Deep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validation

Chang Y.-YPAI-CHI LIRUEY-FENG CHANGYao C.-DChen Y.-YChang W.-Y2022-04-252022-04-25202109302794https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116035947&doi=10.1007%2fs00464-021-08698-2&partnerID=40&md5=00e2f16e503bc54d71e92628e4c40966https://scholars.lib.ntu.edu.tw/handle/123456789/607454Background: 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.Artificial intelligenceDeep learningEndoscopy anatomyadultanatomical locationarticleartificial intelligencecontrolled studyconvolutional neural networkdeep learningesophagogastroduodenoscopyfemalehumanmalemulticlass classificationquality controlretrospective studyupper gastrointestinal tractDeep learning-based endoscopic anatomy classification: an accelerated approach for data preparation and model validationjournal article10.1007/s00464-021-08698-2345864912-s2.0-85116035947