ArticlePathologica2022
Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies.
Article in Pathologica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 29 citations in OpenAlex.
- Detection ofJournal of imaging · 2025Article
- Polarised light scanner for digital pathology.Virchows Archiv : an international journal of pathology · 2025Article
- Deep learning for smartphone-aided detection system of Helicobacter Pylori in gastric biopsy.Scientific reports · 2025Article
- Rapid and Efficient Screening ofDiagnostics (Basel, Switzerland) · 2025Article
- Diagnosing Helicobacter pylori using autoencoders and limited annotations through anomalous staining patterns in IHC whole slide images.International journal of computer assisted radiology and surgery · 2025Article
- Artificial intelligence-based model for diagnosingFrontiers in medicine · 2025Article
- Personalized statistical medicine.The Indian journal of medical research · 2023Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: A common source of concern about digital pathology (DP) is that limited resolution could be a reason for an increased risk of malpractice. A frequent question being raised about this technology is whether it can be used to reliably detect Methods: The method we propose is based on the Warthin-Starry (W-S) silver stain which allows faster detection of HP in virtual slides. A software tool, based on regular expressions, performed a specific search to select 679 biopsies on which a W-S stain was done. From this dataset 185 virtual slides were selected to be assessed by WSI and compared with microscopy slide readings. To determine whether HP infections could be accurately diagnosed with machine learning. AI was used as a service (AIaaS) on a neural network-based web platform trained with 468 images. A test dataset of 210 images was used to assess the classifier performance. Results: In 185 gastric biopsies read with DP we recorded only 4 false positives and 4 false negatives with an overall agreement of 95.6%. Compared with microscopy, defined as the "gold standard" for the diagnosis of HP infections, WSI had a sensitivity and specificity of 0.95 and 0.96, respectively. The ROC curve of our AI classifier generated on a testing dataset of 210 images had an AUC of 0.938. Conclusions: This study demonstrates that DP and AI can be used to reliably identify HP at 20X resolution.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.