Prediction of bone mineral density in CT using deep learning with explainability - Kang, Jeong-Woon; Park, Chunsu; Lee, Dong-Eon; Yoo, Jae-Heung; Kim, MinWoo (Frontiers in Physiology, 2023)
Bone mineral density (BMD) is a key feature in diagnosing bone diseases. Although computational tomography (CT) is a common imaging modality, it seldom provides BMD information in a clinic owing to technical difficulties. Thus, a dual-energy X-ray absorptiometry (DXA) is required to measure BMD at the expense of additional radiation exposure. In this study, a deep learning framework was developed to estimate the BMD from an axial cut of the L1 bone on CT. As a result, the correlation coefficient between BMD estimates and DXA BMD was 0.90. 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 0.875. 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 CT databases.