Objective To observe the surface electromyographic characteristics of the bilateral submen-tal muscles in dysphagia secondary to unilateral brainstem stroke. Methods A total of 25 subjects were recrui-ted. There were 8 stroke patients with dysphagia secondary to a left brainstem stroke and 7 stroke patients with dysphagia secondary to a right brainstem stroke. There were also 10 healthy controls matched in age and gender. The duration and peak amplitude of the submental muscle when swallowing 5 ml of warm water were recorded u-sing a surface electromyograph. Results The average amplitude of the left submental muscle in patients with a left brainstem stroke was significantly longer than that of those with a right brainstem stroke, but no significant differences in average duration were observed. Conversely, the amplitude of the right submental muscle in pa-tients with a right brainstem stroke was significantly longer than that of those with left brainstem stroke, but again there were no significant differences in duration. No significant differences were observed among the healthy con-trols. The amplitude and duration of both the affected and healthy sides of the patients were of course significantly longer or stronger than those of the healthy controls. Conclusion The swallowing function of the bilateral sub-mental muscles may be impaired among unilateral stroke survivors with dysphagia. The damage on the affected side is more severe than on the opposite side.

This study was aimed to explore the treatment regularity of chronic hepatitis B virus by the famous traditional Chinese medicine (TCM) Professor Wu Shoushan in Hubei province on the basis of the Clinical Research Information Integration of TCM.Clinical data were collected from 767 outpatients with chronic hepatitis B virus and analyzed by classification,association and clustering,according to data integration,remittance and integration.The results showed that from herbs used in 259 prescriptions,the top 5 frequently used herbs were poria,capillaris,dried tangerine peel,salvia and forsythia.In the aspect of herbal combination,the herbal pairs with the highest associations were porialicorice-atractylodes,poria-salvia-atractylodes,capillaries-poria-licorice,and etc.In the data mining of core prescription,the modified classic prescriptions with the highest frequency were the Hao-Qin Qing-Dan decoction,DaAn pill,and Ban-Xia Bai-Zhu Tian-Ma decoction.It was concluded that through the data mining method,we found that the TCM syndrome differentiation rules and methods of Professor Wu Shoushan in the treatment of chronic hepatitis B were mainly from toxin,phlegm,stasis and deficiency.

Objective To investigate the correlation between the types of constitution in TCM and the sleep status in the PLA Navy divers. Methods Eighty-nine PLA Navy divers who performed 10m diving professional training were selected. Constitution in TCM was classified and determined by the standardized standard Constitution in Chinese Medicine Questionnaire, sleep status was evaluated by the Pittsburgh Sleep Quality Index. Results 62.9% of Navy divers were the mild constitution in TCM. The eight kinds of the biased constitution in TCM are ranked with yang deficiency, phlegm-dampness, dampness-heat, qi deficiency, yin deficiency, blood stasis, qi stagnation and special intrinsic quality. Among 78 Navy divers with good sleep quality, there were 49 divers (72.1%) with the mild constitution in TCM and 19 (27.9%) with the biased constitution in TCM. Among 21 Navy divers with poor sleep quality, there were 7 divers (33.3%) with the mild constitution in TCM (accounting for) and 14 (66.7%) with the biased constitution in TCM. For Navy divers with poor sleep quality, the sleep quality scores were positive correlated with the blood stasis constitution in TCM (r=0.481,P<0.05). Conclusion Sleep status is correlated with the types of the constitution in TCM, and regulating constitution in TCM can improve sleep quality in PLA Navy divers.

Objective:To study the effect of quality control circle(QCC)on the reasonable ratio of clinical prescriptions. Methods:The dispensed prescriptions in orthopedic emergency department were reviewed in our hospital,and the reasons of unreasonable prescriptions were analyzed. According to the QCC technique,the activities were implemented,the standardized work process was made out and the results were studied. Results:After the six-month QCC activities,the unreasonable ratio of emergency orthopedics prescriptions was reduced from 70% to 21% ,and the target yield rate was 140% and the improvement rate was 70% . Conclusion:The QCC has obvious effect on the improvement of reasonable ratio of emergency orthopedics prescriptions.

Objective To explore the correlation between TCM constitution and sleep status in escort troops. Methods Standardized TCM Constitution Questionnaire and Pittsburgh Sleep Quality Index (PSQI) were used to investigate escort troops. Factors of PSQI and constitution types were analyzed. Results The results of PSQI showed the number of escort troops who had bad sleeping (PSQI>7) was 64, which accounted for 46%of the escort troops, among which, 45 people (70.3%) were justified as biased constitution, while 19 were normal constitution (29.7%). The top three biased constitution in bad sleeping were:qi deficiency, phlegm-dampness and dampness-heat. PSQI factors such as sleep disorder, daytime dysfunction and total score had positive correlation with qi deficiency, yang deficiency, yin deficiency, dampness-heat and blood stasis, and negative correlation with normal constitution. Conclusion Sleep condition of escort troops has correlation with TCM constitution, which indicates that sleep status could be improved by regulating TCM constitution.

This study was aimed to analyze the compatibility principles of herbs for the treatment of jaundice with the method of association rule,and to provide reference for tradition Chinese medicine (TCM) diagnosis and treatment of jaundice.A total of 3404 prescription data of inpatient medical records for the diagnosis of jaundice from 1960 to 1978 were recorded and processed.Then,herbs with high frequency were identified and analyzed the compatibility principles of herbs by Apriori algorithm.The results showed that from 3 404 prescriptions,there were 327 kinds of different herbs,with the average of 10 to 12 herbs in one prescription.From the herb frequency results,we found that the frequency of the top 50 herbs benefits of dieresis and dampness-removing medicine,heat-clearing medicine,deficiency-reinforcing medicine,qi-regulating medicine,blood stasis removing medicine,food stagnation removing medicine,dampnessremoving medicine,purgative medicine,phlegm-removing medicine,and etc.In addition,we identified 27 drug pairs (the associations of 2 herbs) and 24 angle drug pairs (the associations of 3 herbs).It was concluded that the application of association rules to analyze the law of jaundice medication which reflected the treatment experience of jaundice in this period.It can provide a new idea for the further research.It can also provide a way of thinking for our further study.

Objective:To construct a deep learning-based diagnostic system for gastrointestinal submucosal tumor (SMT) under endoscopic ultrasonography (EUS), so as to help endoscopists diagnose SMT.Methods:From January 1, 2019 to December 15, 2021, at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University, 245 patients with SMT confirmed by pathological diagnosis who underwent EUS and endoscopic submucosal dissection were enrolled. A total of 3 400 EUS images were collected. Among the images, 2 722 EUS images were used for training of lesion segmentation model, while 2 209 EUS images were used for training of stromal tumor and leiomyoma classification model; 283 and 191 images were selected as independent test sets to evaluate lesion segmentation model and classification model, respectively. Thirty images were selected as an independent data set for human-machine competition to compare the lesion classification accuracy between lesion classification models and 6 endoscopists. The performance of the segmentation model was evaluated by indexes such as Intersection-over-Union and Dice coefficient. The performance of the classification model was evaluated by accuracy. Chi-square test was used for statistical analysis.Results:The average Intersection-over-Union and Dice coefficient of lesion segmentation model were 0.754 and 0.835, respectively, and the accuracy, recall and F1 score were 95.2%, 98.9% and 97.0%, respectively. Based on the lesion segmentation, the accuracy of classification model increased from 70.2% to 92.1%. The results of human-machine competition showed that the accuracy of classification model in differential diagnosis of stromal tumor and leiomyoma was 86.7% (26/30), which was superior to that of 4 out of the 6 endoscopists(56.7%, 17/30; 56.7%, 17/30; 53.3%, 16/30; 60.0%, 18/30; respectively), and the differences were statistically significant( χ2=7.11, 7.36, 8.10, 6.13; all P<0.05). There was no significant difference between the accuracy of the other 2 endoscopists(76.7%, 23/30; 73.3%, 22/30; respectively) and model(both P<0.05). Conclusion:This system could be used for the auxiliary diagnosis of SMT under ultrasonic endoscope in the future, and to provide a powerful evidence for the selection of subsequent treatment decisions.

Objective:To develop an artificial intelligence-based system for measuring the size of gastrointestinal lesions under white light endoscopy in real time.Methods:The system consisted of 3 models. Model 1 was used to identify the biopsy forceps and mark the contour of the forceps in continuous pictures of the video. The results of model 1 were submitted to model 2 and classified into open and closed forceps. And model 3 was used to identify the lesions and mark the boundary of lesions in real time. Then the length of the lesions was compared with the contour of the forceps to calculate the size of lesions. Dataset 1 consisted of 4 835 images collected retrospectively from January 1, 2017 to November 30, 2019 in Renmin Hospital of Wuhan University, which were used for model training and validation. Dataset 2 consisted of images collected prospectively from December 1, 2019 to June 4, 2020 at the Endoscopy Center of Renmin Hospital of Wuhan University, which were used to test the ability of the model to segment the boundary of the biopsy forceps and lesions. Dataset 3 consisted of 302 images of 151 simulated lesions, each of which included one image of a larger tilt angle (45° from the vertical line of the lesion) and one image of a smaller tilt angle (10° from the vertical line of the lesion) to test the ability of the model to measure the lesion size with the biopsy forceps in different states. Dataset 4 was a video test set, which consisted of prospectively collected videos taken from the Endoscopy Center of Renmin Hospital of Wuhan University from August 5, 2019 to September 4, 2020. The accuracy of model 1 in identifying the presence or absence of biopsy forceps, model 2 in classifying the status of biopsy forceps (open or closed) and model 3 in identifying the presence or absence of lesions were observed with the results of endoscopist review or endoscopic surgery pathology as the gold standard. Intersection over union (IoU) was used to evaluate the segmentation effect of biopsy forceps in model 1 and lesion segmentation effect in model 3, and the absolute error and relative error were used to evaluate the ability of the system to measure lesion size.Results:(1)A total of 1 252 images were included in dataset 2, including 821 images of forceps (401 images of open forceps and 420 images of closed forceps), 431 images of non-forceps, 640 images of lesions and 612 images of non-lesions. Model 1 judged 433 images of non-forceps (430 images were accurate) and 819 images of forceps (818 images were accurate), and the accuracy was 99.68% (1 248/1 252). Based on the data of 818 images of forceps to evaluate the accuracy of model 1 on judging the segmentation effect of biopsy forceps lobe, the mean IoU was 0.91 (95% CI: 0.90-0.92). The classification accuracy of model 2 was evaluated by using 818 forceps pictures accurately judged by model 1. Model 2 judged 384 open forceps pictures (382 accurate) and 434 closed forceps pictures (416 accurate), and the classification accuracy of model 2 was 97.56% (798/818). Model 3 judged 654 images containing lesions (626 images were accurate) and 598 images of non-lesions (584 images were accurate), and the accuracy was 96.65% (1 210/1 252). Based on 626 images of lesions accurately judged by model 3, the mean IoU was 0.86 (95% CI: 0.85-0.87). (2) In dataset 3, the mean absolute error of systematic lesion size measurement was 0.17 mm (95% CI: 0.08-0.28 mm) and the mean relative error was 3.77% (95% CI: 0.00%-10.85%) when the tilt angle of biopsy forceps was small. The mean absolute error of systematic lesion size measurement was 0.17 mm (95% CI: 0.09-0.26 mm) and the mean relative error was 4.02% (95% CI: 2.90%-5.14%) when the biopsy forceps was tilted at a large angle. (3) In dataset 4, a total of 780 images of 59 endoscopic examination videos of 59 patients were included. The mean absolute error of systematic lesion size measurement was 0.24 mm (95% CI: 0.00-0.67 mm), and the mean relative error was 9.74% (95% CI: 0.00%-29.83%). Conclusion:The system could measure the size of endoscopic gastrointestinal lesions accurately and may improve the accuracy of endoscopists.

Objective:To assess the diagnostic efficacy of upper gastrointestinal endoscopic image assisted diagnosis system (ENDOANGEL-LD) based on artificial intelligence (AI) for detecting gastric lesions and neoplastic lesions under white light endoscopy.Methods:The diagnostic efficacy of ENDOANGEL-LD was tested using image testing dataset and video testing dataset, respectively. The image testing dataset included 300 images of gastric neoplastic lesions, 505 images of non-neoplastic lesions and 990 images of normal stomach of 191 patients in Renmin Hospital of Wuhan University from June 2019 to September 2019. Video testing dataset was from 83 videos (38 gastric neoplastic lesions and 45 non-neoplastic lesions) of 78 patients in Renmin Hospital of Wuhan University from November 2020 to April 2021. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD for image testing dataset were calculated. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD in video testing dataset for gastric neoplastic lesions were compared with those of four senior endoscopists.Results:In the image testing dataset, the accuracy, the sensitivity, the specificity of ENDOANGEL-LD for gastric lesions were 93.9% (1 685/1 795), 98.0% (789/805) and 90.5% (896/990) respectively; while the accuracy, the sensitivity and the specificity of ENDOANGEL-LD for gastric neoplastic lesions were 88.7% (714/805), 91.0% (273/300) and 87.3% (441/505) respectively. In the video testing dataset, the sensitivity [100.0% (38/38) VS 85.5% (130/152), χ2=6.220, P=0.013] of ENDOANGEL-LD was higher than that of four senior endoscopists. The accuracy [81.9% (68/83) VS 72.0% (239/332), χ2=3.408, P=0.065] and the specificity [ 66.7% (30/45) VS 60.6% (109/180), χ2=0.569, P=0.451] of ENDOANGEL-LD were comparable with those of four senior endoscopists. Conclusion:The ENDOANGEL-LD can accurately detect gastric lesions and further diagnose neoplastic lesions to help endoscopists in clinical work.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.