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.

Keywords: Artificial Intelligence; convolutional neural network; early esophageal cancer; endoscopic; meta-analysis.

Figure 1

Flowchart summarizing the study selection process

Figure 2

Forest plots of the sensitivity and specificity of CNN-based AI based on endoscopic images for the diagnosis of early EC. The dots correspond to the individual studies included in this analysis, and both sides of the line represent the 95% CI. The narrower the line is, the greater the accuracy of the study and the greater the weight. The diamond corresponds to the pooled result. The intermediate vertical line represents an invalid line. Q statistic test card square value (Chi-square), degree of freedom (df), P values, and I² statistic test results (inconsistency [I²]) correspond to heterogeneity test results. The Q test was used to assess heterogeneity, while the I² test was used to measure the size of heterogeneity. Heterogeneity was considered when P was less than 0.01. If I² was <25%, no heterogeneity was noted. If the value of I² was between 25% and 50%, the degree of heterogeneity was considered to be small. If the value of I² was between 50% and 75%, heterogeneity was noted. If I² was >75%, large heterogeneity was noted. AI = artificial intelligence, CI = confidence interval, CNN = Convolutional neural network, EC = esophageal cancer, FN = false negatives, FP = false positives, TN = true negatives, TP = true positives

Figure 3

Hierarchical summary of SROC plots of CNN-based AI for the diagnosis of early EC. The ellipse represents 95% CI for this estimate. Numbers correspond to the sensitivity and specificity of individual studies included in this analysis. AI = artificial intelligence, CI = confidence interval, CNN = Convolutional neural network, EC = esophageal cancer