Content continues after advertisement

Research Note: Machine Learning Algorithm for Diagnosing Hypoadrenocorticism in Dogs

Internal Medicine

July/August 2021

Sign in to Print/View PDF

Canine hypoadrenocorticism (CHA) is a life-threatening condition that affects 3 out of every 1000 dogs. CHA mimics many disease processes, including kidney, hepatic, and GI disease. Prognosis is excellent with appropriate treatment. This study used machine learning methods to aid in the diagnosis of CHA.  Results of CBC and serum chemistry profiles were collected from 908 control dogs and 133 dogs with confirmed CHA and used as data for the machine algorithms. The model showed a sensitivity of 96.3% and specificity of 97.2%. Although prospective studies are needed to validate these methods, they demonstrated diagnostic performance similar to resting cortisol values (regardless of glucocorticoid or mineralocorticoid deficiency status) and employed an easy-to-use graphic interface.


For global readers, a calculator to convert laboratory values, dosages, and other measurements to SI units can be found here.

All Clinician's Brief content is reviewed for accuracy at the time of publication. Previously published content may not reflect recent developments in research and practice.

Material from Clinician's Brief may not be reproduced, distributed, or used in whole or in part without prior permission of Educational Concepts, LLC. For questions or inquiries please contact us.


Clinician's Brief:
The Podcast
Listen as host Alyssa Watson, DVM, talks with the authors of your favorite Clinician’s Brief articles. Dig deeper and explore the conversations behind the content here.
Clinician's Brief provides relevant diagnostic and treatment information for small animal practitioners. It has been ranked the #1 most essential publication by small animal veterinarians for 9 years.*

*2007-2017 PERQ and Essential Media Studies

© Educational Concepts, L.L.C. dba Brief Media ™ All Rights Reserved. Terms & Conditions | DMCA Copyright | Privacy Policy | Acceptable Use Policy