Evaluation of the effectiveness of a portable device for diagnosing cataract.

Purpose: Access to cataract treatment and diagnostic tools continues to be hindered by financial and logistical barriers. Thus, photography-based cataract analysis via portable devices offers a promising solution for the detection of cataracts in remote regions. In this study, the accuracy of a portable device that is based on the Lens Opacities Classification III System for diagnosing cataracts was analyzed.

Methods: Photographs of the anterior segment of the eye were taken in a low-light environment, and the pupillary region markings were automatically delineated using infrared photography. The captured images were automatically analyzed using a convolutional neural network. The study group included patients with cataracts, and the control group included patients without cataracts.

Results: A total of 270 eyes were analyzed, which included 143 eyes with cataracts and 127 control eyes. A total of 599 photos were analyzed. The isolated nuclear cataract was the most frequently detected subtype (37.5%), followed by a nuclear cataract associated with a cortical cataract (30.3%). The device's accuracy was 88.5% (Confidence intervals (CI), 83.19%-94.69%), specificity was 84.62% (CI 71.79%-97.30%), positive predictive value was 91.78% (CI 74.36%-97.30%), and negative predictive value was 82.50% (CI 74.36%-97.30%).

Conclusion: The portable device is a simplified user-friendly cataract screening technique that can interpret results in remote regions. This innovation could mitigate the occurrence of cataract-induced blindness and prevent premature surgical interventions in early-stage cataracts.