A Machine Learning-Based System for Real-Time Polyp Detection (DeFrame): A Retrospective Study

Shuijiao Chen; Shuang Lu; Yingxin Tang; Dechun Wang; Xinzi Sun; Jun Yi; Benyuan Liu; Yu Cao; Yongheng Chen; Xiaowei Liu

doi:10.3389/fmed.2022.852553

A Machine Learning-Based System for Real-Time Polyp Detection (DeFrame): A Retrospective Study

Front Med (Lausanne). 2022 May 31:9:852553. doi: 10.3389/fmed.2022.852553. eCollection 2022.

Authors

Shuijiao Chen^{1

2

3}, Shuang Lu¹, Yingxin Tang⁴, Dechun Wang⁵, Xinzi Sun⁵, Jun Yi^{1

2

3}, Benyuan Liu⁵, Yu Cao⁵, Yongheng Chen⁶, Xiaowei Liu^{1

2

3}

Affiliations

¹ Department of Gastroenterology, Xiangya Hospital of Central South University, Changsha, China.
² National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University, Changsha, China.
³ Hunan International Scientific and Technological Cooperation Base of Artificial Intelligence Computer Aided Diagnosis and Treatment for Digestive Disease, Hunan, China.
⁴ HighWise Medical Technology Co., Ltd., Suzhou, China.
⁵ Department of Computer Science, The University of Massachusetts Lowell, Lowell, MA, United States.
⁶ Department of Oncology, NHC Key Laboratory of Cancer Proteomics & State Local Joint Engineering Laboratory for Anticancer Drugs, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.

Abstract

Background and aims: Recent studies have shown that artificial intelligence-based computer-aided detection systems possess great potential in reducing the heterogeneous performance of doctors during endoscopy. However, most existing studies are based on high-quality static images available in open-source databases with relatively small data volumes, and, hence, are not applicable for routine clinical practice. This research aims to integrate multiple deep learning algorithms and develop a system (DeFrame) that can be used to accurately detect intestinal polyps in real time during clinical endoscopy.

Methods: A total of 681 colonoscopy videos were collected for retrospective analysis at Xiangya Hospital of Central South University from June 2019 to June 2020. To train the machine learning (ML)-based system, 6,833 images were extracted from 48 collected videos, and 1,544 images were collected from public datasets. The DeFrame system was further validated with two datasets, consisting of 24,486 images extracted from 176 collected videos and 12,283 images extracted from 259 collected videos. The remaining 198 collected full-length videos were used for the final test of the system. The measurement metrics were sensitivity and specificity in validation dataset 1, precision, recall and F1 score in validation dataset 2, and the overall performance when tested in the complete video perspective.

Results: A sensitivity and specificity of 79.54 and 95.83%, respectively, was obtained for the DeFrame system for detecting intestinal polyps. The recall and precision of the system for polyp detection were determined to be 95.43 and 92.12%, respectively. When tested using full colonoscopy videos, the system achieved a recall of 100% and precision of 80.80%.

Conclusion: We have developed a fast, accurate, and reliable DeFrame system for detecting polyps, which, to some extent, is feasible for use in routine clinical practice.

Keywords: artificial intelligence; colonoscopy; computer-aided detection; convolutional neural networks; deep learning.