Kozłowska Natalia, Borowska Marta, Jasiński Tomasz, Wierzbicka Małgorzata, Domino Małgorzata
Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Animals (Basel). 2025 Sep 22;15(18):2758. doi: 10.3390/ani15182758.
In human medicine, computer-aided diagnosis (CAD) is increasingly employed for screening, identifying, and monitoring early endoscopic signs of various diseases. However, its potential-despite proven benefits in human healthcare-remains largely underexplored in equine veterinary medicine. This study aimed to quantify endoscopic signs of pharyngeal lymphoid hyperplasia (PLH) as digital data and to assess their effectiveness in CAD of PLH in comparison and in combination with clinical data reflecting respiratory tract disease. Endoscopic images of the pharynx were collected from 70 horses clinically assessed as either healthy or affected by PLH. Digital data were extracted using an object detection-based processing technique and first-order statistics (FOS). The data were transformed using linear discriminant analysis (LDA) and classified with the random forest (RF) algorithm. Classification metrics were then calculated. When considering digital and clinical data, high classification performance was achieved (0.76 accuracy, 0.83 precision, 0.78 recall, and 0.76 F1 score), with the highest importance assigned to selected FOS features: Number of Objects and Neighbors, and Tracheal Auscultation. The proposed protocol of digitizing standard respiratory tract diagnostic methods provides effective discrimination of PLH grades, supporting the clinical value of CAD in veterinary medicine and paving the way for further research in digital medical diagnostics.
在人类医学中，计算机辅助诊断（CAD）越来越多地用于筛查、识别和监测各种疾病的早期内镜迹象。然而，尽管其在人类医疗保健中已被证明有好处，但其在马兽医医学中的潜力仍在很大程度上未被充分探索。本研究旨在将咽淋巴组织增生（PLH）的内镜迹象量化为数字数据，并评估其在PLH的CAD中与反映呼吸道疾病的临床数据相比及结合使用时的有效性。从70匹经临床评估为健康或患有PLH的马身上收集咽部的内镜图像。使用基于目标检测的处理技术和一阶统计量（FOS）提取数字数据。数据通过线性判别分析（LDA）进行转换，并使用随机森林（RF）算法进行分类。然后计算分类指标。当考虑数字和临床数据时，实现了较高的分类性能（准确率0.76、精确率0.83、召回率0.78和F1分数0.76），选定的FOS特征：目标数量和邻居数量以及气管听诊被赋予了最高的重要性。所提出的将标准呼吸道诊断方法数字化的方案能够有效区分PLH等级，支持CAD在兽医医学中的临床价值，并为数字医学诊断的进一步研究铺平道路。
