A study on the diagnosis of the Helicobacter pylori coccoid form with artificial intelligence technology - Zhong, Zishao; Wang, Xin; Li, Jianmin; Zhang, Beiping; Yan, Lijuan; Xu, Shuchang; Chen, Guangxia; Gao, Hengjun (Frontiers in Microbiology, 2022)
Background: Helicobacter pylori coccoid transform (HPCT) causes refractory H. pylori infection and should be given more attention in infection management. However, manual HPCT recognition on slides is time-consuming and labor-intensive and depends on experienced pathologists; thus, HPCT diagnosis is rarely performed and often overlooked. Therefore, simple HPCT 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 HPCT. 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 HPCT 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). Conclusions: HPCT identification using AI has the advantage of high accuracy and efficiency with the potential to assist or replace pathologists in clinical practice for HPCT identification.