Familial Hypercholesterolemia (FH) is a genetic disorder characterized by elevated levels of Low-Density Lipoprotein (LDL) cholesterol or its associated genes. Early-stage and accurate categorization of FH is of significance allowing for timely interventions to mitigate the risk of life-threatening conditions. Conventional diagnosis approach, however, is complex, costly, and a challenging interpretation task even for experienced clinicians resulting in high underdiagnosis rates. Although there has been a recent surge of interest in using Machine Learning (ML) models for early FH detection, existing solutions only consider a binary classification task solely using classical ML models. Despite its significance, application of Deep Learning (DL) for FH detection is in its infancy, possibly, due to categorical nature of the underlying clinical data. The paper addresses this gap by introducing the FH-TabNet, which is a multi-stage tabular DL network for multi-class (Definite, Probable, Possible, and Unlikely) FH detection. The FH-TabNet initially involves applying a deep tabular data learning architecture (TabNet) for primary categorization into healthy (Possible/Unlikely) and patient (Probable/Definite) classes. Subsequently, independent TabNet classifiers are applied to each subgroup, enabling refined classification. The model's performance is evaluated through 5-fold cross-validation illustrating superior performance in categorizing FH patients, particularly in the challenging low-prevalence subcategories.

HealthTech AI company NuraLogix has announced that it has developed AI models that can predict a person's risk of Type 2 Diabetes, Hypercholesterolemia, Hypertriglyceridemia and Hypertension. This is important, says the company, because it will someday allow people to screen themselves using any device with a video camera such as a smartphone or tablet. Researchers at the company trained their machine learning-based models on the facial blood flow patterns of tens of thousands of subjects who had been diagnosed with the above conditions. All of the models predicted with a minimum Area Under the Curve (AUC) 0.80. Compare this with the classic Framingham model for predicting cardiovascular disease which had an AUC in the range of 0.70, says the company. "This technology is a disruptive tool for population health," says Dr. Keith Thompson, Primary Care Physician of over 30 years, and Chief Medical Officer at NuraLogix, "We live in an era where one half of the globe is without access to health care, and in both developed and underdeveloped countries, there are shortages of Human Health resources."

A Stanford University-led team of scientists has developed a machine learning tool that can analyse electronic healthcare records (EHR) to identify individuals who are likely to have familial hypercholesterolemia (FH), an underdiagnosed genetic cause of elevated low-density lipoprotein (LDL) cholesterol, which puts patients at a 20-fold increased risk of coronary artery disease. In separate test runs the classifier, described today in npj Digital Medicine, correctly identified more than 80% of cases--its positive predictive value (PPV)--and demonstrated 99% specificity. The team says the classifier could help to flag up patients who are most likely to have FH, so that they and their families can undergo further genetic testing. "Theoretically, when someone comes into the clinic with high cholesterol or heart disease, we would run this algorithm," said Nigam Shah, MBBS, PhD, Stanford University associate professor of medicine and biomedical data science. "If they're flagged, it means there's an 80% chance that they have FH. Those few individuals could then get sequenced to confirm the diagnosis and could start an LDL-lowering treatment right away."