iHealthScreen Inc. Introduces An Automated Stroke Prediction Model, based on AI and Color Fundus Imaging

iHealthScreen is the First Company to Develop An AI- and Color-Fundus- Image-Based Stroke Prediction model, Funded by the National Institutes of Health (NIH)

NEW YORK–(BUSINESS WIRE)–#AIiPredictTM late Stroke prediction model provides a fully automated prediction score for incident stroke and identifies the individuals who are at risk of stroke within 5-years. These results were presented at the international conference: Society for Brain Mapping and Therapeutics World Congress and show that the prediction model may help prevent stroke, saving millions of people from death or disability.

Once high-resolution images of the patient’s eyes have been captured using a color fundus camera and submitted to the iPredictTM AI System, the stroke prediction results are available in a fully automated report in less than 60 seconds. The entire test can easily and reliably be completed within 5 minutes.

This prediction model offers an overall accuracy of 82.4% for identifying an individual at risk of having an incident stroke within 5-years.

iPredict achieved higher accuracy than existing stroke prediction models such as Framingham and Chads scores (iPredict-stroke accuracy 82.4% compared to Framingham score 64.9%, and the CHADS2VASC score achieved 63.8% accuracy respectively on the same dataset).

iPredict’s stroke model can be used to alert physicians of the need to take further preventive measures for these patients in the primary care setting.

The company also has AI-based screening tools for early diagnosis of diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma which are CE certified and TGA/Australian health approved.

iHealthScreen company is open to partnerships for distribution and/or co-development of its products in various territories. For more information: https://www.iHealthScreen.org


Alauddin Bhuiyan, Ph.D.

CEO, iHealthScreen Inc.

E: bhuiyan@ihealthscreen.org
T: +1 718 926 9000

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