The effect of incorporating domain knowledge with deep learning in identifying benign and malignant gastric whitish lesions: A retrospective study.

BACKGROUND AND AIM: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the effect of adding domain knowledge in model construction. METHODS: We collected 4558 images from two institutions to train and test models. We first developed two sole DL models (1 and 2) using supervised and semi-supervised algorithms. Then we selected diagnosis-related features through literature research and developed feature-extraction models to determine features including boundary, surface, roundness, depression, and location. Then predictions of the five feature-extraction models and sole DL model were combined and inputted into seven machine-learning (ML) based fitting-diagnosis models. The optimal model was selected as ENDOANGEL-WD (whitish-diagnosis) and compared with endoscopists. RESULTS: Sole DL 2 had higher sensitivity (83.12% vs 68.67%, Bonferroni adjusted P = 0.024) than sole DL 1. Adding domain knowledge, the decision tree performed best among the seven ML models, achieving higher specificity than DL 1 (84.38% vs 72.27%, Bonferroni adjusted P < 0.05) and higher accuracy than DL 2 (80.47%, Bonferroni adjusted P < 0.001) and was selected as ENDOANGEL-WD. ENDOANGEL-WD showed better accuracy compared with 10 endoscopists (75.70%, P < 0.001). CONCLUSIONS: We developed a novel system ENDOANGEL-WD combining domain knowledge and traditional DL to detect gastric whitish neoplasms. Adding domain knowledge improved the performance of traditional DL, which provided a novel solution for establishing diagnostic models for other rare diseases potentially.

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination.

BACKGROUND: Double-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE. DESIGN AND METHODS: A total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus. RESULTS: For the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts. CONCLUSIONS: We established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.

Looking back, looking forward: lessons from COVID-19 communication measurement, evaluation and learning (MEL).

Problem: Communication is an integral component of an emergency response, including to the coronavirus disease (COVID-19) pandemic. Designing effective communication requires systematic measurement, evaluation and learning. Context: In the Western Pacific Region, the World Health Organization (WHO) responded to the COVID-19 pandemic by using the Communication for Health (C4H) approach. This included the development and application of a robust measurement, evaluation and learning (MEL) framework to assess the effectiveness of COVID-19 communication, and to share and apply lessons in real time to continuously strengthen the pandemic response. Action: MEL was applied during the planning, implementation and summative evaluation phases of COVID-19 communication, with evidence-based insights and recommendations continuously integrated in succeeding phases of the COVID-19 response. Lessons learned: This article captures good practices that helped WHO to implement MEL during the COVID-19 pandemic. It focuses on lessons from the evaluation process, including the importance of planning, data integration, collaboration, partnerships, piggybacking, using existing data and leveraging digital media. Discussion: Despite some limitations, the systematic application of MEL to COVID-19 communication shows its value in the planning and implementation of effective, evidence-based communication to address public health challenges. It enables the evaluation of outcomes and reflection on lessons identified to strengthen the response to the current pandemic and future emergencies.

Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy.

White light endoscopy is the most pivotal tool for detecting early gastric neoplasms. Previous artificial intelligence (AI) systems were primarily unexplainable, affecting their clinical credibility and acceptability. We aimed to develop an explainable AI named ENDOANGEL-ED (explainable diagnosis) to solve this problem. A total of 4482 images and 296 videos with focal lesions from 3279 patients from eight hospitals were used for training, validating, and testing ENDOANGEL-ED. A traditional sole deep learning (DL) model was trained using the same dataset. The performance of ENDOANGEL-ED and sole DL was evaluated on six levels: internal and external images, internal and external videos, consecutive videos, and man-machine comparison with 77 endoscopists in videos. Furthermore, a multi-reader, multi-case study was conducted to evaluate the ENDOANGEL-ED's effectiveness. A scale was used to compare the overall acceptance of endoscopists to traditional and explainable AI systems. The ENDOANGEL-ED showed high performance in the image and video tests. In man-machine comparison, the accuracy of ENDOANGEL-ED was significantly higher than that of all endoscopists in internal (81.10% vs. 70.61%, p < 0.001) and external videos (88.24% vs. 78.49%, p < 0.001). With ENDOANGEL-ED's assistance, the accuracy of endoscopists significantly improved (70.61% vs. 79.63%, p < 0.001). Compared with the traditional AI, the explainable AI increased the endoscopists' trust and acceptance (4.42 vs. 3.74, p < 0.001; 4.52 vs. 4.00, p < 0.001). In conclusion, we developed a real-time explainable AI that showed high performance, higher clinical credibility, and acceptance than traditional DL models and greatly improved the diagnostic ability of endoscopists.

Assessment of the Role of Artificial Intelligence in the Association Between Time of Day and Colonoscopy Quality.

Importance: Time of day was associated with a decline in adenoma detection during colonoscopy. Artificial intelligence (AI) systems are effective in improving the adenoma detection rate (ADR), but the performance of AI during different times of the day remains unknown. Objective: To validate whether the assistance of an AI system could overcome the time-related decline in ADR during colonoscopy. Design, Setting, and Participants: This cohort study is a secondary analysis of 2 prospective randomized controlled trials (RCT) from Renmin Hospital of Wuhan University. Consecutive patients undergoing colonoscopy were randomly assigned to either the AI-assisted group or unassisted group from June 18, 2019, to September 6, 2019, and July 1, 2020, to October 15, 2020. The ADR of early and late colonoscopy sessions per half day were compared before and after the intervention of the AI system. Data were analyzed from March to June 2022. Exposure: Conventional colonoscopy or AI-assisted colonoscopy. Main Outcomes and Measures: Adenoma detection rate. Results: A total of 1780 patients (mean [SD] age, 48.61 [13.35] years, 837 [47.02%] women) were enrolled. A total of 1041 procedures (58.48%) were performed in early sessions, with 357 randomized into the unassisted group (34.29%) and 684 into the AI group (65.71%). A total of 739 procedures (41.52%) were performed in late sessions, with 263 randomized into the unassisted group (35.59%) and 476 into the AI group (64.41%). In the unassisted group, the ADR in early sessions was significantly higher compared with that of late sessions (13.73% vs 5.70%; P = .005; OR, 2.42; 95% CI, 1.31-4.47). After the intervention of the AI system, as expected, no statistically significant difference was found (22.95% vs 22.06%, P = .78; OR, 0.96; 95% CI; 0.71-1.29). Furthermore, the AI systems showed better assistance ability on ADR in late sessions compared with early sessions (odds ratio, 3.81; 95% CI, 2.10-6.91 vs 1.60; 95% CI, 1.10-2.34). Conclusions and Relevance: In this cohort study, AI systems showed higher assistance ability in late sessions per half day, which suggests the potential to maintain high quality and homogeneity of colonoscopies and further improve endoscopist performance in large screening programs and centers with high workloads.

Impact of Artificial Intelligence on Colonoscopy Surveillance After Polyp Removal: A Pooled Analysis of Randomized Trials.

BACKGROUND AND AIMS: Artificial intelligence (AI) tools aimed at improving polyp detection have been shown to increase the adenoma detection rate during colonoscopy. However, it is unknown how increased polyp detection rates by AI affect the burden of patient surveillance after polyp removal. METHODS: We conducted a pooled analysis of 9 randomized controlled trials (5 in China, 2 in Italy, 1 in Japan, and 1 in the United States) comparing colonoscopy with or without AI detection aids. The primary outcome was the proportion of patients recommended to undergo intensive surveillance (ie, 3-year interval). We analyzed intervals for AI and non-AI colonoscopies for the U.S. and European recommendations separately. We estimated proportions by calculating relative risks using the Mantel-Haenszel method. RESULTS: A total of 5796 patients (51% male, mean 53 years of age) were included; 2894 underwent AI-assisted colonoscopy and 2902 non-AI colonoscopy. When following U.S. guidelines, the proportion of patients recommended intensive surveillance increased from 8.4% (95% CI, 7.4%-9.5%) in the non-AI group to 11.3% (95% CI, 10.2%-12.6%) in the AI group (absolute difference, 2.9% [95% CI, 1.4%-4.4%]; risk ratio, 1.35 [95% CI, 1.16-1.57]). When following European guidelines, it increased from 6.1% (95% CI, 5.3%-7.0%) to 7.4% (95% CI, 6.5%-8.4%) (absolute difference, 1.3% [95% CI, 0.01%-2.6%]; risk ratio, 1.22 [95% CI, 1.01-1.47]). CONCLUSIONS: The use of AI during colonoscopy increased the proportion of patients requiring intensive colonoscopy surveillance by approximately 35% in the United States and 20% in Europe (absolute increases of 2.9% and 1.3%, respectively). While this may contribute to improved cancer prevention, it significantly adds patient burden and healthcare costs.

The impact of enterovirus A71 vaccination program on hand, foot, and mouth disease in Guangdong, China: A longitudinal surveillance study.

Enterovirus A71 (EV71) vaccination program was introduced in 2016 in China. Based on a longitudinal surveillance dataset from 2012 to 2019 in Guangdong, China, we estimated the impact of the EV71 vaccination program on hand, foot, and mouth disease (HFMD) incidence, by using a counterfactual prediction made from synthetic control approach integrated with a Bayesian time-series model. We observed a relative reduction of 41.4% for EV71-associated HFMD cases during the post-vaccination period of 2017-2019, corresponding to 26,226 cases averted. The reduction of EV71-associated HFMD cases raised with the elevation of EV71 vaccine coverage by year. We found an indirect effect for the children aged 6-14 years who were less likely to be vaccinated. Whereas, the EV71 vaccine may not protect against non-EV71-associated HFMD. This study provides a template for ongoing public health surveillance of EV71 vaccine effectiveness with a counterfactual study design. Our results show strong evidence of the EV71 vaccination program working on reducing EV71-associated HFMD in real-world settings. The finding will benefit policy-making of EV71 vaccination and the prevention of HFMD.

Real-time use of artificial intelligence for diagnosing early gastric cancer by magnifying image-enhanced endoscopy: a multicenter diagnostic study (with videos).

BACKGROUND AND AIMS: Endoscopy is a pivotal method for detecting early gastric cancer (EGC). However, skill among endoscopists varies greatly. Here, we proposed a deep learning-based system named ENDOANGEL-ME to diagnose EGC in magnifying image-enhanced endoscopy (M-IEE). METHODS: M-IEE images were retrospectively obtained from 6 hospitals in China, including 4667 images for training and validation, 1324 images for internal tests, and 4702 images for external tests. One hundred eighty-seven stored videos from 2 hospitals were used to evaluate the performance of ENDOANGEL-ME and endoscopists and to assess the effect of ENDOANGEL-ME on improving the performance of endoscopists. Prospective consecutive patients undergoing M-IEE were enrolled from August 17, 2020 to August 2, 2021 in Renmin Hospital of Wuhan University to assess the applicability of ENDOANGEL-ME in clinical practice. RESULTS: A total of 3099 patients undergoing M-IEE were enrolled in this study. The diagnostic accuracy of ENDOANGEL-ME for diagnosing EGC was 88.44% and 90.49% in internal and external images, respectively. In 93 internal videos, ENDOANGEL-ME achieved an accuracy of 90.32% for diagnosing EGC, significantly superior to that of senior endoscopists (70.16% ± 8.78%). In 94 external videos, with the assistance of ENDOANGEL-ME, endoscopists showed improved accuracy and sensitivity (85.64% vs 80.32% and 82.03% vs 67.19%, respectively). In 194 prospective consecutive patients with 251 lesions, ENDOANGEL-ME achieved a sensitivity of 92.59% (25/27) and an accuracy of 83.67% (210/251) in real clinical practice. CONCLUSIONS: This multicenter diagnostic study showed that ENDOANGEL-ME can be well applied in the clinical setting. (Clinical trial registration number: ChiCTR2000035116.).

Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study.

BACKGROUND: Tandem colonoscopy studies have found that about one in five adenomas are missed at colonoscopy. It remains debatable whether the combination of a computer-aided polyp detection (CADe) system with a computer-aided quality improvement (CAQ) system for real-time monitoring of withdrawal speed results in additional benefits in adenoma detection or if the synergetic effect may be harmed due to excessive visual burden resulting from information overload. This study aimed to evaluate the interaction effect on improving the adenoma detection rate (ADR). METHODS: This single-center, randomized, four-group, parallel, controlled study was performed at Renmin Hospital of Wuhan University. Between 1 July and 15 October 2020, 1076 patients were randomly allocated into four treatment groups: control 271, CADe 268, CAQ 269, and CADe plus CAQ (COMBO) 268. The primary outcome was ADR. RESULTS: The ADR in the control, CADe, CAQ, and COMBO groups was 14.76 % (95 % confidence interval [CI] 10.54 to 18.98), 21.27 % (95 %CI 16.37 to 26.17), 24.54 % (95 %CI 19.39 to 29.68), and 30.60 % (95 %CI 25.08 to 36.11), respectively. The ADR was higher in the COMBO group compared with the CADe group (21.27 % vs. 30.6 %, P = 0.024, odds ratio [OR] 1.284, 95 %CI 1.033 to 1.596) but not compared with the CAQ group (24.54 % vs. 30.6 %, P = 0.213, OR 1.309, 95 %CI 0.857 to 2.000, respectively). CONCLUSIONS: CAQ significantly improved the efficacy of CADe in a four-group, parallel, controlled study. No significant difference in the ADR or polyp detection rate was found between CAQ and COMBO.

Impact of Computer-Assisted System on the Learning Curve and Quality in Esophagogastroduodenoscopy: Randomized Controlled Trial.

Background and Aims: To investigate the impact of the computer-assisted system on esophagogastroduodenoscopy (EGD) training for novice trainees in a prospective randomized controlled trial. Methods: We have constructed a computer-aided system (CAD) using retrospective images based on deep learning which could automatically monitor the 26 anatomical landmarks of the upper digestive tract and document standard photos. Six novice trainees were allocated and grouped into the CAD group and control group. Each of them took the training course, pre and post-test, and EGD examination scored by two experts. The CAD group was trained with the assistance of the CAD system and the control group without. Results: Both groups achieved great improvements in EGD skills. The CAD group received a higher examination grading score in the EGD examination (72.83 ± 16.12 vs. 67.26 ± 15.64, p = 0.039), especially in the mucosa observation (26.40 ± 6.13 vs. 24.11 ± 6.21, p = 0.020) and quality of collected images (7.29 ± 1.09 vs. 6.70 ± 1.05). The CAD showed a lower blind spot rate (2.19 ± 2.28 vs. 3.92 ± 3.30, p = 0.008) compared with the control group. Conclusion: The artificial intelligence assistant system displayed assistant capacity on standard EGD training, and assisted trainees in achieving a learning curve with high operation quality, which has great potential for application. Clinical Trial Registration: This trial is registered at https:/clinicaltrials.gov/, number NCT04682821.

Co-benefits of nonpharmaceutical intervention against COVID-19 on infectious diseases in China: A large population-based observational study.

Background: Nonpharmaceutical interventions (NPIs) are public health measures that aim to suppress the transmission of infectious diseases, including border restrictions, quarantine and isolation, community management, social distancing, face mask usage, and personal hygiene. This research aimed to assess the co-benefits of NPIs against COVID-19 on notifiable infectious diseases (NIDs) in Guangdong Province, China. Methods: Based on NID data from the Notifiable Infectious Diseases Surveillance System in Guangdong, we first compared the incidence of NIDs during the emergency response period (weeks 4-53 of 2020) with those in the same period of 2015-2019 and then compared that with the expected incidence during the synchronous period of 2020 for each city by using a Bayesian structural time series model. Findings: A total of 514,341 cases of 39 types of NIDs were reported in Guangdong during the emergency response period in 2020, which decreased by 50·7% compared with the synchronous period during 2015-2019. It was estimated that the number of 39 NIDs during the emergency response in 2020 was 65·6% (95% credible interval [CI]: 64·0% - 68·2%) lower than expected, which means that 982,356 (95% CI: 913,443 - 1,105,170) cases were averted. The largest reduction (82·1%) was found for children aged 0-14 years. For different categories of NIDs, natural focal diseases and insect-borne infectious diseases had the greatest reduction (89·4%), followed by respiratory infectious diseases (87·4%), intestinal infectious diseases (59·4%), and blood-borne and sexually transmitted infections (18·2%). Dengue, influenza, and hand-foot-and-mouth disease were reduced by 99·3%, 95·1%, and 76·2%, respectively. Larger reductions were found in the regions with developed economies and a higher number of COVID-19 cases. Interpretation: NPIs against COVID-19 may have a large co-benefit on the prevention of other infectious diseases in Guangdong, China, and the effects have heterogeneity in populations, diseases, time and space. Funding: Key-Area Research and Development Program of Guangdong Province.

The impacts of precipitation patterns on dengue epidemics in Guangzhou city.

Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67-55.50 mm per week, Pre_LMD of 3-4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control.

A Gastrointestinal Endoscopy Quality Control System Incorporated With Deep Learning Improved Endoscopist Performance in a Pretest and Post-Test Trial.

INTRODUCTION: Gastrointestinal endoscopic quality is operator-dependent. To ensure the endoscopy quality, we constructed an endoscopic audit and feedback system named Endo.Adm and evaluated its effect in a form of pretest and posttest trial. METHODS: Endo.Adm system was developed using Python and Deep Convolutional Neural Ne2rk models. Sixteen endoscopists were recruited from Renmin Hospital of Wuhan University and were randomly assigned to undergo feedback of Endo.Adm or not (8 for the feedback group and 8 for the control group). The feedback group received weekly quality report cards which were automatically generated by Endo.Adm. We then compared the adenoma detection rate (ADR) and gastric precancerous conditions detection rate between baseline and postintervention phase for endoscopists in each group to evaluate the impact of Endo.Adm feedback. In total, 1,191 colonoscopies and 3,515 gastroscopies were included for analysis. RESULTS: ADR was increased after Endo.Adm feedback (10.8%-20.3%, P < 0.01,

Propionibacterium acnes overabundance in gastric cancer promote M2 polarization of macrophages via a TLR4/PI3K/Akt signaling.

OBJECTIVE: Eradication of Helicobacter pylori (H. pylori) could not completely prevent the progression of gastric cancer (GC), suggesting that non-H. pylori bacteria may participate in the carcinogenesis of GC. The dysbiosis of microbiota in the stomach of GC has gradually been investigated, while the detailed mechanism that promotes GC in this process has not been elucidated. We aimed to identify a non-H. pylori bacteria that contribute to GC. DESIGN: GC tissues and adjacent normal tissues were collected to identify bacteria that significantly increased in GC tissues by 16S rRNA gene sequencing and fluorescence in situ hybridization (FISH) analysis. CCK8, wound healing assay, and trans-well assay were performed to analyze the tumor-promoting effect of this bacteria. Next, we detailed the mechanism for tumor-promoting effect of the bacteria by immunofluorescence, RT-qPCR, and western-blotting analysis. RESULTS: Comparing the microbial community from GC tissues and adjacent normal tissues, we found that Propionibacterium acnes (P. acnes) significantly increased in GC tissues, especially in H. pylori-negative tissues. We further found that the abundance of P. acnes correlated with TNM stages of GC patients. Interestingly, condition medium (CM) from P. acnes-primed macrophages promoted migration of GC cells, while P. acnes only could not. We next proved that P. acnes triggers M2 polarization of macrophages via TLR4/PI3K/Akt signaling. CONCLUSIONS: Together, our finding identified that P. acnes could be a possible agent for the progression of GC besides H. pylori. M2 polarization of macrophages could be promoted by P. acnes via TLR4/PI3K/Akt signaling, thus triggers the progression of GC.

Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial.

BACKGROUND: Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial. METHODS: ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting. RESULTS: 1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P < 0.001) and longer inspection time (5.40 [SD 3.82] vs. 4.38 [SD 3.91] minutes; P < 0.001). In the ENDOANGEL group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all three EGCs (one mucosal carcinoma and two high grade neoplasias) and two advanced gastric cancers, with a per-lesion accuracy of 84.7 %, sensitivity of 100 %, and specificity of 84.3 % for detecting gastric cancer. CONCLUSIONS: In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study.

BACKGROUND: The study aimed to construct an intelligent difficulty scoring and assistance system (DSAS) for endoscopic retrograde cholangiopancreatography (ERCP) treatment of common bile duct (CBD) stones. METHODS: 1954 cholangiograms were collected from three hospitals for training and testing the DSAS. The D-LinkNet34 and U-Net were adopted to segment the CBD, stones, and duodenoscope. Based on the segmentation results, the stone size, distal CBD diameter, distal CBD arm, and distal CBD angulation were estimated. The performance of segmentation and estimation was assessed by mean intersection over union (mIoU) and average relative error. A technical difficulty scoring scale, which was used for assessing the technical difficulty of CBD stone removal, was developed and validated. We also analyzed the relationship between scores evaluated by the DSAS and clinical indicators including stone clearance rate and need for endoscopic papillary large-balloon dilation (EPLBD) and lithotripsy. RESULTS: The mIoU values of the stone, CBD, and duodenoscope segmentation were 68.35 %, 86.42 %, and 95.85 %, respectively. The estimation performance of the DSAS was superior to nonexpert endoscopists. In addition, the technical difficulty scoring performance of the DSAS was more consistent with expert endoscopists than two nonexpert endoscopists. A DSAS assessment score ≥ 2 was correlated with lower stone clearance rates and more frequent EPLBD. CONCLUSIONS: An intelligent DSAS based on deep learning was developed. The DSAS could assist endoscopists by automatically scoring the technical difficulty of CBD stone extraction, and guiding the choice of therapeutic approach and appropriate accessories during ERCP.

The time-varying transmission dynamics of COVID-19 and synchronous public health interventions in China.

OBJECTIVES: We aimed to estimate the time-varying transmission dynamics of COVID-19 in China, Wuhan City, and Guangdong province, and compare to that of severe acute respiratory syndrome (SARS). METHODS: Data on COVID-19 cases in China up to 20 March 2020 was collected from epidemiological investigations or official websites. Data on SARS cases in Guangdong Province, Beijing, and Hong Kong during 2002-3 was also obtained. We estimated the doubling time, basic reproduction number (R0), and time-varying reproduction number (Rt) of COVID-19 and SARS. RESULTS: As of 20 March 2020, 80,739 locally acquired COVID-19 cases were identified in mainland China, with most cases reported between 20 January and 29 February 2020. The R0 value of COVID-19 in China and Wuhan was 5.0 and 4.8, respectively, which was greater than the R0 value of SARS in Guangdong (R0 = 2.3), Hong Kong (R0 = 2.3), and Beijing (R0 = 2.6). At the start of the COVID-19 epidemic, the Rt value in China peaked at 8.4 and then declined quickly to below 1.0 in one month. With SARS, the Rt curve saw fluctuations with more than one peak, the highest peak was lower than that for COVID-19. CONCLUSIONS: COVID-19 has much higher transmissibility than SARS, however, a series of prevention and control interventions to suppress the outbreak were effective. Sustained efforts are needed to prevent the rebound of the epidemic in the context of the global pandemic.

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.

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.