Chest x-ray automated triage: a semiologic approach designed for clinical implementation, exploiting different types of labels through a combination of four Deep Learning architectures

TL;DR

This study develops a deep learning-based triage tool for chest x-ray interpretation that leverages heterogeneous labels and combines multiple architectures for improved clinical utility and integration into hospital workflows.

Contribution

It introduces a novel late fusion approach combining four deep learning architectures trained on diverse annotations, enabling effective use of heterogeneous datasets for clinical chest x-ray triage.

Findings

Achieved AUC of 0.87 on local test data for abnormality detection.

Sensitivity of 86% and specificity of 88% on local population.

Demonstrated model interpretability with heatmaps showing true and false positives.

Abstract

BACKGROUND AND OBJECTIVES: The multiple chest x-ray datasets released in the last years have ground-truth labels intended for different computer vision tasks, suggesting that performance in automated chest-xray interpretation might improve by using a method that can exploit diverse types of annotations. This work presents a Deep Learning method based on the late fusion of different convolutional architectures, that allows training with heterogeneous data with a simple implementation, and evaluates its performance on independent test data. We focused on obtaining a clinically useful tool that could be successfully integrated into a hospital workflow. MATERIALS AND METHODS: Based on expert opinion, we selected four target chest x-ray findings, namely lung opacities, fractures, pneumothorax and pleural effusion. For each finding we defined the most adequate type of ground-truth label, and built four training datasets combining images from public chest x-ray datasets and our institutional archive. We trained four different Deep Learning architectures and combined their outputs with a late fusion strategy, obtaining a unified tool. Performance was measured on two test datasets: an external openly-available dataset, and a retrospective institutional dataset, to estimate performance on local population. RESULTS: The external and local test sets had 4376 and 1064 images, respectively, for which the model showed an area under the Receiver Operating Characteristics curve of 0.75 (95%CI: 0.74-0.76) and 0.87 (95%CI: 0.86-0.89) in the detection of abnormal chest x-rays. For the local population, a sensitivity of 86% (95%CI: 84-90), and a specificity of 88% (95%CI: 86-90) were obtained, with no significant differences between demographic subgroups. We present examples of heatmaps to show the accomplished level of interpretability, examining true and false positives.