A new method allows hospitals to share patient data privately

The approach can be used to create an Artificial Intelligence system that will help clinicians better identify and treat brain tumors.

Scientists at the University of Pennsylvania School of Medicine have developed an emerging technique called federated learning that allows hospitals to share patient data privately.

Through this approach, scientists have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.

Interestingly, the model can be used among hospitals around the world. Doctors can then train on top of this shared model by inputting their patient brain scans. Their new model will then be transferred to a centralized server. The models will eventually be reconciled into a consensus model that has gained knowledge from each of the hospitals and is therefore clinically useful.

Spyridon Bakas, Ph.D., an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania said, “The more data the computational model sees, the better it learns the problem, and the better it can address the question that it was designed to answer. Traditionally, machine learning has used data from a single institution, and then it became apparent that those models do not perform or generalize well on data from other institutions.”

“The federated learning model will need to be validated and approved by the U.S. Food and Drug Administration before it can be licensed and commercialized as a clinical tool for physicians. But if and when the model is commercialized, it would help radiologists, radiation oncologists, and neurosurgeons make important decisions about patient care. Nearly 80,000 people will be diagnosed with a brain tumor this year, according to the American Brain Tumor Association.”

“Studies have shown that, when it comes to tumor boundaries, not only can different physicians have different opinions, but the same physician assessing the same scan can see different tumor boundary definitions on one day of the week versus the next. Artificial Intelligence allows a physician to have more precise information about where a tumor ends, which directly affects a patient’s treatment and prognosis.”

The study was started with a model known as International Brain Tumor Segmentation, or BraTS. This challenge provides a dataset that includes more than 2,600 brain scans captured with magnetic resonance imaging (MRI) from 660 patients. Next, ten hospitals participated in the study by training AI models with their patient data. The federated learning technique was then used to aggregate the data and create the consensus model.

Scientists compared federated learning to models trained by single institutions, as well as to other collaborative-learning approaches. The effectiveness of each method was measured by testing them against scans that were annotated manually by neurologists. When compared to a model trained with centralized data that did not protect patient privacy, federated learning was able to perform almost (99 percent) identically. The findings also indicated that increased access to data through data private, multi-institutional collaborations can benefit model performance.

Study co-author Rivka Colen, MD, an associate professor of Radiology at the University of Pittsburgh School of Medicine, said that this paper and the larger federated learning project open up possibilities for even more uses of Artificial Intelligence in health care.

“I think it’s a huge game-changer. Radiomics is to radiology what genomics was to pathology. AI will revolutionize this field, because, right now, as a radiologist, most of what we do is descriptive. With deep learning, we’re able to extract information that is hidden in this layer of digitized images.”

Journal Reference:
  1. Sheller et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. DOI: 10.1038/s41598-020-69250-1

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