Esophagogastroduodenoscopy (EGD) has been widely applied for gastrointestinal (GI) examinations. However, there is a lack of mature technology to evaluate the quality of the EGD inspection process. In this Letter, the authors design a multi-task anatomy detection convolutional neural network (MT-AD-CNN) to evaluate the EGD inspection quality by combining the detection task of the upper digestive tract with ten anatomical structures and the classification task of informative video frames. The authors' model is able to eliminate non-informative frames of the gastroscopic videos and detect the anatomies in real time. Specifically, a sub-branch is added to the detection network to classify NBI images, informative and non-informative images. By doing so, the detected box will be only displayed on the informative frames, which can reduce the false-positive rate. They can determine the video frames on which each anatomical location is effectively examined, so that they can analyse the diagnosis quality. Their method reaches the performance of 93.74% mean average precision for the detection task and 98.77% accuracy for the classification task. Their model can reflect the detailed circumstance of the gastroscopy examination process, which shows application potential in improving the quality of examinations.
Keywords: EGD inspection process; EGD inspection quality; MT-AD-CNN; anatomies; authors design; biological organs; biomedical optical imaging; classification task; detected box; detection network; diagnosis quality; endoscopes; gastrointestinal examinations; gastroscopic videos; gastroscopy examination process; image classification; informative frames; informative video frames; inspection; learning (artificial intelligence); medical image processing; multitask anatomy detection convolutional neural network; multitask convolutional neural networks; neural nets; noninformative frames; noninformative images; patient diagnosis; upper digestive tract; upper gastrointestinal anatomy detection.