Canadian Association of Radiologists Journal-journal De L Association Canadienne Des Radiologistes 

About: Canadian Association of Radiologists Journal-journal De L Association Canadienne Des Radiologistes is an academic journal. The journal publishes majorly in the area(s): Magnetic resonance imaging & Biopsy. Over the lifetime, 786 publications have been published receiving 6165 citations.

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.

Abstract: Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.

CT Imaging and Differential Diagnosis of COVID-19.

Abstract: Since the beginning of 2020, coronavirus disease 2019 (COVID-19) has spread throughout China. This study explains the findings from lung computed tomography images of some patients with COVID-19 treated in this medical institution and discusses the difference between COVID-19 and other lung diseases.

Canadian Association of Radiologists consensus guidelines for the prevention of contrast-induced nephropathy: update 2012.

Abstract: The development of acute renal failure significantly complicates intravascular contrast medium (CM) use and is linked with high morbidity and mortality. The increasing use of CM, an aging population, and an increase in chronic kidney disease (CKD) will result in an increased incidence of contrast-induced nephropathy (CIN)-unless preventive measures are used. The Canadian Association of Radiologists has developed these guidelines as a practical approach to risk stratification and prevention of CIN. The major risk factor predicting CIN is preexisting CKD, which can be predicted from the glomerular filtration rate (GFR). In terms of being an absolute measure, serum creatinine (SCr) is an unreliable measure of renal function. Patients with GFR >60 mL/min have a very low risk of CIN, and preventive measures are generally unnecessary. When GFR is <60 mL/min, preventive measures should be instituted. The risk of CIN is greatest in patients with GFR <30 mL/min. Preventive measures: Alternative imaging that does not require CM should be considered. Fluid volume loading is the single most important protective measure. Nephrotoxic medications should be discontinued 48 hours prior to the study. CM volume and frequency of administration should be minimized, but satisfactory image quality should still be maintained. High-osmolar contrast should be avoided in patients with renal impairment. There is some evidence to suggest that iso-osmolar contrast reduces the risk of CIN among patients with renal impairment, but further study is necessary to determine whether iso-osmolar contrast is superior to low-osmolar contrast. Acetylcysteine (AC) has been advocated to reduce the incidence of CIN; however, not all studies have shown a benefit, and it is difficult to formulate evidence-based recommendations at this time. Its use may be considered in high-risk patients but is not considered mandatory.

Sample-Size Determination Methodologies for Machine Learning in Medical Imaging Research: A Systematic Review

Abstract: Purpose The required training sample size for a particular machine learning (ML) model applied to medical imaging data is often unknown. The purpose of this study was to provide a descriptive review of current sample-size determination methodologies in ML applied to medical imaging and to propose recommendations for future work in the field. Methods We conducted a systematic literature search of articles using Medline and Embase with keywords including “machine learning,” “image,” and “sample size.” The search included articles published between 1946 and 2018. Data regarding the ML task, sample size, and train-test pipeline were collected. Results A total of 167 articles were identified, of which 22 were included for qualitative analysis. There were only 4 studies that discussed sample-size determination methodologies, and 18 that tested the effect of sample size on model performance as part of an exploratory analysis. The observed methods could be categorized as pre hoc model-based approaches, which relied on features of the algorithm, or post hoc curve-fitting approaches requiring empirical testing to model and extrapolate algorithm performance as a function of sample size. Between studies, we observed great variability in performance testing procedures used for curve-fitting, model assessment methods, and reporting of confidence in sample sizes. Conclusions Our study highlights the scarcity of research in training set size determination methodologies applied to ML in medical imaging, emphasizes the need to standardize current reporting practices, and guides future work in development and streamlining of pre hoc and post hoc sample size approaches.

Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Abstract: Artificial intelligence (AI) software that analyzes medical images is becoming increasingly prevalent. Unlike earlier generations of AI software, which relied on expert knowledge to identify imaging features, machine learning approaches automatically learn to recognize these features. However, the promise of accurate personalized medicine can only be fulfilled with access to large quantities of medical data from patients. This data could be used for purposes such as predicting disease, diagnosis, treatment optimization, and prognostication. Radiology is positioned to lead development and implementation of AI algorithms and to manage the associated ethical and legal challenges. This white paper from the Canadian Association of Radiologists provides a framework for study of the legal and ethical issues related to AI in medical imaging, related to patient data (privacy, confidentiality, ownership, and sharing); algorithms (levels of autonomy, liability, and jurisprudence); practice (best practices and current legal framework); and finally, opportunities in AI from the perspective of a universal health care system.