AI and machine learning have the potential to redefine
the management of several GI disorders.
John Halamka, M.D., president, Mayo Clinic Platform, and
Paul Cerrato, senior research analyst and communications specialist, Mayo
Clinic Platform, wrote this article.
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 deaths
in 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.2
The 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.
