Próchnica zębów – Rokowania, prognozy i postęp choroby

Próchnica zębów
Rokowania, prognozy i postęp choroby

Próchnica zębów jest przewlekłą chorobą infekcyjną prowadzącą do demineralizacji i ubytków w strukturze zęba, co w zaawansowanych stadiach może skutkować utratą zębów oraz powikłaniami takimi jak ropień zęba i rozprzestrzenianie się infekcji systemowej. Epidemiologicznie, próchnica dotyka znaczną część populacji: w Australii 1/3 dorosłych powyżej 15 roku życia ma nieleczoną próchnicę, a w USA 96% osób powyżej 65 lat miało próchnicę, z 1/6 z nich posiadającą nieleczone ubytki. Kluczowe czynniki ryzyka obejmują wcześniejsze doświadczenia próchnicowe, spożycie napojów słodzonych cukrem w dzieciństwie, skład bakteryjny śliny przed 12 miesiącem życia oraz higienę jamy ustnej. Wczesne stadium próchnicy, w tym próchnica szkliwa, może być skutecznie zatrzymane lub odwrócone poprzez odpowiednie leczenie i profilaktykę, w tym stosowanie fluoru oraz utrzymanie prawidłowego pH śliny i higieny jamy ustnej. Zaawansowana próchnica zębiny i korzenia wiąże się z ryzykiem utraty zęba i poważnych infekcji.

Prognozy i przewidywanie wyników leczenia próchnicy zębów

Naturalne rokowanie nieleczonej próchnicy
Czynniki wpływające na rokowanie
Prognozy leczenia na różnych etapach

Nowoczesne metody przewidywania próchnicy

Uczenie maszynowe w przewidywaniu próchnicy
Wczesna diagnoza i interwencja
Ograniczenia i wyzwania modeli predykcyjnych

Implikacje dla praktyki klinicznej

Znaczenie okresu wczesnego dzieciństwa
Kolejne rozdziały

Prognozy i przewidywanie wyników leczenia próchnicy zębów

Próchnica zębów (dental caries) to choroba infekcyjna, która prowadzi do pogorszenia struktury zęba, a jej najczęstszym wynikiem jest powstawanie ubytków próchniczych (cavities). Jest to jedna z najczęstszych chorób, z którymi ludzie żyją przez całe życie.¹² Nieleczona próchnica prowadzi do powiększania się ubytków, a w konsekwencji do utraty zębów, co może wpływać na odżywianie i obniżać jakość życia.³ Prognozy dotyczące przebiegu próchnicy i wyników jej leczenia są kluczowe dla skutecznej interwencji i zapobiegania poważnym powikłaniom.

Naturalne rokowanie nieleczonej próchnicy

Nieleczona próchnica ma przewidywalny, niekorzystny przebieg. Ubytki próchnicze powiększają się, chyba że bakterie zostaną zatrzymane lub usunięte.⁴ Gdy próchnica nie jest leczona przez dłuższy czas, może prowadzić do utraty znacznej części zęba i konieczności jego ekstrakcji. Zaawansowana próchnica może spowodować poważną infekcję wewnątrz zęba i pod dziąsłami (ropień zęba). Infekcja ta może rozprzestrzenić się po całym organizmie, a w rzadkich przypadkach zakażenie z ropnia zęba może być śmiertelne.⁵

Wczesna próchnica nie daje objawów, dlatego regularne wizyty kontrolne u dentysty są niezbędne.⁶ Według danych statystycznych z Australii, 1 na 3 dorosłych powyżej 15 roku życia ma nieleczoną próchnicę, a do 5 roku życia 1 na 3 dzieci ma próchnicę zębów mlecznych.⁷ W Stanach Zjednoczonych niemal wszyscy dorośli (96%) w wieku 65 lat lub starsi mieli próchnicę, a 1 na 6 osób w tej grupie wiekowej ma nieleczone ubytki.⁸

Czynniki wpływające na rokowanie

Skuteczne przewidywanie przebiegu próchnicy wymaga uwzględnienia wielu czynników ryzyka. Badania wykorzystujące uczenie maszynowe (ML) zidentyfikowały najważniejsze predyktory rozwoju próchnicy:⁹¹⁰

Wcześniejsze doświadczenia próchnicowe (szczególnie w wieku 13 i 17 lat)
Spożycie napojów słodzonych cukrem (ilość w wieku 9-13 lat i częstotliwość w wieku 13-17 lat)
Skład bakteryjny śliny we wczesnym okresie życia (przed 12 miesiącem życia)
Higiena jamy ustnej

¹¹¹²

Badania wskazują, że długotrwała ekspozycja na dietę bogatą w cukry przez około 5-10 lat może prowadzić do próchnicowych ubytków próchniczych.¹³ Dodatkowo, interakcje bakteryjne we wczesnym okresie życia mogą predysponować dzieci do próchnicy, co potwierdza zależną od czasu interpretację hipotezy ekologicznej rozwoju próchnicy.¹⁴

Prognozy leczenia na różnych etapach

Rokowanie w przypadku próchnicy zależy w dużej mierze od etapu, na którym rozpoczęto leczenie:¹⁵

Wczesna próchnica – może być leczona przez stomatologa, co powstrzyma jej rozwój w ubytek. Wczesne leczenie może zatrzymać, a nawet wyleczyć próchnicę.¹⁶
Próchnica szkliwa – badania wykazały, że przy prawidłowym pH śliny i dobrych nawykach higienicznych proces demineralizacji może zostać zahamowany.¹⁷
Próchnica zębiny – ryzyko demineralizacji tkanki zębiny obserwowano w grupach z wysokim wskaźnikiem DMFT (próchnica, braki zębowe, wypełnienia) i niewystarczającym poziomem higieny jamy ustnej (S-OHI).¹⁸
Zaawansowana próchnica – gdy próchnica postępuje do korzenia, istnieje ryzyko utraty zęba lub rozwoju bolesnego ropnia.¹⁹

U większości osób z próchnicą nie występują długoterminowe problemy, pod warunkiem wczesnej interwencji. Zabiegi z zastosowaniem fluoru mogą zatrzymać próchnicę zębów we wczesnych stadiach.²⁰ Im szybciej leczona jest próchnica, tym większa szansa na przewidywalny wynik i optymalne zdrowie jamy ustnej.²¹

Nowoczesne metody przewidywania próchnicy

Uczenie maszynowe w przewidywaniu próchnicy

Sztuczna inteligencja (AI) i uczenie maszynowe (ML) mają znaczący wpływ na stomatologię, szczególnie w zakresie algorytmów przetwarzania obrazu do wykrywania próchnicy z obrazów radiograficznych.²² Metody te osiągają wysoką dokładność w przewidywaniu próchnicy u młodych dorosłych na podstawie danych longitudinalnych.²³

Najlepiej działający model LASSO regression osiągnął współczynniki RMSE 0,70, R² 0,44 i MAE 0,48, co świadczy o jego skuteczności w przewidywaniu próchnicy.²⁴ Natomiast model DCP wykorzystujący algorytmy takie jak random forest (RF), support vector machine (SVM), gradient-boosted decision trees (GBDT) i logistic regression (LR) wykazał wysoką precyzję przewidywania próchnicy z dokładnością 92% przy zastosowaniu RF jako algorytmu klasyfikacyjnego.²⁵

Modele te mogą w przyszłości, po dalszym rozwoju i walidacji z danymi z różnych populacji, być wykorzystywane przez specjalistów zdrowia publicznego i decydentów jako narzędzie przesiewowe do identyfikacji ryzyka próchnicy u młodych dorosłych i stosowania bardziej ukierunkowanych interwencji.²⁶

Wczesna diagnoza i interwencja

Wczesne wykrywanie próchnicy zębów jest kluczowe dla skutecznego zapobiegania i leczenia.²⁷ Badania wykazały, że zastosowanie mikrobiologicznej analizy bakterii w ślinie dziecka już w wieku 12 miesięcy może przewidzieć przyszłe ryzyko próchnicy, zanim będzie można wykryć Streptococcus mutans, bakterię silnie związaną z próchnicą.²⁸²⁹

Nadzorowana klasyfikacja przyszłego statusu próchnicy wczesnego dzieciństwa (ECC) przy użyciu próbek śliny z wieku 12 miesięcy, wykorzystująca dane dotyczące całego bakteriomu (AUC-ROC 0,78 95% CI (0,71-0,85)), pozwala przewidzieć przyszły status ECC zanim można wykryć S. mutans.³⁰ Badania wykazały, że szanse na przyszłą diagnozę ECC były 8 (95% CI: (3, 22)) razy wyższe u dzieci przypisanych do określonych typów bakterii w ślinie.³¹

Ograniczenia i wyzwania modeli predykcyjnych

Pomimo obiecujących wyników, modele predykcyjne próchnicy napotykają na pewne ograniczenia:³²

Poleganie na małych i homogenicznych zbiorach danych ogranicza zdolność modeli do generalizacji w różnych populacjach i zróżnicowanych warunkach klinicznych.
Ryzyko przeuczenia (overfitting) i brak standaryzacji w metodologiach szkolenia, walidacji i oceny jakości stanowią istotne bariery dla szerszego zastosowania klinicznego tych modeli AI.
Istnieje potrzeba zapewnienia, że modele są klinicznie istotne i interpretowalne, zaprojektowane i testowane z uwzględnieniem rzeczywistych procesów podejmowania decyzji klinicznych.

³³

Chociaż modele uczenia głębokiego (DL) wykazują wysoką wydajność techniczną, często dorównując lub przewyższając zdolności diagnostyczne wykwalifikowanych stomatologów, konieczne jest rozwijanie bardziej zróżnicowanych zbiorów danych i standaryzacji metodologii, aby modele te mogły być skutecznie stosowane w praktyce klinicznej.³⁴

Implikacje dla praktyki klinicznej

Skuteczne prognozowanie próchnicy ma istotne implikacje dla praktyki klinicznej. Wczesna identyfikacja pacjentów wysokiego ryzyka umożliwia ukierunkowane interwencje profilaktyczne, które mogą zapobiec rozwojowi próchnicy lub zatrzymać ją we wczesnych stadiach.³⁵³⁶

Modele predykcyjne mogą być szczególnie wartościowe w opracowywaniu profilaktycznej opieki stomatologicznej i tworzeniu planów higieny jamy ustnej i diety dla pacjentów podatnych na próchnicę.³⁷ Zastosowanie AI w wykrywaniu próchnicy przyniosło imponujące wyniki, co pokazują badania Lee i wsp. (2018).³⁸

Wdrożenie modelu DCP może zastąpić czasochłonny proces wykrywania próchnicy, ponieważ stomatolodzy i pacjenci mogą wykorzystać wyniki do oceny przyszłego nasilenia potencjalnie poważnych infekcji stomatologicznych.³⁹ Jest to szczególnie istotne, biorąc pod uwagę fakt, że u dzieci próchnica może wpływać na ich rozwój, odżywianie, mowę i rozwój szczęki.⁴⁰

Regularny kontakt ze stomatologiem pozostaje kluczowy dla monitorowania i wczesnego wykrywania próchnicy. Wielu ekspertów zdrowia stomatologicznego zaleca wizytę u stomatologa co 6 miesięcy.⁴¹ Takie podejście, w połączeniu z nowoczesnymi metodami przewidywania i wczesnej interwencji, oferuje najlepsze szanse na pomyślne wyniki leczenia próchnicy.

Znaczenie okresu wczesnego dzieciństwa

Badania podkreślają znaczenie pierwszych 2 lat życia jako podatnego okresu na formowanie się próchnicotwórczego mikrobiologicznego środowiska jamy ustnej.⁴² Zbiorowiska bakteryjne, które tworzą się przed 12 miesiącem życia, mogą promować lub hamować ekologiczną sukcesję prowadzącą do dominacji S. mutans i rozwoju próchnicy.⁴³

Analiza wspiera rozwojową interpretację hipotezy ekologicznej i wskazuje, że interakcje ekologiczne i sukcesje we wczesnym okresie życia, oprócz etiologicznych czynników ryzyka, takich jak dieta i higiena jamy ustnej, mogą predysponować dzieci do próchnicy wczesnego dzieciństwa.⁴⁴

Ta wiedza podkreśla znaczenie wczesnych interwencji i monitorowania składu mikrobiologicznego śliny w celu identyfikacji dzieci o podwyższonym ryzyku próchnicy zanim pojawią się kliniczne objawy.⁴⁵

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Materiały źródłowe

#1 DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
https://www.mdpi.com/2076-3417/12/6/3043
Dental caries is an infectious disease that deteriorates the tooth structure, with tooth cavities as the most common result. […] Research on dental caries has been carried out for early detection due to pain and cost of treatment. […] The results of the proposed paper show that ML is highly recommended for dental professionals in assisting them in decision making for the early detection and treatment of dental caries. […] Early detection of dental caries can be resolved with preventive treatment and restorative treatment. […] Machine learning (ML) has become an essential instrument to comprehend and analyze data as massive as the survey mentioned above and is being used in various ways in the medical area. […] Unlike the conventional method, ML can predict carious teeth in the population with surveys or basic information before performing a detailed diagnosis by an expert.
#2 About Cavities (Tooth Decay) | Oral Health | CDC
https://www.cdc.gov/oral-health/about/cavities-tooth-decay.html
Cavities (also called tooth decay or dental caries) are one of the most common diseases people get and live with during their lifetime. Cavities will get bigger unless the bacteria are stopped or removed. […] For people of all ages, cavities that are not stopped lead to tooth loss. […] Cavities grow and, unless stopped, lead to tooth loss. Missing teeth can affect nutrition and lower your quality of life. […] Nearly all adults (96%) aged 65 years or older have had a cavity. […] 1 in 6 adults aged 65 years or older have untreated cavities.
#3 About Cavities (Tooth Decay) | Oral Health | CDC
https://www.cdc.gov/oral-health/about/cavities-tooth-decay.html
Cavities (also called tooth decay or dental caries) are one of the most common diseases people get and live with during their lifetime. Cavities will get bigger unless the bacteria are stopped or removed. […] For people of all ages, cavities that are not stopped lead to tooth loss. […] Cavities grow and, unless stopped, lead to tooth loss. Missing teeth can affect nutrition and lower your quality of life. […] Nearly all adults (96%) aged 65 years or older have had a cavity. […] 1 in 6 adults aged 65 years or older have untreated cavities.
#4 About Cavities (Tooth Decay) | Oral Health | CDC
https://www.cdc.gov/oral-health/about/cavities-tooth-decay.html
Cavities (also called tooth decay or dental caries) are one of the most common diseases people get and live with during their lifetime. Cavities will get bigger unless the bacteria are stopped or removed. […] For people of all ages, cavities that are not stopped lead to tooth loss. […] Cavities grow and, unless stopped, lead to tooth loss. Missing teeth can affect nutrition and lower your quality of life. […] Nearly all adults (96%) aged 65 years or older have had a cavity. […] 1 in 6 adults aged 65 years or older have untreated cavities.
#5 Cavities (Tooth Decay): Symptoms, Causes & Treatment
https://my.clevelandclinic.org/health/diseases/10946-cavities
Cavities are holes, or areas of tooth decay, that form in your teeth surfaces. […] The sooner you treat a cavity, the better your chance for a predictable outcome and optimal oral health. […] When tooth decay goes untreated for too long, you can lose a large portion of your tooth and need an extraction. Advanced tooth decay can lead to a severe infection inside your tooth and under your gums (tooth abscess). This infection can spread throughout your body. Rarely, infection from a tooth abscess can be fatal. […] Most people with cavities don’t experience any long-term problems. Because cavities develop slowly, it’s important to get regular dental checkups. Fluoride treatments can stop tooth decay in its early stages. Once tooth decay advances to the root, you risk losing the tooth or developing a painful abscess (infection).
#6 Tooth decay | healthdirect
https://www.healthdirect.gov.au/tooth-decay
Tooth decay is caused by plaque and may lead to a cavity (hole) in your tooth. […] Tooth decay can affect people of all ages, even very young children. […] In Australia, 1 in 3 adults over the age of 15 years has untreated tooth decay. […] Similarly, by 5 years of age, 1 in 3 children has tooth decay in their baby teeth. […] Early tooth decay has no symptoms, so you should visit your dental practitioner regularly. Your dental practitioner will check your teeth for tooth decay. […] Many dental health experts recommend seeing your dental practitioner every 6 months. […] Early treatment can stop or even cure tooth decay. […] Mild tooth decay can be treated by a dental practitioner. […] This will stop the early decay from progressing into a hole. […] If tooth decay is not treated, it can cause pain, tooth abscess, swelling or pus around a tooth, damage or broken teeth, and chewing problems. […] In children, tooth decay can affect their development, nutrition, speech, and jaw development.
#7 Tooth decay | healthdirect
https://www.healthdirect.gov.au/tooth-decay
Tooth decay is caused by plaque and may lead to a cavity (hole) in your tooth. […] Tooth decay can affect people of all ages, even very young children. […] In Australia, 1 in 3 adults over the age of 15 years has untreated tooth decay. […] Similarly, by 5 years of age, 1 in 3 children has tooth decay in their baby teeth. […] Early tooth decay has no symptoms, so you should visit your dental practitioner regularly. Your dental practitioner will check your teeth for tooth decay. […] Many dental health experts recommend seeing your dental practitioner every 6 months. […] Early treatment can stop or even cure tooth decay. […] Mild tooth decay can be treated by a dental practitioner. […] This will stop the early decay from progressing into a hole. […] If tooth decay is not treated, it can cause pain, tooth abscess, swelling or pus around a tooth, damage or broken teeth, and chewing problems. […] In children, tooth decay can affect their development, nutrition, speech, and jaw development.
#8 About Cavities (Tooth Decay) | Oral Health | CDC
https://www.cdc.gov/oral-health/about/cavities-tooth-decay.html
Cavities (also called tooth decay or dental caries) are one of the most common diseases people get and live with during their lifetime. Cavities will get bigger unless the bacteria are stopped or removed. […] For people of all ages, cavities that are not stopped lead to tooth loss. […] Cavities grow and, unless stopped, lead to tooth loss. Missing teeth can affect nutrition and lower your quality of life. […] Nearly all adults (96%) aged 65 years or older have had a cavity. […] 1 in 6 adults aged 65 years or older have untreated cavities.
#9 Predicting dental caries outcomes in young adults using machine learning approach
https://pmc.ncbi.nlm.nih.gov/articles/PMC11069237/
To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. […] The prevalence of cavitated level caries experience at age 23 (mean D2+MFS count) was 4.75. […] Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23. […] Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. […] Our model could, in the future, after further development and validation with other diverse population data, be used by public health specialists and policy-makers as a screening tool to identify the risk of caries in young adults and apply more targeted interventions.
#10 Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach
https://pmc.ncbi.nlm.nih.gov/articles/PMC10602064/
To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. […] The prevalence of cavitated level caries experience at age 23 (mean D2+MFS count) was 4.75. […] Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23. […] Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. […] Our model suggests that continued exposure to sugary diet for about 5 to 10 years could result in cavitated caries. […] We identified four variables (age 13 caries experience, age 17 caries experience, the amount of sugar-sweetened beverages intake from age 9 to 13, and frequency of sugar-sweetened beverages intake from age 13 to 17) as the most important ones in the prediction of age 23 cavitated caries counts.
#11 Predicting dental caries outcomes in young adults using machine learning approach
https://pmc.ncbi.nlm.nih.gov/articles/PMC11069237/
To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. […] The prevalence of cavitated level caries experience at age 23 (mean D2+MFS count) was 4.75. […] Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23. […] Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. […] Our model could, in the future, after further development and validation with other diverse population data, be used by public health specialists and policy-makers as a screening tool to identify the risk of caries in young adults and apply more targeted interventions.
#12 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Bacterial communities which assemble before 12 months of age can promote or inhibit an ecological succession to S. mutans dominance and cariogenesis. […] The odds of future ECC diagnosis were 8 (95%CI: (3, 22)) times higher for children assigned to the Streptococcus ASV8-Neisseria ASV12 CST as compared to children assigned to the H. parainfluenzae-Neisseria ASV9-Gemella ASV2 CST at 12 months after controlling for maternal education, count of emerged primary teeth, mode of birth delivery, breastfeeding, antibiotic exposure within 3 months and visit of case diagnosis (P value 0.001, Table 3). […] We showed that bacteriome-wide information classified future ECC status before reliable detection of salivary S. mutans. […] Our findings highlight the first 2 years of life as a susceptible period for assembly of a cariogenic oral microbial community.
#13 Predicting Dental Caries Outcomes in Young Adults Using Machine Learning Approach
https://pmc.ncbi.nlm.nih.gov/articles/PMC10602064/
To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. […] The prevalence of cavitated level caries experience at age 23 (mean D2+MFS count) was 4.75. […] Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23. […] Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. […] Our model suggests that continued exposure to sugary diet for about 5 to 10 years could result in cavitated caries. […] We identified four variables (age 13 caries experience, age 17 caries experience, the amount of sugar-sweetened beverages intake from age 9 to 13, and frequency of sugar-sweetened beverages intake from age 13 to 17) as the most important ones in the prediction of age 23 cavitated caries counts.
#14 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Early childhood caries (ECC) dental caries (cavities) occurring in primary teeth up to age 6 years is a prevalent childhood oral disease with a microbial etiology. […] In this incident, density sampled case-control study of 189 children followed from 2 months to 5 years, we use the salivary bacteriome to (1) prospectively test the ecological hypothesis of ECC in salivary bacteriome communities and (2) identify co-occurring salivary bacterial communities predicting future ECC. […] Supervised classification of future ECC case status using salivary samples from age 12 months using bacteriome-wide data (AUC-ROC 0.78 95% CI (0.710.85)) predicts future ECC status before S. mutans can be detected. […] Early-life bacterial interactions predisposed children to ECC, supporting a time-dependent interpretation of the ecological hypothesis.
#15 Cavities (Tooth Decay): Symptoms, Causes & Treatment
https://my.clevelandclinic.org/health/diseases/10946-cavities
Cavities are holes, or areas of tooth decay, that form in your teeth surfaces. […] The sooner you treat a cavity, the better your chance for a predictable outcome and optimal oral health. […] When tooth decay goes untreated for too long, you can lose a large portion of your tooth and need an extraction. Advanced tooth decay can lead to a severe infection inside your tooth and under your gums (tooth abscess). This infection can spread throughout your body. Rarely, infection from a tooth abscess can be fatal. […] Most people with cavities don’t experience any long-term problems. Because cavities develop slowly, it’s important to get regular dental checkups. Fluoride treatments can stop tooth decay in its early stages. Once tooth decay advances to the root, you risk losing the tooth or developing a painful abscess (infection).
#16 Tooth decay | healthdirect
https://www.healthdirect.gov.au/tooth-decay
Tooth decay is caused by plaque and may lead to a cavity (hole) in your tooth. […] Tooth decay can affect people of all ages, even very young children. […] In Australia, 1 in 3 adults over the age of 15 years has untreated tooth decay. […] Similarly, by 5 years of age, 1 in 3 children has tooth decay in their baby teeth. […] Early tooth decay has no symptoms, so you should visit your dental practitioner regularly. Your dental practitioner will check your teeth for tooth decay. […] Many dental health experts recommend seeing your dental practitioner every 6 months. […] Early treatment can stop or even cure tooth decay. […] Mild tooth decay can be treated by a dental practitioner. […] This will stop the early decay from progressing into a hole. […] If tooth decay is not treated, it can cause pain, tooth abscess, swelling or pus around a tooth, damage or broken teeth, and chewing problems. […] In children, tooth decay can affect their development, nutrition, speech, and jaw development.
#17 Prognosis method for risk assessment of dental caries induced by chocolate comsumption
https://www.scielo.org.mx/scielo.php?pid=S1870-199X2015000100004&script=sci_arttext&tlng=en
Prognosis method for risk assessment of dental caries induced by chocolate consumption. The aim of the present study was to propose a valuation method of stomatological caries risk due to chocolate consumption, based on DMFT and or S-OHI in 12 to 13 year old adolescents. A prognosis method for assessment of stomatological risk of enamel caries caused by chocolate consumption was proposed. This method was based on DMFT assessment; for caries in dentin, the method used was based on DMFT and S-OHI assessment. After Sublime brand chocolate consumption, a significant salivary pH variation could be observed, which reached levels considered non-critical for enamel demineralization at all DMFT and S-OHI level, whereas for dentin tissue risk of demineralization was observed in the high DMFT, deficient S-OHI group.
#18 Prognosis method for risk assessment of dental caries induced by chocolate comsumption
https://www.scielo.org.mx/scielo.php?pid=S1870-199X2015000100004&script=sci_arttext&tlng=en
Prognosis method for risk assessment of dental caries induced by chocolate consumption. The aim of the present study was to propose a valuation method of stomatological caries risk due to chocolate consumption, based on DMFT and or S-OHI in 12 to 13 year old adolescents. A prognosis method for assessment of stomatological risk of enamel caries caused by chocolate consumption was proposed. This method was based on DMFT assessment; for caries in dentin, the method used was based on DMFT and S-OHI assessment. After Sublime brand chocolate consumption, a significant salivary pH variation could be observed, which reached levels considered non-critical for enamel demineralization at all DMFT and S-OHI level, whereas for dentin tissue risk of demineralization was observed in the high DMFT, deficient S-OHI group.
#19 Cavities (Tooth Decay): Symptoms, Causes & Treatment
https://my.clevelandclinic.org/health/diseases/10946-cavities
Cavities are holes, or areas of tooth decay, that form in your teeth surfaces. […] The sooner you treat a cavity, the better your chance for a predictable outcome and optimal oral health. […] When tooth decay goes untreated for too long, you can lose a large portion of your tooth and need an extraction. Advanced tooth decay can lead to a severe infection inside your tooth and under your gums (tooth abscess). This infection can spread throughout your body. Rarely, infection from a tooth abscess can be fatal. […] Most people with cavities don’t experience any long-term problems. Because cavities develop slowly, it’s important to get regular dental checkups. Fluoride treatments can stop tooth decay in its early stages. Once tooth decay advances to the root, you risk losing the tooth or developing a painful abscess (infection).
#20 Cavities (Tooth Decay): Symptoms, Causes & Treatment
https://my.clevelandclinic.org/health/diseases/10946-cavities
Cavities are holes, or areas of tooth decay, that form in your teeth surfaces. […] The sooner you treat a cavity, the better your chance for a predictable outcome and optimal oral health. […] When tooth decay goes untreated for too long, you can lose a large portion of your tooth and need an extraction. Advanced tooth decay can lead to a severe infection inside your tooth and under your gums (tooth abscess). This infection can spread throughout your body. Rarely, infection from a tooth abscess can be fatal. […] Most people with cavities don’t experience any long-term problems. Because cavities develop slowly, it’s important to get regular dental checkups. Fluoride treatments can stop tooth decay in its early stages. Once tooth decay advances to the root, you risk losing the tooth or developing a painful abscess (infection).
#21 Cavities (Tooth Decay): Symptoms, Causes & Treatment
https://my.clevelandclinic.org/health/diseases/10946-cavities
Cavities are holes, or areas of tooth decay, that form in your teeth surfaces. […] The sooner you treat a cavity, the better your chance for a predictable outcome and optimal oral health. […] When tooth decay goes untreated for too long, you can lose a large portion of your tooth and need an extraction. Advanced tooth decay can lead to a severe infection inside your tooth and under your gums (tooth abscess). This infection can spread throughout your body. Rarely, infection from a tooth abscess can be fatal. […] Most people with cavities don’t experience any long-term problems. Because cavities develop slowly, it’s important to get regular dental checkups. Fluoride treatments can stop tooth decay in its early stages. Once tooth decay advances to the root, you risk losing the tooth or developing a painful abscess (infection).
#22 Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review [PeerJ]
https://peerj.com/articles/cs-2371/
In recent years, artificial intelligence (AI) and deep learning (DL) have made a considerable impact in dentistry, specifically in advancing image processing algorithms for detecting caries from radiographical images. […] This study provides an overview aimed at evaluating and comparing reviews that focus on the detection of dental caries (DC) using DL algorithms from 2D radiographs. […] The advancement of DL algorithms in detecting and predicting DC through radiographic imaging is a significant breakthrough. These algorithms excel in extracting subtle features from radiographic images and applying machine learning techniques to achieve highly accurate predictions, often outperforming human experts. This advancement holds immense potential to transform diagnostic processes in dentistry, promising to considerably improve patient outcomes.
#23 Predicting dental caries outcomes in young adults using machine learning approach
https://pmc.ncbi.nlm.nih.gov/articles/PMC11069237/
To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. […] The prevalence of cavitated level caries experience at age 23 (mean D2+MFS count) was 4.75. […] Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23. […] Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. […] Our model could, in the future, after further development and validation with other diverse population data, be used by public health specialists and policy-makers as a screening tool to identify the risk of caries in young adults and apply more targeted interventions.
#24 Predicting dental caries outcomes in young adults using machine learning approach
https://pmc.ncbi.nlm.nih.gov/articles/PMC11069237/
The best performing model was from the LASSO regression, with a RMSE of 0.70, R2 of 0.44, and MAE of 0.48. […] The assessment of variable importance showed that 4 of the 51 independent variables (age 13 caries count, age 17 caries count, the amount of sugar-sweetened beverages intake from age 9 to 13, and the frequency of sugar-sweetened beverages intake from age 13 to 17) were important in the prediction of and all were positively associated with the cavitated caries outcome count at age 23. […] Our ML model generated an accurate, sensitive, and precise model for caries prediction of caries in young adults using longitudinally obtained exposure variables.
#25 DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
https://www.mdpi.com/2076-3417/12/6/3043
The proposed model, called DCP, uses methods such as random forest (RF), support vector machine (SVM), gradient-boosted decision trees (GBDT), and logistic regression (LR). […] The DCP model will replace the time-consuming process of detecting dental caries, as dentists and patients can use the results to evaluate the future crescendo of potentially severe dental infections. […] The proposed model highly predicted dental caries with an accuracy of 92% while using RF as a classification algorithm. […] The presented results of RF can be seen as highly competitive compared to other prediction methods applied.
#26 Predicting dental caries outcomes in young adults using machine learning approach
https://pmc.ncbi.nlm.nih.gov/articles/PMC11069237/
To predict the dental caries outcomes in young adults from a set of longitudinally-obtained predictor variables and identify the most important predictors using machine learning techniques. […] The prevalence of cavitated level caries experience at age 23 (mean D2+MFS count) was 4.75. […] Previous caries experience at age 13 and age 17 and sugar-sweetened beverages intakes at age 13 and age 17 were found to be the four most important predictors of cavitated caries count at age 23. […] Our machine learning model showed high accuracy and precision in the prediction of caries in young adults from a longitudinally-obtained predictor variables. […] Our model could, in the future, after further development and validation with other diverse population data, be used by public health specialists and policy-makers as a screening tool to identify the risk of caries in young adults and apply more targeted interventions.
#27 DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
https://www.mdpi.com/2076-3417/12/6/3043
Dental caries is an infectious disease that deteriorates the tooth structure, with tooth cavities as the most common result. […] Research on dental caries has been carried out for early detection due to pain and cost of treatment. […] The results of the proposed paper show that ML is highly recommended for dental professionals in assisting them in decision making for the early detection and treatment of dental caries. […] Early detection of dental caries can be resolved with preventive treatment and restorative treatment. […] Machine learning (ML) has become an essential instrument to comprehend and analyze data as massive as the survey mentioned above and is being used in various ways in the medical area. […] Unlike the conventional method, ML can predict carious teeth in the population with surveys or basic information before performing a detailed diagnosis by an expert.
#28 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Early childhood caries (ECC) dental caries (cavities) occurring in primary teeth up to age 6 years is a prevalent childhood oral disease with a microbial etiology. […] In this incident, density sampled case-control study of 189 children followed from 2 months to 5 years, we use the salivary bacteriome to (1) prospectively test the ecological hypothesis of ECC in salivary bacteriome communities and (2) identify co-occurring salivary bacterial communities predicting future ECC. […] Supervised classification of future ECC case status using salivary samples from age 12 months using bacteriome-wide data (AUC-ROC 0.78 95% CI (0.710.85)) predicts future ECC status before S. mutans can be detected. […] Early-life bacterial interactions predisposed children to ECC, supporting a time-dependent interpretation of the ecological hypothesis.
#29 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Bacterial communities which assemble before 12 months of age can promote or inhibit an ecological succession to S. mutans dominance and cariogenesis. […] The odds of future ECC diagnosis were 8 (95%CI: (3, 22)) times higher for children assigned to the Streptococcus ASV8-Neisseria ASV12 CST as compared to children assigned to the H. parainfluenzae-Neisseria ASV9-Gemella ASV2 CST at 12 months after controlling for maternal education, count of emerged primary teeth, mode of birth delivery, breastfeeding, antibiotic exposure within 3 months and visit of case diagnosis (P value 0.001, Table 3). […] We showed that bacteriome-wide information classified future ECC status before reliable detection of salivary S. mutans. […] Our findings highlight the first 2 years of life as a susceptible period for assembly of a cariogenic oral microbial community.
#30 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Early childhood caries (ECC) dental caries (cavities) occurring in primary teeth up to age 6 years is a prevalent childhood oral disease with a microbial etiology. […] In this incident, density sampled case-control study of 189 children followed from 2 months to 5 years, we use the salivary bacteriome to (1) prospectively test the ecological hypothesis of ECC in salivary bacteriome communities and (2) identify co-occurring salivary bacterial communities predicting future ECC. […] Supervised classification of future ECC case status using salivary samples from age 12 months using bacteriome-wide data (AUC-ROC 0.78 95% CI (0.710.85)) predicts future ECC status before S. mutans can be detected. […] Early-life bacterial interactions predisposed children to ECC, supporting a time-dependent interpretation of the ecological hypothesis.
#31 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Bacterial communities which assemble before 12 months of age can promote or inhibit an ecological succession to S. mutans dominance and cariogenesis. […] The odds of future ECC diagnosis were 8 (95%CI: (3, 22)) times higher for children assigned to the Streptococcus ASV8-Neisseria ASV12 CST as compared to children assigned to the H. parainfluenzae-Neisseria ASV9-Gemella ASV2 CST at 12 months after controlling for maternal education, count of emerged primary teeth, mode of birth delivery, breastfeeding, antibiotic exposure within 3 months and visit of case diagnosis (P value 0.001, Table 3). […] We showed that bacteriome-wide information classified future ECC status before reliable detection of salivary S. mutans. […] Our findings highlight the first 2 years of life as a susceptible period for assembly of a cariogenic oral microbial community.
#32 Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review [PeerJ]
https://peerj.com/articles/cs-2371/
The reviewed studies consistently demonstrate that DL and CNN models can achieve high levels of accuracy in identifying and predicting DC. These models often match or surpass the diagnostic capabilities of trained dental professionals, highlighting their potential as powerful tools in dental care. […] A recurrent limitation across the studies is the reliance on small and homogeneous datasets. This restricts the models ability to generalize across diverse populations and varying clinical conditions, underscoring the need for larger, more diverse datasets in future research. […] The risk of overfitting and the lack of standardization in training, validation, and quality assessment methodologies pose significant barriers to the broader clinical application of these AI models. Addressing these issues is crucial for the development of robust, reliable AI tools. […] While DL models show high technical performance, there is a pressing need to ensure that these models are clinically relevant and interpretable. Models must be designed and tested with real-world clinical decision-making processes in mind to be truly effective in improving patient care.
#33 Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review [PeerJ]
https://peerj.com/articles/cs-2371/
The reviewed studies consistently demonstrate that DL and CNN models can achieve high levels of accuracy in identifying and predicting DC. These models often match or surpass the diagnostic capabilities of trained dental professionals, highlighting their potential as powerful tools in dental care. […] A recurrent limitation across the studies is the reliance on small and homogeneous datasets. This restricts the models ability to generalize across diverse populations and varying clinical conditions, underscoring the need for larger, more diverse datasets in future research. […] The risk of overfitting and the lack of standardization in training, validation, and quality assessment methodologies pose significant barriers to the broader clinical application of these AI models. Addressing these issues is crucial for the development of robust, reliable AI tools. […] While DL models show high technical performance, there is a pressing need to ensure that these models are clinically relevant and interpretable. Models must be designed and tested with real-world clinical decision-making processes in mind to be truly effective in improving patient care.
#34 Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review [PeerJ]
https://peerj.com/articles/cs-2371/
The reviewed studies consistently demonstrate that DL and CNN models can achieve high levels of accuracy in identifying and predicting DC. These models often match or surpass the diagnostic capabilities of trained dental professionals, highlighting their potential as powerful tools in dental care. […] A recurrent limitation across the studies is the reliance on small and homogeneous datasets. This restricts the models ability to generalize across diverse populations and varying clinical conditions, underscoring the need for larger, more diverse datasets in future research. […] The risk of overfitting and the lack of standardization in training, validation, and quality assessment methodologies pose significant barriers to the broader clinical application of these AI models. Addressing these issues is crucial for the development of robust, reliable AI tools. […] While DL models show high technical performance, there is a pressing need to ensure that these models are clinically relevant and interpretable. Models must be designed and tested with real-world clinical decision-making processes in mind to be truly effective in improving patient care.
#35 DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
https://www.mdpi.com/2076-3417/12/6/3043
Dental caries is an infectious disease that deteriorates the tooth structure, with tooth cavities as the most common result. […] Research on dental caries has been carried out for early detection due to pain and cost of treatment. […] The results of the proposed paper show that ML is highly recommended for dental professionals in assisting them in decision making for the early detection and treatment of dental caries. […] Early detection of dental caries can be resolved with preventive treatment and restorative treatment. […] Machine learning (ML) has become an essential instrument to comprehend and analyze data as massive as the survey mentioned above and is being used in various ways in the medical area. […] Unlike the conventional method, ML can predict carious teeth in the population with surveys or basic information before performing a detailed diagnosis by an expert.
#36 Tooth decay | healthdirect
https://www.healthdirect.gov.au/tooth-decay
Tooth decay is caused by plaque and may lead to a cavity (hole) in your tooth. […] Tooth decay can affect people of all ages, even very young children. […] In Australia, 1 in 3 adults over the age of 15 years has untreated tooth decay. […] Similarly, by 5 years of age, 1 in 3 children has tooth decay in their baby teeth. […] Early tooth decay has no symptoms, so you should visit your dental practitioner regularly. Your dental practitioner will check your teeth for tooth decay. […] Many dental health experts recommend seeing your dental practitioner every 6 months. […] Early treatment can stop or even cure tooth decay. […] Mild tooth decay can be treated by a dental practitioner. […] This will stop the early decay from progressing into a hole. […] If tooth decay is not treated, it can cause pain, tooth abscess, swelling or pus around a tooth, damage or broken teeth, and chewing problems. […] In children, tooth decay can affect their development, nutrition, speech, and jaw development.
#37 Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review [PeerJ]
https://peerj.com/articles/cs-2371/
The field of DL in caries detection is highly dynamic, exhibiting substantial variations in methodologies and outcomes across studies. Several systematic reviews (SRs) and meta-analyses (MAs) have been performed to evaluate the accuracy, sensitivity, and specificity of DL algorithms in caries detection. […] The effectiveness of these models in clinical settings for identifying patients at increased risk of dental caries (DC) is noteworthy. They assist in improving diagnostic and treatment processes as well as patient outcomes. […] The predictive models insights are valuable in devising preventive dental care and creating oral hygiene and diet plans for patients prone to DC. […] The application of AI in detecting DC has produced impressive results, as demonstrated by Lee et al.’s (2018) study.
#38 Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review [PeerJ]
https://peerj.com/articles/cs-2371/
The field of DL in caries detection is highly dynamic, exhibiting substantial variations in methodologies and outcomes across studies. Several systematic reviews (SRs) and meta-analyses (MAs) have been performed to evaluate the accuracy, sensitivity, and specificity of DL algorithms in caries detection. […] The effectiveness of these models in clinical settings for identifying patients at increased risk of dental caries (DC) is noteworthy. They assist in improving diagnostic and treatment processes as well as patient outcomes. […] The predictive models insights are valuable in devising preventive dental care and creating oral hygiene and diet plans for patients prone to DC. […] The application of AI in detecting DC has produced impressive results, as demonstrated by Lee et al.’s (2018) study.
#39 DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
https://www.mdpi.com/2076-3417/12/6/3043
The proposed model, called DCP, uses methods such as random forest (RF), support vector machine (SVM), gradient-boosted decision trees (GBDT), and logistic regression (LR). […] The DCP model will replace the time-consuming process of detecting dental caries, as dentists and patients can use the results to evaluate the future crescendo of potentially severe dental infections. […] The proposed model highly predicted dental caries with an accuracy of 92% while using RF as a classification algorithm. […] The presented results of RF can be seen as highly competitive compared to other prediction methods applied.
#40 Tooth decay | healthdirect
https://www.healthdirect.gov.au/tooth-decay
Tooth decay is caused by plaque and may lead to a cavity (hole) in your tooth. […] Tooth decay can affect people of all ages, even very young children. […] In Australia, 1 in 3 adults over the age of 15 years has untreated tooth decay. […] Similarly, by 5 years of age, 1 in 3 children has tooth decay in their baby teeth. […] Early tooth decay has no symptoms, so you should visit your dental practitioner regularly. Your dental practitioner will check your teeth for tooth decay. […] Many dental health experts recommend seeing your dental practitioner every 6 months. […] Early treatment can stop or even cure tooth decay. […] Mild tooth decay can be treated by a dental practitioner. […] This will stop the early decay from progressing into a hole. […] If tooth decay is not treated, it can cause pain, tooth abscess, swelling or pus around a tooth, damage or broken teeth, and chewing problems. […] In children, tooth decay can affect their development, nutrition, speech, and jaw development.
#41 Tooth decay | healthdirect
https://www.healthdirect.gov.au/tooth-decay
Tooth decay is caused by plaque and may lead to a cavity (hole) in your tooth. […] Tooth decay can affect people of all ages, even very young children. […] In Australia, 1 in 3 adults over the age of 15 years has untreated tooth decay. […] Similarly, by 5 years of age, 1 in 3 children has tooth decay in their baby teeth. […] Early tooth decay has no symptoms, so you should visit your dental practitioner regularly. Your dental practitioner will check your teeth for tooth decay. […] Many dental health experts recommend seeing your dental practitioner every 6 months. […] Early treatment can stop or even cure tooth decay. […] Mild tooth decay can be treated by a dental practitioner. […] This will stop the early decay from progressing into a hole. […] If tooth decay is not treated, it can cause pain, tooth abscess, swelling or pus around a tooth, damage or broken teeth, and chewing problems. […] In children, tooth decay can affect their development, nutrition, speech, and jaw development.
#42 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Bacterial communities which assemble before 12 months of age can promote or inhibit an ecological succession to S. mutans dominance and cariogenesis. […] The odds of future ECC diagnosis were 8 (95%CI: (3, 22)) times higher for children assigned to the Streptococcus ASV8-Neisseria ASV12 CST as compared to children assigned to the H. parainfluenzae-Neisseria ASV9-Gemella ASV2 CST at 12 months after controlling for maternal education, count of emerged primary teeth, mode of birth delivery, breastfeeding, antibiotic exposure within 3 months and visit of case diagnosis (P value 0.001, Table 3). […] We showed that bacteriome-wide information classified future ECC status before reliable detection of salivary S. mutans. […] Our findings highlight the first 2 years of life as a susceptible period for assembly of a cariogenic oral microbial community.
#43 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Bacterial communities which assemble before 12 months of age can promote or inhibit an ecological succession to S. mutans dominance and cariogenesis. […] The odds of future ECC diagnosis were 8 (95%CI: (3, 22)) times higher for children assigned to the Streptococcus ASV8-Neisseria ASV12 CST as compared to children assigned to the H. parainfluenzae-Neisseria ASV9-Gemella ASV2 CST at 12 months after controlling for maternal education, count of emerged primary teeth, mode of birth delivery, breastfeeding, antibiotic exposure within 3 months and visit of case diagnosis (P value 0.001, Table 3). […] We showed that bacteriome-wide information classified future ECC status before reliable detection of salivary S. mutans. […] Our findings highlight the first 2 years of life as a susceptible period for assembly of a cariogenic oral microbial community.
#44 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Our observations on the suitability of diversity measures vs other clustering techniques to detect fine scale differences are applicable in other microbial contexts. […] Overall, our analyses support a developmental interpretation of the ecological hypothesis and raise the possibility that ecological interactions and successions in early life, in addition to etiologic risk factors such as diet and oral hygiene, may predispose children to ECC.
#45 Evaluating the ecological hypothesis: early life salivary microbiome assembly predicts dental caries in a longitudinal case-control study | Microbiome | Full Text
https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-022-01442-5
Bacterial communities which assemble before 12 months of age can promote or inhibit an ecological succession to S. mutans dominance and cariogenesis. […] The odds of future ECC diagnosis were 8 (95%CI: (3, 22)) times higher for children assigned to the Streptococcus ASV8-Neisseria ASV12 CST as compared to children assigned to the H. parainfluenzae-Neisseria ASV9-Gemella ASV2 CST at 12 months after controlling for maternal education, count of emerged primary teeth, mode of birth delivery, breastfeeding, antibiotic exposure within 3 months and visit of case diagnosis (P value 0.001, Table 3). […] We showed that bacteriome-wide information classified future ECC status before reliable detection of salivary S. mutans. […] Our findings highlight the first 2 years of life as a susceptible period for assembly of a cariogenic oral microbial community.