On February 25, 2020 Proscia, a leading provider of artificial intelligence (AI) enabled digital pathology software, reported that it has released the findings of a new study on the first deep learning system with proven accuracy in real laboratory environments (Press release, Proscia, FEB 25, 2020, View Source [SID1234554749]). Published in Scientific Reports, a journal from Nature Research, the study is the largest AI validation study conducted in pathology to date and supports the growing impact of AI in cancer diagnosis. The paper’s results serve as the foundation for enabling faster and more accurate diagnosis to improve treatment decisions and patient outcomes.
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The antiquated standard of care for diagnosing cancer relies on the pathologist’s assessment of patterns in tissue as viewed under a microscope. This manual and subjective practice cannot keep pace with the growing demand for diagnostic services amid a rapidly declining pathologist population and can lead to a lack of confidence in treatment decisions.
Proscia’s study, titled "Tailored for Real-World: A Whole Slide Image Classification System Validated on Uncurated Multi-Site Data Emulating the Prospective Pathology Workload," describes a deep learning system that achieves 98% accuracy in classifying whole slide images of skin biopsies in real laboratory settings. To achieve this consistent performance, the system was designed using high-quality and diverse de-identified data. It was developed using thousands of images from Dermatopathology Laboratory of Central States, one of the largest dermatopathology laboratories in the United States, and tested on an uncurated set of 13,537 images from Cockerell Dermatopathology, Thomas Jefferson University Hospital, and University of Florida to account for the wide variety of diseases seen in practice. This volume of test data, along with the different methods of biopsy, preparation of tissue, tissue staining procedures, and digital scanning processes used across the test laboratories, indicate that the AI is generalizable across multiple laboratory settings.
"The size, scope, and complexity of our study sets a new standard for demonstrating the real-world viability of artificial intelligence in pathology," said Mike Bonham, MD, Ph.D., Chief Medical Officer of Proscia. "We have finally passed the inflection point in generating scientific evidence that AI can drive accuracy and efficiency gains in practice."
Proscia conducted this study to validate its DermAI application and bring AI into the pathology laboratory. Launched in June 2019, DermAI is the first in a series of AI applications on Proscia’s Concentriq platform.* By using deep learning to automatically classify hundreds of variants of skin diseases, DermAI is driving much-needed confidence, quality, and efficiency gains with capabilities including intelligent workload balancing, case prioritization, automated QA, and 100% AI re-review. This first-of-its-kind pathology solution can reduce costly errors and process added volume to meet the global cancer burden.
Dr. Thomas G. Olsen, founder of Dermatopathology Laboratory of Central States and an investigator in the validation study, collaborated with Proscia on the design and clinical requirements for DermAI. "The true promise of digital pathology lies in deep learning, which will transform the practice of pathology for the first time since the introduction of the microscope," commented Dr. Olsen. "It’s exciting to have been part of moving AI-enabled digital pathology beyond academic efforts and into real-world practice applications."
With additional AI applications forthcoming, Proscia is committed to advancing diagnostics and research for other high-impact areas of pathology. Most recently, the company announced a collaboration with Johns Hopkins School of Medicine to bring AI applications to multiple subspecialties.
"The results of our research show robust generalization to data from multiple labs and scanners. The validation study design was a deliberate departure from the examples we’ve seen in pathology to date," said Julianna Ianni, Ph.D., Proscia’s Director of AI Research. "The burden of proof for AI is higher than it was a year ago, as laboratories are increasingly looking to deploy practical solutions to enhance their workflows."
The full study, published in Scientific Reports on February 21, 2020, can be found online here. To learn more about the study, please register for Proscia’s upcoming webinar, "Overcoming Real-World Variability: Inside Pathology’s Most Comprehensive AI Validation Study," on March 31, 2020 at 12PM ET.