دورية أكاديمية
Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy
| العنوان: | Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy |
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| المؤلفون: | Yuta Kumazu, Nao Kobayashi, Naoki Kitamura, Elleuch Rayan, Paul Neculoiu, Toshihiro Misumi, Yudai Hojo, Tatsuro Nakamura, Tsutomu Kumamoto, Yasunori Kurahashi, Yoshinori Ishida, Munetaka Masuda, Hisashi Shinohara |
| المصدر: | Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
| بيانات النشر: | Nature Portfolio, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Abstract The prediction of anatomical structures within the surgical field by artificial intelligence (AI) is expected to support surgeons’ experience and cognitive skills. We aimed to develop a deep-learning model to automatically segment loose connective tissue fibers (LCTFs) that define a safe dissection plane. The annotation was performed on video frames capturing a robot-assisted gastrectomy performed by trained surgeons. A deep-learning model based on U-net was developed to output segmentation results. Twenty randomly sampled frames were provided to evaluate model performance by comparing Recall and F1/Dice scores with a ground truth and with a two-item questionnaire on sensitivity and misrecognition that was completed by 20 surgeons. The model produced high Recall scores (mean 0.606, maximum 0.861). Mean F1/Dice scores reached 0.549 (range 0.335–0.691), showing acceptable spatial overlap of the objects. Surgeon evaluators gave a mean sensitivity score of 3.52 (with 88.0% assigning the highest score of 4; range 2.45–3.95). The mean misrecognition score was a low 0.14 (range 0–0.7), indicating very few acknowledged over-detection failures. Thus, AI can be trained to predict fine, difficult-to-discern anatomical structures at a level convincing to expert surgeons. This technology may help reduce adverse events by determining safe dissection planes. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-021-00557-3 |
| URL الوصول: | https://doaj.org/article/c7e960f6df7145dda92b6b75c1be79d4 |
| رقم الأكسشن: | edsdoj.7e960f6df7145dda92b6b75c1be79d4 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-021-00557-3 |