AIMC Topic: Genetic Testing

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Improving the Detection of Potential Cases of Familial Hypercholesterolemia: Could Machine Learning Be Part of the Solution?

Journal of the American Heart Association
BACKGROUND: Familial hypercholesterolemia (FH), while highly prevalent, is a significantly underdiagnosed monogenic disorder. Improved detection could reduce the large number of cardiovascular events attributable to poor case finding. We aimed to ass...

Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction.

Reproductive biology and endocrinology : RB&E
BACKGROUND: The best method for selecting embryos ploidy is preimplantation genetic testing for aneuploidies (PGT-A). However, it takes more labour, money, and experience. As such, more approachable, non- invasive techniques were still needed. Analys...

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes.

The Canadian journal of cardiology
Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particularly among young individuals. Recent advances in our understanding of these syndromes have improved patient diagnosis and care, yet certain clinical ...

An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases.

Human genetics
Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an...

Improving newborn screening in India: Disease gaps and quality control.

Clinica chimica acta; international journal of clinical chemistry
In India, newborn screening (NBS) is essential for detecting health problems in infants. Despite significant progress, significant gaps and challenges persist. India has made great strides in genomics dueto the existence of the National Institute of ...

Applying data science methodologies with artificial intelligence variant reinterpretation to map and estimate genetic disorder prevalence utilizing clinical data.

American journal of medical genetics. Part A
Data science methodologies can be utilized to ascertain and analyze clinical genetic data that is often unstructured and rarely used outside of patient encounters. Genetic variants from all genetic testing resulting to a large pediatric healthcare sy...

The ENGAGE study: evaluation of a conversational virtual agent that provides tailored pre-test genetic education to cancer patients.

Journal of cancer survivorship : research and practice
PURPOSE: Novel approaches are needed to ensure all patients with cancer have access to quality genetic education before genetic testing to enable informed treatment decisions. The purpose of this study was to test the use of an artificial intelligenc...

The neglected emotional drawbacks of the prioritization of embryos to transfer.

Reproductive biomedicine online
In recent years, increasing efforts have been made to develop advanced techniques that could predict the potential of implantation of each single embryo and prioritize the transfer of those at higher chance. The most promising include non-invasive pr...

Improved accuracy in colorectal cancer tissue decomposition through refinement of established deep learning solutions.

Scientific reports
Hematoxylin and eosin-stained biopsy slides are regularly available for colorectal cancer patients. These slides are often not used to define objective biomarkers for patient stratification and treatment selection. Standard biomarkers often pertain t...

Applications of artificial intelligence in clinical laboratory genomics.

American journal of medical genetics. Part C, Seminars in medical genetics
The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately ev...