We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

AI Model Accurately Predicts Whether Crohn Disease Will Recur After Surgery

By HospiMedica International staff writers
Posted on 11 May 2022
Print article
Image: AI can predict postoperative recurrence of Crohn disease (Photo courtesy of Pexels)
Image: AI can predict postoperative recurrence of Crohn disease (Photo courtesy of Pexels)

The 10-year rate of postoperative symptomatic recurrence of Crohn disease, a chronic inflammatory gastrointestinal disease, is estimated at 40%. Although there are scoring systems to evaluate Crohn disease activity and the existence of postoperative recurrence, no scoring system had been developed to predict whether Crohn disease might recur. Using an artificial intelligence (AI) tool that emulates how humans visualize and is trained to recognize and classify images, investigators have now constructed a model that predicts the postoperative recurrence of Crohn disease with high accuracy by evaluating histological images.

The AI tool developed by researchers at Osaka University (Suita, Japan) also revealed previously unrecognized differences in adipose cells and significant differences in the extent of mast cell infiltration in the subserosa, or outer lining of the intestine, comparing patients with and without disease recurrence. The new study included 68 patients with Crohn disease who underwent bowel resection between January 2007 and July 2018. They were classified into two groups according to the presence or absence of postoperative disease recurrence within two years after surgery. Each group was sorted into two subgroups, one for training an AI model and the other for validation. For training, whole slide images of surgical specimens were cropped into tile images, labeled for presence or absence of postsurgical recurrence, and then processed by EfficientNet-b5, a commercially available AI model designed to perform image classification.

When the model was tested with unlabeled images, the results indicated that the deep learning model accurately classified the unlabeled images according to the presence or absence of disease occurrence. Next, predictive heat maps were generated to identify areas and histological features from which the machine learning model could predict recurrence with high accuracy. The images included all layers of the intestinal wall. The heatmaps showed that the machine learning model yielded correct predictions in the subserosal adipose tissue layer. However, in other areas, such as the mucosal and proper muscular layers, the model was less accurate. Images with the most accurate predictions were extracted from the test datasets of the non-recurrence and recurrence groups. Among these images, the best predictive results all contained adipose tissue.

Because the machine learning model achieved accurate predictions from images of subserosal tissue, the investigators hypothesized that subserosal adipose cell morphologies differed between the recurrence and the nonrecurrence groups. Adipose cells in the recurrence group had a significantly smaller cell size, higher flattening, and smaller center to center cell distance values than those in the nonrecurrence group. The investigators also hypothesized that the differences in adipocyte morphology between the two groups were associated with some degree or type of inflammatory condition in the tissue. They found that the recurrence group had a significantly higher number of mast cells infiltrating the subserosal adipose tissue, indicating that the cells are associated with the recurrence of Crohn disease and the “adipocyte shrinkage” phenomenon. To the investigators’ knowledge, these findings are the first to link postoperative recurrence of Crohn disease with the histology of subserosal adipose cells and mast cell infiltration.

“Most of the analysis of histopathological images using AI in the past have targeted malignant tumors,” explained lead investigators Takahiro Matsui, MD, PhD, and Eiichi Morii, MD, PhD, Department of Pathology, Osaka University Graduate School of Medicine, Osaka, Japan. “We aimed to obtain clinically useful information for a wider variety of diseases by analyzing histopathology images using AI. We focused on Crohn disease, in which postoperative recurrence is a clinical problem.”

“Our findings enable stratification by prognosis of postoperative Crohn disease patients. Many drugs, including biologicals, are used to prevent Crohn disease recurrence, and proper stratification can enable more intensive and successful treatment of high-risk patients,” observed Dr. Matsui and Dr. Morii.

Related Links:
Osaka University

Gold Supplier
Creatinine Meter
StatSensor Xpress Creatinine Meter
New
Hyper-Hypothermia Blanket
Maxi-Therm
New
Bedside Transfusion Administration Software
BloodTrack Tx
New
X-Ray System
Straight Arm

Print article
Radcal

Channels

Critical Care

view channel
Image: An earbud prototype that has been wired for data collection (Photo courtesy of MUSC)

Earbuds to Outperform Smartwatches in Monitoring Blood Pressure

While blood pressure cuffs are considered the most accurate method of measurement, they require the user to sit down, put on the cuff, and stay still. This can be inconvenient and may lead to errors in... Read more

Health IT

view channel
Image: Using digital data can improve health outcomes (Photo courtesy of Unsplash)

Electronic Health Records May Be Key to Improving Patient Care, Study Finds

When a patient gets transferred from a hospital to a nearby specialist or rehabilitation facility, it is often difficult for personnel at the new facility to access the patient’s electronic health records... Read more

Business

view channel
Image: The demand for endometrial ablation devices is increasing due to rising prevalence of gynecological disorders (Photo courtesy of Pexels)

Global Endometrial Ablation Market Driven by Rising Prevalence of Gynecological Disorders

Gynecological disorders, such as menorrhagia, PCOD, abnormal vaginal bleeding, affect millions of women globally every year and are on the rise. Abnormal Uterine Bleeding (AUB) is the most common disorder... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.