Researchers have developed a machine learning algorithm to accurately detect heart murmurs in dogs, one of the main indicators of cardiac disease, which affects a large proportion of some smaller breeds such as King Charles Spaniels.
The research team, led by the University of Cambridge, adapted an algorithm originally designed for humans and found it could automatically detect and grade heart murmurs in dogs, based on audio recordings from digital stethoscopes. In tests, the algorithm detected heart murmurs with a sensitivity of 90%, a similar accuracy to expert cardiologists.
Heart murmurs are a key indicator of mitral valve disease, the most common heart condition in adult dogs. Roughly one in 30 dogs seen by a veterinarian has a heart murmur, although the prevalence is higher in small breed dogs and older dogs.
Since mitral valve disease and other heart conditions are so common in dogs, early detection is crucial as timely medication can extend their lives. The technology developed by the Cambridge team could offer an affordable and effective screening tool for primary care veterinarians, and improve quality of life for dogs. The results are reported in the Journal of Veterinary Internal Medicine.
"Heart disease in humans is a huge health issue, but in dogs it's an even bigger problem," said first author Dr Andrew McDonald from Cambridge's Department of Engineering. "Most smaller dog breeds will have heart disease when they get older, but obviously dogs can't communicate in the same way that humans can, so it's up to primary care vets to detect heart disease early enough so it can be treated."
Professor Anurag Agarwal, who led the research, is a specialist in acoustics and bioengineering. "As far as we're aware, there are no existing databases of heart sounds in dogs, which is why we started out with a database of heart sounds in humans," he said. "Mammalian hearts are fairly similar, and when things go wrong, they tend to go wrong in similar ways."
The researchers started with a database of heart sounds from about 1000 human patients and developed a machine learning algorithm to replicate whether a heart murmur had been detected by a cardiologist. They then adapted the algorithm so it could be used with heart sounds from dogs.
The researchers gathered data from almost 800 dogs who were undergoing routine heart examination at four veterinary specialist centres in the UK. All dogs received a full physical examination and heart scan (echocardiogram) by a cardiologist to grade any heart murmurs and identify cardiac disease, and heart sounds were recorded using an electronic stethoscope. By an order of magnitude, this is the largest dataset of dog heart sounds ever created.
"Mitral valve disease mainly affects smaller dogs, but to test and improve our algorithm, we wanted to get data from dogs of all shapes, sizes and ages," said co-author Professor Jose Novo Matos from Cambridge's Department of Veterinary Medicine, a specialist in small animal cardiology. "The more data we have to train it, the more useful our algorithm will be, both for vets and for dog owners."
The researchers fine-tuned the algorithm so it could both detect and grade heart murmurs based on the audio recordings, and differentiate between murmurs associated with mild disease and those reflecting advanced heart disease that required further treatment.
"Grading a heart murmur and determining whether the heart disease needs treatment requires a lot of experience, referral to a veterinary cardiologist, and expensive specialised heart scans," said Novo Matos. "We want to empower general practitioners to detect heart disease and assess its severity to help owners make the best decisions for their dogs."
Analysis of the algorithm's performance found it agreed with the cardiologist's assessment in over half of cases, and in 90% of cases, it was within a single grade of the cardiologist's assessment. The researchers say this is a promising result, as it is common for there to be significant variability in how different vets grade heart murmurs.
"The grade of heart murmur is a useful differentiator for determining next steps and treatments, and we've automated that process," said McDonald. "For vets and nurses without as much stethoscope skill, and even those who are incredibly skilled with a stethoscope, we believe this algorithm could be a highly valuable tool."
In humans with valve disease, the only treatment is surgery, but for dogs, effective medication is available. "Knowing when to medicate is so important, in order to give dogs the best quality of life possible for as long as possible," said Agarwal. "We want to empower vets to help make those decisions."
"So many people talk about AI as a threat to jobs, but for me, I see it as a tool that will make me a better cardiologist," said Novo Matos. "We can't perform heart scans on every dog in this country - we just don't have enough time or specialists to screen every dog with a murmur. But tools like these could help vets and owners, so we can quickly identify those dogs who are most in need of treatment."
The research was supported in part by the Kennel Club Charitable Trust, the Medical Research Council, and Emmanuel College Cambridge.
Reference:
Andrew McDonald et al. 'A machine learning algorithm to grade canine heart murmurs and stage preclinical myxomatous mitral valve disease.' Journal of Veterinary Internal Medicine (2024). DOI: 10.1111/jvim.17224
