SHANGHAI, China, Nov. 01, 2018 (GLOBE NEWSWIRE) -- Shanghai Wision AI Co., Ltd, a leader in developing computer-aided diagnostic algorithms and systems to improve the accuracy and effectiveness of diagnostic imaging, today announced results of a study validating a novel machine-learning algorithm that improves detection of adenomatous polyps during colonoscopy. Researchers at Wision AI conducted the study in collaboration with clinicians at the Center for Advanced Endoscopy at Beth Israel Deaconess Medical Center (BIDMC), Harvard Medical School and the Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, and the results appear in the current issue of Nature Biomedical Engineering. Built on the same network architecture used to develop self-driving cars, the Wision AI algorithm is designed to enable “self-driving” in colonoscopy procedures.

“Previous studies have shown that every one percent increase in the rate of detecting precancerous polyps results in a three percent decrease in the risk of interval colon cancer,” said Tyler Berzin, MD, Co-Director, GI Endoscopy, and Director, Advanced Endoscopy Fellowship at BIDMC and Assistant Professor of Medicine at Harvard Medical School. “This underscores the importance of accurate polyp detection. The encouraging results obtained using Wision AI demonstrate that a novel deep-learning algorithm can automatically detect polyps during colonoscopy, opening new doors to increasing the effectiveness of screening colonoscopy and enabling a new quality control metric that may improve endoscopy skills.”

Detecting and removing precancerous polyps during colonoscopy is the gold standard in preventing colon cancer, a leading cause of cancer death. However, the adenoma miss rate among the more than 14 million colonoscopies performed in the United States each year is 6 – 27 percent. The inability to recognize polyps within the visual field is a key reason that precancerous polyps go undetected. Studies show that having a second set of eyes on the monitor during colonoscopy procedures can increase detection rates by up to 30 percent. The Wision AI algorithm can serve as this second view by highlighting polyps directly on the monitor.
A key challenge in developing AI-based algorithms for use in clinical settings is that the dataset used to validate the algorithm is typically very small compared with the development dataset. This can result in “over-fitting” of the algorithm in a manner that limits its efficacy in real-world clinical scenarios. Additionally, in most cases, a single dataset is collected and divided for both training and validation, which may result in similar data being used for both steps and therefore reducing the rigor of the validation process. In contrast, the Wision AI algorithm was validated on large, prospectively developed datasets that were collected independently from the training dataset and were several-fold larger than the training dataset. This more rigorous validation approach that Wision AI utilizes is designed to increase the performance of the algorithm in real-world clinical settings.

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