Classification of lumbar spine disorders using large language models and MRI segmentation.

ABSTRACT:

Background

MRI is critical for diagnosing lumbar spine disorders but its complexity challenges diagnostic accuracy. This study proposes a BERT-based large language model (LLM) to enhance precision in classifying lumbar spine disorders through the integration of MRI data, textual reports, and numerical measurements.

Methods

The segmentation quality of MRI data is evaluated using dice coefficients (cut-off: 0.92) and intersection over union (IoU) metrics (cut-off: 0.88) to ensure precise anatomical feature extraction. The CNN extracts key lumbar spine features, such as lumbar lordotic angle (LLA) and disc heights, which are tokenized as direct scalar values representing positional relationships. A data source of 28,065 patients with various disorders, including degenerative di

SUBMITTER: Dong R 

PROVIDER: S-EPMC11571895 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature