ABSTRACT
Objective:To evaluate deep learning in improving the diagnostic rate of adenomatous and non-adenomatous polyps.Methods:Non-magnifying narrow band imaging (NBI) polyp images obtained from Endoscopy Center of Renmin Hospital, Wuhan University were divided into three datasets. Dataset 1 (2 699 adenomatous and 1 846 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used for model training and validation of the diagnosis system. Dataset 2 (288 adenomatous and 210 non-adenomatous non-magnifying NBI polyp images from January 2018 to October 2020) was used to compare the accuracy of polyp classification between the system and endoscopists. At the same time, the accuracy of 4 trainees in polyp classification with and without the assistance of this system was compared. Dataset 3 (203 adenomatous and 141 non-adenomatous non-magnifying NBI polyp images from November 2020 to January 2021) was used to prospectively test the system.Results:The accuracy of the system in polyp classification was 90.16% (449/498) in dataset 2, superior to that of endoscopists. With the assistance of the system, the accuracy of colorectal polyp diagnosis was significantly improved. In the prospective study, the accuracy of the system was 89.53% (308/344).Conclusion:The colorectal polyp classification system based on deep learning can significantly improve the accuracy of trainees in polyp classification.
ABSTRACT
Objective:To establish a deep convolutional neural network (DCNN) model based on YOLO and ResNet algorithm for automatic detection of colorectal polyps and to test its function.Methods:Colonoscopy images and videos collected from the database of Digestive Endoscopy Center of Renmin Hospital of Wuhan University from January 2018 to March 2019 were divided into three databases (database 1, 3, 4). The public database CVC-ClinicDB (composed of 612 polyp images extracted from 29 colonoscopy videos provided by Barcelona Hospital, Spain) was used as the database 2. Database 1 (4 700 colonoscopy images from January 2018 to November 2018, including 3 700 intestinal polyp images and 1 000 non-polyp images) was used for establishing training and verifying the DCNN model. Database 2 (CVC-ClinicDB) and database 3 (720 colonoscopy images from January 2019 to March 2019, including 320 intestinal polyp images and 400 non-polyp images) were used for testing the DCNN model on image detection. Database 4 (15 colonoscopy videos in December 2019, containing 33 polyps) was used for testing the DCNN model on video detection. The sensitivity, specificity, accuracy and false positive rate of the DCNN model for detecting intestinal polyps were calculated.Results:The sensitivity of the DCNN model for detecting intestinal polyps in database 2 was 93.19% (602/646). In database 3, the DCNN model showed the accuracy of 95.00% (684/720), sensitivity of 98.13% (314/320), specificity of 92.50% (370/400), and false positive rate of 7.50% (30/400) for detecting intestinal polyps. In database 4, the DCNN model achieved a per-polyp-sensitivity of 100.00% (33/33), a per-image-accuracy of 96.29% (133 840/138 998), a per-image-sensitivity of 90.24% (4 066/4 506), a per-image-specificity of 96.49% (129 774/134 492), and a per-image-false positive rate of 3.51% (4 718/134 492).Conclusion:The DCNN model constructed in the study has a high sensitivity and specificity for automatic detection of colorectal polyps both in the colonoscopy images and videos, has a low false positive rate in the videos, and has the potential to assist endoscopists in diagnosis of colorectal polyps.
ABSTRACT
Objective To compare the epidemiological characteristics of COVID-19 in Guangzhou and Wenzhou, and evaluate the effectiveness of their prevention and control measures. Methods Data of COVID-19 cases reported in Guangzhou and Wenzhou as of 29 February, 2020 were collected. The incidence curves of COVID-19 in two cities were constructed. The real time reproduction number ( R t ) of COVID-19 in two cities was calculated respectively. Results A total of 346 and 465 confirmed COVID-19 cases were analysed in Guangzhou and Wenzhou, respectively. In two cities, most cases were aged 30-59 years (Guangzhou: 54.9%; Wenzhou: 70.3%). The incidence curve peaked on 27 January, 2020 in Guangzhou and on 26 January, 2020 in Wenzhou, then began to decline in both cities. The peaks of imported COVID-19 cases from Hubei occurred earlier than the peak of COVID-19 incidences in two cities, and the peak of imported cases from Hubei occurred earlier in Wenzhou than in Guangzhou. In early epidemic phase, imported cases were predominant in both cities, then the number of local cases increased and gradually took the dominance in Wenzhou. In Guangzhou, the imported cases was still predominant. Despite the different epidemic pattern, the R t and the number of COVID-19 cases declined after strict prevention and control measures were taken in Guangzhou and in Wenzhou. Conclusion The time and scale specific differences of imported COVID-19 resulted in different epidemic patterns in two cities, but the spread of the disease were effectively controlled after taking strict prevention and control measures.
ABSTRACT
Objective@#To evaluate the exported risk of novel coronavirus pneumonia (NCP) from Hubei Province and the imported risk in various provinces across China.@*Methods@#Data of reported NCP cases and Baidu Migration Indexin all provinces of the country as of February 14, 2020 were collected. The correlation analysis between cumulative number of reported cases and the migration index from Hubei was performed, and the imported risks from Hubei to different provinces across China were further evaluated.@*Results@#A total of 49 970 confirmed cases were reported nationwide, of which 37 884 were in Hubei Province. The average daily migration index from Hubei to other provinces was 312.09, Wuhan and other cities in Hubei were 117.95 and 194.16, respectively. The cumulative NCP cases of provinces was positively correlated with the migration index derived from Hubei province, also in Wuhan and other cities in Hubei, with correlation coefficients of 0.84, 0.84, and 0.81. In linear model, population migration from Hubei Province, Wuhan and other cities in Hubei account for 71.2%, 70.1%, and 66.3% of the variation, respectively. The period of high exported risk from Hubei occurred before January 27, of which the risks before January 23 mainly came from Wuhan, and then mainly from other cities in Hubei. Hunan Province, Henan Province and Guangdong Province ranked the top three in terms of cumulative imported risk (the cumulative risk indices were 58.61, 54.75 and 49.62 respectively).@*Conclusion@#The epidemic in each province was mainly caused by the importation of Hubei Province. Taking measures such as restricting the migration of population in Hubei Province and strengthening quarantine measures for immigrants from Hubei Province may greatly reduce the risk of continued spread of the epidemic.
ABSTRACT
Objective To assess the imported risk of COVID-19 in Guangdong province and its cities, and conduct early warning. Methods Data of reported COVID-19 cases and Baidu Migration Index of 21 cities in Guangdong province and other provinces of China as of February 25, 2020 were collected. The imported risk index of each city in Guangdong province were calculated, and then correlation analysis was performed between reported cases and the imported risk index to identify lag time. Finally, we classified the early warming levels of epidemic by imported risk index. Results A total of 1 347 confirmed cases were reported in Guangdong province, and 90.0% of the cases were clustered in the Pearl River Delta region. The average daily imported risk index of Guangdong was 44.03. Among the imported risk sources of each city, the highest risk of almost all cities came from Hubei province, except for Zhanjiang from Hainan province. In addition, the neighboring provinces of Guangdong province also had a greater impact. The correlation between the imported risk index with a lag of 4 days and the daily reported cases was the strongest (correlation coefficient: 0.73). The early warning base on cumulative 4-day risk of each city showed that Dongguan, Shenzhen, Zhongshan, Guangzhou, Foshan and Huizhou have high imported risks in the next 4 days, with imported risk indexes of 38.85, 21.59, 11.67, 11.25, 6.19 and 5.92, and the highest risk still comes from Hubei province. Conclusions Cities with a large number of migrants in Guangdong province have a higher risk of import. Hubei province and neighboring provinces in Guangdong province are the main source of the imported risk. Each city must strengthen the health management of migrants in high-risk provinces and reduce the imported risk of Guangdong province.