Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches

Yuanren Tong,Ji Li,Yue Li,Dong Wu,Keming Lu,Yucong Lin,Jiaming Qian,Aiming Yang,Sheng Yu,Yingyun Yang

doi:10.1186/s12911-020-01277-w

Abstract

BackgroundDifferentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms.MethodsA total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had undergone colonoscopy examinations in the Peking Union Medical College Hospital from January 2008 to November 2018 were enrolled. The input was the description of the endoscopic image in the form of free text. Word segmentation and key word filtering were conducted as data preprocessing. Random forest (RF) and convolutional neural network (CNN) approaches were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, and CD and ITB) and a three-class classifier (UC, CD and ITB) were built.ResultsThe classifiers built in this research performed well, and the CNN had better performance in general. The RF sensitivities/specificities of UC-CD, UC-ITB, and CD-ITB were 0.89/0.84, 0.83/0.82, and 0.72/0.77, respectively, while the values for the CNN of CD-ITB were 0.90/0.77. The precisions/recalls of UC-CD-ITB when employing RF were 0.97/0.97, 0.65/0.53, and 0.68/0.76, respectively, and when employing the CNN were 0.99/0.97, 0.87/0.83, and 0.52/0.81, respectively.ConclusionsClassifiers built by RF and CNN approaches had excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were achieved as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases.ConferenceThe abstract of this article has won the first prize of the Young Investigator Award during the Asian Pacific Digestive Week (APDW) 2019 held in Kolkata, India.

Highlights

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), are idiopathic and chronic digestive tract inflammatory diseases with repeated remission and relapses
Conference: The abstract of this article has won the first prize of the Young Investigator Award during the Asian Pacific Digestive Week (APDW) 2019 held in Kolkata, India
● Endoscopy is very important in differential diagnosis among UC, CD and intestinal tuberculosis (ITB)

Summary

Introduction

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), are idiopathic and chronic digestive tract inflammatory diseases with repeated remission and relapses. The ultimate courses and prognoses of IBD and ITB can be different. ITB can be cured with early diagnosis and proper antituberculosis treatment, while recurrences of CD and UC are common and require life-long follow-up. Misdiagnosis and inappropriate treatment can cause either a prolonged disease course or severe adverse effects [1]. Timely and accurate diagnosis and differentiation of IBD and ITB are very important, especially in China, which has the third-highest tuberculosis incidence rate in the world according to the World Health Organization [2]. Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) using endoscopy is challenging. We aimed to realize automatic differential diagnosis among these diseases through machine learning algorithms

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