publication date: Jul. 19, 2019
Paige.AI publishes study on clinical-grade pathology
Paige.AI announced the publication of an article in Nature Medicine describing an AI system for computational pathology that achieves clinical-grade accuracy levels.
The paper provides scientific evidence that pathologists’ work in diagnosing and treating cancer can be complemented and aided through the deployment of computational decision-support systems to improve patient care.
The team of scientists developed deep learning algorithms to build a system that can detect prostate cancer, skin cancer, and breast cancer with near-perfect accuracy. These algorithms are based on a dataset of nearly 45,000 de-identified, digitized slide images from more than 15,000 cancer patients from 44 countries.
Thomas Fuchs, co-founder and chief scientific officer of Paige.AI, led the work at his Memorial Sloan Kettering Cancer Center lab along with his student Gabriele Campanella.
The publication of the study’s findings was the result of collaboration between numerous researchers and clinicians, and made possible by Paige.AI’s partnership with MSK. All data were de-identified and did not contain any protected health information or label text.
The paper outlines how a series of novel algorithms were created using datasets ten times larger than those manually curated performed better and also are more generalizable. The significance of this development hinges on the fact curating datasets can be prohibitively expensive and time intensive.
By eliminating the need to curate datasets, it can now develop highly accurate algorithms that can be built into clinical decision support products to help pathologists around the world drive better patient care.
“It demonstrates that AI has the potential to support pathologists in delivering quantitative and more accurate diagnoses, improving treatment … Continue reading Paige.AI publishes study on clinical-grade pathology
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