https://scholars.lib.ntu.edu.tw/handle/123456789/607454
標題: | Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation | 作者: | Chang Y.-Y PAI-CHI LI RUEY-FENG CHANG Yao C.-D Chen Y.-Y Chang W.-Y |
關鍵字: | Artificial intelligence;Deep learning;Endoscopy anatomy;adult;anatomical location;article;artificial intelligence;controlled study;convolutional neural network;deep learning;esophagogastroduodenoscopy;female;human;male;multiclass classification;quality control;retrospective study;upper gastrointestinal tract | 公開日期: | 2021 | 來源出版物: | Surgical Endoscopy | 摘要: | Background: Photodocumentation during endoscopy procedures is one of the indicators for endoscopy performance quality; however, this indicator is difficult to measure and audit in the endoscopy unit. Emerging artificial intelligence technology may solve this problem, which requires a large amount of material for model development. We developed a deep learning-based endoscopic anatomy classification system through convolutional neural networks with an accelerated data preparation approach. Patients and methods: We retrospectively collected 8,041 images from esophagogastroduodenoscopy (EGD) procedures and labeled them using two experts for nine anatomical locations of the upper gastrointestinal tract. A base model for EGD image multiclass classification was first developed, and an additional 6,091 images were enrolled and classified by the base model. A total of 5,963 images were manually confirmed and added to develop the subsequent enhanced model. Additional internal and external endoscopy image datasets were used to test the model performance. Results: The base model achieved total accuracy of 96.29%. For the enhanced model, the total accuracy was 96.64%. The overall accuracy improved with the enhanced model compared with the base model for the internal test dataset without narrowband images (93.05% vs. 91.25%, p < 0.01) or with narrowband images (92.74% vs. 90.46%, p < 0.01). The total accuracy was 92.56% of the enhanced model on the external test dataset. Conclusions: We constructed a deep learning-based model with an accelerated approach that can be used for quality control in endoscopy units. The model was also validated with both internal and external datasets with high accuracy. ? 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116035947&doi=10.1007%2fs00464-021-08698-2&partnerID=40&md5=00e2f16e503bc54d71e92628e4c40966 https://scholars.lib.ntu.edu.tw/handle/123456789/607454 |
ISSN: | 09302794 | DOI: | 10.1007/s00464-021-08698-2 |
顯示於: | 資訊工程學系 |
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