Osteoporosis, a disease characterized by weakened bones and increased fracture risk, has long been a subject of extensive scientific investigation. The advent of machine learning (ML) has ushered in a new era of artificial intelligence (AI) capabilities, particularly in fields dealing with complex datasets where traditional analytical methods fall short. Osteoporosis research is one such area where Machine Learning Solutions offer significant potential, despite certain technical and clinical considerations. This review explores the landscape of machine learning applications in osteoporosis, addressing key concerns and highlighting the benefits for stakeholders aiming to enhance osteoporosis management through AI.
A comprehensive search across PubMed and Web of Science databases yielded 89 relevant studies. These studies investigated machine learning applications across four critical domains of osteoporosis management: evaluating bone properties, classifying osteoporosis, detecting fractures, and predicting fracture risk. An assessment of reporting and methodological quality, using a 12-point checklist, revealed that the studies generally exhibited moderate quality, with scores ranging from 2 to 11 (mode score 6). Several limitations were commonly observed, including incomplete reporting, especially regarding model selection processes, inadequate data partitioning, and a scarcity of external validation studies.
However, the application of machine learning in analyzing medical images for opportunistic osteoporosis diagnosis and fracture detection stands out as a particularly promising area. This represents a major contribution of machine learning solutions to the field of osteoporosis. Furthermore, ongoing research into developing machine learning models to identify novel risk factors for fractures and improve the accuracy of fracture prediction models presents another highly promising avenue. Certain studies have also begun to explore the potential of machine learning models to inform and enhance clinical decision-making in osteoporosis care.
To mitigate common challenges and ensure the robust development and application of machine learning solutions in this domain, the adoption of standardized checklists for model development and result reporting is highly recommended. This will promote transparency, reproducibility, and ultimately, the responsible and effective integration of machine learning into osteoporosis management.
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