July 14, 2022
Release Subtitle:
Researchers have developed a new AI model which diagnoses and classifies
neoplastic lesions in patients with IBD with high accuracy
Release Summary Text:
The treatment of inflammatory bowel disease (IBD)-associated dysplasia
is often challenging due to diagnostic techniques that are unable to
accurately differentiate and classify the severity of neoplastic
lesions. Now, researchers from Okayama University in Japan have
developed an AI-system based on a conventional neural network, by
training it with endoscopic images of neoplastic lesions. This system
displayed a higher diagnostic ability in the classification of the
lesions, as compared to that of endoscopists.
Full text of release:
The incidence of inflammatory bowel disease (IBD)—an intractable disease
characterized by chronic inflammation of the gastrointestinal (GI)
tract—has increased significantly in Japan. Chronic inflammation
associated with IBD often leads to the development of cancer in the
colorectal region.
For patients who have visible or low-grade dysplasia (abnormal cell
growth which may not be malignant), endoscopic resection, a technique
used to remove cancerous lesions, and colonoscopy are usually employed.
However, for patients with a high rate of neoplasia (severe cell growth
which is malignant), a total proctocolectomy, i.e., complete removal of
the colon and rectum is the standard treatment, which is highly
detrimental to their quality of lives.
Hence, identifying the severity and grade of the neoplasia during
diagnosis is essential before proceeding with treatment. Unfortunately,
the presence of inflammation in the colorectal region makes it difficult
for endoscopists to classify the type of IBD neoplasia (IBDN). This
leaves biopsy as the only viable option, which is associated with high
risks and often leads to inaccurate diagnoses, highlighting the need for
a simpler diagnostic technique with high accuracy.
To this end, a team of researchers from Okayama University Graduate
School of Medicine, including Assistant Professor Hideaki Kinugasa,
Doctor Shumpei Yamamoto, Professor Sakiko Hiraoka, and Professor Yoshiro
Kawahara conducted a pilot study to develop an artificial intelligence
(AI) system that classifies IBDN lesions accurately. In addition, as
part of this study, which was published in Gastroenterology and Hepatology first on May 29, 2022, they compared the diagnostic ability of endoscopists with that of the new AI system.
First, the team used a conventional neural network (CNN)—a type of
neural network used for the analysis of visual imagery—known as
Efficient-Net-B3, to develop the AI-system’s prototype. They trained
this system using 862 endoscopic images of 99 IBDN lesions from patients
with IBD derived from two hospitals between 2003 and 2021, and
validated it using a deep-learning framework. Next, they asked
endoscopists with over 8 years of experience in gastrointestinal
endoscopy to analyse the images and classify the lesions into two types
based on the need for proctocolectomy, and compared their classification
to that of the AI-system.
As a result of data-augmentation, the AI-system generated approximately
six million images from the original data set, which were then used to
analyse clinicopathological characteristics of patients and the lesions.
Based on these analyses, the team found that most patients had
ulcerative colitis—a type of IBD, with more than 95% of them presenting
pancolitis and left-sided colitis. Moreover, the AI-system displayed an
image-based diagnostic ability with 64.5% sensitivity, 89.5%
specificity, and 80.6% accuracy, and a lesion-based diagnostic ability
with 74.4% sensitivity, 85% specificity, and 80.8% accuracy. What’s
interesting is that the correct diagnosis rate of the AI system was
79.0, while that of endoscopists was 77.8.
What do these findings imply? “Our
AI-system prototype proved successful in determining the degree of
malignancy of IBD-tumors and is valuable enough to contribute to
clinical practice in the coming years”, said Assistant Prof. Kinugasa in response.
The team also highlighted that combining this AI-based automatic
diagnosis of neoplastic lesions with existing endoscopic diagnostic
techniques might provide superior diagnostic results in real-time.
While discussing the system’s additional advantages and real-life applications, Assistant Prof. Kinugasa added, “Using
this AI-system can ensure that endoscopists do not misdiagnose IBD
neoplastic lesions, patients receive prompt treatment, and more
appropriate treatment strategies are developed and applied for early as
well as advanced stages of IBD”.
Here’s hoping that this AI-system revolutionizes the diagnosis of
IBD-associated neoplasia and improves the lives of patients with IBD in
Japan and the rest of the world!
Release URL:
https://www.eurekalert.org/news-releases/958630
Reference:
The diagnostic ability to classify neoplasias occurring in inflammatory
bowel disease by artificial intelligence and endoscopists: A pilot study
Journal: Journal of Gastroenterology and Hepatology
DOI:10.1111/jgh.15904
Contact Person:Hideaki Kinugasa
Dr. Hideki Kinugasa is an Assistant Professor at the Department of
Gastroenterology and Hepatology at Okayama University Graduate School of
Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan. His
research is primarily focused on colorectal cancer, gastrointestinal
cancer, circulating tumor DNA, liquid biopsy, fusobacterium, pancreatic
neoplasms, and endoscopic diagnosis and treatment. Within the last year,
he has already contributed to the publication of 49 research papers and
his work has been cited by various researchers.
https://www.okayama-u.ac.jp/eng/research_highlights/index_id164.html
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