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Journal of equine veterinary science2020; 90; 102973; doi: 10.1016/j.jevs.2020.102973

Using Artificial Intelligence to Predict Survivability Likelihood and Need for Surgery in Horses Presented With Acute Abdomen (Colic).

Abstract: Artificial intelligence and machine learning have promising applications in several medical fields of diagnosis, imaging, and laboratory testing procedures. However, the use of this technology in the veterinary medicine field is lagging behind, and there are many areas where it could be used with potentially successful outcomes and results. In this study, two critical predictions were explored in horses presented with acute abdomen (colic) using this technology. Those were the need for surgical intervention and survivability likelihood of affected horses based on clinical data (history, clinical examination findings, and diagnostic procedures). The two prediction parameters were explored using the application of Decision Trees, Multilayer Perceptron, Bayes Network, and Naïve Bayes. The machine learning algorithms were able to predict the need for surgery and survivability likelihood of horses presented with acute abdomen (colic) with 76% and 85% accuracy, respectively. The application of this technology in the different clinical fields of veterinary medicine appears to be of a value and warrants further investigation and testing.
Copyright © 2020 Elsevier Inc. All rights reserved.
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Publication Date: 2020-03-19 PubMed ID: 32534764DOI: 10.1016/j.jevs.2020.102973Google Scholar: Lookup
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  • Journal Article
  • Research Support
  • Non-U.S. Gov't

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.

This research article investigates how artificial intelligence (AI) and machine learning tools can assess the need for surgery and predict survival rates in horses afflicted with severe abdominal pain, commonly known as colic.

Research Purpose and Rationale

  • The authors have set out to demonstrate the potential of AI and machine learning in veterinary medicine, an area where the use of such technology is relatively underdeveloped.
  • They specifically focus on testing these tools on assessing the need for surgical treatment and predicting survival rates among horses presented with acute abdomen, better known as colic.

Approach and Methodology

  • The machine learning algorithms put to use in this study are Decision Trees, Multilayer Perceptron, Bayes Network, and Naïve Bayes.
  • The system was trained on available clinical data, such as the patient’s history, clinical examination findings, and diagnostic procedures.

Key Findings

  • The machine learning algorithms predicted with 76% accuracy whether surgery would be required for the horses affected with colic.
  • In terms of survival likelihood, the algorithms were slightly more accurate, predicting with 85% accuracy whether the horses would survive.

Implications

  • The research carries significant implications for the veterinary medicine field, showcasing the potential for AI and machine learning to improve outcomes and decision-making in clinical scenarios.
  • The authors advocate for further investigation and testing to expand the application of this technology in various clinical fields of veterinary medicine.

Cite This Article

APA
Fraiwan MA, Abutarbush SM. (2020). Using Artificial Intelligence to Predict Survivability Likelihood and Need for Surgery in Horses Presented With Acute Abdomen (Colic). J Equine Vet Sci, 90, 102973. https://doi.org/10.1016/j.jevs.2020.102973

Publication

Journal of equine veterinary science
ISSN: 0737-0806
NlmUniqueID: 8216840
Country: United States
Language: English
Volume: 90
Pages: 102973
PII: S0737-0806(20)30064-2

Researcher Affiliations

Fraiwan, Mohammad A
  • Department of Computer Engineering, Faculty of Computer and Information Technology, Jordan University of Science and Technology, Irbid, Jordan. Electronic address: mafraiwan@just.edu.jo.
Abutarbush, Sameeh M
  • Department of Clinical Veterinary Medical Sciences, Faculty of Veterinary Medicine, Jordan University of Science and Technology, Irbid, Jordan.

MeSH Terms

  • Abdomen, Acute / diagnosis
  • Abdomen, Acute / veterinary
  • Animals
  • Artificial Intelligence
  • Bayes Theorem
  • Colic / veterinary
  • Horse Diseases
  • Horses

Citations

This article has been cited 17 times.
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