Researchers have developed an AI model that accurately predicts which keratoconus patients require treatment by analyzing OCT eye scans and clinical data.
Key Details
- 1Study presented at the 43rd Congress of the European Society of Cataract and Refractive Surgeons (ESCRS).
- 2AI analyzed 36,673 OCT images from 6,684 patients with keratoconus.
- 3Algorithm could stratify two-thirds of patients as low-risk and one-third as high-risk for disease progression using data from a single visit.
- 4Including data from a second visit increased prediction accuracy to up to 90% of patients correctly categorized.
- 5Cross-linking treatment prevents vision loss and corneal transplantation in most cases if given early.
- 6Algorithm will undergo further safety testing before clinical deployment; researchers plan to expand to other eye conditions.
Why It Matters
Accurate early prediction of keratoconus progression through AI-augmented imaging could enable timely intervention, reduce unnecessary monitoring, and optimize healthcare resources, potentially preventing vision loss in young patients. This approach showcases the growing clinical impact of AI in ophthalmic imaging and patient stratification.
Source
EurekAlert
Related News
•EurekAlert
UVA's $4M NIH Grant Backs AI to Optimize Cardiac MRI Therapies
UVA secures $4 million NIH funding to develop GenStrain, an AI model using MRI data to personalize cardiac pacing therapies.
•EurekAlert
Machine Learning Models Predict Cancer Risk with Routine Health Data
Researchers developed machine learning models using routinely collected health data to predict individual risk for 20 common cancers, outperforming traditional methods for several cancer types.
•EurekAlert
Duke Receives $25M to Lead National Trustworthy AI Hub for Dementia and Aging
Duke University has launched a $25 million national center to make AI for aging and dementia clinically robust and trustworthy.