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Animals : an open access journal from MDPI2025; 15(18); 2758; doi: 10.3390/ani15182758

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

Abstract: 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.
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Publication Date: 2025-09-22 PubMed ID: 41008003PubMed Central: PMC12466614DOI: 10.3390/ani15182758Google 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.

Overview

  • This study evaluates a computer-aided diagnosis (CAD) system for detecting pharyngeal lymphoid hyperplasia (PLH) in horses by analyzing digital endoscopic images using object detection techniques and combining these results with clinical data.
  • The goal is to demonstrate the effectiveness of CAD in veterinary medicine, similar to its growing use in human medical diagnostics.

Background and Motivation

  • Computer-aided diagnosis (CAD) is widely used in human medicine to enhance disease screening, identification, and monitoring, especially with endoscopic images.
  • Despite its success in human healthcare, CAD’s application in veterinary medicine, particularly for respiratory diseases in horses, remains limited and underexplored.
  • Pharyngeal lymphoid hyperplasia (PLH) is a condition in horses involving inflammation of lymphoid tissue in the pharynx, which can affect respiratory health.
  • Detecting and grading PLH currently relies on clinical assessment and endoscopic examination, which can be subjective and variable.

Objective

  • To develop and validate a CAD protocol that quantitatively assesses digital endoscopic images of the equine pharynx for signs of PLH.
  • To compare the effectiveness of digital (image-based) data and clinical data alone, as well as in combination, for diagnosing PLH.

Methods

  • Data Collection: Endoscopic images were gathered from 70 horses clinically categorized as either healthy or affected by PLH.
  • Digital Data Extraction: Applied an object detection-based processing technique to the images to identify specific features related to PLH.
  • Statistical Feature Computation: Calculated first-order statistics (FOS) concerning identified objects within the images, such as the number of objects and their neighborhood characteristics.
  • Data Transformation: Used linear discriminant analysis (LDA) to transform and reduce dimensionality of the extracted features for enhanced classification.
  • Classification: Employed a random forest (RF) algorithm to classify the samples into PLH-affected or healthy categories based on the transformed features.
  • Integration with Clinical Data: Also incorporated clinical data such as auscultation findings relating to the respiratory tract to examine combined diagnostic performance.
  • Performance Evaluation: Computed classification metrics including accuracy, precision, recall, and F1 score to assess the effectiveness of the models.

Results

  • The combined use of digital endoscopic data and clinical data resulted in high classification performance:
    • Accuracy: 0.76 (76%)
    • Precision: 0.83 (83%)
    • Recall: 0.78 (78%)
    • F1 Score: 0.76 (76%)
  • Among the features, the most significant contributors to classification accuracy were:
    • Number of detected objects in the image
    • Number of neighboring objects (a spatial relationship measure)
    • Tracheal auscultation results (a clinical measure)

Conclusions and Implications

  • The study successfully demonstrated that digitizing respiratory tract diagnostic methods using object detection and statistical analysis of endoscopic images can effectively discriminate grades of PLH in horses.
  • The CAD approach showed promise as a diagnostic support tool, augmenting clinical assessments in equine veterinary medicine.
  • Integrating clinical data with image-based digital features improves the accuracy of diagnosis.
  • This work lays a foundation for future research into digital medical diagnostics in veterinary practice and highlights the potential to apply advanced image processing and machine learning methods.

Cite This Article

APA
Kozłowska N, Borowska M, Jasiński T, Wierzbicka M, Domino M. (2025). Computer-Aided Diagnosis of Equine Pharyngeal Lymphoid Hyperplasia Using the Object Detection-Based Processing Technique of Digital Endoscopic Images. Animals (Basel), 15(18), 2758. https://doi.org/10.3390/ani15182758

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 15
Issue: 18
PII: 2758

Researcher Affiliations

Kozłowska, Natalia
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Borowska, Marta
  • Institute of Biomedical Engineering, Faculty of Mechanical Engineering, Białystok University of Technology, 15-351 Bialystok, Poland.
Jasiński, Tomasz
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Wierzbicka, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.
Domino, Małgorzata
  • Department of Large Animal Diseases and Clinic, Institute of Veterinary Medicine, Warsaw University of Life Sciences, 02-787 Warsaw, Poland.

Grant Funding

  • W/WM-IIB/2/2024 / the Polish Ministry of Science and Higher Education

Conflict of Interest Statement

The authors declare no conflicts of interest.

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