Materiały źródłowe

• #1 Osteoporosis prognosis, life expectancy, and more

  https://www.medicalnewstoday.com/articles/osteoporosis-prognosis

  The outlook for osteoporosis mainly depends on how early someone seeks treatment and whether they continue with it. Treatment can slow or stop bone loss, keeping bones stronger for longer. […] Osteoporosis is not a terminal illness, but it does tend to worsen with time, especially without treatment. The condition does not directly influence life expectancy, though people who experience fractures from osteoporosis may have a shorter life span. Osteoporosis may also worsen the outlook of other diagnoses. […] With treatment, the outlook for osteoporosis is generally good. Treatment can slow or even reverse bone loss. […] All people steadily lose bone mineral density with age. Therefore, without treatment, osteoporosis tends to steadily worsen. […] The most important factor affecting someone’s outlook for osteoporosis is whether they seek treatment and how early in the disease they do so.

• #2 Osteoporosis: Prognosis, Complications, and Treatment

  https://www.verywellhealth.com/osteoporosis-prognosis-6979280

  People living with osteoporosis experience a reduced quality of life and lower life expectancy rates. The length of time a person can live with osteoporosis depends on their treatment and the age when they were diagnosed. […] When looking at the average, research has shown that males who begin treatment before 60 and women who begin treatment before 75 can expect to live 15 years or more following their diagnosis. […] Osteoporosis is a progressive condition. While it is not considered fatal in and of itself, it does shorten a person’s life expectancy. That is especially true if someone does not receive adequate treatment. However, people with osteoporosis can expect to live 15 years or more after their diagnosis if they take the proper medications and make the necessary lifestyle changes.

• #3 Osteoporosis: Prognosis, Complications, and Treatment

  https://www.verywellhealth.com/osteoporosis-prognosis-6979280

  People living with osteoporosis experience a reduced quality of life and lower life expectancy rates. The length of time a person can live with osteoporosis depends on their treatment and the age when they were diagnosed. […] When looking at the average, research has shown that males who begin treatment before 60 and women who begin treatment before 75 can expect to live 15 years or more following their diagnosis. […] Osteoporosis is a progressive condition. While it is not considered fatal in and of itself, it does shorten a person’s life expectancy. That is especially true if someone does not receive adequate treatment. However, people with osteoporosis can expect to live 15 years or more after their diagnosis if they take the proper medications and make the necessary lifestyle changes.

• #4 Osteoporosis: Symptoms, Causes and Treatment

  https://my.clevelandclinic.org/health/diseases/4443-osteoporosis

  You should expect to manage osteoporosis for a long time, usually the rest of your life. You’ll need regular appointments with a healthcare provider and bone density tests. Your provider will monitor any changes in your bone density and will adjust your treatments as needed. […] Osteoporosis itself isn’t fatal and won’t change your life expectancy (how long you’ll live). But it can make you more likely to experience a bone fracture (and can increase your risk of more severe breaks or complications from a fracture). Some studies have found that hip fractures in adults older than 65 lead to reduced mobility and an earlier death. […] The best way to prevent bone fractures is catching osteoporosis before it can hurt you. Visit a healthcare provider for regular checkups. Ask them when you’ll need bone density tests and how often you should have follow-up tests to monitor your bone health.

• #5 Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data | Scientific Reports

  https://www.nature.com/articles/s41598-024-56114-1

  Osteoporosis is a major public health concern that significantly increases the risk of fractures. […] The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. […] In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies. […] The adverse outcomes of osteoporosis such as hip and vertebral fractures are associated with substantial morbidity, mortality, and economic costs. […] Unfortunately, osteoporosis often remains undiagnosed and untreated until the occurrence of a debilitating fracture, hence proactive identification of high-risk individuals is a critical step in mitigating the disease burden.

• #6 Osteoporosis prognosis, life expectancy, and more

  https://www.medicalnewstoday.com/articles/osteoporosis-prognosis

  The outlook for osteoporosis mainly depends on how early someone seeks treatment and whether they continue with it. Treatment can slow or stop bone loss, keeping bones stronger for longer. […] Osteoporosis is not a terminal illness, but it does tend to worsen with time, especially without treatment. The condition does not directly influence life expectancy, though people who experience fractures from osteoporosis may have a shorter life span. Osteoporosis may also worsen the outlook of other diagnoses. […] With treatment, the outlook for osteoporosis is generally good. Treatment can slow or even reverse bone loss. […] All people steadily lose bone mineral density with age. Therefore, without treatment, osteoporosis tends to steadily worsen. […] The most important factor affecting someone’s outlook for osteoporosis is whether they seek treatment and how early in the disease they do so.

• #7 Osteoporosis prognosis, life expectancy, and more

  https://www.medicalnewstoday.com/articles/osteoporosis-prognosis

  Most people with the condition have a close-to-typical life expectancy. […] Osteoporosis can also influence life expectancy indirectly. […] Osteoporosis is not directly fatal, and it is not a terminal illness. However, it may lead to significant pain, weakness, and disability if a person experiences fractures. […] With treatment, the outlook for osteoporosis can be favorable. Early diagnosis and treatment can mean a person stops losing as much bone density as they age, and in some cases, may even reverse bone loss to an extent. […] However, without intervention, osteoporosis will worsen. The right treatment, coupled with lifestyle changes, can improve outcomes.

• #8 Osteoporosis: Clinical Evaluation – Endotext – NCBI Bookshelf

  https://www.ncbi.nlm.nih.gov/books/NBK279049/

  Osteoporosis is a common systemic skeletal disease characterized by low bone strength that results in an increased risk of fracture. Fractures are associated with serious clinical consequences, including pain, disability, loss of independence, and death, as well as high healthcare costs. Early identification and intervention with patients at high risk for fracture is needed to reduce the burden of osteoporotic fractures. […] The presence of clinical risk factors (CRFs) that are independent of BMD, particularly advancing age, prior fracture, and recency/number/severity of fracture(s), can identify patients at high-risk for fracture by providing information on fracture risk that is complementary to BMD. […] The combination of BMD and clinical risk factors (CRFs) predicts fracture risk better than BMD or CRFs alone. A fracture risk assessment tool (FRAX) combines CRFs and femoral neck BMD in a computer-based algorithm that estimates the 10-year probability of hip fracture and major osteoporotic fracture.

• #9 Osteoporosis: Clinical Evaluation – Endotext – NCBI Bookshelf

  https://www.ncbi.nlm.nih.gov/books/NBK279049/

  Osteoporosis is a common systemic skeletal disease characterized by low bone strength that results in an increased risk of fracture. Fractures are associated with serious clinical consequences, including pain, disability, loss of independence, and death, as well as high healthcare costs. Early identification and intervention with patients at high risk for fracture is needed to reduce the burden of osteoporotic fractures. […] The presence of clinical risk factors (CRFs) that are independent of BMD, particularly advancing age, prior fracture, and recency/number/severity of fracture(s), can identify patients at high-risk for fracture by providing information on fracture risk that is complementary to BMD. […] The combination of BMD and clinical risk factors (CRFs) predicts fracture risk better than BMD or CRFs alone. A fracture risk assessment tool (FRAX) combines CRFs and femoral neck BMD in a computer-based algorithm that estimates the 10-year probability of hip fracture and major osteoporotic fracture.

• #10 Osteoporosis: Clinical Evaluation – Endotext – NCBI Bookshelf

  https://www.ncbi.nlm.nih.gov/books/NBK279049/

  The use of FRAX provides a quantitative estimation of fracture risk that is based on robust data in large populations of men and women with ethnic and geographic diversity. […] To generate a valid FRAX output, the responses to CRF questions must be correct; for example, an incorrect entry of self-reported rheumatoid arthritis or use of glucocorticoids could skew the results toward overestimation of fracture risk. […] The reported prevalence of secondary osteoporosis varies depending on the study population, the extent of the medical evaluation, and definitions for laboratory abnormalities. It is likely that many or most patients with osteoporosis have clinically significant contributing factors that may influence patient management.

• #11 Predicting risk of osteoporotic fracture in men and women in England and Wales: prospective derivation and validation of QFractureScores | The BMJ

  https://www.bmj.com/content/339/bmj.b4229

  Objective To develop and validate two new fracture risk algorithms (QFractureScores) for estimating the individual risk of osteoporotic fracture or hip fracture over 10 years. […] Use of hormone replacement therapy (HRT), age, body mass index (BMI), smoking status, recorded alcohol use, parental history of osteoporosis, rheumatoid arthritis, cardiovascular disease, type 2 diabetes, asthma, tricyclic antidepressants, corticosteroids, history of falls, menopausal symptoms, chronic liver disease, gastrointestinal malabsorption, and other endocrine disorders were significantly and independently associated with risk of osteoporotic fracture in women. […] The hip fracture algorithm had the best performance among men and women. It explained 63.94% of the variation in women and 63.19% of the variation in men.

• #12 Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy | Scientific Reports

  https://www.nature.com/articles/s41598-021-93152-5

  Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. […] We developed a computational model from 8981 clinical variables, including demographic data, diagnoses, laboratory results, medications, and initial BMD results, taken from 10-year period of electronic medical records to predict BMD response after treatment. […] The results showed that the average treatment response of the recommended regimens was 9.54% higher than the actual regimens. In summary, our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and personalising the most appropriate treatment regimen for an individual patient. […] The goals of osteoporosis treatment are to reduce risk of osteoporotic fractures and improve bone mineral density (BMD), a gold standard tool for diagnosing osteoporosis and assessing response to therapy.

• #13 Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy | Scientific Reports

  https://www.nature.com/articles/s41598-021-93152-5

  We believe that machine learning-based decision support systems could be potentially leveraged with relevant patient information to inform a new way of improving therapeutic effectiveness in osteoporosis treatment. […] We proposed a personalized osteoporosis treatment approach guided by a machine learning model prediction. […] The dataset of 13,562 osteoporosis treatment profiles taken from January 2011 to December 2019 were used for this study. […] The Random Forests model achieved the highest overall performance in the testing dataset with Accuracy = 0.69, Precision = 0.70, Recall = 0.89, F1-score = 0.78, ROC = 0.70. […] The proposed machine-learning model identifies possible BMD response patterns of different treatments based on complex personal clinical data. […] The difference of average probability between actual and recommended regimens were 8.36% in the response group, 11.47% in the inadequate response group, and 9.54% in the whole testing dataset.

• #14 Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy | Scientific Reports

  https://www.nature.com/articles/s41598-021-93152-5

  This study demonstrated the development of a machine learning-based clinical decision support system. […] This method is an automated data pipeline process which can be potentially integrated into hospital EMR systems. […] This alternative approach can aid physicians to select an optimal therapeutic regimen in order to maximize a patient-specific treatment outcome.

• #15 Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network | BMC Public Health | Full Text

  https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-11002-5

  Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults. […] The univariate logistic model found that taking calcium tablet (odds ratio [OR]=0.431), SBP (OR=1.010), fracture (OR=1.796), coronary heart disease (OR=4.299), drinking alcohol (OR=1.835), physical exercise (OR=0.747) and other factors were related to the risk of osteoporosis. […] Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research. […] The ANN model results showed that if the probability was more significant than 0.330, osteoporosis would occur. Further researches are needed to validate our model to predict the risk of osteoporosis in adults.

• #16 Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data | Scientific Reports

  https://www.nature.com/articles/s41598-024-56114-1

  Early prediction and identification of osteoporosis can pave the way for timely interventions, potentially decelerating or even reversing bone loss. […] This study, by capitalizing on nationwide primary healthcare data from Germany, offers a non-invasive and efficient means to predict osteoporosis risk based on health indicators and chronic conditions. […] The achieved lift value of 1.9 for the stacker model accentuates its ability to effectively identify osteoporosis cases compared to random selection, validating its clinical utility. […] The significance of lipid metabolism disorders in predicting osteoporosis in our model presented intriguing insights. […] The SHAP values, an advanced tool for model interpretability, were instrumental in determining the salience of each feature within our predictive framework. Age and gender emerged as the most paramount factors, a finding that resonates with the broader osteoporosis literature.

• #17 Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data | Scientific Reports

  https://www.nature.com/articles/s41598-024-56114-1

  The imperative of early osteoporosis prediction has never been clearer. […] The choice of algorithms in the present study was pivotal in ensuring robust prediction performance. […] The optimal threshold probability of 0.52 derived from the Youden index underscores the balanced consideration of both sensitivity (true positive rate) and specificity (true negative rate) in the study. […] The current research serves as a testament to the potential of machine learning in advancing osteoporosis prediction, highlighting a novel approach that melds the power of various predictive algorithms.

• #18 PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach | BMC Research Notes | Full Text

  https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-025-07089-3

  Osteoporosis, prevalent among the elderly population, is primarily diagnosed through bone mineral density (BMD) testing, which has limitations in early detection. […] This study demonstrates the potential of machine learning models in assessing an individuals risk of developing osteoporosis, a condition that significantly impacts quality of life and imposes substantial healthcare costs. […] Importantly, identifying height, age, and sex as top predictors offers critical insights into the demographic and physiological factors that clinicians should consider when evaluating patients risk profiles. […] The LightGBM model outperformed others with an F1 score of 0.914, an MCC of 0.831, and an AUC of 0.970 on the training set. […] On the test set, it achieved an F1 score of 0.912, an MCC of 0.826, and an AUC of 0.972.

• #19 PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach | BMC Research Notes | Full Text

  https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-025-07089-3

  Therefore, the osteoporosis prediction model constructed using the LightGBM demonstrated better generalizability and robustness. […] Our study has several limitations that should be acknowledged and considered in future research. […] These findings suggest that machine learning can be comprehensively applied to healthcare big data for risk analysis of certain diseases.

• #20 Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247330

  Osteoporosis has increased and developed into a serious public health concern worldwide. […] The prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT showed high validity with more than 93% accuracy, specificity, and negative predictive value. […] The prediction accuracy of osteoporosis was 95.9% and 96.0% in the training and validation cohorts, respectively. […] Therefore, screening using this method may contribute to reduce or prevent the unnecessary duplication check and cost of DXA. […] Overall, opportunistic screening of femoral osteoporosis with machine-learning analysis and APCT has shown high potential feasibility.

• #21 Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy | Scientific Reports

  https://www.nature.com/articles/s41598-021-93152-5

  Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. […] We developed a computational model from 8981 clinical variables, including demographic data, diagnoses, laboratory results, medications, and initial BMD results, taken from 10-year period of electronic medical records to predict BMD response after treatment. […] The results showed that the average treatment response of the recommended regimens was 9.54% higher than the actual regimens. In summary, our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and personalising the most appropriate treatment regimen for an individual patient. […] The goals of osteoporosis treatment are to reduce risk of osteoporotic fractures and improve bone mineral density (BMD), a gold standard tool for diagnosing osteoporosis and assessing response to therapy.

• #22 Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy | Scientific Reports

  https://www.nature.com/articles/s41598-021-93152-5

  A decrease of less than 3% of lumbar BMD or less than 5% total hip or femoral neck BMD are considered response to therapy, while those who have new fractures or a BMD decrease exceeding the aforementioned criteria are considered inadequate response. […] Although there is an increasing range of approved therapeutic options; for instance, weak antiresorptive, potent antiresorptive, or anabolic agents that efficiently improve BMD and prevent fractures, one fourth of osteoporosis patients receiving treatment fail to respond with BMD improvement. […] The failure rate is high because the treatment outcome does not only depend on treatment regimens and dosage. […] These strategies seem to help to increase the successful rate of treatment outcomes and are reasonable to apply in clinical practice, but it is hard to find a clear and discrete protocol to make universal decisions because of the uncertainty of clinical adjustment and complex information regarding individual clinical factors and personal history.

• #23 Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy | Scientific Reports

  https://www.nature.com/articles/s41598-021-93152-5

  A decrease of less than 3% of lumbar BMD or less than 5% total hip or femoral neck BMD are considered response to therapy, while those who have new fractures or a BMD decrease exceeding the aforementioned criteria are considered inadequate response. […] Although there is an increasing range of approved therapeutic options; for instance, weak antiresorptive, potent antiresorptive, or anabolic agents that efficiently improve BMD and prevent fractures, one fourth of osteoporosis patients receiving treatment fail to respond with BMD improvement. […] The failure rate is high because the treatment outcome does not only depend on treatment regimens and dosage. […] These strategies seem to help to increase the successful rate of treatment outcomes and are reasonable to apply in clinical practice, but it is hard to find a clear and discrete protocol to make universal decisions because of the uncertainty of clinical adjustment and complex information regarding individual clinical factors and personal history.

• #24 Osteoporosis: Symptoms, Causes and Treatment

  https://my.clevelandclinic.org/health/diseases/4443-osteoporosis

  You should expect to manage osteoporosis for a long time, usually the rest of your life. You’ll need regular appointments with a healthcare provider and bone density tests. Your provider will monitor any changes in your bone density and will adjust your treatments as needed. […] Osteoporosis itself isn’t fatal and won’t change your life expectancy (how long you’ll live). But it can make you more likely to experience a bone fracture (and can increase your risk of more severe breaks or complications from a fracture). Some studies have found that hip fractures in adults older than 65 lead to reduced mobility and an earlier death. […] The best way to prevent bone fractures is catching osteoporosis before it can hurt you. Visit a healthcare provider for regular checkups. Ask them when you’ll need bone density tests and how often you should have follow-up tests to monitor your bone health.

• #25 Osteoporosis: Symptoms, Causes and Treatment

  https://my.clevelandclinic.org/health/diseases/4443-osteoporosis

  You should expect to manage osteoporosis for a long time, usually the rest of your life. You’ll need regular appointments with a healthcare provider and bone density tests. Your provider will monitor any changes in your bone density and will adjust your treatments as needed. […] Osteoporosis itself isn’t fatal and won’t change your life expectancy (how long you’ll live). But it can make you more likely to experience a bone fracture (and can increase your risk of more severe breaks or complications from a fracture). Some studies have found that hip fractures in adults older than 65 lead to reduced mobility and an earlier death. […] The best way to prevent bone fractures is catching osteoporosis before it can hurt you. Visit a healthcare provider for regular checkups. Ask them when you’ll need bone density tests and how often you should have follow-up tests to monitor your bone health.

• #26 Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data | Scientific Reports

  https://www.nature.com/articles/s41598-024-56114-1

  Early prediction and identification of osteoporosis can pave the way for timely interventions, potentially decelerating or even reversing bone loss. […] This study, by capitalizing on nationwide primary healthcare data from Germany, offers a non-invasive and efficient means to predict osteoporosis risk based on health indicators and chronic conditions. […] The achieved lift value of 1.9 for the stacker model accentuates its ability to effectively identify osteoporosis cases compared to random selection, validating its clinical utility. […] The significance of lipid metabolism disorders in predicting osteoporosis in our model presented intriguing insights. […] The SHAP values, an advanced tool for model interpretability, were instrumental in determining the salience of each feature within our predictive framework. Age and gender emerged as the most paramount factors, a finding that resonates with the broader osteoporosis literature.

• #27 Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data | Scientific Reports

  https://www.nature.com/articles/s41598-024-56114-1

  Osteoporosis is a major public health concern that significantly increases the risk of fractures. […] The aim of this study was to develop a Machine Learning based predictive model to screen individuals at high risk of osteoporosis based on chronic disease data, thus facilitating early detection and personalized management. […] In this study, a predictive model for osteoporosis based on chronic disease data was developed using machine learning. The model shows great potential in early detection and risk stratification of osteoporosis, ultimately facilitating personalized prevention and management strategies. […] The adverse outcomes of osteoporosis such as hip and vertebral fractures are associated with substantial morbidity, mortality, and economic costs. […] Unfortunately, osteoporosis often remains undiagnosed and untreated until the occurrence of a debilitating fracture, hence proactive identification of high-risk individuals is a critical step in mitigating the disease burden.

• #28 Osteoporosis Self-Assessment Tool for Asians Can Predict Neurologic Prognosis in Patients with Isolated Moderate Traumatic Brain Injury | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0132685

  Osteoporosis Self-Assessment Tool for Asians (OSTA) has been proved to be a simple and effective tool for recognizing osteoporosis risk. […] In addition to labeling the cohort harboring osteoporotic risk, OSTA index could predict neurologic prognosis in patients with isolated moderate traumatic brain injury. […] Higher ISS, lower OSTA index and exposure to neurosurgery are the independent risk factors for poorer recovery from isolated moderate traumatic brain injury. The OSTA index could provide additional merit in traumatic prognostication in addition to labelling the cohort harboring osteoporotic risk.

• #29 PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach | BMC Research Notes | Full Text

  https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-025-07089-3

  Osteoporosis, prevalent among the elderly population, is primarily diagnosed through bone mineral density (BMD) testing, which has limitations in early detection. […] This study demonstrates the potential of machine learning models in assessing an individuals risk of developing osteoporosis, a condition that significantly impacts quality of life and imposes substantial healthcare costs. […] Importantly, identifying height, age, and sex as top predictors offers critical insights into the demographic and physiological factors that clinicians should consider when evaluating patients risk profiles. […] The LightGBM model outperformed others with an F1 score of 0.914, an MCC of 0.831, and an AUC of 0.970 on the training set. […] On the test set, it achieved an F1 score of 0.912, an MCC of 0.826, and an AUC of 0.972.

• #30 Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy | Scientific Reports

  https://www.nature.com/articles/s41598-021-93152-5

  We believe that machine learning-based decision support systems could be potentially leveraged with relevant patient information to inform a new way of improving therapeutic effectiveness in osteoporosis treatment. […] We proposed a personalized osteoporosis treatment approach guided by a machine learning model prediction. […] The dataset of 13,562 osteoporosis treatment profiles taken from January 2011 to December 2019 were used for this study. […] The Random Forests model achieved the highest overall performance in the testing dataset with Accuracy = 0.69, Precision = 0.70, Recall = 0.89, F1-score = 0.78, ROC = 0.70. […] The proposed machine-learning model identifies possible BMD response patterns of different treatments based on complex personal clinical data. […] The difference of average probability between actual and recommended regimens were 8.36% in the response group, 11.47% in the inadequate response group, and 9.54% in the whole testing dataset.

• #31 Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy | Scientific Reports

  https://www.nature.com/articles/s41598-021-93152-5

  This study demonstrated the development of a machine learning-based clinical decision support system. […] This method is an automated data pipeline process which can be potentially integrated into hospital EMR systems. […] This alternative approach can aid physicians to select an optimal therapeutic regimen in order to maximize a patient-specific treatment outcome.

• #32 Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247330

  Osteoporosis has increased and developed into a serious public health concern worldwide. […] The prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT showed high validity with more than 93% accuracy, specificity, and negative predictive value. […] The prediction accuracy of osteoporosis was 95.9% and 96.0% in the training and validation cohorts, respectively. […] Therefore, screening using this method may contribute to reduce or prevent the unnecessary duplication check and cost of DXA. […] Overall, opportunistic screening of femoral osteoporosis with machine-learning analysis and APCT has shown high potential feasibility.

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