Transforming Healthcare with AI
Accelerate diagnosis, improve patient outcomes, and empower clinicians with AI-driven insights for cardiology and neurology — built on real-world data.
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Cardiology
Explore our research-use-only cardiology tools for risk assessment, patient stratification, and algorithmic ECG interpretation.
Echonet EF Predictor: Predict left ventricular ejection fraction (EF) from echocardiogram videos using deep learning.
ASCVD: Estimate a patient’s 10-year risk of atherosclerotic cardiovascular disease.
Blood Pressure Category: Classify patients based on blood pressure measurements for risk stratification.
CHA2DS2VASc: Calculate stroke risk in patients with atrial fibrillation.
ECG Interpreter: Analyze ECG data and provide automated interpretations for research purposes.
Neurology
Explore our research-use-only neurology tools for Alzheimer’s disease risk assessment, diagnosis, and progression prediction.
Alzheimer Risk Screener (Rule-Based)
Estimate potential Alzheimer’s risk using key clinical and demographic features.
Alzheimer Diagnostic Classifiers
Predict cognitive state (CN, MCI, AD) using a range of features from basic clinical tests to advanced multimodal biomarkers. All models were trained on ADNI data.
2-Year Cognitive Progress Predictors
Forecast cognitive decline over 2 years using basic and advanced biomarkers, including neuroimaging and cognitive test scores.
Built on ADNI data to assist research in Alzheimer’s progression.
Related Papers
Explore peer-reviewed research and technical insights behind Clinovia’s AI-powered cardiovascular and neurodegenerative risk assessment tools.
Advancing Cardiovascular Risk Prediction: A Review of Machine Learning Models and Their Clinical Potential
Journal: Life (MDPI), 2025
This review examines the latest AI techniques for predicting cardiovascular risk, addressing strengths, limitations, and steps toward clinical integration.
Read Full ReviewXGBoost Models Based on Non-Imaging Features for Prediction of Mild Cognitive Impairment in Older Adults
Journal: Scientific Reports, 2025
Uses features like demographics and cognitive scores (no imaging) to model MCI, leveraging SHAP for feature interpretability.
View on Scientific ReportsPredicting Deterioration in Mild Cognitive Impairment with Survival Transformers, XGBoost & Cox Models
Repository: arXiv (2024)
Compares survival transformer and XGBoost models in forecasting cognitive decline, highlighting their predictive accuracy and stability.
Read on arXivWho Benefits from Clinovia.ai
Clinovia.ai’s Research Use Only (RUO) solutions empower hospitals, contract research organizations (CROs), and pharmaceutical companies to accelerate discovery and improve evidence-based decision making in neurology and cardiology.
Hospitals
Evaluate AI-driven diagnostics in controlled research settings to explore new approaches for cardiovascular and cognitive assessment.
Contract Research Organizations (CROs)
Integrate Clinovia models into clinical studies to enhance analytics, validate biomarkers, and generate explainable research insights for sponsors.
Pharmaceutical Companies
Apply Clinovia’s AI for patient stratification, drug efficacy studies, and post-market safety monitoring in cardiology and neurology.