Artificial intelligence (AI) has been positioned as being the most important recent advancement in radiology, if not the most potentially disruptive. Singapore radiologists have been quick to embrace this technology as part of the natural progression of the discipline toward a vision of how clinical medicine, empowered by technology, can achieve our national healthcare objectives of delivering value-based and patient-centric care. In this article, we consider 3 core questions relating to AI in radiology, and review the barriers to the widespread adoption of AI in radiology. We propose solutions and describe a "Centaur" model as a promising avenue for enabling the interfacing between AI and radiologists. Finally, we introduce The Radiological AI, Data Science and Imaging Informatics (RADII) subsection of the Singapore Radiological Society. RADII is an enabling body, which together with key technological and institutional stakeholders, will champion research, development and evaluation of AI for radiology applications.
Artificial Intelligence ; Humans ; Image Processing, Computer-Assisted ; Machine Learning ; Neural Networks (Computer) ; Radiology ; Singapore ; Societies, Medical

Betacoronavirus ; Coronavirus Infections ; diagnosis ; Diagnosis, Differential ; Diagnostic Imaging ; methods ; Humans ; Male ; Middle Aged ; Pandemics ; Pneumonia, Viral ; diagnosis ; virology ; Radiography, Thoracic ; methods ; Tomography, X-Ray Computed ; methods

INTRODUCTIONThis study aimed to assess the accuracy and outcomes of coronary computed tomography angiography (CCTA) performed in a regional hospital in Singapore.

METHODSThe Changi General Hospital CCTA database was retrospectively analysed over a 24-month period. Electronic hospital records, catheter coronary angiography (CCA) and CCTA electronic databases were used to gather data on major adverse cardiovascular events (MACE) and CCA results. CCTA findings were deemed positive if coronary artery stenosis ≥ 50% was reported or if the stenosis was classified as moderate or severe. CCA findings were considered positive if coronary artery stenosis ≥ 50% was reported.

RESULTSThe database query returned 679 patients who had undergone CCTA for the evaluation of suspected coronary artery disease. Of the 101 patients in the per-patient accuracy analysis group, there were six true negatives, one false negative, 81 true positives and 13 false positives, resulting in a negative predictive value of 85.7% and positive predictive value of 86.2%. The mean age of the study sample was 53 ± 13 years and 255 (37.6%) patients were female. Mean duration of patient follow-up was 360 days. Of the 513 negative CCTA patients, none developed MACE during the follow-up period, and of the 164 positive CCTA patients, 19 (11.6%) developed MACE (p < 0.001).

CONCLUSIONAnalysis of CCTA studies suggested accuracy and outcomes that were consistent with published clinical data. There was a one-year MACE-free warranty period following negative CCTA findings.

INTRODUCTION: We aimed to assess the attitudes and learner needs of radiology residents and faculty radiologists regarding artificial intelligence (AI) and machine learning (ML) in radiology. METHODS: A web-based questionnaire, designed using SurveyMonkey, was sent out to residents and faculty radiologists in all three radiology residency programmes in Singapore. The questionnaire comprised four sections and aimed to evaluate respondents' current experience, attempts at self-learning, perceptions of career prospects and expectations of an AI/ML curriculum in their residency programme. Respondents' anonymity was ensured. RESULTS: A total of 125 respondents (86 male, 39 female; 70 residents, 55 faculty radiologists) completed the questionnaire. The majority agreed that AI/ML will drastically change radiology practice (88.8%) and makes radiology more exciting (76.0%), and most would still choose to specialise in radiology if given a choice (80.0%). 64.8% viewed themselves as novices in their understanding of AI/ML, 76.0% planned to further advance their AI/ML knowledge and 67.2% were keen to get involved in an AI/ML research project. An overwhelming majority (84.8%) believed that AI/ML knowledge should be taught during residency, and most opined that this was as important as imaging physics and clinical skills/knowledge curricula (80.0% and 72.8%, respectively). More than half thought that their residency programme had not adequately implemented AI/ML teaching (59.2%). In subgroup analyses, male and tech-savvy respondents were more involved in AI/ML activities, leading to better technical understanding. CONCLUSION A growing optimism towards radiology undergoing technological transformation and AI/ML implementation has led to a strong demand for an AI/ML curriculum in residency education.

INTRODUCTION: Chest radiographs (CXRs) are widely used for the screening and management of COVID-19. This article describes the radiographic features of COVID-19 based on an initial national cohort of patients. METHODS: This is a retrospective review of swab-positive patients with COVID-19 who were admitted to four different hospitals in Singapore between 22 January and 9 March 2020. Initial and follow-up CXRs were reviewed by three experienced radiologists to identify the predominant pattern and distribution of lung parenchymal abnormalities. RESULTS: In total, 347 CXRs of 96 patients were reviewed. Initial CXRs were abnormal in 41 (42.7%) out of 96 patients. The mean time from onset of symptoms to CXR abnormality was 5.3 ± 4.7 days. The predominant pattern of lung abnormality was ground-glass opacity on initial CXRs (51.2%) and consolidation on follow-up CXRs (51.0%). Multifocal bilateral abnormalities in mixed central and peripheral distribution were observed in 63.4% and 59.2% of abnormal initial and follow-up CXRs, respectively. The lower zones were involved in 90.2% of initial CXRs and 93.9% of follow-up CXRs. CONCLUSION In a cohort of swab-positive patients, including those identified from contact tracing, we found a lower incidence of CXR abnormalities than was previously reported. The most common pattern was ground-glass opacity or consolidation, but mixed central and peripheral involvement was more common than peripheral involvement alone.
COVID-19 ; Humans ; Lung/diagnostic imaging* ; Radiography, Thoracic ; Retrospective Studies ; SARS-CoV-2 ; Singapore

Lung cancer is the leading cause of cancer-related death around the world, being the top cause of cancer-related deaths among men and the second most common cause of cancer-related deaths among women in Singapore. Currently, no screening programme for lung cancer exists in Singapore. Since there is mounting evidence indicating a different epidemiology of lung cancer in Asian countries, including Singapore, compared to the rest of the world, a unique and adaptive approach must be taken for a screening programme to be successful at reducing mortality while maintaining cost-effectiveness and a favourable risk-benefit ratio. This review article promotes the use of low-dose computed tomography of the chest and explores the radiological challenges and future directions.