News | Digital Pathology | July 16, 2019

Paige Announces Clinical-grade Artificial Intelligence in Pathology

Article published in Nature Medicine provides further scientific evidence for deployment of computational decision support systems to improve patient care

Paige Announces Clinical-grade Artificial Intelligence in Pathology

July 16, 2019 — Computational pathology company Paige announced the publication of an article in Nature Medicine describing an artificial intelligence (AI) system for computational pathology that achieves clinical-grade accuracy levels. The paper provides further 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 responsible for the work described in the article developed specially-designed 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 vast dataset of nearly 45,000 de-identified, digitized slide images from more than 15,000 cancer patients from 44 countries.

The paper outlines how a series of novel algorithms, created using datasets 10 times larger than those that have been manually curated, performed better and also are more generalizable. The significance of this new development hinges on the fact that curating datasets can be prohibitively expensive and time intensive. By eliminating the need to curate datasets, Paige can now develop many more highly accurate algorithms that can be built into clinical decision support products to help pathologists around the world drive better patient care.

Paige plans to commercialize several of these solutions to address the most pressing needs in pathology to improve patient care. The company has already built on the academic work described in Nature Medicine to develop a clinical product, based on technology currently under review by the U.S. Food and Drug Administration (FDA) as a designated Breakthrough Device, for an intended indication different than the one described in the article.

All data collection, research and analysis for this research was conducted exclusively at Memorial Sloan Kettering (MSK) in New York City. The publication of the study’s findings was the result of collaboration between numerous researchers and clinicians, and made possible by Paige’s partnership with MSK. All data were de-identified and did not contain any protected health information or label text. 

Watch the VIDEO: Integrating Digital Pathology With Radiology

For more information: www.nature.com/nm

Reference

1. Campanella G., Hanna M.G., Geneslaw L., et al. Clinical-grade Computational Pathology using Weakly Supervised Deep Learning on Whole Slide Images. Nature Medicine, July 15, 2019. https://doi.org/10.1038/s41591-019-0508-1

Related Content

Infinitt PACS 7.0 is a faster, more powerful viewer that was built from the ground up to support AI for image analysis and for operational/ workflow improvements in radiology
News | PACS | November 19, 2019
November 19, 2019 — Infinitt North America will be highlighting a next generation,...
 LifeImage Baystate
News | Artificial Intelligence | November 18, 2019
November 18, 2019 — Baystate Health, the premier integrated health syst
News | Artificial Intelligence | November 18, 2019
November 18, 2019 — Visage Imaging, Inc. (Visage), a wholly owned subsidi
The study finds it's possible to use commercial facial recognition software to identify people from brain MRI that includes imagery of the face
News | Magnetic Resonance Imaging (MRI) | November 15, 2019
November 15, 2019 — Though identifying data typically are removed from medical image files before they are shared for
EchoGo uses artificial intelligence (AI) to calculate cardiac ultrasound left ventricular ejection fraction (EF), the most frequently used measurement of heart function, left ventricular volumes (LV) and, for the first time for an AI application, automated cardiac strain.

EchoGo uses artificial intelligence (AI) to calculate cardiac ultrasound left ventricular ejection fraction (EF), the most frequently used measurement of heart function, left ventricular volumes (LV) and, for the first time for an AI application, automated cardiac strain.

News | Cardiovascular Ultrasound | November 14, 2019
November 14, 2019 — Ultromics has received 510(k) clearance from the U.S.
 MaxQ AI
News | Artificial Intelligence | November 13, 2019
November 13, 2019 – MaxQ AI announced a new partnership agreement with...
 Paxera Ultima 360
News | Enterprise Imaging | November 12, 2019
November 12, 2019 — Medical Imaging developer PaxeraHealth will showcase the