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

## TL;DR

A deep learning model can diagnose lumbar central canal stenosis using abdominal CT scans with accuracy similar to lumbar CT scans.

## Contribution

Developed a U-Net-based deep learning algorithm for automatic diagnosis of lumbar central canal stenosis using abdominal CT.

## Key findings

- The deep learning model achieved a Dice similarity coefficient of 0.85 and ICC of 0.82 for dural sac segmentation.
- Abdominal CT showed diagnostic accuracy (85%) comparable to lumbar CT (83%) for central canal stenosis.
- The algorithm's classification accuracy for LCCS was 84% with high consistency between abdominal and lumbar CT scans.

## Abstract

To develop a deep learning algorithm for diagnosing lumbar central canal stenosis (LCCS) using abdominal CT (ACT) and lumbar spine CT (LCT).

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 mm2 or < 100 mm2, 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.

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.

The deep learning algorithm automatically diagnosed LCCS on LCTs and ACTs. ACT had a diagnostic performance for LCCS comparable to that of LCT.