Evaluation of deep learning-based quantitative computed tomography for opportunistic osteoporosis screening

• Published in: Scientific reports Vol. 14; no. 1; p. 363
• Main Authors: Oh, Sangseok, Kang, Woo Young, Park, Heejun, Yang, Zepa, Lee, Jemyoung, Kim, Changwon, Woo, Ok Hee, Hong, Suk-Joo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05.01.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/699; Automation; Bone Density; Bone Diseases, Metabolic; Bone mineral density; Computed tomography; Deep Learning; Dual energy X-ray absorptiometry; Humanities and Social Sciences; Humans; Medical imaging; multidisciplinary; Osteoporosis; Osteoporosis - diagnostic imaging; Science; Science (multidisciplinary); Spine; Spine (lumbar); Tomography; Tomography, X-Ray Computed; Vertebrae
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-45824-7

To evaluate diagnostic efficacy of deep learning (DL)-based automated bone mineral density (BMD) measurement for opportunistic screening of osteoporosis with routine computed tomography (CT) scans. A DL-based automated quantitative computed tomography (DL-QCT) solution was evaluated with 112 routine clinical CT scans from 84 patients who underwent either chest (N:39), lumbar spine (N:34), or abdominal CT (N:39) scan. The automated BMD measurements (DL-BMD) on L1 and L2 vertebral bodies from DL-QCT were validated with manual BMD (m-BMD) measurement from conventional asynchronous QCT using Pearson’s correlation and intraclass correlation. Receiver operating characteristic curve (ROC) analysis identified the diagnostic ability of DL-BMD for low BMD and osteoporosis, determined by dual-energy X-ray absorptiometry (DXA) and m-BMD. Excellent concordance were seen between m-BMD and DL-BMD in total CT scans (r = 0.961/0.979). The ROC-derived AUC of DL-BMD compared to that of central DXA for the low-BMD and osteoporosis patients was 0.847 and 0.770 respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to central DXA for low BMD were 75.0%, 75.0%, and 75.0%, respectively, and those for osteoporosis were 68.0%, 80.5%, and 77.7%. The AUC of DL-BMD compared to the m-BMD for low BMD and osteoporosis diagnosis were 0.990 and 0.943, respectively. The sensitivity, specificity, and accuracy of DL-BMD compared to m-BMD for low BMD were 95.5%, 93.5%, and 94.6%, and those for osteoporosis were 88.2%, 94.5%, and 92.9%, respectively. DL-BMD exhibited excellent agreement with m-BMD on L1 and L2 vertebrae in the various routine clinical CT scans and had comparable diagnostic performance for detecting the low-BMD and osteoporosis on conventional QCT.