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  • 1
    Online Resource
    Online Resource
    Development of a Deep-Learning Algorithm for Small Bowel-Lesion Detection and a Study of the Improvement in the False-Positive Rate
    MDPI AG ; 2022
    In:  Journal of Clinical Medicine Vol. 11, No. 13 ( 2022-06-26), p. 3682-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 11, No. 13 ( 2022-06-26), p. 3682-
    Abstract: Deep learning has recently been gaining attention as a promising technology to improve the identification of lesions, and deep-learning algorithms for lesion detection have been actively developed in small-bowel capsule endoscopy (SBCE). We developed a detection algorithm for abnormal findings by deep learning (convolutional neural network) the SBCE imaging data of 30 cases with abnormal findings. To enable the detection of a wide variety of abnormal findings, the training data were balanced to include all major findings identified in SBCE (bleeding, angiodysplasia, ulceration, and neoplastic lesions). To reduce the false-positive rate, “findings that may be responsible for hemorrhage” and “findings that may require therapeutic intervention” were extracted from the images of abnormal findings and added to the training dataset. For the performance evaluation, the sensitivity and the specificity were calculated using 271 detectable findings in 35 cases. The sensitivity was calculated using 68,494 images of non-abnormal findings. The sensitivity and specificity were 93.4% and 97.8%, respectively. The average number of images detected by the algorithm as having abnormal findings was 7514. We developed an image-reading support system using deep learning for SBCE and obtained a good detection performance.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm11133682
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662592-1
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  • 2
    Online Resource
    Online Resource
    The Entire Intestinal Tract Surveillance Using Capsule Endoscopy after Immune Checkpoint Inhibitor Administration: A Prospective Observational Study
    MDPI AG ; 2021
    In:  Diagnostics Vol. 11, No. 3 ( 2021-03-18), p. 543-
    Details
    In: Diagnostics, MDPI AG, Vol. 11, No. 3 ( 2021-03-18), p. 543-
    Abstract: Background: Despite the proven efficacy of immune checkpoint inhibitors (ICIs) against various types of malignancies, they have been found to induce immune-related adverse events, such as enterocolitis; however, the clinical features of ICI-induced enterocolitis remain to be sufficiently elucidated, which is significant, considering the importance of early detection in the appropriate management and treatment of ICI-induced enterocolitis. Therefore, the current study aimed to determine the utility of capsule endoscopy as a screening tool for ICI-induced enterocolitis. Methods: This single-center, prospective, observational study was conducted on patients with malignancy who received any ICI between April 2016 and July 2020 at Keio University Hospital. Next, second-generation capsule endoscopy (CCE-2) was performed on day 60 after ICI initiation to explore the entire gastrointestinal tract. Results: Among the 30 patients enrolled herein, 23 underwent CCE-2. Accordingly, a total of 23 findings were observed in 14 (60.8%) patients at any portion of the gastrointestinal tract (7 patients in the colon, 4 patients in the small intestine, 2 patients in both the colon and the small intestine, and 1 patient in the stomach). After capsule endoscopy, 2 patients (8.7%) developed ICI-induced enterocolitis: both had significantly higher Capsule Scoring of Ulcerative Colitis than those who had not developed ICI-induced enterocolitis (p = 0.0455). No adverse events related to CCE-2 were observed. Conclusions: CCE-2 might be a safe and useful entire intestinal tract screening method for the early detection of ICI-induced enterocolitis in patients with malignancies.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics11030543
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662336-5
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