Dataset Information

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

ABSTRACT: BACKGROUND:Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. OBJECTIVE:This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images. METHODS:Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. RESULTS:Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. CONCLUSIONS:An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. TRIAL REGISTRATION:PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957.

SUBMITTER: Bang CS

PROVIDER: S-EPMC7527948 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

Bang Chang Seok CS Lee Jae Jun JJ Baik Gwang Ho GH

Journal of medical Internet research 20200916 9

<h4>Background</h4>Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition ...[more]

PMID: 32936088

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Project description:IntroductionStandards for Reporting of Diagnostic Accuracy Study (STARD) was developed to improve the completeness and transparency of reporting in studies investigating diagnostic test accuracy. However, its current form, STARD 2015 does not address the issues and challenges raised by artificial intelligence (AI)-centred interventions. As such, we propose an AI-specific version of the STARD checklist (STARD-AI), which focuses on the reporting of AI diagnostic test accuracy studies. This paper describes the methods that will be used to develop STARD-AI.Methods and analysisThe development of the STARD-AI checklist can be distilled into six stages. (1) A project organisation phase has been undertaken, during which a Project Team and a Steering Committee were established; (2) An item generation process has been completed following a literature review, a patient and public involvement and engagement exercise and an online scoping survey of international experts; (3) A three-round modified Delphi consensus methodology is underway, which will culminate in a teleconference consensus meeting of experts; (4) Thereafter, the Project Team will draft the initial STARD-AI checklist and the accompanying documents; (5) A piloting phase among expert users will be undertaken to identify items which are either unclear or missing. This process, consisting of surveys and semistructured interviews, will contribute towards the explanation and elaboration document and (6) On finalisation of the manuscripts, the group's efforts turn towards an organised dissemination and implementation strategy to maximise end-user adoption.Ethics and disseminationEthical approval has been granted by the Joint Research Compliance Office at Imperial College London (reference number: 19IC5679). A dissemination strategy will be aimed towards five groups of stakeholders: (1) academia, (2) policy, (3) guidelines and regulation, (4) industry and (5) public and non-specific stakeholders. We anticipate that dissemination will take place in Q3 of 2021.

Project description:ImportanceA high proportion of suspicious pigmented skin lesions referred for investigation are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the patient pathway are needed to reduce the pressure on secondary care and pathology services.ObjectiveTo determine the accuracy of an artificial intelligence algorithm in identifying melanoma in dermoscopic images of lesions taken with smartphone and digital single-lens reflex (DSLR) cameras.Design, setting, and participantsThis prospective, multicenter, single-arm, masked diagnostic trial took place in dermatology and plastic surgery clinics in 7 UK hospitals. Dermoscopic images of suspicious and control skin lesions from 514 patients with at least 1 suspicious pigmented skin lesion scheduled for biopsy were captured on 3 different cameras. Data were collected from January 2017 to July 2018. Clinicians and the Deep Ensemble for Recognition of Malignancy, a deterministic artificial intelligence algorithm trained to identify melanoma in dermoscopic images of pigmented skin lesions using deep learning techniques, assessed the likelihood of melanoma. Initial data analysis was conducted in September 2018; further analysis was conducted from February 2019 to August 2019.InterventionsClinician and algorithmic assessment of melanoma.Main outcomes and measuresArea under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of the algorithmic and specialist assessment, determined using histopathology diagnosis as the criterion standard.ResultsThe study population of 514 patients included 279 women (55.7%) and 484 white patients (96.8%), with a mean (SD) age of 52.1 (18.6) years. A total of 1550 images of skin lesions were included in the analysis (551 [35.6%] biopsied lesions; 999 [64.4%] control lesions); 286 images (18.6%) were used to train the algorithm, and a further 849 (54.8%) images were missing or unsuitable for analysis. Of the biopsied lesions that were assessed by the algorithm and specialists, 125 (22.7%) were diagnosed as melanoma. Of these, 77 (16.7%) were used for the primary analysis. The algorithm achieved an AUROC of 90.1% (95% CI, 86.3%-94.0%) for biopsied lesions and 95.8% (95% CI, 94.1%-97.6%) for all lesions using iPhone 6s images; an AUROC of 85.8% (95% CI, 81.0%-90.7%) for biopsied lesions and 93.8% (95% CI, 91.4%-96.2%) for all lesions using Galaxy S6 images; and an AUROC of 86.9% (95% CI, 80.8%-93.0%) for biopsied lesions and 91.8% (95% CI, 87.5%-96.1%) for all lesions using DSLR camera images. At 100% sensitivity, the algorithm achieved a specificity of 64.8% with iPhone 6s images. Specialists achieved an AUROC of 77.8% (95% CI, 72.5%-81.9%) and a specificity of 69.9%.Conclusions and relevanceIn this study, the algorithm demonstrated an ability to identify melanoma from dermoscopic images of selected lesions with an accuracy similar to that of specialists.

Project description:ObjectiveHighly sensitive guaiac-based faecal occult blood (Hemoccult SENSA) and Helicobacter pylori stool antigen testing might help detect upper gastrointestinal lesions when appended to a colorectal cancer screening programme with faecal immunochemical testing. We evaluated the diagnostic accuracies of two stool tests in detecting upper gastrointestinal lesions.DesignCross-sectional design.SettingHospital-based and community-based screening settings.ParticipantsA hospital-based deviation cohort of 3172 participants to evaluate test performance and a community-based validation cohort of 3621 to verify the findings.InterventionsThree types of stool tests with bidirectional endoscopy as the reference standard.OutcomesSensitivity, specificity and positive and negative likelihood ratios.ResultsFor detecting upper gastrointestinal lesions in cases with negative immunochemical tests, the sensitivity, specificity, and positive and negative likelihood ratios of the guaiac-based and H pylori antigen tests were 16.3% (95% CI 13.3% to 19.8%), 90.1% (88.9% to 91.2%), 1.64 (1.31 to 2.07), and 0.93 (0.89 to 0.97), respectively, and 52.5% (48.1% to 56.9%), 80.6% (79.0% to 82.1%), 2.71 (2.41 to 3.04) and 0.59 (0.54 to 0.65), respectively. For detecting upper gastrointestinal lesions in cases with normal colonoscopy, the results of the guaiac-based and H pylori antigen tests were 17.9% (14.8% to 21.5%), 90.1% (88.9% to 91.2%), 1.81 (1.45 to 2.26) and 0.91 (0.87 to 0.95), respectively, and 53.1% (48.6% to 57.4%), 80.7% (79.1% to 82.2%), 2.75 (2.45 to 3.08) and 0.58 (0.53 to 0.64), respectively. Within the community, positive predictive values of the immunochemical and H pylori antigen tests were 36.0% (26.0% to 46.0%) and 31.9% (28.3% to 35.5%), respectively, for detecting lower and upper gastrointestinal lesions, which were similar to expected values.ConclusionsThe H pylori stool antigen test is more accurate than the guaiac-based test in the screening of upper gastrointestinal lesions in a population with high prevalence of H pylori infection and upper gastrointestinal lesions. It is applicable to add the H pylori antigen test to the immunochemical test for pan detection.Trial registrationNCT01341197 (ClinicalTrial.gov).

Dataset Information

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

Publications

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

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