We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
Sekisui Diagnostics UK Ltd.

Download Mobile App




Artificial Intelligence Improves Colonoscopy Accuracy

By HospiMedica International staff writers
Posted on 01 Mar 2023
Print article
Image: AI can identify cancer risks and eliminate them on the spot during colonoscopy (Photo courtesy of Pexels)
Image: AI can identify cancer risks and eliminate them on the spot during colonoscopy (Photo courtesy of Pexels)

Colon cancer is one of the most deadly cancers in the world today and its incidence rate has been steadily increasing among younger people. Colonoscopy is currently considered the best way to detect and prevent colorectal cancer. However, it is hard to spot precancerous polyps in patients who are most at risk, especially those with IBDs like Crohn's Disease and Ulcerative Colitis. The pre-cancerous lesions they develop can be very flat or only slightly raised, making them difficult to catch during colonoscopy. Recent studies suggest that over half of post-colonoscopy cases of colon cancer appear due to missed lesions in previous examinations. To address this problem, scientists are now exploring the use of AI to locate these hard-to-see polyps.

Researchers at Mayo Clinic (Rochester, MN, USA) are studying the use of AI to enhance polyp detection rate in colonoscopy. AI is being used by gastroenterologists for a variety of gastrointestinal conditions with the aim of identifying signs earlier and making them easier to treat. In the case of colon cancer, the AI system scans the real-time video feed from the colonoscopy and highlights potential polyps with small red boxes, helping doctors to spot them more quickly. Adding AI to traditional colonoscopies can help physicians better detect polyps that may have been missed otherwise. 

Mayo Clinic carries out around 800 to 900 surveillance colonoscopies on IBD patients annually which has provided it with a rich databank for developing AI systems to improve the process. This data serves as "ground truth" or real-world examples that are used to train AI algorithms. The team will annotate images from a sample of 1000 patients, watching all colonoscopy videos and marking lesions in frames from different angles. The annotated images will then be fed to a computer to create AI machine learning algorithms that can teach the machine how to detect polyps associated with IBD. The researchers are also creating a new digital endoscopy platform that will film all in-house procedures, correlate them with medical records, and then integrate AI back into the procedures as applicable.

"We’re all familiar with facial recognition software. Instead of training the AI to recognize faces, we train it to recognize polyps," said  James East, M.D., a gastroenterologist at Mayo Clinic Healthcare in London.

Related Links:
Mayo Clinic 

Gold Member
SARS‑CoV‑2/Flu A/Flu B/RSV Sample-To-Answer Test
SARS‑CoV‑2/Flu A/Flu B/RSV Cartridge (CE-IVD)
Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Silver Member
Compact 14-Day Uninterrupted Holter ECG
NR-314P
New
Computerized Spirometer
DatospirAira

Print article

Channels

Patient Care

view channel
Image: The newly-launched solution can transform operating room scheduling and boost utilization rates (Photo courtesy of Fujitsu)

Surgical Capacity Optimization Solution Helps Hospitals Boost OR Utilization

An innovative solution has the capability to transform surgical capacity utilization by targeting the root cause of surgical block time inefficiencies. Fujitsu Limited’s (Tokyo, Japan) Surgical Capacity... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The Quantra Hemostasis System has received US FDA special 510(k) clearance for use with its Quantra QStat Cartridge (Photo courtesy of HemoSonics)

Critical Bleeding Management System to Help Hospitals Further Standardize Viscoelastic Testing

Surgical procedures are often accompanied by significant blood loss and the subsequent high likelihood of the need for allogeneic blood transfusions. These transfusions, while critical, are linked to various... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.