OphthaPredict: Automatic Classification of Conjunctivitis Using Deep Learning Architecture

Conjunctivitis is a common infectious ophthalmic ailment that is known to cause significant discomfort and even loss of vision in some cases. The distinction between ‘Normal,’ ‘Viral,’ and ‘Bacterial’ causes of ‘Pink eye’ is presently difficult and assessed through culture reports and is inferred using deep learning architecture. An artificial intelligence-assisted classification of conjunctivitis can be of importance and help in its early diagnosis. The present study aimed to propose an end-to-end, automated conjunctivitis classification using a pre-trained deep learning architecture called EfficientNet. A three-labeled classification of conjunctivitis for ‘Normal,’ ‘Viral,’ and ‘Bacterial’ was performed. The model was deployed in real time as a web page called ‘OpthaPredict’ and achieved accuracy and precision of up to 99%. The study contributes to the development of more precise and reliable methods for predicting the type of conjunctivitis in real-time. The implications of the present study extend to the roots of the Indian healthcare system, such as Auxiliary Nurse Midwives (ANMs), Accredited Social Health Activist (ASHA) workers, Primary Care Physicians (PCPs), and early career ophthalmologists who rely on accurate results for informed decision-making.