Related Experiment Video For Artificial Intelligence
Updated: Sep 4, 2025
Diagnosis of Neoplasia in Barrett’s Esophagus using Vital-dye Enhanced Fluorescence Imaging
Published on: May 11, 2014
Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on
Hongbiao Ma1, Longlun Wang2, Yinlin Chen1
1Department of Thoracic Surgery, Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
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.
