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Machine learning-assisted screening for canine Cushing's syndrome.

Young-Jae Yoo1, Kyungchang Jeong2, Hanbit Seo2

  • 1Laboratory of Veterinary Internal Medicine, College of Veterinary Medicine, Chungbuk National University, Cheongju, Republic of Korea.

The Veterinary Quarterly
|December 23, 2025
PubMed
Summary

Machine learning aids canine Cushing's syndrome (CS) diagnosis using routine tests. This approach achieved 88.5% accuracy, improving diagnostic efficiency for this common endocrine disorder in dogs.

Keywords:
artificial intelligenceboosted treedoggradient boostinghypercortisolismnon-adrenal illnesspolydipsiapolyuria

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Area of Science:

  • Veterinary Medicine
  • Endocrinology
  • Machine Learning in Diagnostics

Background:

  • Cushing's syndrome (CS) is a prevalent endocrine disorder in dogs, often presenting with variable clinical signs.
  • Accurate diagnosis of CS is challenging due to its complex presentation, hindering timely intervention.
  • Current diagnostic pathways can be resource-intensive and may not always identify suitable candidates for advanced testing.

Purpose of the Study:

  • To develop and validate a machine learning model for assisting in the diagnosis of canine Cushing's syndrome.
  • To utilize routinely available screening diagnostics for improved diagnostic accuracy.
  • To enhance the efficiency and accessibility of Cushing's syndrome diagnosis in veterinary practice.

Main Methods:

  • A boosted tree algorithm (gradient boosting) was trained on a dataset comprising complete blood count, serum chemistry panel, and urinalysis parameters.
  • Data included 153 control dogs and 152 dogs with confirmed Cushing's syndrome.
  • The model was trained on 80% of the data and validated on the remaining 20%.

Main Results:

  • The machine learning model achieved an overall accuracy of 88.5% (95% CI: 80.5-96.5%).
  • Sensitivity was 83.3% (95% CI: 70.7-96.7%) and specificity was 93.5% (95% CI: 84.9-100%).
  • The area under the receiver operating characteristic curve was 0.912 (95% CI: 0.835-0.988), indicating excellent diagnostic discriminatory ability.

Conclusions:

  • Machine learning algorithms can effectively assist in diagnosing canine Cushing's syndrome using standard screening diagnostics.
  • The developed model demonstrates high accuracy and discriminatory power, offering a valuable tool for veterinarians.
  • Implementation of a user-friendly interface can improve diagnostic efficiency and potentially enhance owner satisfaction.