Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method

Teng Zhou; Guoqiang Han; Bing Nan Li; Zhizhe Lin; Edward J Ciaccio; Peter H Green; Jing Qin

doi:10.1016/j.compbiomed.2017.03.031

Quantitative analysis of patients with celiac disease by video capsule endoscopy: A deep learning method

Comput Biol Med. 2017 Jun 1:85:1-6. doi: 10.1016/j.compbiomed.2017.03.031. Epub 2017 Apr 8.

Authors

Teng Zhou¹, Guoqiang Han¹, Bing Nan Li², Zhizhe Lin³, Edward J Ciaccio⁴, Peter H Green⁴, Jing Qin⁵

Affiliations

¹ School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China.
² Department of Biomedical Engineering, Hefei University of Technology, Hefei 230009, China. Electronic address: bingoon@ieee.org.
³ Affiliated Shantou Hospital of Sun Yat-sen University, Shantou Central Hospital, Shantou 515000, China.
⁴ Department of Medicine, Celiac Disease Center, Columbia University, New York, USA.
⁵ Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong.

PMID: 28412572
DOI: 10.1016/j.compbiomed.2017.03.031

Abstract

Background: Celiac disease is one of the most common diseases in the world. Capsule endoscopy is an alternative way to visualize the entire small intestine without invasiveness to the patient. It is useful to characterize celiac disease, but hours are need to manually analyze the retrospective data of a single patient. Computer-aided quantitative analysis by a deep learning method helps in alleviating the workload during analysis of the retrospective videos.

Method: Capsule endoscopy clips from 6 celiac disease patients and 5 controls were preprocessed for training. The frames with a large field of opaque extraluminal fluid or air bubbles were removed automatically by using a pre-selection algorithm. Then the frames were cropped and the intensity was corrected prior to frame rotation in the proposed new method. The GoogLeNet is trained with these frames. Then, the clips of capsule endoscopy from 5 additional celiac disease patients and 5 additional control patients are used for testing. The trained GoogLeNet was able to distinguish the frames from capsule endoscopy clips of celiac disease patients vs controls. Quantitative measurement with evaluation of the confidence was developed to assess the severity level of pathology in the subjects.

Results: Relying on the evaluation confidence, the GoogLeNet achieved 100% sensitivity and specificity for the testing set. The t-test confirmed the evaluation confidence is significant to distinguish celiac disease patients from controls. Furthermore, it is found that the evaluation confidence may also relate to the severity level of small bowel mucosal lesions.

Conclusions: A deep convolutional neural network was established for quantitative measurement of the existence and degree of pathology throughout the small intestine, which may improve computer-aided clinical techniques to assess mucosal atrophy and other etiologies in real-time with videocapsule endoscopy.

Keywords: Celiac disease; Deep learning; GoogLeNet; Quantitative analysis; Videocapsule endoscopy.

MeSH terms

Algorithms
Capsule Endoscopy / methods*
Celiac Disease / diagnostic imaging*
Humans
Image Interpretation, Computer-Assisted / methods*
Machine Learning*
Neural Networks, Computer