5Background: Computed tomography (CT) is the preferred imaging method for diagnosing 2019 4 6 novel coronavirus (COVID19) pneumonia. Our research aimed to construct a system based on 4 7 deep learning for detecting COVID-19 pneumonia on high resolution CT, relieve working 4 8 pressure of radiologists and contribute to the control of the epidemic. 4 9

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Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

ObjectiveEsophagogastroduodenoscopy (EGD) is the pivotal procedure in the diagnosis of upper gastrointestinal lesions. However, there are significant variations in EGD performance among endoscopists, impairing the discovery rate of gastric cancers and precursor lesions. The aim of this study was to construct a real-time quality improving system, WISENSE, to monitor blind spots, time the procedure and automatically generate photodocumentation during EGD and thus raise the quality of everyday endoscopy.DesignWISENSE system was developed using the methods of deep convolutional neural networks and deep reinforcement learning. Patients referred because of health examination, symptoms, surveillance were recruited from Renmin hospital of Wuhan University. Enrolled patients were randomly assigned to groups that underwent EGD with or without the assistance of WISENSE. The primary end point was to ascertain if there was a difference in the rate of blind spots between WISENSE-assisted group and the control group.ResultsWISENSE monitored blind spots with an accuracy of 90.40% in real EGD videos. A total of 324 patients were recruited and randomised. 153 and 150 patients were analysed in the WISENSE and control group, respectively. Blind spot rate was lower in WISENSE group compared with the control (5.86% vs 22.46%, p<0.001), and the mean difference was −15.39% (95% CI −19.23 to −11.54). There was no significant adverse event.ConclusionsWISENSE significantly reduced blind spot rate of EGD procedure and could be used to improve the quality of everyday endoscopy.Trial registration numberChiCTR1800014809; Results.

Background Gastric cancer is the third most lethal malignancy worldwide. A novel deep convolution neural network (DCNN) to perform visual tasks has been recently developed. The aim of this study was to build a system using the DCNN to detect early gastric cancer (EGC) without blind spots during esophagogastroduodenoscopy (EGD). Methods 3170 gastric cancer and 5981 benign images were collected to train the DCNN to detect EGC. A total of 24549 images from different parts of stomach were collected to train the DCNN to monitor blind spots. Class activation maps were developed to automatically cover suspicious cancerous regions. A grid model for the stomach was used to indicate the existence of blind spots in unprocessed EGD videos. Results The DCNN identified EGC from non-malignancy with an accuracy of 92.5 %, a sensitivity of 94.0 %, a specificity of 91.0 %, a positive predictive value of 91.3 %, and a negative predictive value of 93.8 %, outperforming all levels of endoscopists. In the task of classifying gastric locations into 10 or 26 parts, the DCNN achieved an accuracy of 90 % or 65.9 %, on a par with the performance of experts. In real-time unprocessed EGD videos, the DCNN achieved automated performance for detecting EGC and monitoring blind spots. Conclusions We developed a system based on a DCNN to accurately detect EGC and recognize gastric locations better than endoscopists, and proactively track suspicious cancerous lesions and monitor blind spots during EGD.

Background and study aims: Qualified esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). Our previous report showed that artificial intelligence system could monitor blind spots during EGD. Here, we updated the system to a new one (named ENDOANGEL), verified its effectiveness on improving endoscopy quality and pre-tested its performance on detecting EGC in a multi-center randomized controlled trial. Patients and methods: ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD examination in 5 hospitals were randomly assigned to ENDOANGEL-assisted (EA) group or normal control (NC) group. The primary outcome was the number of blind spots. The second outcome includes performance of ENDOANGEL on predicting EGC in clinical setting. Results: 1,050 patients were recruited and randomized. 498 and 504 patients in EA and NC groups were respectively analyzed. Compared with NC, the number of blind spots was less (5.382±4.315 vs. 9.821±4.978, p<0.001) and the inspection time was prolonged (5.400±3.821 min vs. 4.379±3.907 min, p<0.001) in EA group. In the 498 patients from EA group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all 3 EGC (1 mucosal carcinoma and 2 high-grade neoplasia) and 2 advanced gastric cancer, with a per-lesion accuracy of 84.69%, sensitivity of 100% and specificity of 84.29% for detecting GC. Conclusions: The results of the multi-center study confirmed that ENDOANGEL is an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

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