Improving radiologist detection of meniscal abnormality on undersampled, deep learning reconstructed knee MRI.

ABSTRACT:

Background

Accurate interpretation of meniscal anomalies on knee MRI is critical for diagnosis and treatment planning, with artificial intelligence emerging as a promising tool to support and enhance this process through automated anomaly detection.

Purpose

To evaluate the impact of an artificial intelligence (AI) anomaly detection assistant on radiologists' interpretation of meniscal anomalies in undersampled, deep learning (DL)-reconstructed knee MRI and assess the relationship between reconstruction quality metrics and anomaly detection performance.

Materials and methods

This retrospective study included 947 knee MRI examinations; 51 were excluded for poor image quality, leaving 896 participants (mean age, 44.7 ± 15.3 years; 472 women). Using 8-fold undersampled da

SUBMITTER: Konovalova N 

PROVIDER: S-EPMC12021832 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature