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
Meta-Analysis
. 2022 Mar;55(5):528-540.
doi: 10.1111/apt.16778. Epub 2022 Jan 30.

Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases

Pierfrancesco Visaggi  1 , Brigida Barberio  2 , Dario Gregori  3 , Danila Azzolina  3   4 , Matteo Martinato  3 , Cesare Hassan  5 , Prateek Sharma  6 , Edoardo Savarino  2 , Nicola de Bortoli  1
Affiliations
Meta-Analysis

Systematic review with meta-analysis: artificial intelligence in the diagnosis of oesophageal diseases

Pierfrancesco Visaggi et al. Aliment Pharmacol Ther. 2022 Mar.

Erratum in

  • Corrigendum.
    [No authors listed] [No authors listed] Aliment Pharmacol Ther. 2022 Sep;56(5):928. doi: 10.1111/apt.17166. Aliment Pharmacol Ther. 2022. PMID: 35934865 Free PMC article. No abstract available.

Abstract

Background: Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs).

Aim: We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD.

Methods: We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated.

Results: For the diagnosis of Barrett's neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models' performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively.

Conclusions: AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.

Keywords: Barrett’s oesophagus; IPCL; artificial intelligence; gastroesophageal reflux disease; gastrointestinal endoscopy; oesophageal cancer.

PubMed Disclaimer

Figures

FIGURE 1
FIGURE 1
Quality in methodology of included studies
FIGURE 2
FIGURE 2
Flow diagram of assessment of studies identified in the meta‐analysis
FIGURE 3
FIGURE 3
Performance of artificial intelligence in the diagnosis of Barrett's neoplasia
FIGURE 4
FIGURE 4
Performance of artificial intelligence in the diagnosis of oesophageal squamous cell carcinoma
See this image and copyright information in PMC

References

    1. Le Berre C, Sandborn WJ, Aridhi S, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology. 2020;158:76‐94.e2. - PubMed
    1. Ebigbo A, Palm C, Probst A, et al. A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology. Endosc Int Open. 2019;7:E1616‐E1623. - PMC - PubMed
    1. Sana MK, Hussain ZM, Shah PA, Maqsood MH. Artificial intelligence in celiac disease. Comput Biol Med. 2020;125:103996. - PubMed
    1. Hassan C, Spadaccini M, Iannone A, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta‐analysis. Gastrointest Endosc. 2021;93:77‐85.e6. - PubMed
    1. Barua I, Vinsard DG, Jodal HC, et al. Artificial intelligence for polyp detection during colonoscopy: a systematic review and meta‐analysis. Endoscopy. 2021;53:277‐284. - PubMed