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

Abstract

BackgroundHelicobacter 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 methodsWe 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.ResultsFor 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).ConclusionHPCF 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.

Applications of Artificial Intelligence in Gastroenterology and Hepatology

“…A specific area in which AI may be particularly useful in GI pathology is automating time consuming processes. One example in the literature is identifying coccoid forms of H. pylori, which are associated with refractory infection [82].…”

Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning

“…The YOLO series represents one-stage algorithms, which are more suited to practical applications than two-stage algorithms (such as Faster R-CNN) owing to their better balance between accuracy and speed 23 . Zhong et al 24 pointed that the YOLO model was superior to the Faster R-CNN model for the Helicobacter pylori detection task. YOLO-v7 leverages a trainable bag-of-freebies approach, enabling significant improvements in precision for real-time detection tasks without incurring additional inference costs.…”

Pathohistological Changes in the Gastric Mucosa in Correlation with the Immunohistochemically Detected Spiral and Coccoid Forms of Helicobacter pylori

“…The importance of the coccoid form is emphasized in increasing research using machine learning for the detection of both forms [ 27 ]. Analyzing the form of bacteria on a histological preparation is extremely tedious and time consuming.…”

“…Analyzing the form of bacteria on a histological preparation is extremely tedious and time consuming. Chen et al presented a machine learning program that can recognize the coccoid and spiral forms of H. pylori on a preparation, which will certainly aid pathologists during analysis [ 27 ].…”