A study on the diagnosis of the Helicobacter pylori coccoid form with artificial intelligence technology

Zishao Zhong; Xin Wang; Jianmin Li; Beiping Zhang; Lijuan Yan; Shuchang Xu; Guangxia Chen; Hengjun Gao

doi:10.3389/fmicb.2022.1008346

A study on the diagnosis of the Helicobacter pylori coccoid form with artificial intelligence technology

Front Microbiol. 2022 Oct 28:13:1008346. doi: 10.3389/fmicb.2022.1008346. eCollection 2022.

Authors

Zishao Zhong^{1

2

3

4}, Xin Wang^{1

2

5}, Jianmin Li⁶, Beiping Zhang^{2

3}, Lijuan Yan², Shuchang Xu^{1

4}, Guangxia Chen⁷, Hengjun Gao^{1

2

4

8}

Affiliations

¹ School of Medicine, Institute of Digestive Disease, Tongji University, Shanghai, China.
² China Center for Helicobacter pylori Molecular Medicine, Shanghai, China.
³ The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
⁴ Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
⁵ School of Chemical Engineering and Technology, China University of Mining and Technology, Xuzhou, China.
⁶ Unicom Guangdong Industrial Internet Co., Ltd, Guangzhou, China.
⁷ Department of Gastroenterology, Xuzhou Municipal Hospital Affiliated to Xuzhou Medical University, Xuzhou, China.
⁸ National Engineering Center for Biochips, Shanghai, China.

Abstract

Background: Helicobacter pylori (H. pylori) is an important pathogenic microorganism that causes gastric cancer, peptic ulcers and dyspepsia, and infects more than half of the world's population. Eradicating H. pylori is the most effective means to prevent and treat these diseases. H. pylori coccoid form (HPCF) causes refractory H. pylori infection and should be given more attention in infection management. However, manual HPCF recognition on slides is time-consuming and labor-intensive and depends on experienced pathologists; thus, HPCF diagnosis is rarely performed and often overlooked. Therefore, simple HPCF diagnostic methods need to be developed.

Materials and methods: We manually labeled 4,547 images from anonymized paraffin-embedded samples in the China Center for H. pylori Molecular Medicine (CCHpMM, Shanghai), followed by training and optimizing the Faster R-CNN and YOLO v5 models to identify HPCF. Mean average precision (mAP) was applied to evaluate and select the model. The artificial intelligence (AI) model interpretation results were compared with those of the pathologists with senior, intermediate, and junior experience levels, using the mean absolute error (MAE) of the coccoid rate as an evaluation metric.

Results: For the HPCF detection task, the YOLO v5 model was superior to the Faster R-CNN model (0.688 vs. 0.568, mean average precision, mAP); the optimized YOLO v5 model had a better performance (0.803 mAP). The MAE of the optimized YOLO v5 model (3.25 MAE) was superior to that of junior pathologists (4.14 MAE, p < 0.05), no worse than intermediate pathologists (3.40 MAE, p > 0.05), and equivalent to a senior pathologist (3.07 MAE, p > 0.05).

Conclusion: HPCF identification using AI has the advantage of high accuracy and efficiency with the potential to assist or replace pathologists in clinical practice for HPCF identification.

Keywords: Helicobacter pylori; artificial intelligence technology; coccoid form; deep learning; diagnosis.