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Abstract #0164

Optimizing Osteoporosis Detection: AutoML -Radiomics Approach Combining Imaging and Demographic Data

Yung-Yin Cheng1,2, Chun-Wen Chen3, Chia-Hong Hsieh1,3, Chun-Han Liao1,4,5, Ming-Cheng Liu6,7, Shao-Chieh Lin1, Chia-Chun Tai8, Tzu-Yu Chiu8, Yu-Zhen Hsieh8, and Yi-Jui Liu8
1Ph.D. program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, 2Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung, Taiwan, 3Department of Radiology, School of Medicine, National Defense Medical Center, Taipei, Taiwan, 4Department of Medical Imaging, Yuanlin Christian Hospital, Changhua, Taiwan, 5Department of Medical Imaging, Changhua Christian Hospital, Changhua, Taiwan, 6Department of Radiology, Taichung Veterans General Hospital, Taichung, Taiwan, 7Ph.D. Program of Electrical and Communications Engineering, Feng Chia University, Taichung, Taiwan, 8Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan

Synopsis

Keywords: Bone/Skeletal, Machine Learning/Artificial Intelligence, AutoML, Radiomics, Osteoporosis, MRI

Motivation: To develop a machine learning model utilizing demographic data and lumbar fat-water MRI as an opportunistic screening for osteoporosis.

Goal(s): To compare the performance of AutoML in predicting osteoporosis using demographic features, radiomics from lumbar fat and water MRI, and their combination.

Approach: A TPOP-radiomics-based classification model was trained using demographic features and radiomic data from lumbar fat-water MRI, differentiating between normal and osteoporosis as identified by DEXA.

Results: The combined model emerged as the most effective from the AutoML process, achieving a mean sensitivity of 0.783 and a mean specificity of 0.867 in distinguishing between normal and osteoporosis.

Impact: Because osteoporosis is often considered a 'silent' disease, routine IDEAL-IQ lumbar scans have the potential to serve as an opportunistic screening tool for reducing the risk of fragility fractures, which are associated with morbidity and mortality.

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Keywords