Machine learning classification of multiple sclerosis in children using optical coherence tomography.

BACKGROUND: In children, multiple sclerosis (MS) is the ultimate diagnosis in only 1/5 to 1/3 of cases after a first episode of central nervous system (CNS) demyelination. As the visual pathway is frequently affected in MS and other CNS demyelinating disorders (DDs), structural retinal imaging such as optical coherence tomography (OCT) can be used to differentiate MS. OBJECTIVE: This study aimed to investigate the utility of machine learning (ML) based on OCT features to identify distinct structural retinal features in children with DDs. METHODS: This study included 512 eyes from 187 (neyes = 374) children with demyelinating diseases and 69 (neyes = 138) controls. Input features of the analysis comprised of 24 auto-segmented OCT features. RESULTS: Random Forest classifier with recursive feature elimination yielded the highest predictive values and identified DDs with 75% and MS with 80% accuracy, while multiclass distinction between MS and monophasic DD was performed with 64% accuracy. A set of eight retinal features were identified as the most important features in this classification. CONCLUSION: This study demonstrates that ML based on OCT features can be used to support a diagnosis of MS in children.

Point-of-care ultrasound in the COVID-19 era: A scoping review.

In the midst of the COVID-19 pandemic, unprecedented pressure has been added to healthcare systems around the globe. Imaging is a crucial component in the management of COVID-19 patients. Point-of-care ultrasound (POCUS) such as hand-carried ultrasound emerges in the COVID-19 era as a tool that can simplify the imaging process of COVID-19 patients, and potentially reduce the strain on healthcare providers and healthcare resources. The preliminary evidence available suggests an increasing role of POCUS in diagnosing, monitoring, and risk-stratifying COVID-19 patients. This scoping review aims to delineate the challenges in imaging COVID-19 patients, discuss the cardiopulmonary complications of COVID-19 and their respective sonographic findings, and summarize the current data and recommendations available. There is currently a critical gap in knowledge in the role of POCUS in the COVID-19 era. Nonetheless, it is crucial to summarize the current preliminary data available in order to help fill this gap in knowledge for future studies.

Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint.

The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future.

Insights from teaching artificial intelligence to medical students in Canada.

Clinical artificial intelligence (AI) applications are rapidly developing but existing medical school curricula provide limited teaching covering this area. Here we describe an AI training curriculum we developed and delivered to Canadian medical undergraduates and provide recommendations for future training.

Health Care Students' Perspectives on Artificial Intelligence: Countrywide Survey in Canada.

BACKGROUND: Artificial intelligence (AI) is no longer a futuristic concept; it is increasingly being integrated into health care. As studies on attitudes toward AI have primarily focused on physicians, there is a need to assess the perspectives of students across health care disciplines to inform future curriculum development. OBJECTIVE: This study aims to explore and identify gaps in the knowledge that Canadian health care students have regarding AI, capture how health care students in different fields differ in their knowledge and perspectives on AI, and present student-identified ways that AI literacy may be incorporated into the health care curriculum. METHODS: The survey was developed from a narrative literature review of topics in attitudinal surveys on AI. The final survey comprised 15 items, including multiple-choice questions, pick-group-rank questions, 11-point Likert scale items, slider scale questions, and narrative questions. We used snowball and convenience sampling methods by distributing an email with a description and a link to the web-based survey to representatives from 18 Canadian schools. RESULTS: A total of 2167 students across 10 different health professions from 18 universities across Canada responded to the survey. Overall, 78.77% (1707/2167) predicted that AI technology would affect their careers within the coming decade and 74.5% (1595/2167) reported a positive outlook toward the emerging role of AI in their respective fields. Attitudes toward AI varied by discipline. Students, even those opposed to AI, identified the need to incorporate a basic understanding of AI into their curricula. CONCLUSIONS: We performed a nationwide survey of health care students across 10 different health professions in Canada. The findings would inform student-identified topics within AI and their preferred delivery formats, which would advance education across different health care professions.

Fabry Cardiomyopathy: Current Practice and Future Directions.

Fabry disease (FD) is an X-linked lysosomal storage disorder caused by mutations in the galactosidase A (GLA) gene that result in deficient galactosidase A enzyme and subsequent accumulation of glycosphingolipids throughout the body. The result is a multi-system disorder characterized by cutaneous, corneal, cardiac, renal, and neurological manifestations. Increased left ventricular wall thickness represents the predominant cardiac manifestation of FD. As the disease progresses, patients may develop arrhythmias, advanced conduction abnormalities, and heart failure. Cardiac biomarkers, point-of-care dried blood spot testing, and advanced imaging modalities including echocardiography with strain imaging and magnetic resonance imaging (MRI) with T1 mapping now allow us to detect Fabry cardiomyopathy much more effectively than in the past. While enzyme replacement therapy (ERT) has been the mainstay of treatment, several promising therapies are now in development, making early diagnosis of FD even more crucial. Ongoing initiatives involving artificial intelligence (AI)-empowered interpretation of echocardiographic images, point-of-care dried blood spot testing in the echocardiography laboratory, and widespread dissemination of point-of-care ultrasound devices to community practices to promote screening may lead to more timely diagnosis of FD. Fabry disease should no longer be considered a rare, untreatable disease, but one that can be effectively identified and treated at an early stage before the development of irreversible end-organ damage.

Development of a Carotid Vulnerable Plaque Phantom Model Evaluated by Pixel Distribution Analysis.

Carotid artery plaque composed of a larger percentage of lipids and/or intra-plaque hemorrhage are considered "vulnerable" or at higher risk for rupture. It is thought that such vulnerable lesions contribute to the majority of cardiovascular events. Ultrasound may facilitate the identification of plaque tissue types associated with risk for rupture. Pixel distribution analysis (PDA) is a plaque composition imaging analysis method that assigns grayscale ranges to corresponding tissue types. The aim of this study was to develop an in vitro vulnerable carotid plaque mimic (phantom) using known rat tissue types (fat, muscle and bone) to establish corresponding PDA ranges. Two sets of PDA grayscale ranges were established: (i) the combined tissue set, which combined tissue subtypes into their respective categories-polyvinyl chloride (representing blood, grayscale range 0-4), muscle (84-95), fat (99-113) and bone (145-175); (ii) Individual tissue set for each tissue subtype-polyvinyl chloride (grayscale range 0-4), neck muscle (68-86), leg muscle (76-86), epididymal fat (91-100), abdomen muscle (104-108), subcutaneous fat (111-120) and bone (145-175). The grayscale pixel range overlaped between tissue types (87-90 and 109-110). These ranges were tested on five simulated polyvinyl chloride heterogeneous plaque types containing epididymal fat, leg muscle, neck muscle, abdominal muscle or bone. The individual tissue set grayscale ranges detected significantly more pixels within the correct tissue category than the combined tissue set ranges (≤10.1%, p < 0.05). This study represents a novel phantom PDA method to assess plaque heterogeneity and may be used to infer tissue type composition in clinical imaging. Additionally, this plaque phantom may serve as a platform for development and testing of novel composition analysis methods.

Novel Ultrasound Methods to Investigate Carotid Artery Plaque Vulnerability.

Features of vulnerable plaque include a high lipid content, an irregular shape, a thin fibrous cap, and neovascularization, but such lesions often fall into the category of nonstenotic, despite being at high risk for rupture, and therefore may be overlooked. In this review, the authors describe state-of-the-art investigative ultrasound methods to assess the activity, quality, and morphology of atherosclerotic plaque to determine vulnerability. Specifically, the authors focus on carotid artery plaque, describing the assessment of plaque activity through the detection of neovascularization using contrast-enhanced ultrasound, the characterization of plaque quality by advanced grayscale and integrated backscatter analysis methods, and the assessment of plaque morphology using three-dimensional ultrasound.