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

Antimicrobial susceptibility of clinical Helicobacter pylori isolates and its eradication by standard triple therapy: a study in west central region of Colombia

The aim of the present study was first to isolate Helicobacter pylori from gastric biopsy specimens and to test their antibiotic susceptibility. Second, it was to evaluate the efficacy of the standard triple therapy from patients of the west central region of Colombia. H. pylori positive patients received standard triple therapy with proton pump inhibitor (PPI) (40 mg b.i.d.), clarithromycin (500 mg b.i.d.), and amoxicillin (1 g b.i.d.) for 14 days. Thereafter, antibiotic susceptibility of the isolates was assessed by E-Test. From 94 patients enrolled, 67 were positive for H. pylori by histology or culture. Overall resistance to metronidazole, levofloxacin, rifampicin, clarithromycin, and amoxicillin was 81%, 26.2%, 23.9%, 19%, and 9.5%, respectively. No resistance was found for tetracycline. A total of 54 patients received standard triple therapy, 48 attended follow-ups testing, and of them, 30 had resistance test reports. Overall eradication rate was 81.2%. Second-line treatment was given to eight patients, four of whom were followed up with a 13C urea breath test (UBT) and remained positive for H. pylori. Eradication was significantly higher in patients with clarithromycin susceptible than in resistant strains (95.6% vs 42.8% P = 0.001). The updated percentages of resistance to clarithromycin in this geographical area had increased, so this value must be considered when choosing the treatment regimen.IMPORTANCEAntibiotic resistance in Helicobacter pylori has increased worldwide, as has resistance to multiple antimicrobials (MDRs), which seriously hampers the successful eradication of the infection. The ideal success rate in eradicating H. pylori infection (≥90%) was not achieved in this study (81.2%). This is the first time that MDR is reported (14.3%) in the region; the resistance to clarithromycin increased over time (3.8%-19%), and levofloxacin (26.2%) and rifampicin (23%) resistant isolates were detected for the first time. With these results, strain susceptibility testing is increasingly important, and the selection of treatment regimen should be based on local antibiotic resistance patterns.

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

BACKGROUND

The coccoid form of Helicobacter pylori (H. pylori) is resistant to antibiotics. There are only a few studies that have analyzed the frequency of coccoid H. pylori in patients with gastritis. The aim of this work was to examine the correlation between the H. pylori form and the pathohistological characteristics of the stomach in patients with gastritis.

MATERIALS AND METHODS

This research was cross-sectional and focused on the gastric mucosa samples of 397 patients from one general hospital in Croatia. Two independent pathologists analyzed the samples regarding the pathohistological characteristics and the form of H. pylori.

RESULTS

There was a statistically significant difference in the gender of patients with H. pylori gastritis. Only the coccoid form of H. pylori was present in 9.6% of patients. There was a statistically significant difference in the frequency of a certain form of the bacterium depending on its localization in the stomach. The intensity of the bacterium was low in the samples where only the coccoid or spiral form was described. In cases of infection in the antrum, premalignant lesions and the coccoid form of H. pylori were more often present.

CONCLUSION

In the diagnosis of H. pylori infection, the determination of the form of the bacterium via immunohistochemistry should be included to increase the rate of eradication therapy and reduce the incidence of gastric malignancy.

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

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Advancing Artificial Intelligence Integration Into the Pathology Workflow: Exploring Opportunities in Gastrointestinal Tract Biopsies

This review aims to present a comprehensive overview of the current landscape of artificial intelligence (AI) applications in the analysis of tubular gastrointestinal biopsies. These publications cover a spectrum of conditions, ranging from inflammatory ailments to malignancies. Moving beyond the conventional diagnosis based on hematoxylin and eosin-stained whole-slide images, the review explores additional implications of AI, including its involvement in interpreting immunohistochemical results, molecular subtyping, and the identification of cellular spatial biomarkers. Furthermore, the review examines how AI can contribute to enhancing the quality and control of diagnostic processes, introducing new workflow options, and addressing the limitations and caveats associated with current AI platforms in this context.

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

Objectives

To explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.

Methods

A total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.

Results

The deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.

Conclusion

This deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.