Using Artificial Intelligence to Assess Ulcerative Colitis
Researchers from Tokyo
Medical and Dental University (TMDU) have developed an artificial intelligence
system that effectively evaluates endoscopic mucosal findings from patients
with ulcerative colitis without the need for biopsy collection.
Assessments of patients with ulcerative colitis (UC), which is a type of inflammatory bowel disease, are usually conducted via endoscopy and histology. But now, researchers from Japan have developed a system that may be more accurate than existing methods and may reduce the need for these patients to undergo invasive medical procedures.
In a study published this February in Gastroenterology,
researchers from Tokyo Medical and Dental University (TMDU) have revealed a
newly developed artificial intelligence (AI) system that can evaluate
endoscopic findings of UC with an accuracy equivalent to that of expert
endoscopists.
Accurate evaluations are critical in providing optimal care for
patients with UC. Previous studies have indicated that both endoscopic
remission, evaluated via assessment of endoscopic procedure, and histological
remission, as indicated by the degree of microscopic inflammation, can predict
patient outcomes, and are thus frequently used as treatment goals. However,
intra- and inter-observer variations occur in both endoscopic and histological
analyses, and histological analysis frequently requires the collection of
tissue via biopsies, which are invasive and costly.
"The interpretation of endoscopic images is subjective and
based on the experience of individual endoscopists, thereby making the
standardization of evaluation and real-time characterization challenging,"
says lead author of the study Kento Takenaka. "To address this, we sought
to develop a deep neural network (DNN) system for consistent, objective, and
real-time analysis of endoscopic images from patients with UC (DNUC)."
To do this, the researchers developed a system with DNNs to
evaluate endoscopic images from patients with UC. DNNs are a type of AI
machine-learning method that are based on the construction of artificial neural
networks.
"We constructed the DNUC algorithm, using 40,758 images of
colonoscopies and 6885 biopsy results from 2012 patients with
UC," says senior author Mamoru Watanabe. "This comprised the training
set for machine-learning, which enabled the algorithm to learn to accurately
evaluate and classify the data."
The researchers then validated the accuracy of the DNUC
algorithm using 4187 endoscopic images and 4104 biopsy specimens from 875
patients with UC.
"We found that the DNUC achieved a level of accuracy that
was equivalent to that of expert endoscopists," says Takenaka. "Thus,
our system was able to predict histologic remission from UC using endoscopic
images only, as opposed to both histological and endoscopic data. This
represents an important development given the costs and risks associated with
biopsies."
The DNUC may be able to identify UC patients who are in
remission without requiring them to undergo biopsy collection and analysis.
This could save time and money for medical institutions, and limit exposure to
invasive medical procedures for individuals with UC.
Alexia Roy | Larix International Pte Ltd
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