Feasibility of deep learning algorithm in diagnosing lumbar central canal stenosis using abdominal CT

• Published in: Skeletal radiology Vol. 54; no. 5; pp. 947 - 957
• Main Authors: Jeon, Yejin, Kim, Bo Ram, Choi, Hyoung In, Lee, Eugene, Kim, Da-Wit, Choi, Boorym, Lee, Joon Woo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2025; Springer Nature B.V
• Subjects: Accuracy; Aged; Aged, 80 and over; Algorithms; Canals (anatomy); Classification; Computed tomography; Correlation coefficients; Deep Learning; Feasibility Studies; Female; Humans; Image processing; Image segmentation; Imaging; Lumbar Vertebrae - diagnostic imaging; Machine learning; Male; Medical imaging; Medicine; Medicine & Public Health; Middle Aged; Nuclear Medicine; Orthopedics; Pathology; Radiographic Image Interpretation, Computer-Assisted - methods; Radiography, Abdominal - methods; Radiology; Retrospective Studies; Scientific; Scientific Article; Segmentation; Sensitivity and Specificity; Spinal Stenosis - diagnostic imaging; Spine (lumbar); Stenosis; Tomography, X-Ray Computed - methods; Central canal stenosis; Deep learning; Computed tomography; Lumbar spine; Artificial Intelligence
• ISSN: 0364-2348; 1432-2161; 1432-2161
• DOI: 10.1007/s00256-024-04796-z

Objective To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT). Materials and methods This retrospective study involved 109 patients undergoing LCTs and ACTs between January 2014 and July 2021. The dural sac on CT images was manually segmented and classified as normal or stenosed (dural sac cross-sectional area ≥ 100 mm 2 or < 100 mm 2 , respectively). A deep learning model based on U-Net architecture was developed to automatically segment the dural sac and classify the central canal stenosis. The classification performance of the model was compared on a testing set (990 images from 9 patients). The accuracy, sensitivity, and specificity of automatic segmentation were quantitatively evaluated by comparing its Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) with those of manual segmentation. Results In total, 990 CT images from nine patients (mean age ± standard deviation, 77 ± 7 years; six men) were evaluated. The algorithm achieved high segmentation performance with a DSC of 0.85 ± 0.10 and ICC of 0.82 (95% confidence interval [CI]: 0.80,0.85). The ICC between ACTs and LCTs on the deep learning algorithm was 0.89 (95%CI: 0.87,0.91). The accuracy of the algorithm in diagnosing LCCS with dichotomous classification was 84%(95%CI: 0.82,0.86). In dataset analysis, the accuracy of ACTs and LCTs was 85%(95%CI: 0.82,0.88) and 83%(95%CI: 0.79,0.86), respectively. The model showed better accuracy for ACT than LCT. Conclusion The deep learning algorithm automatically diagnosed LCCS on LCTs and ACTs. ACT had a diagnostic performance for LCCS comparable to that of LCT.