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Equine veterinary journal2021; 54(5); 847-855; doi: 10.1111/evj.13528

Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis.

Abstract: 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. Objective: A software tool was developed to aid in the diagnosis of equine ophthalmic diseases, especially uveitis. Methods: 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). Conclusions: 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.
© 2021 The Authors. Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
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Publication Date: 2021-11-08 PubMed ID: 34713490DOI: 10.1111/evj.13528Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research presents a software tool developed using artificial intelligence and deep learning methods, to diagnose uveitis and other horse eye diseases. This tool works based on high-resolution images of horse eyes, distinguishing between healthy eyes and those with ophthalmic conditions, and proves accurate in its diagnosis.

Understanding the Research methods

  • The study used advanced and modern technology, which included artificial intelligence, deep learning, and smart device technology to develop a diagnostic tool to identify equine ophthalmic diseases.
  • The diagnostic software was trained using a deep learning approach for image classification, where it analyzed photos of equine eyes to identify signs of uveitis or other ophthalmic diseases.
  • The software utilized four basis networks of different sizes (MobileNetV2, InceptionV3, VGG16, VGG19) with modified top-layers for evaluation.
  • The Convolutional Neural Networks (CNN) used in the study trained on 2346 pictures of equine eyes, which were augmented to 9384 images. A separate dataset of 261 unmodified images was used to evaluate the performance of the trained network.

Significant Results

  • Upon evaluation, the software tool exhibited a high level of accuracy, with 99.82% accuracy on training data and around 96.66% accuracy on validation data when distinguishing between uveitis, other ophthalmic diseases, and healthy eyes.
  • The system developed to detect equine uveitis is a novel and reliable methodology to tell the difference between uveitis and other equine ophthalmic diseases.

Conclusions and Future Implications

  • In the conclusions, the researchers identified a potential source of selection bias for the artificial intelligence. The increased pupil size, mainly present in horses with ophthalmic diseases due to the use of mydriatics, was not evenly distributed in all categories of the dataset.
  • Despite potential bias, this detection system serves as a proof-of-concept for image-based detection of ophthalmic diseases and lays the groundwork for its future use and expansion in diagnosing other equine diseases or ophthalmic diseases in different species.

Cite This Article

APA
May A, Gesell-May S, Müller T, Ertel W. (2021). Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis. Equine Vet J, 54(5), 847-855. https://doi.org/10.1111/evj.13528

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English
Volume: 54
Issue: 5
Pages: 847-855

Researcher Affiliations

May, Anna
  • Equine Hospital, Ludwig Maximilians University Munich, Munich, Germany.
Gesell-May, Stefan
  • Centre for Equine Ophthalmology, Equine Hospital in Parsdorf, Vaterstetten, Germany.
Müller, Tobias
  • Institute for Artificial Intelligence, Ravensburg-Weingarten University, Doggenriedstrasse, Germany.
Ertel, Wolfgang
  • Institute for Artificial Intelligence, Ravensburg-Weingarten University, Doggenriedstrasse, Germany.

MeSH Terms

  • Animals
  • Artificial Intelligence
  • Horse Diseases / diagnosis
  • Horses
  • Neural Networks, Computer
  • Uveitis / diagnosis
  • Uveitis / veterinary

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Citations

This article has been cited 4 times.
  1. Feuser AK, Gesell-May S, Müller T, May A. Artificial Intelligence for Lameness Detection in Horses-A Preliminary Study. Animals (Basel) 2022 Oct 17;12(20).
    doi: 10.3390/ani12202804pubmed: 36290189google scholar: lookup
  2. Akbarein H, Taaghi MH, Mohebbi M, Soufizadeh P. Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review. Vet Med Sci 2025 May;11(3):e70315.
    doi: 10.1002/vms3.70315pubmed: 40173266google scholar: lookup
  3. Xiao S, Dhand NK, Wang Z, Hu K, Thomson PC, House JK, Khatkar MS. Review of applications of deep learning in veterinary diagnostics and animal health. Front Vet Sci 2025;12:1511522.
    doi: 10.3389/fvets.2025.1511522pubmed: 40144529google scholar: lookup
  4. Scharre A, Scholler D, Gesell-May S, Müller T, Zablotski Y, Ertel W, May A. Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases. Equine Vet J 2025 Jan;57(1):47-53.
    doi: 10.1111/evj.14087pubmed: 38567426google scholar: lookup
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