Imaging in the Lion City: Singapore Radiology Country Report

Singapore is a small tropical island city-state with limited natural resources that has achieved remarkable healthcare outcomes through effective long-term planning and judicious investment in human resources and technology. A full-range of medical imaging services is available in the country, with integrated care delivered to patients through a network of both government and private hospitals. Training in diagnostic radiology, interventional radiology, nuclear medicine and diagnostic radiography continue to evolve in Singapore, with an aim to further increase the range of subspecialty medical imaging services available and address projected challenges for the healthcare system in the future, such as an aging population. Continued government investment in technology and biomedical imaging is expected to further expand the scope and depth of medical imaging services in the future

Towards clinical AI fairness: A translational perspective

Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the issue of fairness remains a concern in high-stakes fields such as healthcare. Despite extensive discussion and efforts in algorithm development, AI fairness and clinical concerns have not been adequately addressed. In this paper, we discuss the misalignment between technical and clinical perspectives of AI fairness, highlight the barriers to AI fairness' translation to healthcare, advocate multidisciplinary collaboration to bridge the knowledge gap, and provide possible solutions to address the clinical concerns pertaining to AI fairness

An Uncommon Cause of Abdominal Pain and Vomiting in a 55-Year-Old Woman

Abdominal pain is a symptom that is commonly encountered in clinical practice. Evaluation of abdominal pain involves the exclusion of a myriad of possible diagnosis. This case illustrates an uncommon cause of abdominal pain and vomiting in a 55-year-old female with diagnostic radiological features

Application of a deep learning algorithm in the detection of hip fractures

Summary: This paper describes the development of a deep learning model for prediction of hip fractures on pelvic radiographs (X-rays). Developed using over 40,000 pelvic radiographs from a single institution, the model demonstrated high sensitivity and specificity when applied to a test set of emergency department radiographs. This study approximates the real-world application of a deep learning fracture detection model by including radiographs with sub-optimal image quality, other non-hip fractures, and metallic implants, which were excluded from prior published work. The study also explores the effect of ethnicity on model performance, as well as the accuracy of visualization algorithm for fracture localization

Author Correction: Saliva is more sensitive than nasopharyngeal or nasal swabs for diagnosis of asymptomatic and mild COVID-19 infection (Scientific Reports, (2021), 11, 1, (3134), 10.1038/s41598-021-82787-z)

10.1038/s41598-021-89880-3Scientific Reports1111253

Saliva is more sensitive than nasopharyngeal or nasal swabs for diagnosis of asymptomatic and mild COVID-19 infection

10.1038/s41598-021-82787-zScientific Reports111313

Multiparametric Magnetic Resonance Imaging and Magnetic Resonance Elastography to Evaluate the Early Effects of Bariatric Surgery on Nonalcoholic Fatty Liver Disease

Background. Bariatric surgery is the most effective treatment for morbid obesity and reduces the severity of nonalcoholic fatty liver disease (NAFLD) in the long term. Less is known about the effects of bariatric surgery on liver fat, inflammation, and fibrosis during the early stages following bariatric surgery. Aims. This exploratory study utilises advanced imaging methods to investigate NAFLD and fibrosis changes during the early metabolic transitional period following bariatric surgery. Methods. Nine participants with morbid obesity underwent sleeve gastrectomy. Multiparametric MRI (mpMRI) and magnetic resonance elastography (MRE) were performed at baseline, during the immediate (1 month), and late (6 months) postsurgery period. Liver fat was measured using proton density fat fraction (PDFF), disease activity using iron-correct T1 (cT1), and liver stiffness using MRE. Repeated measured ANOVA was used to assess longitudinal changes and Dunnett’s method for multiple comparisons. Results. All participants (Age 45.1±9.0 years, BMI 39.7±5.3 kg/m2) had elevated hepatic steatosis at baseline (PDFF >5%). In the immediate postsurgery period, PDFF decreased significantly from 14.1±7.4% to 8.9±4.4% (p=0.016) and cT1 from 826.9±80.6 ms to 768.4±50.9 ms (p=0.047). These improvements continued to the later postsurgery period. Bariatric surgery did not reduce liver stiffness measurements. Conclusion. Our findings support using MRI as a noninvasive tool to monitor NAFLD in patient with morbid obesity during the early stages following bariatric surgery

A translational perspective towards clinical AI fairness

Abstract Artificial intelligence (AI) has demonstrated the ability to extract insights from data, but the fairness of such data-driven insights remains a concern in high-stakes fields. Despite extensive developments, issues of AI fairness in clinical contexts have not been adequately addressed. A fair model is normally expected to perform equally across subgroups defined by sensitive variables (e.g., age, gender/sex, race/ethnicity, socio-economic status, etc.). Various fairness measurements have been developed to detect differences between subgroups as evidence of bias, and bias mitigation methods are designed to reduce the differences detected. This perspective of fairness, however, is misaligned with some key considerations in clinical contexts. The set of sensitive variables used in healthcare applications must be carefully examined for relevance and justified by clear clinical motivations. In addition, clinical AI fairness should closely investigate the ethical implications of fairness measurements (e.g., potential conflicts between group- and individual-level fairness) to select suitable and objective metrics. Generally defining AI fairness as “equality” is not necessarily reasonable in clinical settings, as differences may have clinical justifications and do not indicate biases. Instead, “equity” would be an appropriate objective of clinical AI fairness. Moreover, clinical feedback is essential to developing fair and well-performing AI models, and efforts should be made to actively involve clinicians in the process. The adaptation of AI fairness towards healthcare is not self-evident due to misalignments between technical developments and clinical considerations. Multidisciplinary collaboration between AI researchers, clinicians, and ethicists is necessary to bridge the gap and translate AI fairness into real-life benefits