[Does artificial intelligence have a role in osteoporosis management?] / L'intelligence artificielle a-t-elle un rôle dans la prise en charge de l'ostéoporose ?

The individual and societal burden of osteoporosis is high and will continue to increase due to the demographic situation. Applications based on artificial intelligence models can provide concrete solutions at each step of the management of osteoporosis: screening, diagnostic, therapy management and prognostic assessment. The implementation of such models could assist clinicians in their workflow while improving overall patient care.

L'ostéoporose représente un fléau important, à l'échelle individuelle mais aussi sociétale. Avec le vieillissement de la population, le nombre de patients concernés augmente de manière considérable. Des applications basées sur des modèles d'intelligence artificielle nous apportent des solutions de plus en plus concrètes, à chaque étape de la prise en charge de l'ostéoporose : dépistage, diagnostic, prise en charge médicamenteuse et évaluation pronostique. L'implémentation de tels modèles pourrait aider les professionnels de santé, aussi bien dans l'optimisation du flux du travail que dans la prise en charge clinique du patient.

[Screening and management of sarcopenia by the general practitioner]. / Dépistage et prise en charge de la sarcopénie au cabinet.

Sarcopenia is an aging syndrome with multiple contributing factors, characterized by a loss of muscle strength, function and mass. It affects a third of the elderly population, increasing morbidity and mortality, as well as health costs. It should be suspected in the event of a decrease in physical capacities reported or observed during the consultation, in a patient with risk factors. Five questions (SARC-F formulary) or the measure of the gait speed makes screening easy to perform ; the diagnosis is confirmed by supplementary examinations in a specialized center. Treatment consists on performing physical exercises against resistance and ensuring sufficient caloric and protein intake; drug treatments are under study.

La sarcopénie est un syndrome lié au vieillissement avec de multiples facteurs favorisants, caractérisé par une perte de la force, de la fonction et de la masse musculaires. Elle affecte un tiers de la population âgée, chez qui elle augmente la morbidité et la mortalité ainsi que les coûts de la santé. On doit la suspecter en cas de diminution des capacités physiques rapportée ou observée lors de la consultation chez un patient présentant des facteurs de risque. Cinq questions (formulaire SARC-F) ou la mesure de la vitesse de la marche rendent facile le dépistage ; le diagnostic est confirmé par des examens complémentaires dans un centre spécialisé. La prise en charge consiste en la réalisation d'exercices physiques contre résistance en assurant des apports caloriques et protéiques suffisants ; des traitements médicamenteux sont à l'étude.

Standardization of body composition parameters between GE Lunar iDXA and Hologic Horizon A and their clinical impact.

Body composition (BC) measured by DXA differs between devices. We aimed to compare regional and total BC measurements assessed by the Hologic Horizon A and the GE Lunar iDXA devices; to determine device-specific calibration equations for each BC parameter; and to assess the impact of this standardization procedure on the assessment of sarcopenia, lipedema, obesity, and cardiovascular risk with DXA. A total of 926 postmenopausal women (aged 72.9 ± 6.9 yr, height 160.3 ± 6.6 cm, weight 66.1 ± 12.7 kg) underwent BC assessment on each device within 1 h, following the ISCD guidelines. The included sample was split into 80% train and 20% test datasets stratified by age, height, and weight. Inter-device differences in BC parameters were assessed with Bland-Altman analysis, Pearson or Spearman correlation coefficients, and t-tests or Wilcoxon tests. The equations were developed in the train dataset using backward stepwise multiple linear regressions and were evaluated in the test dataset with the R-squared and mean absolute error. We compared the abovementioned BC-derived health conditions before and after standardization in the test set with respect to relative risk, accuracy, Kappa score, and McNemar tests. Total and regional body masses were similar (p>.05) between devices. BMC was greater for all regions in the Lunar device (p<.05), while fat and lean masses differed among regions. Regression equations showed high performance metrics in both datasets. The BC assessment from Hologic classified 2.13 times more sarcopenic cases (McNemar: p<.001), 1.39 times more lipedema (p<.001), 0.40 times less high cardiovascular risk (p<.001), and similarly classified obesity (p>.05), compared to Lunar. After standardization, the differences disappeared (p>.05), and the classification metrics improved. This study discusses how hardware and software differences impact BC assessments. The provided standardization equations address these issues and improve the agreement between devices. Future studies and disease definitions should consider these differences.

Development and reporting of artificial intelligence in osteoporosis management.

An abundance of medical data and enhanced computational power have led to a surge in Artificial Intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17th, 2020, to February 1st, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the MI-CLAIM checklist. The systematic search yielded 97 articles that fell into five areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles) and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7-11) for bone properties assessment, 7.8 (range: 5-11) for osteoporosis classification, 8.4 (range: 7-11) for fracture detection, 7.6 (range: 4-11) for risk prediction, and 9.0 (range: 6-11) for bone segmentation. A 6th area, AI-driven clinical decision support, identified the studies from the five preceding areas which aimed to improve clinician efficiency, diagnostic accuracy and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes.

This review covers the recent advancements in artificial intelligence (AI) for managing osteoporosis, an increasingly prevalent condition that weakens bone tissues and increases fracture risk. Analyzing 97 studies from December 2020 to February 2023, the present work highlights how AI enhances bone properties assessment, osteoporosis classification, fracture detection and classification, risk prediction, and bone segmentation. A systematic qualitative assessment of the studies revealed improvements in study quality compared with the earlier review period, supported by innovative and more explainable AI approaches. AI shows promise in clinical decision support by offering novel screening tools that can help in the earlier identification of the disease, improve clinical workflows and patient prognosis. New pre-processing strategies and advanced model architectures have played a critical role in these improvements. Researchers have enhanced the accuracy and predictive performance of traditional methods by integrating clinical data with imaging data through advanced multi-factorial AI techniques. These innovations, paired with standardized development and validation processes, promise to personalize medicine and enhance patient care in osteoporosis management.

Muscle parameters in fragility fracture risk prediction in older adults: A scoping review.

Half of osteoporotic fractures occur in patients with normal/osteopenic bone density or at intermediate or low estimated risk. Muscle measures have been shown to contribute to fracture risk independently of bone mineral density. The objectives were to review the measurements of muscle health (muscle mass/quantity/quality, strength and function) and their association with incident fragility fractures and to summarize their use in clinical practice. This scoping review follows the PRISMA-ScR guidelines for reporting. Our search strategy covered the three overreaching concepts of 'fragility fractures', 'muscle health assessment' and 'risk'. We retrieved 14 745 references from Medline Ovid SP, EMBASE, Web of Science Core Collection and Google Scholar. We included original and prospective studies on community-dwelling adults aged over 50 years that analysed an association between at least one muscle parameter and incident fragility fractures. We systematically extracted 17 items from each study, including methodology, general characteristics and results. Data were summarized in tables and graphically presented in adjusted forest plots. Sixty-seven articles fulfilled the inclusion criteria. In total, we studied 60 muscle parameters or indexes and 322 fracture risk ratios over 2.8 million person-years (MPY). The median (interquartile range) sample size was 1642 (921-5756), age 69.2 (63.5-73.6) years, follow-up 10.0 (4.4-12.0) years and number of incident fragility fractures 166 (88-277). A lower muscle mass was positively/not/negatively associated with incident fragility fracture in 28 (2.0), 64 (2.5) and 10 (0.2 MPY) analyses. A lower muscle strength was positively/not/negatively associated with fractures in 53 (1.3), 57 (1.7 MPY) and 0 analyses. A lower muscle function was positively/not/negatively associated in 63 (1.9), 45 (1.0 MPY) and 0 analyses. An in-depth analysis shows how each single muscle parameter was associated with each fragility fractures subtype. This review summarizes markers of muscle health and their association with fragility fractures. Measures of muscle strength and function appeared to perform better for fracture risk prediction. Of these, hand grip strength and gait speed are likely to be the most practical measures for inclusion in clinical practice, as in the evaluation of sarcopenia or in further fracture risk assessment scores. Measures of muscle mass did not appear to predict fragility fractures and might benefit from further research, on D3-creatine dilution test, lean mass indexes and artificial intelligence methods.