Treffer: Computer-Aided Diagnosis of Equine Pharyngeal Lymphoid Hyperplasia Using the Object Detection-Based Processing Technique of Digital Endoscopic Images.

Simple Summary: Artificial intelligence is increasingly being applied in medical practice, particularly in computer-aided diagnosis (CAD). While these applications are already common in humans, they have only recently been introduced in veterinary medicine, especially in equine practice. This study aimed to evaluate the effectiveness of CAD in diagnosing one of the respiratory tract diseases—pharyngeal lymphoid hyperplasia (PLH). Since PLH is visually diagnosed based on the size and number of lymphoid follicles within the pharyngeal mucosa, this study employed an object detection-based processing technique to identify lymphoid follicles on endoscopic images and combined it with two digitization approaches—Voronoi diagrams and first-order statistics (FOS)—to quantify endoscopic signs of PLH. A digital data set thus obtained from 70 horses was combined with a clinical data set, representing respiratory tract clinical symptoms, to assess classification performance using the machine learning algorithm. The proposed CAD method achieved the highest classification metrics—0.76 accuracy and 0.83 precision—when both data sets were combined. This performance was higher compared to applying the CAD method to either data set alone. The proposed CAD method provides effective discrimination of PLH grades and may be further applied to the assessment of equine pharyngeal endoscopic images. 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. [ABSTRACT FROM AUTHOR]

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