Smarter Risk Assessments. Better Prevention. Seamless Integration.
Provide Your Patients with the Best Management Options
Our Collaborators
Our Products
OpenDNA’s comprehensive AI-driven risk assessment tools bridge the gap between genetics and clinical care, providing actionable insights that help you predict, prevent, and personalize treatment for complex diseases like cancer and cardiovascular conditions.
CardioRisk+
AI-driven heart disease prediction for proactive cardiovascular care.
CancerRisk+
Assess breast, colon, and prostate cancer risk using polygenic risk scores.
TotalRisk+
The only polygenic-monogenic cancer risk tool for comprehensive genetic assessment.
PolyRisk+
The broadest AI-powered polygenic risk tool spanning eight major disease areas.
Enhance patient care
with full-spectrum polygenic and monogenic genetic insights
Engage and
empower patients
with personalized risk assessments
Lead in
innovation
with AI-powered precision medicine
Seamlessly
integrate
into your clinical workflow
Our Clinical Results
Proven to Improve Patient Outcomes
AI-based Coronary Risk Assessment Integrating Polygenic Risk With Coronary Calcium
The AI model demonstrated a significant association with the risk of CHD events (p=0.001) and outperformed the PCE (AUC 0.69 vs. 0.66), further enhanced by adding the CAC score (AUC 0.74 vs 0.66). The AI model was also more strongly associated with the CAC score (p=0.003) than the PCE (p=0.041). Combined with CAC, the AI model achieved 80% sensitivity, an NRI of 22.54%, and a 100% NPV. In summary, the AI model outperformed the PCE and the PRS alone in predicting CHD events and nearly doubled the sensitivity over the PCE. Although CAC scoring alone provided greater predictive power than the AI model, the highest predictive accuracy was observed by integrating CAC into the AI model.
Discussion: These findings highlight the potential of combining AI-driven risk assessment models with PRS and CAC to enhance early CHD risk stratification and guide therapy in those who traditional scores might overlook, particularly in younger middle-aged adults.
View the Poster Presented at the American College of Cardiology (ACC) Scientific Session 2025
Advanced Machine Learning Techniques For Coronary Artery Disease Risk Prediction In Early Middle-Aged Adults
Forward feature selection identified 22 CAD risk factors, including the PRS. The final model yielded an AUC of 0.81 in the training dataset and 0.80 in the testing dataset. At the 7.5% risk threshold, the model demonstrated higher sensitivity (0.511 vs. 0.346) and comparable specificity (0.863 vs. 0.866) compared to the ACC ASCVD model. The Net Reclassification Index (NRI) improved by 13.79%, emphasizing the model’s accuracy and alignment.
Discussion: These findings highlight the potential of combining genetic information with clinical and lifestyle factors through machine learning to improve CAD risk prediction in early middle-aged adults. The use of a stacking ensemble approach enabled modeling of complex relationships, yielding superior performance over standard risk models.
Impact of Polygenic Risk Score (PRS) for Coronary Artery Disease on Physician Decision-Making and Patient Care
PRS findings impacted clinical decision-making in 67% of cases (100 participants). The most frequent physician response was raising patient awareness and offering patient education (73 cases), while emphasizing PRS as a tool for risk communication. In 4 specific cases, PRS findings led to new statin recommendations, while 23 cases resulted in other management modifications, including lifestyle adjustments and increased risk monitoring.
Discussion: These findings highlight the potential of PRS in enhancing risk communication and clinical decision-making, primarily by reinforcing patient awareness rather than directly by altering pharmacologic management. Further research is needed to optimize PRS implementation and assess its long-term clinical impact.
Read the full article in Frontiers in Genetics, 2025
Patient Case Study: Started Statins Following CardioRisk+ Test
Michael Chen, MD – Shady Grove Medical Center, Washington Adventist Hospital, Cardiac Associates.
A 50-year-old patient with no known coronary artery disease (CAD) and normal results on prior cardiac evaluations had consistently declined statin therapy, despite physician recommendations based on age and borderline LDL-C levels. The patient felt their overall risk was low and was concerned about potential side effects.
As part of a preventive health initiative, the patient underwent polygenic risk testing. Results revealed an 80th percentile genetic risk for CAD, indicating a significantly elevated inherited predisposition compared to the general population.
This new genetic information shifted the patient’s perception of risk. With a clearer understanding of their long-term vulnerability, the patient chose to begin statin therapy and committed to additional lifestyle changes.
What Do Clinicians Say?
“The development of a health problem is the result of a complex interplay between nature and nurture: your genes and environmental/behavioral factors. Prior to our partnership with OpenDNA, the nature part was a black box. Now, using the data obtained from up to a million different genes we can better personalize the care for each of our members. What a powerful tool.“
William Harper, MD, FACP, Founder of Harper Health
Our Solution
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Advanced Genetic Testing:
Simple buccal swab for genomic insights -
Gen AI-Powered Decision Support:
Real-time, actionable and personalized recommendations -
Automated EMR Integration:
Effortlessly pulls and analyzes patient data -
Interactive Patient Engagement:
“What-if” risk modeling for informed decision-making
