Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
Cervical spondylosis, a complex and prevalent condition, demands precise and
efficient diagnostic techniques for accurate assessment. While MRI offers
detailed visualization of cervical spine anatomy, manual interpretation remains
labor-intensive and prone to error. To address this, we developed an innovative
AI-assisted Expert-based Diagnosis System that automates both segmentation and
diagnosis of cervical spondylosis using MRI. Leveraging a dataset of 960
cervical MRI images from patients with cervical disc herniation, our system
features a pathology-guided segmentation model capable of accurately segmenting
key cervical anatomical structures. The segmentation is followed by an
expert-based diagnostic framework that automates the calculation of critical
clinical indicators. Our segmentation model achieved an impressive average Dice
coefficient exceeding 0.90 across four cervical spinal anatomies and
demonstrated enhanced accuracy in herniation areas. Diagnostic evaluation
further showcased the system precision, with a mean absolute error (MAE) of
2.44 degree for the C2-C7 Cobb angle and 3.60 precentage for the Maximum Spinal
Cord Compression (MSCC) coefficient. In addition, our method delivered high
accuracy, precision, recall, and F1 scores in herniation localization, K-line
status assessment, and T2 hyperintensity detection. Comparative analysis
demonstrates that our system outperforms existing methods, establishing a new
benchmark for segmentation and diagnostic tasks for cervical spondylosis.
