Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis. (8th November 2021)

• Record Type: Journal Article
• Authors: May, Anna; Gesell‐May, Stefan; Müller, Tobias; Ertel, Wolfgang
• Is Part Of: Equine veterinary journal. Volume 54:Number 5(2022)
• Page Start: 847
• Page End: 855
• Publication Date: 2021-11-08
• Subjects: artificial intelligence -- blindness -- equine uveitis -- horse -- machine/deep learning -- ophthalmology; Horses -- Diseases -- Periodicals; 636.108905
• DOI: 10.1111/evj.13528
• Languages: English
• ISSNs: 0425-1644

Abstract

Summary: Background: Due to recent developments in artificial intelligence, deep learning, and smart‐device‐technology, diagnostic software may be developed which can be executed offline as an app on smartphones using their high‐resolution cameras and increasing processing power to directly analyse photos taken on the device. Objectives: A software tool was developed to aid in the diagnosis of equine ophthalmic diseases, especially uveitis. Study design: Prospective comparison of software and clinical diagnoses. Methods: A deep learning approach for image classification was used to train software by analysing photographs of equine eyes to make a statement on whether the horse was displaying signs of uveitis or other ophthalmic diseases. Four basis networks of different sizes (MobileNetV2, InceptionV3, VGG16, VGG19) with modified top‐layers were evaluated. Convolutional Neural Networks (CNN) were trained on 2346 pictures of equine eyes, which were augmented to 9384 images. 261 separate unmodified images were used to evaluate the performance of the trained network. Results: Cross validation showed accuracy of 99.82% on training data and 96.66% on validation data when distinguishing between three categories (uveitis, other ophthalmic diseases, healthy). Main limitations: One source of selection bias for the artificial intelligence presumably was the increased pupil size, which was mainly present in horses with ophthalmic diseases due to the use of mydriatics, and was not homogeneously dispersed in all categories of the dataset. Conclusions: Our system for detection of equine uveitis is unique and novel and can differentiate between uveitis and other equine ophthalmic diseases. Its development also serves as a proof‐of‐concept for image‐based detection of ophthalmic diseases in general and as a basis for its further use and expansion.