Development of a Deep-Learning Algorithm for Small Bowel-Lesion Detection and a Study of the Improvement in the False-Positive Rate

Naoki Hosoe, Tomofumi Horie, Anna Tojo, Hinako Sakurai, Yukie Hayashi, Kenji Jose Luis Limpias Kamiya, Tomohisa Sujino, Kaoru Takabayashi, Haruhiko Ogata, Takanori Kanai

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number3682
JournalJournal of Clinical Medicine
Volume11
Issue number13
DOIs
Publication statusPublished - 2022 Jul 1

Keywords

  • angioectasia
  • deep learning
  • obscure gastrointestinal bleeding
  • tumor
  • video-capsule endoscopy

ASJC Scopus subject areas

  • Medicine(all)

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