Titre
Machine Learning Solutions for Osteoporosis-A Review.
Type
synthèse (review)
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Smets, J.
Auteure/Auteur
Shevroja, E.
Auteure/Auteur
Hügle, T.
Auteure/Auteur
Leslie, W.D.
Auteure/Auteur
Hans, D.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1523-4681
Statut éditorial
Publié
Date de publication
2021-05
Volume
36
Numéro
5
Première page
833
Dernière page/numéro d’article
851
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't ; Review
Publication Status: ppublish
Publication Status: ppublish
Résumé
Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).
PID Serval
serval:BIB_9E8118118CDD
PMID
Date de création
2021-03-30T09:47:39.923Z
Date de création dans IRIS
2025-05-21T02:49:11Z