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Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis.

ABSTRACT: Background and study aims? Recently, a growing body of evidence has been amassed on evaluation of artificial intelligence (AI) known as deep learning in computer-aided diagnosis of gastrointestinal lesions by means of convolutional neural networks (CNN). We conducted this meta-analysis to study pooled rates of performance for CNN-based AI in diagnosis of gastrointestinal neoplasia from endoscopic images. Methods? Multiple databases were searched (from inception to November 2019) and studies that reported on the performance of AI by means of CNN in the diagnosis of gastrointestinal tumors were selected. A random effects model was used and pooled accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Pooled rates were categorized based on the gastrointestinal location of lesion (esophagus, stomach and colorectum). Results? Nineteen studies were included in our final analysis. The pooled accuracy of CNN in esophageal neoplasia was 87.2?% (76-93.6) and NPV was 92.1?% (85.9-95.7); the accuracy in lesions of stomach was 85.8?% (79.8-90.3) and NPV was 92.1?% (85.9-95.7); and in colorectal neoplasia the accuracy was 89.9?% (82-94.7) and NPV was 94.3?% (86.4-97.7). Conclusions? Based on our meta-analysis, CNN-based AI achieved high accuracy in diagnosis of lesions in esophagus, stomach, and colorectum.

SUBMITTER: Mohan BP

PROVIDER: S-EPMC7581460 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis.

Mohan Babu P BP Khan Shahab R SR Kassab Lena L LL Ponnada Suresh S Dulai Parambir S PS Kochhar Gursimran S GS

Endoscopy international open 20201022 11

<b>Background and study aims </b> Recently, a growing body of evidence has been amassed on evaluation of artificial intelligence (AI) known as deep learning in computer-aided diagnosis of gastrointestinal lesions by means of convolutional neural networks (CNN). We conducted this meta-analysis to study pooled rates of performance for CNN-based AI in diagnosis of gastrointestinal neoplasia from endoscopic images. <b>Methods </b> Multiple databases were searched (from inception to November 2019) an ...[more]

PMID: 33140014

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Dataset Information

Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis.

Publications

Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis.

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