AI Outperforms Humans in Diagnosing Tissue Pathology

A “deep learning” artificial intelligence model developed at Washington State University can identify pathology, or signs of disease, in images of animal and human tissue much faster, and often more accurately, than people.

The development, detailed in Scientific Reports, could dramatically speed up the pace of disease-related research. It also holds potential for improved medical diagnosis, such as detecting cancer from a biopsy image in a matter of minutes, a process that typically takes a human pathologist several hours.

“This AI-based deep learning program was very, very accurate at looking at these tissues,” said Michael Skinner, a WSU biologist and co-corresponding author on the paper. “It could revolutionize this type of medicine for both animals and humans, essentially better facilitating these kinds of analysis.”

To develop the AI model, computer scientists Colin Greeley, a former WSU graduate student, and his advising professor Lawrence Holder trained it using images from past epigenetic studies conducted by Skinner’s laboratory. These studies involved molecular-level signs of disease in kidney, testes, ovarian and prostate tissues from rats and mice. The researchers then tested the AI with images from other studies, including studies identifying breast cancer and lymph node metastasis.

The researchers found that the new AI deep learning model not only correctly identified pathologies quickly but did so faster than previous models – and in some cases found instances that a trained human team had missed.

“I think we now have a way to identify disease and tissue that is faster and more accurate than humans,” said Holder, a co-corresponding author on the study.

Traditionally, this type of analysis required painstaking work by teams of specially trained people who examine and annotate tissue slides using a microscope—often checking each other’s work to reduce human error.

In Skinner’s research on epigenetics, which involves studying changes to molecular processes that influence gene behavior without changing the DNA itself, this analysis could take a year or even more for large studies. Now with the new AI deep learning model, they can get the same data within a couple weeks, Skinner said.

Deep learning is an AI method that attempts to mimic the human brain, a method that goes beyond traditional machine learning, Holder said. Instead, a deep learning model is structured with a network of neurons and synapses. If the model makes a mistake, it “learns” from it, using a process called backpropagation, making a bunch of changes throughout its network to fix the error, so it will not repeat it.

The research team designed the WSU deep learning model to handle extremely high-resolution, gigapixel images, meaning they contain billions of pixels. To deal with the large file sizes of these images, which can slow down even the best computer, the researchers designed the AI model to look at smaller, individual tiles but still place them in context of larger sections but in lower resolution, a process that acts sort of like zooming in and out on a microscope.

This deep learning model is already attracting other researchers, and Holder’s team is currently collaborating with WSU veterinary medicine researchers on diagnosing disease in deer and elk tissue samples.

The authors also point to the model’s potential for improving research and diagnosis in humans particularly for cancer and other gene-related diseases. As long as there is data, such as annotated images identifying cancer in tissues, researchers could train the AI model to do that work, Holder said.

“The network that we’ve designed is state-of-the-art,” Holder said. “We did comparisons to several other systems and other data sets for this paper, and it beat them all."

Reference: Greeley C, Holder L, Nilsson EE, Skinner MK. Scalable deep learning artificial intelligence histopathology slide analysis and validation. Sci Rep. 2024;14(1):26748. doi: 10.1038/s41598-024-76807-x

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