LOS ANGELES, CA, UNITED STATES - Nov 5, 2018 - 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 Peoples 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.
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. Click
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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.
The algorithm was developed using 5,545 images (65.5 percent
containing polyps and 34.5 percent without polyps) from the
colonoscopy reports of 1,290 patients. Experienced endoscopists
annotated the presence of polyps in all images used in the development
dataset, and the algorithm was then validated on four independent
datasets: two sets for image analysis (A and B) and two sets for video
analysis (C and D).
Key findings from the study include:
Validation on dataset A, which included 27,113 images from patients
undergoing colonoscopy at the Endoscopy Center of Sichuan Provincial
Peoples Hospital, found a per-image-sensitivity of 94.4 percent and a
per-image-specificity of 95.9 percent.
The per-image-sensitivity in a subset of 1,280 images with polyps that
are typically hard to detect was 91.7 percent.
Validation on dataset B, based on a public database of 612 colonoscopy
images acquired from the Hospital Clinic of Barcelona, found a
per-image-sensitivity of 88.2 percent. The use of this dataset allowed
for generalization of the validation data to a broader patient
population.
Validation on dataset C included a series of colonoscopy videos
containing 138 polyps, found a per-image sensitivity of 91.6 percent
among 60,914 frames of video, and a per-polyp sensitivity of 100
percent.
Validation on dataset D, which contained 54 colonoscopy videos without
any polyps, found a per-image-specificity of 95.4 percent among
1,072,483 frames.
The total processing time for each image frame was 76.8 milliseconds,
including preprocessing and displaying times before and after
execution of the deep-learning algorithm. Implementation in a
real-time system resulted in a processing rate of 30 frames per second
with Nvidia Titan X GPUs.
The authors conclude that this automatic polyp-detection system based
on deep learning has high overall performance in both colonoscopy
images and real-time videos.
Wision AI is committed to realizing the clinical value of AI and
mathematical medicine in a variety of indications, including
gastroenterology, ophthalmology, neurology, and radiation-based
imaging, said JingJia Liu, Chief Executive Officer at Wision AI. The
results of this study demonstrate the power of our rigorous approach
to developing deep-learning algorithms, which utilizes distinct
datasets for training and validation and results in high levels of
specificity and sensitivity that have the potential to improve
diagnostic screening methods that are known to reduce disease risk,
improve health outcomes and save lives.
The first clinical trial of this technology had been completed early
this year, and results from this study demonstrated a significantly
improved adenoma detection rate (ADR) in the AI-aided group. Full
results from this first clinical trial were presented at the United
European Gastroenterology Week 2018 in Vienna last week by the authors
of the Nature Biomedical Engineering publication, and Pu Wang, MD, a
gastroenterologist at Sichuan Provincial Hospital, received the
National Scholar Award for this cutting-edge research.
About Wision AI
Shanghai Wision AI Co., Ltd is a leader in developing computer-aided
diagnostic algorithms and systems to improve the accuracy and
effectiveness of diagnostic imaging. Based in Shanghai, China, the
company has extensive expertise in mathematics, algorithm development
and software and hardware engineering and works closely with top-tier
medical institutions in China and around the world. The company
integrates medical knowledge into flexible and scalable models that
leverage cutting-edge convolutional neural network and general-purpose
computing to achieve high sensitivity and specificity in detection,
segmentation and measurement in diagnostic imaging. Wision AI is
advancing its transformative mathematical medicine approach in
multiple clinical settings, including gastroenterology, ophthalmology,
neurology, and radiomics. To learn more about Wision AI, go to
http://www.wision.com/