AUTHOR=Kang Jeong-Woon , Park Chunsu , Lee Dong-Eon , Yoo Jae-Heung , Kim MinWoo 

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

JOURNAL=Frontiers in Physiology

VOLUME=13

YEAR=2023

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2022.1061911

DOI=10.3389/fphys.2022.1061911

ISSN=1664-042X

ABSTRACT=<p>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 <inline-formula id="inf1"><mml:math id="m1" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mn>1.0</mml:mn></mml:mrow></mml:math></inline-formula>), 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.</p>