Comprehensive comparative analysis of explainable deep learning model for differentiation of brucellar spondylitis and tuberculous spondylitis through MRI sequences.

ABSTRACT:

Background

The differentiation of brucellar spondylitis (BS) from tuberculous spondylitis (TS) on magnetic resonance imaging (MRI) is a critical clinical challenge. While deep learning holds promise, the optimal architectural strategy for integrating information from multi-sequence MRI remains unclear. This study systematically compared distinct deep learning architectures to identify a valid and effective integration strategy for this diagnostic problem.

Methods

In this retrospective, single-center diagnostic study, we included 235 patients with surgically and pathologically confirmed BS (n = 82) or TS (n = 153) from January 2014 to December 2024. We systematically evaluated four distinct architectural strategies for processing sagittal T1-weighted, T2-weighted, and fat-sup

SUBMITTER: Yasin P 

PROVIDER: S-EPMC12849075 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature