Artificial Intelligence Helps Detect Dry Eye Disease

Dry Eye Disease (DED) is a prevalent ocular condition, impacting as many as 30% of individuals worldwide. Its onset can significantly impede the quality of life for various demographics. Timely screening and prognosis play a crucial role in managing the disease progression. Nonetheless, achieving this can pose challenges (1^✔ ✔Trusted Source
AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions

Go to source). In this research endeavor, scientists endeavor to leverage artificial intelligence (AI) to facilitate early screening and prognosis of DED. By harnessing AI technology, screening becomes more accessible to individuals, while also enabling personalized therapeutic interventions for patients.

Researchers published their results in Big Data Mining and Analytics.

Understanding the Impact and Symptoms of Dry Eye Disease

DED can affect a wide array of people, including those who wear contact lenses, makeup, stay up late, look at screens for a long time and are over 30 years old. Symptoms of this disease are dry eyes, irritation and burning, tears, eye fatigue and pain. One can easily see how this disease has the potential to drastically impact a large portion of the modern world’s population. Here is where the combined efforts of ophthalmic disease detection and the world of computer scientists and engineers can help.

“By addressing challenges, imparting insights, and delineating future research pathways, it contributes substantially to the advancement of ophthalmic disease detection through sophisticated technological modalities,” said Mini Han Wang, author and researcher.

There are seven facets to this AI-based disease detection. Timely intervention via the AI screening process and correct prognosis is the first part. The use of exhaustive surveys for DED through AI is another, and this is a supporting principle to ensure a level of thoroughness and trustworthiness throughout the process. A systematic approach follows, as well as the marriage of computer science and engineering with ophthalmology. Then, the standards for DED detection must be devised and upheld for future researchers and practitioners, which will naturally lead to the advancement of the field. Finally, all the research, methodologies and tools must be compiled so researchers, scholars and practitioners can have all of the information currently out there available to them.

While the ophthalmologists set the guidelines regarding the framework of the disease and flags for diagnosis, the AI does a lot of the heavy lifting. Ideally, this AI would use images and videos taken from a user’s cell phone to help reach users across the world. The AI can then utilize these images, as well as risk factors in the patient’s life, to make a smart and well-informed prognosis.

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The use of AI detection for DED holds a lot of promise, especially considering the risk factors are often normal activities in many people’s everyday lives. To make the detection methods accessible enough and accurate enough, further research needs to be done.

“However, there are still challenges for engineers to select the diagnostic standards and combinations of different types of datasets. By using trustworthy algorithms, images and videos captured from phones for accessibility purposes, a holistic approach to healthcare for early screening is possible,” said Wang.

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With continued testing and collaboration between engineers and ophthalmologists, there is great potential for this method of testing to be useful in contributing to early screening of DED and subsequent therapeutic actions taken for the patient to reduce a worsening condition or to recover some quality of life.

Reference:

1. AI-Based Advanced Approaches and Dry Eye Disease Detection Based on Multi-Source Evidence: Cases, Applications, Issues, and Future Directions - (https://ieeexplore.ieee.org/document/10506809/)

Source-Eurekalert