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. (July 2021)

• Record Type: Journal Article
• Authors: Mosquera, Candelaria; Diaz, Facundo Nahuel; Binder, Fernando; Rabellino, José Martín; Benitez, Sonia Elizabeth; Beresñak, Alejandro Daniel; Seehaus, Alberto; Ducrey, Gabriel; Ocantos, Jorge Alberto; Luna, Daniel Roberto
• Is Part Of: Computer methods and programs in biomedicine. Volume 206(2021)
• Publication Date: 2021-07
• Subjects: Radiography -- Artificial intelligence -- Deep Learning -- Clinical decision support systems -- Chest; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106130
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Detection of four main radiological findings in chest x-rays with a semiologic approach. Images with different types of labels were exploited by using a late fusion of four convolutional architectures. Trained on heterogeneous data from a combination of public and institutional datasets. Achieved an area under the curve of 0.87 in detection of abnormality in local retrospective collection of chest x-rays. Designed as a clinically useful tool that could be successfully integrated into a hospital workflow 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 x-ray 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 suitable 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. The performance was measured on two test datasets: an external openly-available dataset, and a retrospective institutional dataset, to estimate performance on the 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. Conclusion: This study presents a new approach for exploiting heterogeneous labels from different chest x-ray datasets, by choosing Deep Learning architectures according to the radiological characteristics of each pathological finding. We estimated the tool's performance on the local population, obtaining results comparable to state-of-the-art metrics. We believe this approach is closer to the actual reading process of chest x-rays by professionals, and therefore more likely to be successful in a real clinical setting.