Equine Hospital, Ludwig Maximilians University Munich, Munich, Germany.
Centre for Equine Ophthalmology, Equine Hospital in Parsdorf, Vaterstetten, Germany.
Equine Vet J. 2022 Sep;54(5):847-855. doi: 10.1111/evj.13528. Epub 2021 Nov 8.
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.
A software tool was developed to aid in the diagnosis of equine ophthalmic diseases, especially uveitis.
Prospective comparison of software and clinical diagnoses.
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.
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).
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.
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.
由于人工智能、深度学习和智能设备技术的最新发展，可能会开发出可离线运行的诊断软件，作为智能手机上的应用程序，利用其高分辨率摄像头和不断提高的处理能力，直接分析设备上拍摄的照片。
开发一种软件工具，辅助诊断马眼科疾病，特别是葡萄膜炎。
软件与临床诊断的前瞻性比较。
使用图像分类的深度学习方法，通过分析马眼照片来训练软件，以确定马是否出现葡萄膜炎或其他眼病的迹象。评估了四个不同大小的基础网络（MobileNetV2、InceptionV3、VGG16、VGG19）和经过修改的顶层。将卷积神经网络（CNN）在 2346 张马眼照片上进行训练，并将其扩充到 9384 张图像。使用 261 张未经修改的独立图像来评估训练网络的性能。
在区分三种类别（葡萄膜炎、其他眼病、健康）时，交叉验证显示训练数据的准确率为 99.82%，验证数据的准确率为 96.66%。
人工智能的一个选择偏差来源可能是瞳孔增大，由于使用了散瞳剂，主要出现在有眼病的马中，而在数据集的所有类别中没有均匀分布。
我们的马葡萄膜炎检测系统是独特和新颖的，可以区分葡萄膜炎和其他马眼病。它的开发也为一般眼病的基于图像的检测提供了概念验证，并为其进一步的使用和扩展奠定了基础。
