Overview
The client is one of the leading healthcare technology providers in the US, offering technology that empowers care delivery solutions for ophthalmic hospitals.
Challenges
Detecting infection in the retina images, ‘cotton wool’ spots
Transforming and automating the entire manual diagnosis process
Calculating the spread of the infection and identifying the severity of the problem
Improve the accuracy of diagnosis
Outcomes
- The deep-learning algorithm can be extended to simultaneously identify multiple retinal abnormalities, helping practitioners improve the early diagnosis of retinal diseases in underdeveloped areas, thus addressing the triple mandates of care, viz., - accessibility, affordability, and availability
- This automated detection system can reduce manual effort, and primary eye care services can be provided in remote areas to overcome the scarcity of doctors
- Accomplished a 95 percent decrease in exam time versus ophthalmologists working alone. The model helped lower exam time by 75 percent when combined with an ophthalmologist
