Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey

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.

A chest radiography-based artificial intelligence deep-learning model to predict severe Covid-19 patient outcomes: the CAPE (Covid-19 AI Predictive Engine) Model

BackgroundChest radiography may be used together with deep-learning models to prognosticate COVID-19 patient outcomes PurposeT o evaluate the performance of a deep-learning model for the prediction of severe patient outcomes from COVID-19 pneumonia on chest radiographs. MethodsA deep-learning model (CAPE: Covid-19 AI Predictive Engine) was trained on 2337 CXR images including 2103 used only for validation while training. The prospective test set consisted of CXR images (n=70) obtained from RT-PCR confirmed COVID-19 pneumonia patients between 1 January and 30 April 2020 in a single center. The radiographs were analyzed by the AI model. Model performance was obtained by receiver operating characteristic curve analysis. ResultsIn the prospective test set, the mean age of the patients was 46 (+/-16.2) years (84.2% male). The deep-learning model accurately predicted outcomes of ICU admission/mortality from COVID-19 pneumonia with an AUC of 0.79 (95% CI 0.79-0.96). Compared to traditional risk scoring systems for pneumonia based upon laboratory and clinical parameters, the model matched the EWS and MulBTSA risk scoring systems and outperformed CURB-65. ConclusionsA deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 on chest radiographs. Key ResultsA deep-learning model was able to predict severe patient outcomes (ICU admission and mortality) from COVID-19 from chest radiographs with an AUC of 0.79, which is comparable to traditional risk scoring systems for pneumonia. Summary StatementThis is a chest radiography-based AI model to prognosticate the risk of severe COVID-19 pneumonia outcomes.

Accuracy and clinical outcomes of coronary CT angiography for patients with suspected coronary artery disease: a single-centre study in Singapore

<p><b>INTRODUCTION</b>This study aimed to assess the accuracy and outcomes of coronary computed tomography angiography (CCTA) performed in a regional hospital in Singapore.</p><p><b>METHODS</b>The 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.</p><p><b>RESULTS</b>The 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).</p><p><b>CONCLUSION</b>Analysis 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.</p>