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 hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




AI-Assisted Imaging to Assist Endoscopists in Colonoscopy Procedures

By HospiMedica International staff writers
Posted on 04 Dec 2024
Print article
Image: Synthetic images generated by each diffusion model contrasted with the corresponding real textural images of four types of polyps (Photo courtesy of UT at Austin)
Image: Synthetic images generated by each diffusion model contrasted with the corresponding real textural images of four types of polyps (Photo courtesy of UT at Austin)

Colorectal cancer is a major health concern in the United States, with the likelihood of developing the disease being 1 in 25 for women and 1 in 23 for men. Polyps, which are precursors to cancer, can be identified and removed through colonoscopies, of which 15 million are conducted annually in the U.S. These procedures are crucial for diagnosing not only cancer but also conditions like Crohn’s disease, ulcerative colitis, and other colon and rectal disorders. Approximately 30% of polyps are adenomas, a type of precancerous growth, yet the detection rate of adenomas ranges from 7% to 60%. This variation in detection may be attributed to factors such as the skill of the endoscopist, inadequate colon preparation for the procedure, or the challenging locations of some polyps that may be missed. It is also vital that adenomas are fully removed, as any remaining tissue could develop into a cancerous tumor. In a bid to improve the detection of adenomas and other colorectal diseases, a team of interdisciplinary researchers is turning to artificial intelligence (AI) to assist doctors in spotting polyps that even the best current technology may miss.

While AI tools for adenoma detection already exist, they have not significantly improved the identification of smaller or more subtle polyps. These tools mostly identify the adenomas that are already visible to most endoscopists. Researchers from the University of Texas at Austin (Austin, TX, USA) are focusing on detecting the more challenging, smaller polyps by analyzing each colonoscopy image pixel by pixel. Their AI algorithm is being trained to locate adenomas and precisely outline them, ensuring the tumor is detected and completely removed. The pixel-based segmentation helps address variations in colon contours, lighting issues, and even incomplete colon preparations during procedures. This advanced AI is designed to assist endoscopists in distinguishing between healthy and diseased tissue, even when the differences are subtle.

Currently, the only way to evaluate a polyp is through an endoscope, which provides a limited front view. However, the researchers are also developing a device that would allow endoscopists to “feel” the colon lining or a potential polyp before deciding whether to remove it. This device, an inflatable tactile sensor, would be attached to the endoscope. Once a polyp is identified, the endoscopist could move a donut-shaped balloon, about two inches in diameter, over the suspicious area. Using a joystick, the balloon would be inflated with air or liquid, and a sensor covering the balloon would provide tactile feedback about whether the area feels hard or soft, rough or smooth. (Hard or rough surfaces typically indicate a more problematic area.) This tactile sensor functions like a virtual fingertip on any surface and generates its own image, known as a vision-based tactile sensor. The team aims to have the tactile sensor ready for clinical trials within five years.

Related Links:
University of Texas at Austin

Gold Member
12-Channel ECG
CM1200B
Gold Member
Real-Time Diagnostics Onscreen Viewer
GEMweb Live
New
Mattress Replacement System
Carilex DualPlus
New
Fetal and Maternal Monitor
F9 Series

Print article

Channels

Critical Care

view channel
Image: The UbiqVue 2A Multiparameter System is based around a wearable ciosensor with chest-based SpO2 (Photo courtesy of LifeSignals)

Multiparameter System Featuring Wearable Biosensor Enables Near Real-Time Patient Monitoring

A novel cloud-based system featuring a wearable biosensor with chest-based SpO2 monitoring enables continuous patient monitoring across both hospital and out-of-hospital care settings. The UbiqVue 2A... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... 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 acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.