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News Article

Artificial intelligence aids in the diagnosis of hypoadrenocorticism in dogs

Machine learning algorithms confidently identify the condition

Veterinarians at the University of California, Davis School of Veterinary Medicine have developed an algorithm utilizing artificial intelligence (AI) to detect Addison’s disease (hypoadrenocorticism) in dogs.

UC Davis’ Krystle Reagan and Chen Gilor (with UC Davis at time of development; currently at the University of Florida) teamed with an electrical and computer engineer to develop the AI algorithm, which has an accuracy rate greater than 99 percent. In an article in Domestic Animal Endocrinology, the team says that their method outperforms other screening tests based on routine laboratory blood work.

Addison’s disease is notoriously difficult to recognize and can go undetected for years. Dogs have vague clinical signs that mimic other conditions such as kidney and intestinal disease.

“Anecdotally, we see dogs with Addison’s disease come through the clinic, and they’ve been misdiagnosed for two to three years,” said Dr. Reagan. “Once Addison’s is properly detected, though, it is generally easy to treat with an excellent prognosis for the patient.”

“We set out to create an alert system that uses information from routine screening tests,” Dr. Reagan continued. “The alert should be able to inform veterinarians when Addison’s disease is likely, and prompt further investigation.”

Their AI-powered algorithm does just that. When a sick dog visits a veterinarian, often the first tests ordered are routine blood tests. The loss of hormones associated with Addison’s disease results in subtle irregularities in those tests that can be confused with other disease processes. The team used this routine blood work to train an AI program to detect complex patterns from more than 1,000 dogs previously treated at UC Davis. The computer program was able to learn these patterns, and with very high accuracy, determine if a dog has Addison’s disease.

This program can analyse this first line, routine blood work, and alert veterinarians when Addison’s disease is suspected, triggering them to pursue further diagnostic testing – an adrenocorticotropic hormone stimulation test (the gold standard to confirm Addison’s).

The team has filed a non-provisional patent through the UC Davis Office of Research and has a commercialization plan in place to license the program to large laboratories whose services are used by most veterinary practices. The program is anticipated to be available for commercial use by the end of 2020.

Article: Reagan, K. L., Reagan, B. A., Gilor, C. (2020). Machine learning algorithm as a diagnostic tool for hypoadrenocorticism in dogs. Domestic Animal Endocrinology, 72, 106396, doi: 10.1016/j.domaniend.2019.106396

Article details

  • Date
  • 18 February 2020
  • Source
  • University of California, Davis
  • Subject(s)
  • Dogs, Cats, and other Companion Animals