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Use of artificial intelligence for detection of gastric lesions by magnetically controlled capsule endoscopy

  • Author Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.
    Ji Xia
    Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
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  • Author Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.
    Tian Xia
    Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.
    Jun Pan
    Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.
    Fei Gao
    Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.
    Affiliations
    Beijing Medicinovo Technology Co. Ltd., Beijing, China
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  • Shuang Wang
    Affiliations
    Beijing Medicinovo Technology Co. Ltd., Beijing, China
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  • Yang-Yang Qian
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
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  • Heng Wang
    Affiliations
    Beijing Medicinovo Technology Co. Ltd., Beijing, China
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  • Jie Zhao
    Affiliations
    Beijing Medicinovo Technology Co. Ltd., Beijing, China
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  • Xi Jiang
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
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  • Wen-Bin Zou
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
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  • Yuan-Chen Wang
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
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  • Wei Zhou
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
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  • Zhao-Shen Li
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
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  • Zhuan Liao
    Correspondence
    Reprint requests: Zhuan Liao and Zhao-Shen Li, National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, 168 Changhai Road, Shanghai 200433, China.
    Affiliations
    National Clinical Research Center for Digestive Diseases, Department of Gastroenterology, Changhai Hospital, Second Military Medical University, Shanghai, China
    Search for articles by this author
  • Author Footnotes
    ∗ Drs J. Xia, T. Xia, Pan, and Gao contributed equally to this article.

      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 < .001). The kappa value of 2 times repeated reads was 1.

      Conclusions

      The CNN faster-RCNN-based diagnostic program system showed good performance in diagnosing gastric focal lesions in MCE images.

      Abbreviations:

      AI (artificial intelligence), AUC (area under curve), CAD (computer-aided diagnosis), CI (confidence intervals), CNN (convolutional neural networks), MCE (magnetically controlled capsule endoscopy), NPV (negative predictive value), PPV (positive predictive value), RCNN (region-based convolutional neural network)
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