Using deep learning to enhance reporting efficiency and accuracy in degenerative cervical spine MRI

Abstract

BACKGROUND CONTEXT

Cervical spine MRI is essential for evaluating degenerative cervical spondylosis (DCS) but is time-consuming to report and subject to interobserver variability. The integration of artificial intelligence in medical imaging offers potential solutions to enhance productivity and diagnostic consistency.

PURPOSE

To assess whether a transformer-based deep learning model (DLM) can improve the efficiency and accuracy of radiologists in reporting DCS MRIs.

STUDY DESIGN/SETTING

Retrospective study using external DCS MRIs from December 2015 to August 2018.

PATIENT SAMPLE

The test dataset comprised 50 preoperative DCS MRIs (2,555 images) from 50 patients (mean age = 60 years ± SD 14; 13 women [26%]), excluding cases with instrumentation.

OUTCOME MEASURES

Primary outcomes were interpretation time and interobserver agreement (Gwet's kappa) among radiologists grading spinal canal and neural foramina stenosis with and without DLM-assistance.

METHODS

A transformer-based DLM was used to classify spinal canal (grades 0/1/2/3) and neural foramina (grades 0/1/2) stenosis at each disc level. Two experienced musculoskeletal radiologists (both with 12-years-of-experience) provided reference standard labels in consensus. Ten radiologists (0–7 years of experience) graded DCS MRIs with and without DLM-assistance, with a 1-month washout period between sessions to minimize recall bias. Interpretation time and interobserver agreement were assessed.

RESULTS

DLM-assistance significantly improved interpretation time by 69 to 308 s (p<.001), reducing mean time from 159–490 s (SD 27–649) to 90–182 s (SD 42–218). Radiology residents experienced the largest time savings. DLM-assistance improved interobserver agreement across all stenosis gradings compared to baseline. For dichotomous spinal canal grading, residents had the largest improvement in agreement (κ = 0.63 to 0.77, p<.001). Conversely, for dichotomous neural foramina grading, musculoskeletal radiologists had the largest improvement (κ=0.60 to 0.72, p<.001). Notably, independent DLM performance alone was equivalent or superior to all readers.

CONCLUSIONS

The integration of a deep learning model into the radiological assessment of DCS MRI improved radiologists’ interpretation time and interobserver agreement, regardless of experience level.