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Valvular heart disease is an important contributor to cardiovascular morbidity and mortality and remains underdiagnosed. Deep learning analysis of electrocardiography (ECG) may be useful in detecting aortic stenosis (AS), aortic regurgitation (AR), and mitral regurgitation (MR).This study aimed to develop ECG deep learning algorithms to identify moderate or severe AS, AR, and MR alone and in combination.A total of 77,163 patients undergoing ECG within 1 year before echocardiography from 2005-2021 were identified and split into train (n = 43,165), validation (n = 12,950), and test sets (n = 21,048; 7.8% with any of AS, AR, or MR). Model performance was assessed using area under the receiver-operating characteristic (AU-ROC) and precision-recall curves. Outside validation was conducted on an independent data set. Test accuracy was modeled using different disease prevalence levels to simulate screening efficacy using the deep learning model.The deep learning algorithm model accuracy was as follows: AS (AU-ROC: 0.88), AR (AU-ROC: 0.77), MR (AU-ROC: 0.83), and any of AS, AR, or MR (AU-ROC: 0.84; sensitivity 78%, specificity 73%) with similar accuracy in external validation. In screening program modeling, test characteristics were dependent on underlying prevalence and selected sensitivity levels. At a prevalence of 7.8%, the positive and negative predictive values were 20% and 97.6%, respectively.Deep learning analysis of the ECG can accurately detect AS, AR, and MR in this multicenter cohort and may serve as the basis for the development of a valvular heart disease screening program.

We train and validate a semi-supervised, multi-task LSTM on 57,675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep apnea (0.8298). We compare two semi-supervised train- ing methods, semi-supervised sequence learning and heuristic pretraining, and show they outperform hand-engineered biomarkers from the medical literature. We believe our work suggests a new approach to patient risk stratification based on cardiovascular risk scores derived from popular wearables such as Fitbit, Apple Watch, or Android Wear., Presented at AAAI 2018

Up to a third of patients with diabetes are undiagnosed, and therefore go untreated. Widely available consumer technologies could detect diabetes at an early stage. Since diabetes is associated with vascular changes, we examined whether diabetes could be detected using only the photoplethysmography

Gestational exposure to ethanol causes fetal alcohol syndrome, which is associated with cerebellar hypoplasia. Previous in vitro studies demonstrated ethanol-impaired neuronal survival with reduced signaling through the insulin receptor (IRbeta). We examined insulin signaling in an experimental rat model of chronic gestational exposure to ethanol in which the pups exhibited striking cerebellar hypoplasia with increased apoptosis. Immunoprecipitation and Western blot analyses detected reduced levels of tyrosyl-phosphorylated IRbeta, tyrosyl-phosphorylated insulin receptor substrate-1 (IRS-1), and p85-associated IRS-1 but no alterations in IRbeta, IRS-1, or p85 protein expression in cerebellar tissue from ethanol-exposed pups. In addition, ethanol exposure significantly reduced the levels of total phosphoinositol 3-kinase, Akt kinase, phospho-BAD (inactive), and glyceraldehyde-3-phosphate dehydrogenase and increased the levels of glycogen synthase kinase-3 activity, activated BAD, phosphatase and tensin homolog deleted in chromosome 10 (PTEN) protein, and PTEN phosphatase activity in cerebellar tissue. Cerebellar neurons isolated from ethanol-exposed pups had reduced levels of insulin-stimulated phosphoinositol 3-kinase and Akt kinase activities and reduced insulin inhibition of PTEN and glycogen synthase kinase-3 activity. The results demonstrate that cerebellar hypoplasia produced by chronic gestational exposure to ethanol is associated with impaired survival signaling through insulin-regulated pathways, including failure to suppress PTEN function.