A comprehensive deep-learning model for interpreting chest x-rays

Abstract

The study by Jarrel Seah and colleagues,1Seah JCY Tang CHM Buchlak QD et al.Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study.Lancet Digit Health. 2021; 3: 496-506Summary Full Text Full Text PDF PubMed Scopus (11) Google Scholar published in The Lancet Digital Health, shows that radiologists’ performance improved when assisted by a comprehensive chest x-ray deep-learning model. Specifically, 821 681 images (284 649 patients) with 127 chest x-ray findings were trained through EfficientNet, and a deep-learning model was used to assist diagnoses made by 20 experienced radiologists. This deep-learning model is a breakthrough as a support system for radiologists, suggesting synergistic improvements from cooperation between radiologists and artificial intelligence in clinical practice. However, a key issue in the study design should be addressed: the deep-learning model is at a disadvantage compared with a human. Specifically, the training dataset of 520 014 cases was labelled by radiologists using chest x-ray images and clinical reports, and the test dataset of 2568 cases was also labelled by radiologists using anonymised clinical information, past chest x-ray images, and relevant reports on findings from chest CTs. The deep-learning model thus has no opportunity to train using the characteristic features of lesions in consideration of clinical information from chest CT. Chest CT is an imaging method providing three-dimensional data (axial, sagittal, and coronal) that enhances anatomical details of the lung parenchyma, and contributes more detailed information than conventional x-ray, facilitating more precise diagnosis by radiologists. A deep-learning model that can benefit from the interpretation and experience of radiologists is needed. We propose a transfer-learning strategy that transfers characteristic features such as morphology and distribution of lung cancer from CT images to x-ray images. Transfer learning is a method inspired by the human capability to transfer knowledge across domains, and the diagnostic ability of a deep-learning model would improve by shifting information among methods.2Lotter W Diab AR Haslam B et al.Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach.Nat Med. 2021; 27: 244-249Crossref PubMed Scopus (44) Google Scholar Furthermore, the diagnostic decisions of radiologists are structured hierarchically, and the initial diagnosis has fewer potential interpretations than diagnosis by chest CT images.3An G Akiba M Omodaka K Nakazawa T Yokota H Hierarchical deep learning models using transfer learning for disease detection and classification based on small number of medical images.Sci Rep. 2021; 114250Crossref PubMed Scopus (9) Google Scholar A clinical setting that considers the diagnostic process from chest x-ray to CT examination should empower a deep-learning model with increased clinical relevance, which could help radiologists reach a diagnosis through considering additional information at first presentation of chest x-rays.4Larici AR Cicchetti G Marano R et al.Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review.Eur J Radiol. 2020; 131109217Summary Full Text Full Text PDF PubMed Scopus (31) Google Scholar The generalisability of the model in different geographical settings should be explored, since deep learning is a promising technology that can perform quantitative evaluations and share medical resources globally.5LeCun Y Bengio Y Hinton G Deep learning.Nature. 2015; 521: 436-444Crossref PubMed Scopus (36882) Google Scholar The transfer-learning strategy offers the possibility of resolving the uneven distribution of medical resources, including imaging methods, and should contribute to bias mitigation. This deep-learning model of comprehensive chest x-rays is a breakthrough that could accelerate diagnosis by radiologists. We declare no competing interests. Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase studyThis study shows the potential of a comprehensive deep-learning model to improve chest x-ray interpretation across a large breadth of clinical practice. Full-Text PDF Open Access