A Retrospective Cohort Study Evaluating the Safety and Efficacy of Sequential versus Concurrent Intrapleural Instillation of Tissue Plasminogen Activator and DNase for Pleural Infection.

Methods: We conducted a retrospective review of patients with pleural infection requiring intrapleural therapy at two tertiary referral centres. Results: We included 84 (62.2%) and 51 (37.8%) patients who received sequential and concurrent intrapleural therapy, respectively. Patient demographics and clinical characteristics, including age, RAPID score, and percentage of pleural opacity on radiographs before intrapleural therapy, were similar in both groups. Treatment failure rates (defined by either in-hospital mortality, surgical intervention, or 30-day readmission for pleural infection) were 9.5% and 5.9% with sequential and concurrent intrapleural therapy, respectively (p = 0.534). This translates to a treatment success rate of 90.5% and 94.1% for sequential and concurrent intrapleural therapy, respectively. There was no significant difference in the decrease in percentage of pleural effusion size on chest radiographs (15.1% [IQR 6-35.7] versus 26.6% [IQR 9.9-38.7], p = 0.143) between sequential and concurrent therapy, respectively. There were also no significant differences in the rate of pleural bleeding (4.8% versus 9.8%, p = 0.298) and chest pain (13.1% versus 9.8%, p = 0.566) between sequential and concurrent therapy, respectively. Conclusion: Our study adds to the growing literature on the safety and efficacy of concurrent intrapleural therapy in pleural infection.

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

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.

Multimodality Imaging in the Diagnosis and Assessment of Cardiac Amyloidosis.

Amyloidosis is a rare disorder where abnormal protein aggregates are deposited in tissues forming amyloid fibrils, leading to progressive organ failure. Although any organ can be affected, cardiac involvement is the main cause of morbidity and mortality associated with amyloidosis as diagnosis is often delayed due to the indolent nature of the disease in some forms. An early diagnosis of disease and knowledge of the type/subtype of cardiac amyloidosis (CA) are essential for appropriate management and better outcome. Echocardiography is often the first line of investigation for patients suspected of CA and offers superior hemodynamic assessment. Although cardiovascular magnetic resonance (CMR) imaging is not diagnostic of CA, it provides vital clues to diagnosis and has a role in disease quantification and prognostication. Radiolabeled bone seeking tracers are the mainstay of diagnosis of CA and when combined with screening of monoclonal light chains, bone scintigraphy offers high sensitivity in diagnosing transthyretin type of CA. This review aims to describe the noninvasive imaging assessment and approach to diagnosis of patients with suspected CA. Imaging features of echocardiography, nuclear scintigraphy, and CMR are described with a brief mention on computed tomography.

Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia.

BACKGROUND: Chest radiograph (CXR) is a basic diagnostic test in community-acquired pneumonia (CAP) with prognostic value. We developed a CXR-based artificial intelligence (AI) model (CAP AI predictive Engine: CAPE) and prospectively evaluated its discrimination for 30-day mortality. METHODS: Deep-learning model using convolutional neural network (CNN) was trained with a retrospective cohort of 2235 CXRs from 1966 unique adult patients admitted for CAP from 1 January 2019 to 31 December 2019. A single-centre prospective cohort between 11 May 2020 and 15 June 2020 was analysed for model performance. CAPE mortality risk score based on CNN analysis of the first CXR performed for CAP was used to determine the area under the receiver operating characteristic curve (AUC) for 30-day mortality. RESULTS: 315 inpatient episodes for CAP occurred, with 30-day mortality of 19.4% (n=61/315). Non-survivors were older than survivors (mean (SD)age, 80.4 (10.3) vs 69.2 (18.7)); more likely to have dementia (n=27/61 vs n=58/254) and malignancies (n=16/61 vs n=18/254); demonstrate higher serum C reactive protein (mean (SD), 109 mg/L (98.6) vs 59.3 mg/L (69.7)) and serum procalcitonin (mean (SD), 11.3 (27.8) µg/L vs 1.4 (5.9) µg/L). The AUC for CAPE mortality risk score for 30-day mortality was 0.79 (95% CI 0.73 to 0.85, p<0.001); Pneumonia Severity Index (PSI) 0.80 (95% CI 0.74 to 0.86, p<0.001); Confusion of new onset, blood Urea nitrogen, Respiratory rate, Blood pressure, 65 (CURB-65) score 0.76 (95% CI 0.70 to 0.81, p<0.001), respectively. CAPE combined with CURB-65 model has an AUC of 0.83 (95% CI 0.77 to 0.88, p<0.001). The best performing model was CAPE incorporated with PSI, with an AUC of 0.84 (95% CI 0.79 to 0.89, p<0.001). CONCLUSION: CXR-based CAPE mortality risk score was comparable to traditional pneumonia severity scores and improved its discrimination when combined.

Radiographic features of COVID-19 based on an initial cohort of 96 patients in Singapore.

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.

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.

"Catch Me If You Can": Recurrent ST-Segment Elevation Myocardial Infarction Due to Aortic Thrombus.

A 42-year-old male patient presented with recurrent inferior ST-segment elevation myocardial infarction with minimal atherosclerotic disease on intracoronary imaging. Transesophageal echocardiogram and computed tomography aortogram revealed the underlying cause to be a mobile aortic thrombus in the right coronary cusp, prolapsing into and out of the right coronary ostium. (Level of Difficulty: Beginner.).

A practical and adaptive approach to lung cancer screening: a review of international evidence and position on CT lung cancer screening in the Singaporean population by the College of Radiologists 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.

Artificial Intelligence and Radiology in Singapore: Championing a New Age of Augmented Imaging for Unsurpassed Patient Care.

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.

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

INTRODUCTION: This study aimed to assess the accuracy and outcomes of coronary computed tomography angiography (CCTA) performed in a regional hospital in Singapore. METHODS: 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. RESULTS: 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). CONCLUSION: 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.

The future of radiology augmented with Artificial Intelligence: A strategy for success.

The rapid development of Artificial Intelligence/deep learning technology and its implementation into routine clinical imaging will cause a major transformation to the practice of radiology. Strategic positioning will ensure the successful transition of radiologists into their new roles as augmented clinicians. This paper describes an overall vision on how to achieve a smooth transition through the practice of augmented radiology where radiologists-in-the-loop ensure the safe implementation of Artificial Intelligence systems.

Saccular Kommerell aneurysm, a potential pitfall on MDCT imaging - A review of imaging features and potential mimics.

Saccular Kommerell aneurysm represents a potential pitfall on Multidetector CT (MDCT) imaging, mimicking conditions such as saccular aneurysm of the thoracic aorta, ductus diverticulum and dilated Kommerell diverticulum. Accurate diagnosis of this condition is critical in the management of this potentially fatal condition. This paper reviews the MDCT imaging features of Kommerell aneurysms and its mimics and demonstrates how to make an accurate diagnosis through a series of four cases. MDCT features of Kommerell aneurysms, either saccular or fusiform types arising from a Kommerell diverticulum with atherosclerotic plaque and mural thrombus are discussed.

Cardiovascular implantable electronic devices: a review of the dangers and difficulties in MR scanning and attempts to improve safety.

An increasing number of patients are being treated with cardiovascular implantable electronic devices (CIEDs), many of which are MR conditional. There is a lack of literature on the safe scanning of MR conditional CIEDs. This review article discusses MR imaging safety in patients with implanted CIEDs. Guidelines on safe use and indications of imaging patients with MR conditional CIEDs are described, followed by a pictorial essay of the radiographic features of these devices. We also discuss the challenges of monitoring the patient in the MR environment, advances in MRI conditional imaging of devices, availability, limitations and workflow including vendor-specific and other collaborative efforts to simplify the scanning process. Radiologists must be able to facilitate the safe utilization of MR imaging in patients who have CIEDs. A thorough knowledge of the hazards of imaging non-MR compatible devices is required as well as knowing how to correctly identify and manage the imaging of patients with MR conditional CIEDs. Finally, we propose steps required to facilitate the safe scanning of patients with MR conditional CIEDs adopted in our institution and a contingency plan in the event that an inadvertent MR scan of a patient with a MRI unsafe CIED should occur. MAIN MESSAGES: • Risks of MR imaging in patients who have CIEDs have been worked around. • There are many technical limitations in enabling safe MR scanning of CIEDs. • Radiological identification of MRI-conditional status of CIEDs is useful. • Standardizing conditions for safe MRI scanning is important. • We offer example algorithms for facilitating safe MRI scanning of CIEDs.

Clinical performance of high-resolution late gadolinium enhancement imaging with compressed sensing.

PURPOSE: To evaluate diagnostic image quality of 3D late gadolinium enhancement (LGE) with high isotropic spatial resolution (∼1.4 mm3 ) images reconstructed from randomly undersampled k-space using LOw-dimensional-structure Self-learning and Thresholding (LOST). MATERIALS AND METHODS: We prospectively enrolled 270 patients (181 men; 55 ± 14 years) referred for myocardial viability assessment. 3D LGE with isotropic spatial resolution of 1.4 ± 0.1 mm3 was acquired at 1.5T using a LOST acceleration rate of 3 to 5. In a subset of 121 patients, 3D LGE or phase-sensitive LGE were acquired with parallel imaging with an acceleration rate of 2 for comparison. Two readers evaluated image quality using a scale of 1 (poor) to 4 (excellent) and assessed for scar presence. The McNemar test statistic was used to compare the proportion of detected scar between the two sequences. We assessed the association between image quality and characteristics (age, gender, torso dimension, weight, heart rate), using generalized linear models. RESULTS: Overall, LGE detection proportions for 3D LGE with LOST were similar between readers 1 and 2 (16.30% vs. 18.15%). For image quality, readers gave 85.9% and 80.0%, respectively, for images categorized as good or excellent. Overall proportion of scar presence was not statistically different from conventional 3D LGE (28% vs. 33% [P = 0.17] for reader 1 and 26% vs. 31% [P = 0.37] for reader 2). Increasing subject heart rate was associated with lower image quality (estimated slope = -0.009 (P = 0.001)). CONCLUSION: High-resolution 3D LGE with LOST yields good to excellent image quality in >80% of patients and identifies patients with LV scar at the same rate as conventional 3D LGE. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1829-1838.

Reproducibility of myocardial T1 and T2 relaxation time measurement using slice-interleaved T1 and T2 mapping sequences.

PURPOSE: To assess measurement reproducibility and image quality of myocardial T1 and T2 maps using free-breathing slice-interleaved T1 and T2 mapping sequences at 1.5 Tesla (T). MATERIALS AND METHODS: Eleven healthy subjects (33 ± 16 years; 6 males) underwent a slice-interleaved T1 and T2 mapping test/retest cardiac MR study at 1.5T on 2 days. For each day, subjects were imaged in two sessions with removal out of the magnet and repositioning before the subsequent session. We studied measurement reproducibility as well as the required sample size for sufficient statistical power to detect a predefined change in T1 and T2 . In a separate prospective study, we assessed T1 and T2 map image quality in 241 patients (54 ± 15 years; 73 women) with known/suspected cardiovascular disease referred for clinical cardiac MR. A subjective quality score was used to assess a segment-based image quality. RESULTS: In the healthy cohort, the slice-interleaved T1 measurements were highly reproducible, with global coefficients of variation (CVs) of 2.4% between subjects, 2.1% between days, and 1.7% between sessions. Slice-interleaved T2 mapping sequences provided similar reproducibility with global CVs of 7.2% between subjects, 6.3% between days, and 5.0 between sessions. A lower variability resulted in a reduction of the required number of subjects to achieve a certain statistical power when compared with other T1 mapping sequences. In the subjective image quality assessment, >80% of myocardial segments had interpretable data. CONCLUSION: Slice-interleaved T1 and T2 mapping sequences yield highly reproducible T1 and T2 measurements with >80% of interpretable myocardial segments. J. Magn. Reson. Imaging 2016;44:1159-1167.