Real-Time Artificial Intelligence-Based Histologic Classifications of Colorectal Polyps Using Narrow-Band Imaging

Yi Lu; Jiachuan Wu; Xianhua Zhuo; Minhui Hu; Yongpeng Chen; Yuxuan Luo; Yue Feng; Min Zhi; Chujun Li; Jiachen Sun

doi:10.3389/fonc.2022.879239

Real-Time Artificial Intelligence-Based Histologic Classifications of Colorectal Polyps Using Narrow-Band Imaging

Front Oncol. 2022 Apr 26:12:879239. doi: 10.3389/fonc.2022.879239. eCollection 2022.

Authors

Yi Lu^{1

2}, Jiachuan Wu³, Xianhua Zhuo^{1

4}, Minhui Hu^{1

2}, Yongpeng Chen^{1

2}, Yuxuan Luo⁵, Yue Feng⁵, Min Zhi^{2

6}, Chujun Li^{1

2}, Jiachen Sun^{1

2}

Affiliations

¹ Department of Gastrointestinal Endoscopy, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
² Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases , the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
³ Digestive Endoscopy Center, Guangdong Second Provincial General Hospital, Guangzhou, China.
⁴ Department of Otorhinolaryngology, the Second Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
⁵ Tianjin Economic-Technological Development Area (TEDA) Yujin Digestive Health Industry Research Institute, Tianjin, China.
⁶ Department of Gastroenterology, the Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Abstract

Background and aims: With the development of artificial intelligence (AI), we have become capable of applying real-time computer-aided detection (CAD) in clinical practice. Our aim is to develop an AI-based CAD-N and optimize its diagnostic performance with narrow-band imaging (NBI) images.

Methods: We developed the CAD-N model with ResNeSt using NBI images for real-time assessment of the histopathology of colorectal polyps (type 1, hyperplastic or inflammatory polyps; type 2, adenomatous polyps, intramucosal cancer, or superficial submucosal invasive cancer; type 3, deep submucosal invasive cancer; and type 4, normal mucosa). We also collected 116 consecutive polyp videos to validate the accuracy of the CAD-N.

Results: A total of 10,573 images (7,032 images from 650 polyps and 3,541 normal mucous membrane images) from 478 patients were finally chosen for analysis. The sensitivity, specificity, PPV, NPV, and accuracy for each type of the CAD-N in the test set were 89.86%, 97.88%, 93.13%, 96.79%, and 95.93% for type 1; 93.91%, 95.49%, 91.80%, 96.69%, and 94.94% for type 2; 90.21%, 99.29%, 90.21%, 99.29%, and 98.68% for type 3; and 94.86%, 97.28%, 94.73%, 97.35%, and 96.45% for type 4, respectively. The overall accuracy was 93%. We also built models for polyps ≤5 mm, and the sensitivity, specificity, PPV, NPV, and accuracy for them were 96.81%, 94.08%, 95%, 95.97%, and 95.59%, respectively. Video validation results showed that the sensitivity, specificity, and accuracy of the CAD-N were 84.62%, 86.27%, and 85.34%, respectively.

Conclusions: We have developed real-time AI-based histologic classifications of colorectal polyps using NBI images with good accuracy, which may help in clinical management and documentation of optical histology results.

Keywords: NBI; NICE; artificial intelligence; colorectal; polyp.