In the fall of 2022, Penn Gastroenterology and Hepatology received a grant for the Department to incorporate GI Genius™, the first FDA-approved device to use artificial intelligence (AI) to assist clinicians in colon polyp detection in real time during a colonoscopy.

"This specific grant was to offer AI technology for underserved communities," says Penn Gastroenterologist Neilanjan Nandi, MD, FACP. "We applied for this grant in order to enhance colon polyp detection rates in our West Philadelphia community with the ultimate goal to further reduce colon cancer in our local population."

Background:

• AI is the study of algorithms that give machines the ability to reason and perform cognitive functions, and encompasses the field of machine learning and its inter-related sub-domains.
• Colorectal cancer (CRC) is the third leading cause of death from cancer in the United States. Preventative screening for CRC can significantly reduce disease risk and mortality. A collection of challenges, including lack of access to care and irregularities in insurance coverage, have historically presented barriers to screening in medically underserved populations.

Improving access to care in the underserved community is among the founding missions of Penn Medicine. With regard to CRC in particular, this means access to colonoscopy and other essential screening modalities, and participation in screening programs. Cohort studies in targeted populations suggest that colonoscopy can bring about substantial reduction in CRC mortality.

Periodic screening and surveillance colonoscopy can substantially reduce the risk of colorectal cancer development and reduce associated mortality by catching polyps early. However, despite regular lifetime adherence to a colon polyp surveillance schedule, there is still a 5-9 percent risk of developing an "interval" colon cancer. This inherent small risk is due to the potential miss small polyps during a colonoscopy that may evade detection and transform over time into a cancer. High quality preparation and high quality endoscopic skill are necessary to maximize the adenoma detection rate (ADR). The national ADR benchmark is 26 percent across the country. Penn medicine gastroenterologists currently far surpass this benchmark significantly at ~38 percent ADR. While early detection of stage 1 – 3 colon cancer is associated with at least 85 percent five-year remission rates, optimizing ADR to find and resect adenomas is the key to complete colon cancer prevention. Therefore, tools that can aid in the increased visualization of colon adenomas can help the endoscopist remove these polyps to ultimately prevent future colon cancer pathogenesis.

The aim of GI Genius™ is to apply AI technology during colonoscopy in order to detect more colon polyps and reduce adenoma miss rates so that people don't develop interval colon cancers" says Dr. Nandi.

GI Genius: Endoscopy Powered by AI

GI Genius ™ is composed of an artificial intelligence algorithm that aids endoscopists in highlighting portions of the colon where a potential polyp may be lurking. During colonoscopy, the system superimposes real time markers on the endoscopic camera video feed that suggest the presence of a potential lesion being identified. These signal to the clinician that further assessment and intervention may be needed. In real-time, the endoscopist may need to provide closer visual inspection, tissue sampling, and/or polyp resection or ablation. Ultimately, it's to the clinician's clinical discretion how to manage a colon polyp per their best clinical judgement and per standardized clinical practice guidelines.

The safety and effectiveness of GI Genius™ was studied through a multicenter, prospective, randomized, controlled study with 700 individuals ranging in age from 40-80 years old. These individuals were undergoing colonoscopy for colorectal cancer screening, colon polyp surveillance, positive fecal immunochemical test for blood in the stool, or reported gastrointestinal symptoms of possible colorectal cancer. Study subjects underwent either standard white light colonoscopy alone or standard white light colonoscopy with the GI Genius. Study results illustrated that colonoscopy utilizing GI Genius was able to identify pathology-confirmed adenomas or carcinomas in 55.1 percent of patients compared to 42.0 percent of patients with standard colonoscopy, an observed difference of 13 percent.

"We are still in the early days of adopting AI in colon cancer prevention as routine, but the data suggests that it can improve ADR and it may become a standard of care in the years to come," says Dr. Nandi. Remaining challenges include the additional financial costs of new technology and insurance reimbursement. In the interim, Penn Presybterian Gastroenterology is proud to pilot this technology at no cost to our community.

AI at Penn Medicine

The study of artificial Intelligence is being advanced throughout Penn Medicine at both the Perelman School of Medicine and the Abramson Cancer Center.

Recent developments include:

• Daniel Alejandro Hashimoto, MD, MTR, is working with AI in the context of Surgery. An AI innovator, Dr. Hashimoto is the co-founder of the Surgical AI and Innovation Laboratory at the Perelman School of Medicine, and is the Director of the Penn Computer Assisted Surgery and Outcomes (PCASO) Laboratory.
• A Penn led effort to incorporate machine learning in the largest global effort to identify and predict glioblastoma tumor boundaries without compromising patient privacy. The dataset created involved 6,314 glioblastoma (GBM) patients at 71 sites around the globe and allowed the development of a model able to enhance identification and prediction of boundaries in three tumor sub-compartments, without compromising patient privacy.
• An odor-based test that sniffs out vapors emanating from blood samples was able to distinguish between benign and pancreatic and ovarian cancer cells with up to 95 percent accuracy, according to a new study from researchers at the University of Pennsylvania and Penn's Perelman School of Medicine.
• A free, open-source automated machine learning system for data analysis from the Penn Medicine Institute for Biomedical Informatics. Designed for anyone to use, from a high school student looking to gain insight on their baseball team's statistics, to trained researchers looking for associations between cancer and environmental factors. "Penn AI," the first widely available tool of its kind, seeks to lower the barrier for entry into artificial intelligence, allowing users to bring in their own datasets or use the several hundred that are available for download within the tool.
• An NIH funded project at the Penn Center for Biomedical Image Computing and Analytics applying advanced artificial intelligence (AI) methods to integrate and find patterns in genetic, imaging, and clinical data from over 60,00 Alzheimer's patients — representing one of the largest and most ambitious research undertakings of its kind.