Gastroenterology Embraces Artificial Intelligence
AI and machine learning have the potential to redefine the management of several GI disorders.
Colonoscopy is one of the true success stories in modern medicine. Studies have demonstrated that colonoscopy screening detects the cancer at a much earlier stage, reducing the risk of invasive tumors and metastatic disease, and reducing mortality. However, while colorectal cancer is highly preventable, it is the third leading cause of cancer-related deathsin the U.S. About 148,000 individuals develop the malignancy and over 53,000 die from it each year. We asked ourselves a question: can AI improve the detection of this and related gastrointestinal disorders?
As we explained in The Digital Reconstruction of Healthcare, one of the challenges in making an accurate diagnosis of GI disease is differentiating between disorders that look similar at the cellular level. For example, because environmental enteropathy and celiac disease overlap histopathologically, deep learning algorithms have been designed to analyze biopsy slides to detect the subtle differences between the two conditions. Syed et al.1 used a combination of convolutional and deconvolutional neural networks in a prospective analysis of over 3,000 biopsy images from 102 children. They were able to tell the differences between environmental enteropathy, celiac disease, and normal controls with an accuracy rating of 93.4%, and a false negative rate of 2.4%. Most of these mistakes occurred when comparing celiac patients to healthy controls.
The investigators also identified several biomarkers that may help separate the two GI disorders: interleukin 9, interleukin 6, interleukin 1b, and interferon-induced protein 10 were all helpful in making an accurate prediction regarding the correct diagnosis. The potential benefits to this deep learning approach become obvious when one considers the arduous process that patients have to endure to reach a definitive diagnosis of either disorder: typically, they must undergo 4 to 6 biopsies and may need several endoscopic procedures to sample various sections of the intestinal tract because the disorder may affect only specific areas along the lining and leave other areas intact.
Several randomized controlled trials have been conducted to support the use of ML in gastroenterology. Chinese investigators, working in conjunction with Beth Israel Deaconess Medical Center and Harvard Medical School, tested a convolutional neural network to determine if it was capable of improving the detection of precancerous colorectal polyps in real time.2The need for a better system of detecting these growths is evident, given the fact that more than 1 in 4 adenomas are missed during coloscopies. To address the problem, Wang et al. randomized more than 500 patients to routine colonoscopy and more than 500 to computer-assisted colonoscopies. In the final analysis, the adenoma detection rate (ADR) was higher in the ML-assisted group (29.1% vs. 20.3%, P < 0.001). The higher ADR occurred because the algorithm was capable of detecting a greater number of smaller adenomas (185 vs. 102). There were no significant differences in the detection of large polyps.
Nayantara Coelho-Prabhu, M.D., a gastroenterologist at Mayo Clinic, points out, however, that the clinical relevance of detection of diminutive polyps remains to be determined. “Yet, there is definite clinical importance in the subsequent development of computer assisted diagnosis (CADx) or polyp characterization algorithms. These will help clinicians determine clinically relevant polyps, and possibly advance the resect and discard practice. It also will help clinicians adequately assess margins of polyps, so that complete removal can be achieved, thus decreasing future recurrences.”
Randomized clinical trials demonstrated that a convolutional neural network in combination with deep reinforcement learning (collectively called the WISENSE system) can reduce the number of blind spots during endoscopy intended to evaluate the esophagus, stomach, and duodenum in real time. “A total of 324 patients were recruited and randomized; 153 and 150 patients were analysed in the WISENSE and control group, respectively. Blind spot rate was lower in WISENSE group compared with the control (5.86% vs 22.46%, p<0.001) . . .”3
Mayo Clinic’s Endoscopy Center, utilizing Mayo Clinic Platform’s resources, has also been exploring the value of machine learning in GI care with the assistance of Endonet, a comprehensive library of endoscopic videos and images, linked to clinical data including symptoms, diagnoses, pathology, and radiology. These data will include unedited full-length videos as well as video summaries of the procedure including landmarks, specific abnormalities, and anatomical identifiers. Dr. Coelho-Prabhu explains that the idea is to have different user interfaces:
“From the patient’s perspective, it will serve as an electronic video record of all their procedures, and future procedures can be tailored to survey prior abnormal areas as needed.
From a research perspective, this will be a diverse and rich library including large volumes of specialized populations such as Barrett’s esophagus, inflammatory bowel disease, familial polyposis syndromes. The additional strength is that Mayo Clinic provides highly specialized care, especially to these select populations. We can develop AI algorithms to advance medical care using this library. From a hospital system perspective, this would serve as a reference library, guiding endoscopists, including for advanced therapeutic procedures in the future. It also could be used to measure and monitor quality indicators in endoscopy. From an educational standpoint, this library can be developed into a teaching set for both trainee and advanced practitioners looking for CME opportunities. From industry perspective, this database could be used to train/validate commercial AI algorithms.”
AI and machine learning may not be the panacea some technology enthusiasts imagine it to be, but there’s little doubt they are becoming an important partner in the road to more personalized patient care.
References
1. Syed S, Al-Bone M, Khan MN, et al. Assessment of machine learning detection of environmental enteropathy and celiac disease in children. JAMA Network Open. 2019;2:e195822.
2. Wang P, Berzin TM, Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68:1813–1819.
3. Wu L, Zhang J, Zhou W, et al Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut. 2019;68:2161–2169.
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