Artificial neural network optimizes self-examination of osteoporosis risk in women

Abstract

Objective This study aimed to investigate the application of an artificial neural network (ANN) in optimizing the Osteoporosis Self-Assessment Tool for Asians (OSTA) score. Methods OSTA score was calculated for each female participant that underwent dual-energy X-ray absorptiometry examination in two hospitals (one in each of two Chinese cities, Harbin and Ningbo). An ANN model was built using age and weight as input and femoral neck T-score as output. Osteoporosis risk screening by joint application of ANN and OSTA score was evaluated by receiver operating characteristic curve analysis. Results Nearly 90% of women with dual-energy X-ray absorptiometry-determined femoral neck osteoporosis were ≥60 years old. The ANN with age and weight as input and OSTA score both identified osteoporosis, with respective accuracy rates of 78.8% and 78.3%. However, both methods failed to identify osteoporosis in women < 60 years old. Compared with OSTA score alone, combined use of the two tools increased the rate of osteoporosis recognition among women > 80 years old. Conclusions OSTA score-mediated osteoporosis risk screening should be restricted to women ≥60 years old. Joint application of ANN and OSTA improved osteoporosis risk screening among Chinese women > 80 years old.

Machine Learning Solutions for Osteoporosis—A Review

“…( 29,32–34,36–65 ) Osteoporosis classification was made based on lumbar BMD, ( 32–34,37,51 ) hip BMD, ( 38,50,58 ) lumbar and hip BMD, ( 29,39–42,46–48,53,59,60 ) other non‐standard assessments, ( 43,44,49,54–56,65 ) or unspecified. ( 36,45,52,57,61–64 ) Studies identified osteoporosis based on opportunistic imaging from CT, ( 32–34 ) X‐ray, ( 37,38,43–45,55–59,63,64 ) or dental imaging; (36,47–49,53,54,60,62 ) other studies used data from patient characteristics, ( 40,41,50,51,61,65 ) bone biomarkers, (29,39 ) or acoustical responses. ( 42,52 ) As outcome, studies classified osteoporotic versus normal patients, ( 29,36,39,40,43,49,50,52,54–57,62 ) osteoporotic versus non‐osteoporotic patients (based on a BMD T ‐score threshold of –2.5 SD), ( 34,38,44,64 ) normal versus abnormal subjects (based on the BMD T ‐score threshold of −1 SD), ( 33,41,42,45,47,48,58–60,65 ) experimented multiple classifications, ( 46,63 ) or assigned to three classes: osteoporosis (BMD T ‐score ≤ −2.5 SD), osteopenia (−2.5 < BMD T ‐score ≤ −1), and normal (BMD T ‐score > −1 SD).…”

“…( 32,37,51,61 ) The models were internally validated in almost each study, and in two of them the models were also externally validated. ( 32,50 ) Twelve studies validated their model using accuracy with an average performance of 90.1% (range 70.0% to 98.9%); 22 validated their model using AUC with a mean of 0.90 (range 0.74 to 1.00). Surprisingly, six reported near perfect AUCs (AUC ≥0.99), ( 29,43,48,54,56,60 ) indicating potential overfitting with risk for poor generalization.…”

“…( 33,63,65 ) Several studies did not report characteristics of their data set ( 48,55,56,59–64 ) or the model selection process. ( 33,39,41,42,45,46,50,51,53,56–61,64,65 ) Performance was significantly impacted by case prevalence where accuracy dropped from 94.0% to 88.4% when tested on 13% and 50% positive (osteoporotic) cases, respectively. ( 44 ) An image enhancement and standardization step, and combining multiple features, was able to considerably improve results in two studies, respectively.…”

Evaluation of two tools for the early screening of osteoporosis in postmenopausal Chinese women with type 2 diabetes mellitus

“…8,9 Studies demonstrated that OSTA could help to predict fracture risk among postmenopausal women. [10][11][12] The osteoporosis screening tool for Chinese (OSTC) women, developed by Chinese researchers using body weight and age, was shown to effectively predict osteoporosis risk and to determine the appropriate use of BMD testing in community hospitals without sufficient DXA equipment. 13 Considering that T2DM is associated with increased body weight, 14 the OSTA and OSTC might estimate the fracture risk in patients with T2DM.…”

Evolution in fracture risk assessment: artificial versus augmented intelligence

“…In contrast, fewer than 100 of these were in the field of osteoporosis, although this is following the same exponential trajectory with the majority of studies published during the last 2-3 years. Efforts have been made in osteoporosis diagnosis and classification, bone mineral density assessment, fracture detection, fracture risk estimation, and bone image segmentation [7][8][9][10][11][12][13][14]. The majority of these articles used opportunistic data-particularly in imaging.…”