Development, deployment and scaling of operating room-ready artificial intelligence for real-time surgical decision support.

Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites.

What is the educational value and clinical utility of artificial intelligence for intraoperative and postoperative video analysis? A survey of surgeons and trainees.

Introduction: Surgical complications often occur due to lapses in judgment and decision-making. Advances in artificial intelligence (AI) have made it possible to train algorithms that identify anatomy and interpret the surgical field. These algorithms can potentially be used for intraoperative decision-support and postoperative video analysis and feedback.

Use of artificial intelligence for decision-support to avoid high-risk behaviors during laparoscopic cholecystectomy.

Introduction: Bile duct injuries (BDIs) are a significant source of morbidity among patients undergoing laparoscopic cholecystectomy (LC). GoNoGoNet is an artificial intelligence (AI) algorithm that has been developed and validated to identify safe ("Go") and dangerous ("No-Go") zones of dissection during LC, with the potential to prevent BDIs through real-time intraoperative decision-support. This study evaluates GoNoGoNet's ability to predict Go/No-Go zones during LCs with BDIs.

Assessment of Machine Learning-Based Medical Directives to Expedite Care in Pediatric Emergency Medicine.

Importance: Increased wait times and long lengths of stay in emergency departments (EDs) are associated with poor patient outcomes. Systems to improve ED efficiency would be useful. Specifically, minimizing the time to diagnosis by developing novel workflows that expedite test ordering can help accelerate clinical decision-making.

Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery.

Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R-SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data.

EpigenCentral: Portal for DNA methylation data analysis and classification in rare diseases.

Epigenetic processes play a key role in regulating gene expression. Genetic variants that disrupt chromatin-modifying proteins are associated with a broad range of diseases, some of which have specific epigenetic patterns, such as aberrant DNA methylation (DNAm), which may be used as disease biomarkers. While much of the epigenetic research has focused on cancer, there is a paucity of resources devoted to neurodevelopmental disorders (NDDs), which include autism spectrum disorder and many rare, clinically overlapping syndromes.

Correction: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.

Purpose: Computational documentation of genetic disorders is highly reliant on structured data for differential diagnosis, pathogenic variant identification, and patient matchmaking. However, most information on rare diseases (RDs) exists in freeform text, such as academic literature. To increase availability of structured RD data, we developed a crowdsourcing approach for collecting phenotype information using student assignments.

Quality Indicators as Predictors of Future Inspection Performance in Ontario Nursing Homes.

Objectives: There are several mechanisms for monitoring the quality of care in long-term care (LTC), including the use of quality indicators derived from resident assessments and formal inspections. The LTC inspection process is time and resource-intensive, and there may be opportunities to better target inspections. In this study, we aimed to examine whether quality indicators could predict future inspection performance in LTC homes across Ontario, Canada.

Expanding the Boundaries of RNA Sequencing as a Diagnostic Tool for Rare Mendelian Disease.

Gene-panel and whole-exome analyses are now standard methodologies for mutation detection in Mendelian disease. However, the diagnostic yield achieved is at best 50%, leaving the genetic basis for disease unsolved in many individuals. New approaches are thus needed to narrow the diagnostic gap.