# Real-Time Multi-Label Upper Gastrointestinal Anatomy Recognition from Gastroscope Videos
## 5 Conclusions  
In this paper, we propose a novel and effective recursive convolutional neural network, GL-Net, for automatic recognition of the anatomical location of the stomach in EGD videos. GL-Net consists of two partial structures, namely GCN and LSTM, which are used to extract label-dependent and time-dependent features, respectively. The GCN part of our method is able to extract the label dependency of multi-label image recognition, compared to the currently related study of static image based recognition methods for single-label multi-class anatomical location recognition. Meanwhile, the spatial-temporal features extracted by the LSTM part are able to identify adjacent similar frames more accurately. In addition, we designed a real-time system based on the GL-Net method to automatically monitor detailed metrics during EGD (e.g., anatomical examination coverage, effective observation frame statistics, observation statistics of each anatomical site, etc.) and perform statistical analysis on the quality of EGD examinations. A quantitative assessment of the quality of the endoscopist's examination is performed to demonstrate the professional operating habits of the endoscopist and the presence of potential accidents and problems. It also demonstrates the feasibility of implementing endoscopic quality control guidelines using artificial intelligence technology. It can effectively mitigate the subjective and empirical differences among endoscopists, improve the quality of routine endoscopy, and provide a reference for writing endoscopy reports and performing clinical procedures in real time with anatomical positions. In the future, the combination of anatomical position identification results and endoscopic mucosal health condition for comprehensive analysis is expected to further improve the quality control of computer-assisted endoscopy and assist in lesion diagnosis. We believe that computer-aided detection and artificial intelligence techniques will play an increasing role. The rapid changes in model structure in recent years have allowed us to use increasingly advanced approaches to clinical data. However, the characteristics of the data distribution should be considered more in studies, such as the multi-label classification in this paper, which is more clinically realistic than single-label classification, and the potential associations within clinical prior knowledge and tasks, such as the construction of inter-label associations with the spatio-temporal association in this paper. Incorporating researchers' or clinicians' prior knowledge into the model training process is a more specific, accurate, and reliable solution to obtain practical solutions. We believe that in the future development of deep learning medical imaging research work, AI technology and medical knowledge will be further integrated to obtain further technical breakthroughs, as well as playing a greater role in the clinic and being more easily accepted by the public. Conceptualization, T.Y. and H.H. ; methodology, T.Y. and H.H. ; software, X.Z. ; validation, H.L. ; formal analysis, T.Y. ; investigation, J.L. ; resources, J.L. ; data curation, W.H. ; writing--original draft preparation, T.Y. and H.H. ; writing--review and editing, J.L. ; visualization, X.Z. ; supervision, H.D. and J.S. ; project administration, J.L. ; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Key Research and Development Program of Zhejiang, China (No.2021C03111) and the National Natural Science Foundation of China (No.81827804). Ethical review and approval were waived for this study, due to the retrospective design of the study and the fact that all data used were from existing and anonymized clinical datasets. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper. **Data Availability Statement:** Not applicable.