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

• Published in: Frontiers in microbiology Vol. 13; p. 1008346
• Main Authors: Zhong, Zishao, Wang, Xin, Li, Jianmin, Zhang, Beiping, Yan, Lijuan, Xu, Shuchang, Chen, Guangxia, Gao, Hengjun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 28-10-2022
• Subjects: artificial intelligence technology; coccoid form; deep learning; diagnosis; Helicobacter pylori; Microbiology; deep learning; diagnosis; Helicobacter pylori; coccoid form; artificial intelligence technology
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2022.1008346

is an important pathogenic microorganism that causes gastric cancer, peptic ulcers and dyspepsia, and infects more than half of the world's population. Eradicating
is the most effective means to prevent and treat these diseases.
coccoid form (HPCF) causes refractory
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.
We manually labeled 4,547 images from anonymized paraffin-embedded samples in the China Center for
Molecular Medicine (
, 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.
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,
 < 0.05), no worse than intermediate pathologists (3.40 MAE,
 > 0.05), and equivalent to a senior pathologist (3.07 MAE,
 > 0.05).
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.