Applications of artificial intelligence in the thorax: a narrative review focusing on thoracic radiology

Abstract

Objective: This review will focus on how AI-and, specifically, deep learning-can be applied to complement aspects of the current healthcare system. We describe how AI-based tools can augment existing clinical workflows by discussing the applications of AI to worklist prioritization and patient triage, the performance-boosting effects of AI as a second reader, and the use of AI to facilitate complex quantifications.We also introduce prominent examples of recent AI applications, such as tuberculosis screening in resourceconstrained environments, the detection of lung cancer with screening CT, and the diagnosis of COVID-19.We also provide examples of prognostic predictions and new discoveries beyond existing clinical practices. Background: Artificial intelligence (AI) has shown promising performance for thoracic diseases, particularly in the field of thoracic radiology. However, it has not yet been established how AI-based image analysis systems can help physicians in clinical practice. Methods: This review included peer-reviewed research articles on AI in the thorax published in English between 2015 and 2021. Conclusions: With advances in technology and appropriate preparation of physicians, AI could address various clinical problems that have not been solved due to a lack of clinical resources or technological limitations.

The adding value of contrast-enhanced CT radiomics: Differentiating tuberculosis from non-tuberculous infectious lesions presenting as solid pulmonary nodules or masses

“…Moreover, there is a great deal of subjectivity and fortuity in the diagnosis of radiologists with different qualifications. Computer-aided diagnosis has become an irreversible trend in clinical work, and artificial intelligence has great prospects in the field of thoracic radiology ( 35 ). The combine RM established in this study is of great significance for the diagnosis of TB, especially in areas of high TB prevalence such as the WHO regions of South-East Asia, Africa, and the Western Pacific.…”

Artificial Intelligence in Paediatric Tuberculosis

“…Triage and worklist prioritisation are important applications of AI in thoracic radiology and may have clinical relevance for TB screening where a timely diagnosis is critical with such a highly transmissible infection [ 8 ]. Of course, this will be reliant on accurate AI image review if the triage tool is to prioritise abnormal radiographs for urgent reporting or a highly specific AI tool using Natural Language Processing (NLP) that can review the clinical indication for suspicion of TB and bring this to the attention of the radiologist.…”

“…Hwang et al [ 49 ] developed a deep learning-based automated detection (DLAD) algorithm and compared radiology and non-radiology physician performance in image interpretation for the detection of active TB with and without AI assistance using six independent external multicentre test data sets. They concluded that both non-radiology physicians and board-certified radiologists showed improvements in sensitivity with the assistance of DLAD thus highlighting the potential of AI as a second reader [ 8 , 28 , 49 ].…”

“…Kim Y et al [ 8 ] reviewed peer-reviewed research articles on AI in the thorax between 2015 and 2021. The review focused on how AI (specifically, deep learning) could be applied to complement aspects of the current health care system.…”

“…The review focused on how AI (specifically, deep learning) could be applied to complement aspects of the current health care system. With advances in technology and appropriate preparation of physicians, AI can address clinical problems that have not been solved due to a lack of clinical resources or technological limitations [ 8 ]. Understanding how these AI products work, their real-world applications, key performance metrics and the value proposition is essential for radiologists to consider when evaluating and reviewing the array of AI products for paediatric applications in TB diagnosis.…”