Developed AI/ML algorithm to detect ‘cotton wool’ infection Spots in retina images for a Leading Healthcare Technology Provider in the US
Overview
The client is one of the leading healthcare technology providers in the US, offering technology that empowers care delivery solutions for ophthalmic hospitals.
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Challenges
Complex Cloud Environment
Detecting infection in the retina images, ‘cotton wool’ spots
Limited Security Expertise
Transforming and automating the entire manual diagnosis process
Evolving Threat Landscape
Calculating the spread of the infection and identifying the severity of the problem
Evolving Threat Landscape
Improve the accuracy of diagnosis
Benefits
Leverage the data for clinical and operational decisions to support and deliver value-based healthcare
Improved Security Posture
Our experienced team with a proven track record ensures that you receive expert guidance every step of the way.
Enhanced Visibility
We offer customized, comprehensive cybersecurity coverage, designed to meet your unique business needs.
Reduced Costs
Utilizing cutting-edge tools, we continuously innovate and improve to stay ahead of emerging threats.
Compliance Adherence
With a focus on collaboration and results, we prioritize building solutions that work effectively for your organization.
Benefits
Leverage the data for clinical and operational decisions to support and deliver value-based healthcare
Improved Security Posture
Our experienced team with a proven track record ensures that you receive expert guidance every step of the way.
Enhanced Visibility
We offer customized, comprehensive cybersecurity coverage, designed to meet your unique business needs.
Reduced Costs
Utilizing cutting-edge tools, we continuously innovate and improve to stay ahead of emerging threats.
Compliance Adherence
With a focus on collaboration and results, we prioritize building solutions that work effectively for your organization.
Outcomes
Leverage the data for clinical and operational decisions to support and deliver value-based healthcare
- 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
