Publications by authors named "Keita Otani"
Article Synopsis
- Researchers improved an AI model called RetinaNet to enhance the diagnosis of various lesions in wireless capsule endoscopy images, targeting ulcers, vascular lesions, and tumors.
- The AI was trained on a substantial dataset from 1234 patients, using over 14 million images to ensure accurate lesion detection.
- Evaluation results showed excellent performance metrics, with mean AUC values above 0.997 and high sensitivity and specificity for accurately identifying different types of small bowel lesions in clinical practice.
Background: Although colorectal neoplasms are the most common abnormalities found in colon capsule endoscopy (CCE), no computer-aided detection method is yet available. We developed an artificial intelligence (AI) system that uses deep learning to automatically detect such lesions in CCE images.
Methods: We trained a deep convolutional neural network system based on a Single Shot MultiBox Detector using 15 933 CCE images of colorectal neoplasms, such as polyps and cancers.
BACKGROUND : Previous computer-aided detection systems for diagnosing lesions in images from wireless capsule endoscopy (WCE) have been limited to a single type of small-bowel lesion. We developed a new artificial intelligence (AI) system able to diagnose multiple types of lesions, including erosions and ulcers, vascular lesions, and tumors. METHODS : We trained the deep neural network system RetinaNet on a data set of 167 patients, which consisted of images of 398 erosions and ulcers, 538 vascular lesions, 4590 tumors, and 34 437 normal tissues.
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