Deep learning for identifying cervical ossification of the posterior longitudinal ligament: a systematic review and meta-analysis.

ABSTRACT:

Background

Ossification of the posterior longitudinal ligament (OPLL) is a significant contributor for unintentional durotomy following anterior spinal surgery, neural compression, and cervical myelopathy. While traditional diagnostic methods like plain radiography are commonly used, they may yield false negatives. The diagnostic accuracy and reliability of artificial intelligence methods for detecting this condition remain largely unexplored. This study aimed to systematically evaluate the performance of deep learning models (DLMs) in diagnosing and predicting cervical OPLL.

Methods

This systematic review assesses the utilization of DLMs in diagnosing and predicting OPLL. Inclusion criteria were defined as the use of DLM for the diagnosis and prediction of cervical OPLL in

SUBMITTER: Corr F 

PROVIDER: S-EPMC11948434 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature