P213 Can artificial intelligence help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neuron network
Y. Li1, Y. Tong1, K. Lu2, S. Yu2, J. Qian1
1Department of Gastroenterology, Peking Union Medical College Hospital, Beijing, China, 2Center for Statistical Science, Tsinghua University, Beijing, China
Background
Differentiating between ulcerative colitis (UC), Crohn’s disease (CD) and intestinal tuberculosis (ITB) is challenging under endoscopy. We aimed to realise automatic differential diagnosis among these diseases through machine learning algorithms.
Methods
A total of 6399 consecutive patients (5128 UC, 875 CD and 396 ITB) who had taken colonoscopy examinations in Peking Union Medical College Hospital from January 2008 to November 2018 was enrolled. The input was the description of the endoscopic image in the form of free-text. Word segmentation and key word infiltration were conducted as data pre-processing. Random forest (RF) and convolutional neural network (CNN) were applied to different disease entities. Three two-class classifiers (UC and CD, UC and ITB, CD and ITB) and a three-class classifier (UC, CD and ITB) were built. Sensitivity/specificity and precision/recall were applied to evaluate the performance of two-class classifiers and the three-class classifier, respectively.
Results
The classifiers built in this research were well-performed and the CNN had a 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, while the CNN of CD-ITB was 0.90/0.77. The precision/recall of UC-CD-ITB was 0.97/0.97, 0.65/0.53 and 0.68/0.76 by RF, respectively, and 0.99/0.97,0.87/0.83 and 0.52/0.81 by CNN, respectively.
Conclusion
Classifiers built by RF and CNN had an excellent performance when classifying UC with CD or ITB. For the differentiation of CD and ITB, high specificity and sensitivity were reached as well. Artificial intelligence through machine learning is very promising in helping unexperienced endoscopists differentiate inflammatory intestinal diseases.