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Everything you need to know about Computed Tomography (CT) & CT Scanning

Stomach: Ai and the Stomach Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Stomach ❯ AI and the Stomach

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  • Background: Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.
    Methods: A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN.
    Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
    Toshiaki Hirasawa et al.
    Gastric Cancer (2018) 21:653–660
  • Results: The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface.
    Conclusion: The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.
    Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
    Toshiaki Hirasawa et al.
    Gastric Cancer (2018) 21:653–660
  • “In conclusion, we developed a CNN system for detecting gastric cancer using stored endoscopic images, which processed extensive independent images in a very short time. The clinically relevant diagnostic ability of the CNN offers a promising applicability to daily clinical practice for reducing the burden of endoscopists as well as telemedicine in remote and rural areas as well as in developing countries where the number of endoscopists is limited.”
    Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
    Toshiaki Hirasawa et al.
    Gastric Cancer (2018) 21:653–660
© 1999-2021 Elliot K. Fishman, MD, FACR. All rights reserved.