Diagnosis and prediction

When it comes to life-threatening diseases, it is critical to catch them before they develop. This may sound impossible, but with the right data, vets can make educated predictions about which animals will develop a disease. Chronic kidney disease (CKD) in cats is a good example — it’s not reversible, and often, by the time it presents, the patient has already suffered kidney damage. 

It’s also a disease that tends to affect older cats, so by the time a veterinarian catches a case o CKD, the cat’s quality of life is likely to be severely impacted. If the disease occurrence can be predicted, however, the patient can be treated before kidney damage occurs, and the cat’s health and quality of life can be dramatically improved.

Researchers recently developed an algorithm to predict CKD before a cat gets sick⁴. It uses AI to predict whether a cat will develop the disease. Trained on Electronic Health Records (EHR) from 20 years of vet visits, the algorithm looked for specific factors that contributed to CKD in more than 100,000 cats across breeds, geographical areas, and ranging in age from one year old to more than 22.

Using this dataset, the team built a recurrent neural network (RNN) that examines blood work for four factors contributing to CKD: creatinine, blood urea nitrogen, urine specific gravity, and age. 

The RNN was able to predict whether a cat will develop CKD within the next two years with greater than 95% accuracy. The false positives were very low — a huge benefit for vets and pet owners who have traditionally dealt with CKD as a difficult-to-detect disease. 

This model, say the researchers, can quickly be implemented in hospital practice or diagnostic laboratory software to directly support veterinarians in making clinical decisions regarding sick cats.