Dataset Information

Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.

ABSTRACT:

Background

Early screening and treatment of esophageal cancer (EC) is particularly important for the survival and prognosis of patients. However, early EC is difficult to diagnose by a routine endoscopic examination. Therefore, convolutional neural network (CNN)-based artificial intelligence (AI) has become a very promising method in the diagnosis of early EC using endoscopic images. The aim of this study was to evaluate the diagnostic performance of CNN-based AI for detecting early EC based on endoscopic images.

Methods

A comprehensive search was performed to identify relevant English articles concerning CNN-based AI in the diagnosis of early EC based on endoscopic images (from the date of database establishment to April 2022). The pooled sensitivity (SEN), pooled specificity (SPE), positive likelihood ratio (LR+), negative likelihood ratio (LR-), diagnostic odds ratio (DOR) with 95% confidence interval (CI), summary receiver operating characteristic (SROC) curve, and area under the curve (AUC) for the accuracy of CNN-based AI in the diagnosis of early EC based on endoscopic images were calculated. We used the I² test to assess heterogeneity and investigated the source of heterogeneity by performing meta-regression analysis. Publication bias was assessed using Deeks' funnel plot asymmetry test.

Results

Seven studies met the eligibility criteria. The SEN and SPE were 0.90 (95% confidence interval [CI]: 0.82-0.94) and 0.91 (95% CI: 0.79-0.96), respectively. The LR+ of the malignant ultrasonic features was 9.8 (95% CI: 3.8-24.8) and the LR- was 0.11 (95% CI: 0.06-0.21), revealing that CNN-based AI exhibited an excellent ability to confirm or exclude early EC on endoscopic images. Additionally, SROC curves showed that the AUC of the CNN-based AI in the diagnosis of early EC based on endoscopic images was 0.95 (95% CI: 0.93-0.97), demonstrating that CNN-based AI has good diagnostic value for early EC based on endoscopic images.

Conclusions

Based on our meta-analysis, CNN-based AI is an excellent diagnostic tool with high sensitivity, specificity, and AUC in the diagnosis of early EC based on endoscopic images.

SUBMITTER: Ma H

PROVIDER: S-EPMC9752541 | biostudies-literature | 2022 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.

Ma Hongbiao H Wang Longlun L Chen Yinlin Y Tian Lu L

Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology Association 20220901 5

<h4>Background</h4>Early screening and treatment of esophageal cancer (EC) is particularly important for the survival and prognosis of patients. However, early EC is difficult to diagnose by a routine endoscopic examination. Therefore, convolutional neural network (CNN)-based artificial intelligence (AI) has become a very promising method in the diagnosis of early EC using endoscopic images. The aim of this study was to evaluate the diagnostic performance of CNN-based AI for detecting early EC b ...[more]

PMID: 35848703

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Dataset Information

Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.

Background

Methods

Results

Conclusions

Publications

Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.

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