The object detection method aids in image reconstruction evaluation and clinical interpretation of meniscal abnormalities

TL;DR

This study explores how deep learning-based image reconstruction quality impacts anomaly detection in knee MRI and assesses an AI assistant's role in improving radiologists' interpretation of meniscal abnormalities.

Contribution

It introduces a box-based reconstruction metric linked to detection performance and demonstrates AI assistance enhances radiologists' accuracy and agreement.

Findings

Box-based SSIM correlates strongly with detection metrics

AI assistance improves radiologists' accuracy from 86% to 88.3%

Significant SSIM changes reduce detection performance

Abstract

This study investigates the relationship between deep learning (DL) image reconstruction quality and anomaly detection performance, and evaluates the efficacy of an artificial intelligence (AI) assistant in enhancing radiologists' interpretation of meniscal anomalies on reconstructed images. A retrospective study was conducted using an in-house reconstruction and anomaly detection pipeline to assess knee MR images from 896 patients. The original and 14 sets of DL-reconstructed images were evaluated using standard reconstruction and object detection metrics, alongside newly developed box-based reconstruction metrics. Two clinical radiologists reviewed a subset of 50 patients' images, both original and AI-assisted reconstructed, with subsequent assessment of their accuracy and performance characteristics. Results indicated that the structural similarity index (SSIM) showed a weaker correlation with anomaly detection metrics (mAP, r=0.64, p=0.01; F1 score, r=0.38, p=0.18), while box-based SSIM had a stronger association with detection performance (mAP, r=0.81, p<0.01; F1 score, r=0.65, p=0.01). Minor SSIM fluctuations did not affect detection outcomes, but significant changes reduced performance. Radiologists' AI-assisted evaluations demonstrated improved accuracy (86.0% without assistance vs. 88.3% with assistance, p<0.05) and interrater agreement (Cohen's kappa, 0.39 without assistance vs. 0.57 with assistance). An additional review led to the incorporation of 17 more lesions into the dataset. The proposed anomaly detection method shows promise in evaluating reconstruction algorithms for automated tasks and aiding radiologists in interpreting DL-reconstructed MR images.