Saudi Journal of Gastroenterology
Home About us Instructions Submission Subscribe Advertise Contact Login    Print this page  Email this page Small font sizeDefault font sizeIncrease font size 
Users Online: 934 
 
SYSTEMATIC REVIEW/META ANALYSIS
Ahead of Print

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


1 Department of Thoracic Surgery, Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
2 Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China

Correspondence Address:
Lu Tian,
Department of Radiology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, 400014
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/sjg.sjg_178_22

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.


Print this article
Search
 Back
 
  Search Pubmed for
 
    -  Ma H
    -  Wang L
    -  Chen Y
    -  Tian L
 Citation Manager
 Article Access Statistics
 Reader Comments
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed449    
    PDF Downloaded6    

Recommend this journal