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- Title
Osteoporosis Prediction Using Machine-Learned Optical Bone Densitometry Data.
- Authors
Miura, Kaname; Tanaka, Shigeo M.; Chotipanich, Chanisa; Chobpenthai, Thanapon; Jantarato, Attapon; Khantachawana, Anak
- Abstract
Optical bone densitometry (OBD) has been developed for the early detection of osteoporosis. In recent years, machine learning (ML) techniques have been actively implemented for the areas of medical diagnosis and screening with the goal of improving diagnostic accuracy. The purpose of this study was to verify the feasibility of using the combination of OBD and ML techniques as a screening tool for osteoporosis. Dual energy X-ray absorptiometry (DXA) and OBD measurements were performed on 203 Thai subjects. From the OBD measurements and readily available demographic data, machine learning techniques were used to predict the T-score measured by the DXA. The T-score predicted using the Ridge regressor had a correlation of r = 0.512 with respect to the reference value. The predicted T-score also showed an AUC of 0.853 for discriminating individuals with osteoporosis. The results obtained suggest that the developed model is reliable enough to be used for screening for osteoporosis.
- Subjects
BONE densitometry; DUAL-energy X-ray absorptiometry; OSTEOPOROSIS; MEDICAL screening; MACHINE learning; DIAGNOSIS
- Publication
Annals of Biomedical Engineering, 2024, Vol 52, Issue 2, p396
- ISSN
0090-6964
- Publication type
Article
- DOI
10.1007/s10439-023-03387-8