Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Nov 4:9:1018937.
doi: 10.3389/fmed.2022.1018937. eCollection 2022.

Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis

Om Parkash  1 , Asra Tus Saleha Siddiqui  1 , Uswa Jiwani  2 , Fahad Rind  3 , Zahra Ali Padhani  4 , Arjumand Rizvi  2 , Zahra Hoodbhoy  5 , Jai K Das  4   5
Affiliations

Diagnostic accuracy of artificial intelligence for detecting gastrointestinal luminal pathologies: A systematic review and meta-analysis

Om Parkash et al. Front Med (Lausanne). .

Abstract

Background: Artificial Intelligence (AI) holds considerable promise for diagnostics in the field of gastroenterology. This systematic review and meta-analysis aims to assess the diagnostic accuracy of AI models compared with the gold standard of experts and histopathology for the diagnosis of various gastrointestinal (GI) luminal pathologies including polyps, neoplasms, and inflammatory bowel disease.

Methods: We searched PubMed, CINAHL, Wiley Cochrane Library, and Web of Science electronic databases to identify studies assessing the diagnostic performance of AI models for GI luminal pathologies. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. We performed a meta-analysis and hierarchical summary receiver operating characteristic curves (HSROC). The risk of bias was assessed using Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool. Subgroup analyses were conducted based on the type of GI luminal disease, AI model, reference standard, and type of data used for analysis. This study is registered with PROSPERO (CRD42021288360).

Findings: We included 73 studies, of which 31 were externally validated and provided sufficient information for inclusion in the meta-analysis. The overall sensitivity of AI for detecting GI luminal pathologies was 91.9% (95% CI: 89.0-94.1) and specificity was 91.7% (95% CI: 87.4-94.7). Deep learning models (sensitivity: 89.8%, specificity: 91.9%) and ensemble methods (sensitivity: 95.4%, specificity: 90.9%) were the most commonly used models in the included studies. Majority of studies (n = 56, 76.7%) had a high risk of selection bias while 74% (n = 54) studies were low risk on reference standard and 67% (n = 49) were low risk for flow and timing bias.

Interpretation: The review suggests high sensitivity and specificity of AI models for the detection of GI luminal pathologies. There is a need for large, multi-center trials in both high income countries and low- and middle- income countries to assess the performance of these AI models in real clinical settings and its impact on diagnosis and prognosis.

Systematic review registration: [https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=288360], identifier [CRD42021288360].

Keywords: artificial intelligence; diagnostic accuracy; gastroenterology; pathologies; systematic review.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Model classification. Source: Brownlee (104).
FIGURE 2
FIGURE 2
Study selection.
FIGURE 3
FIGURE 3
Risk of bias in the included studies.
FIGURE 4
FIGURE 4
Hierarchical summary ROC curve for externally validated studies.
FIGURE 5
FIGURE 5
Hierarchical summary ROC curve for detecting GI luminal diseases. (A) GI polyps and (B) GI neoplasms.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. IHME,. Digestive Diseases — Level 2 Cause. (2019). Available online at: https://www.healthdata.org/results/gbd_summaries/2019/digestive-diseases... (accessed June 30, 2022).
    1. Peery AF, Crockett SD, Murphy CC, Jensen ET, Kim HP, Egberg MD, et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: update 2021. Gastroenterology. (2022) 162:621–44. 10.1053/j.gastro.2021.10.017 - DOI - PMC - PubMed
    1. Pourhoseingholi MA, Vahedi M, Baghestani AR. Burden of gastrointestinal cancer in Asia; an overview. Gastroenterol Hepatol Bed Bench. (2015) 8:19–27. - PMC - PubMed
    1. Chuks NS. Challenges of Gastrointestinal Endoscopy in Resource-Poor Countries. In: Pascu O. editor. Gastrointestinal Endoscopy. Rijeka: IntechOpen; (2011).
    1. Maida M, Alrubaiy L, Bokun T, Bruns T, Castro V, China L, et al. Current challenges and future needs of clinical and endoscopic training in gastroenterology: a European survey. Endosc Int Open. (2020) 8:E525–33. 10.1055/a-1093-0877 - DOI - PMC - PubMed

Publication types

LinkOut - more resources