Prediction of bone mineral density in CT using deep learning with explainability

• Published in: Frontiers in physiology Vol. 13; p. 1061911
• Main Authors: Kang, Jeong-Woon, Park, Chunsu, Lee, Dong-Eon, Yoo, Jae-Heung, Kim, MinWoo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 10.01.2023
• Subjects: bone mineral density; computational tomography; deep learning; dual energy X-ray absorptiometry; explainable artificial intelligence; Physiology; explainable artificial intelligence; bone mineral density; deep learning; dual energy X-ray absorptiometry; computational tomography
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2022.1061911

Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides bone mineral density information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure bone mineral density at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the bone mineral density from an axial cut of the L1 bone on computational tomography. As a result, the correlation coefficient between bone mineral density estimates and dual-energy X-ray absorptiometry bone mineral density was .90. When the samples were categorized into abnormal and normal groups using a standard (T-score = − 1.0 ), the maximum F1 score in the diagnostic test was .875. In addition, it was identified using explainable artificial intelligence techniques that the network intensively sees a local area spanning tissues around the vertebral foramen. This method is well suited as an auxiliary tool in clinical practice and as an automatic screener for identifying latent patients in computational tomography databases.