Researchers have used deep learning to predict success, failure or complications in transplanted kidneys by analyzing pathology slides at scale.

The team suggests its advance stands to improve post-transplant prognostics and extend precision care to allograft medicine.

The Lancet Digital Health published the research report Nov. 15.

Lead author Jesper Kers, PhD, of the University of Amsterdam, senior author Peter Boor, PhD, of Aachen University Hospital in Germany and colleagues trained convolutional neural networks on close to 6,000 digital slide images acquired in allograft biopsies from almost 2,000 patients.

Validating the technique on slides from 1,847 patients as well as a real-world cohort of 101 patients, the team found good accuracy in classifying tissue as normal, rejected or susceptible to other diseases.

While the study used retrospective data and was conducted to prove its concept, Kers and co-authors state that prospective trials will be best carried out after allograft scientists see the usefulness of AI-based diagnostic support, as well as its potential pitfalls.