several approaches. One of the methods involves fellows in pathology annotating hundreds of slides of a cancer—say, breast cancer—and passing those to senior pathologists for review before feeding the annotated slides to Paige.AI. Another approach incorporates the startup’s software into the workflow of practicing pathologists. At Memorial Sloan Kettering, Paige.AI’s accompanying digital slide viewer—something of a Google Maps for viewing cells—is already part of the clinic workflow.
“We have Microsoft Surface stations where they can, with a pen, just paint or correct on [the slides] so the AI can learn continuously from the pathologist,” Fuchs said.
A final approach to training the neural networks involves the incorporation of patient data. By training the neural networks on clinical data, from patient treatment to patient outcome, Fuchs said, Paige.AI will be able to help calculate survival rates.
Anant Madabhushi, a biomedical engineering professor at Case Western Reserve University, said he sees the ability to view patient outcomes as the real value of Paige.AI.
“I think right now digital pathology is hard, deep learning is hard. Everybody thinks that if they get pathology images they can start doing deep learning,” Madabhushi said. “I would argue that the most valuable data is the clinical trial data sets, with outcome information.”
At Case Western Reserve, Madabhushi is part of a team using deep learning to analyze biopsy tissue to determine whether or not chemotherapy would be beneficial for a cancer patient. By accelerating diagnosis and improving accuracy, the test has the potential to reduce misdiagnosis and save patients from unnecessary and grueling chemotherapy treatments, all at a lower cost than tests available today.
For artificial intelligence in pathology, Madabhushi thinks finding that value proposition is crucial for success.
Paige.AI is by no means the only company applying artificial intelligence to make pathology less qualitative and more quantitative.
Boston-area startup PathAI, one of Paige.AI’s most obvious competitors, raised $11 million in November in a round led by General Catalyst Partners. Initially, the startup is partnering with Philips to create a tool to help pathologists diagnose metastatic breast cancer. PathAI’s product has yet to be incorporated into the workflow of pathologists the way Paige.AI’s has at Memorial Sloan Kettering. But Andrew Beck, PathAI’s co-founder and CEO, has published extensively in the fields of cancer biology, cancer pathology, and biomedical informatics, lending credibility to the startup’s expertise in the domain. Google and IBM have their own initiatives in pathology, too.
While people and big organizations are clearly investing in artificial intelligence applied to medicine, Madabhushi is skeptical of the economic viability of many of these endeavors.
“A lot of groups have not really thought through, at least in my mind, what is the value proposition… how are you going to make your money?” Madabhushi said.
In the case of pathology, digitizing slides is time consuming and expensive. And what does AI need in order to be accurate? Not only well-annotated data, but a lot of it.
At the James Cancer Hospital at Ohio State University, biotechnology company Inspirata announced on April 18 that it has just digitized its 500,000th glass histopathology slide, up from 100,000 slides last August. Madabhushi, who also serves as a member of Inspirata’s scientific advisory board, said that’s the rate of progress, even with multiple slide scanners at full operation.
Fuchs said his team is digitizing 40,000 slides a month. While Memorial Sloan Kettering has been digitizing slides for the last five years, at the current rate, it would take some 52 years to digitize the center’s 25 million slides. In a press release, Paige.AI acknowledges it is focusing on digitizing “millions of additional archived slides in the next few years,” but not all 25 million. With the new funding in February, Fuchs said, ramping up Paige.AI’s rate of digitization is a primary objective.
Eventually, Paige.AI’s technology could process digital slides submitted from hospitals with limited access to pathologists. That resource could be life-saving for individuals with rare tumors that might not be immediately identifiable to many pathologists.
While Paige.AI is primarily working with Memorial Sloan Kettering, the startup anticipates eventually working with other cancer centers. Madabhushi said collaboration will be important if Paige.AI is to train algorithms with universal application to the way slides are prepared across different hospitals.
Paige.AI is currently a team of five, not including Memorial Sloan Kettering staff collaborating with the startup. Fuchs said a portion of Paige.AI’s recent funding will go toward expanding and hiring machine-learning engineers.
Fuchs is optimistic about attracting talent.
“What speaks for us is it’s a good cause,” Fuchs said. “You can spend your time stratifying teenagers on Facebook, or you can heal cancer patients. I mean, it makes a difference what you spend your life on.”