Effect of DCNN model-assisted colorectal polyp detection system on the detection of colorectal polyps by junior physicians

doi:10.3969/j.issn.1674-8115.2022.02.011

Abstract

Abstract: Objective

·To explore the effect of a computer-aided detection (CADe) system constructed by a deep convolutional neural network (DCNN) model in artificial intelligence (AI) technology on the detection rate of colorectal polyps among the junior physicians lacking of colonoscopy operation experience.

Methods

·The colonoscopy images from January 2019 to December 2020 and colonoscopy videos from January 2021 to March 2021 from the Endoscopy Center Database of Digestive Endoscopy Center, Tongren Hospital, Shanghai Jiao Tong University School of Medicine were collected. The collected images and videos were divided into dataset 1 (5 908 images) and dataset 6 (360 short videos). Dataset 1 was divided into dataset 1a (4 906 images), dataset 1b (300 images) and dataset 1c (702 images), and dataset 1c was the intercepted images from video dataset 6. Datasets 2 to 5 were the public datasets CVC-ClinicDB, CVC-ColonDB, ETIS-Larib Polyp DB and KVASIR, containing a total of 2 188 images. Dataset 1a and datasets 2?5 were the model training sets, and dataset 1b and dataset 1c were the model testing sets. Ten trained junior physicians with no experience in colonoscopy were randomized into AI-assisted group (Group A, n=5) and non-AI-assisted group (Group B, n=5) to interpret 360 colonoscopy videos and determine the presence of polyps in the videos, respectively. For the first 180 videos, both groups had no AI assistance. For the second 180 videos, the AI-assisted group was supplemented with a DCNN model-assisted colorectal polyp detection system, which processed the video data set through the detection system to mark polyps for the investigator's interpretation, while the non-AI-assisted group watched the original video to interpret the presence of polyps. All the videos were first confirmed by 2 senior endoscopists for the presence of polyps. If there was a dispute, the video was excluded and the diagnosis confirmed by these 2 physicians together was the gold standard. If a polyp was present in the video and the investigator failed to detect it, the diagnosis was considered missed; if no polyp was present in the video and the investigator judged that a polyp was present, the diagnosis was considered misdiagnosed.

Results

·In the first 180 videos without AI assistance, there was no significant difference in the number of missed colon polyps between group A and group B. In the second 180 videos, the number of missed polyps in group A with AI assistance was significantly less than that in group B without AI assistance (P=0.031). Meanwhile, the number of missed polyps in the second 180 videos in group A was less than that in the first 180 videos, and the difference was also statistically significant (P=0.007). In addition, there was no significant difference in the number of misdiagnosed polyps detected between and within the groups.

Conclusion

·The DCNN model-assisted colorectal polyp detection system can significantly improve the polyps detection rate by physicians lacking of colonoscopy operation experience without increasing misdiagnosis of colorectal polyps.

Key words: artificial intelligence (AI), deep convolutional neural network (DCNN), computer-aided detection (CADe), colorectal polyp

CLC Number:

R574.63

Xiaofeng WANG, Lu ZHOU, Leyu YAO, Fan HE, Haixia PENG, Daming YANG, Xiaolin HUANG. Effect of DCNN model-assisted colorectal polyp detection system on the detection of colorectal polyps by junior physicians[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2022, 42(2): 205-210.

Figures/Tables 5

TrendMD

References 32

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Item	First 180 videos	Second 180 videos	P value
Polyp size/n			0.199
6‒10 mm	30	22
<6 mm	72	80
Yamada classification/n			0.387
Type Ⅰ	66	60
Type Ⅱ	36	42

Item	First 180 videos	Second 180 videos	P value
Polyp size/n			0.199
6‒10 mm	30	22
<6 mm	72	80
Yamada classification/n			0.387
Type Ⅰ	66	60
Type Ⅱ	36	42

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