Machine Learning Solutions for Osteoporosis—A Review

Abstract

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).

Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning

“…The best-studied strategy is the use of abdominal CT to predict spine BMD 19 , 20 , 23 , classify osteoporosis based on CT attenuation 22 , simulated BMD 19 , 20 , T -score 23 , or detect osteoporotic fractures 38 ; or use imaging biomarkers to predict the risk of fractures 24 . Julien Smets et al reviewed machine learning solutions for osteoporosis 39 . Among five studies using CT scans to predict BMD, the best correlation coefficient between estimated and CT-simulated spine BMD was 0.94 21 .…”

Radiologische Diagnostik und Prognostik von COVID-19: Einsatz von künstlicher Intelligenz und Zusammenhang mit muskulo-skelettaler Bildgebung

“…Wir haben im Rahmen der Diagnostik Bilanz Studie des BioAsset Projekts [15] KI Verfahren getestet und auch hier konnte eine gute Performance hinsichtlich der automatischen Detektion von Wirbelkörperfrakturen gezeigt werden [12]. Das Potential dieser KI Ansätze ist auch bereits in ersten Reviews im Überblick zusammengestellt worden [44]. Noch sind die Studien begrenzt, verwenden unterschiedliche KI-Modelle, und die Dokumentation der Übertragbarkeit und Robustheit der Prädiktionswerte steht noch aus.…”

A pilot study of a deep learning approach to detect marginal bone loss around implants

“…Convolutional neural networks (CNNs) show excellent object detection and classification performance [ 19 ]. Many studies based on CNNs have been conducted in the field of dentistry [ 20 , 21 ], for tooth numbering [ 22 ] and analysis of dental caries [ 23 ], osteoporosis [ 24 ], periodontal bone loss [ 25 ], submerged primary teeth [ 26 ] and dental implants [ 27 – 29 ]. CNNs learn directly from raw input data and classify images without the requirement for manual feature extraction.…”