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Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy
- Source :
- Gastrointestinal endoscopy. 93(1)
- Publication Year :
- 2020
-
Abstract
- Background and Aims Magnetically controlled capsule endoscopy (MCE) has become an efficient diagnostic modality for gastric diseases. We developed a novel automatic gastric lesion detection system to assist in diagnosis and reduce inter-physician variations. This study aimed to evaluate the diagnostic capability of the computer-aided detection system for MCE images. Methods We developed a novel automatic gastric lesion detection system based on a convolutional neural network (CNN) and faster region-based convolutional neural network (RCNN). A total of 1,023,955 MCE images from 797 patients were used to train and test the system. These images were divided into 7 categories (erosion, polyp, ulcer, submucosal tumor, xanthoma, normal mucosa, and invalid images). The primary endpoint was the sensitivity of the system. Results The system detected gastric focal lesions with 96.2% sensitivity (95% confidence interval [CI], 95.7%-96.5%), 76.2% specificity (95% CI, 75.97%-76.3%), 16.0% positive predictive value (95% CI, 15.7%-16.3%), 99.7% negative predictive value (95% CI, 99.74%-99.79%), and 77.1% accuracy (95% CI, 76.9%-77.3%) (sensitivity was 99.3% for erosions; 96.5% for polyps; 89.3% for ulcers; 87.2% for submucosal tumors; 90.6% for xanthomas; 67.8% for normal; and 96.1% for invalid images). Analysis of the receiver operating characteristic curve showed that the area under the curve for all positive images was 0.84. Image processing time was 44 milliseconds per image for the system and 0.38 ± 0.29 seconds per image for clinicians (P Conclusions The CNN faster-RCNN-based diagnostic program system showed good performance in diagnosing gastric focal lesions in MCE images.
- Subjects :
- Stomach Diseases
Capsule Endoscopy
law.invention
03 medical and health sciences
0302 clinical medicine
Capsule endoscopy
law
Artificial Intelligence
Medicine
Humans
Radiology, Nuclear Medicine and imaging
Receiver operating characteristic
business.industry
Submucosal tumor
Gastroenterology
Area under the curve
Gastric lesions
Confidence interval
ROC Curve
Computer-aided diagnosis
030220 oncology & carcinogenesis
Diagnostic program
030211 gastroenterology & hepatology
Neural Networks, Computer
business
Nuclear medicine
Subjects
Details
- ISSN :
- 10976779
- Volume :
- 93
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Gastrointestinal endoscopy
- Accession number :
- edsair.doi.dedup.....86b8436f5e82435173137df56c749cdb