iHealthScreen is the first company that developed the late AMD prediction model and was awarded a patent by USPTO
NEW YORK–(BUSINESS WIRE)–#AI—iPredictTM late AMD prediction model provides a fully automated prediction score for age-related macular degeneration (AMD), and identifies the individuals who are at risk of developing late AMD in 1 year, 2-year or 5-year. Earlier the results were published in the top scientific journals. The prediction model can potentially help prevent late AMD and save millions of people from blindness.
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 AMD and 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.
The prediction model offers an overall accuracy of 89.61% for identifying an individual at risk of progression to late AMD.
iPredict’s late AMD prediction model can be used as follows:
- iPredict-AMD can detect referable AMD such as intermediate to late AMD and non-referable AMD such as early or none. Once referable AMD is detected, iPredict late AMD prediction model will be utilized and compute the probability score for late AMD.
The company also has AI-based screening tools for early diagnosis of diabetic retinopathy, AMD and glaucoma which are CE certified and TGA/Australian health approved.
If referable stage disease is detected for any of these conditions, the iPredict automated report recommends a visit to an Ophthalmologist for appropriate treatment. Otherwise, in accordance with standards of care, a follow-up visit in one year is suggested.
iPredict is indicated for use by healthcare providers in clinics, hospitals or other healthcare facilities to automatically detect DR, AMD, and glaucoma suspect.
iHealthScreen company is open to partnerships within its products. For more information: https://www.iHealthScreen.org
Contacts
Alauddin Bhuiyan, Ph.D.
CEO, iHealthScreen Inc.
E: [email protected]
T: +1 718 926 9000
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