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• W2952034757 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." @default.
• W2952034757 created "2019-06-27" @default.
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• W2952034757 date "2019-06-10" @default.
• W2952034757 modified "2023-10-17" @default.
• W2952034757 title "Artificial neural network optimizes self-examination of osteoporosis risk in women" @default.
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• W2952034757 doi "https://doi.org/10.1177/0300060519850648" @default.
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