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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.
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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.
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Objective:To construct an artificial intelligence-assisted diagnosis system to recognize the characteristics of Helicobacter pylori ( HP) infection under endoscopy, and evaluate its performance in real clinical cases. Methods:A total of 1 033 cases who underwent 13C-urea breath test and gastroscopy in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from January 2020 to March 2021 were collected retrospectively. Patients with positive results of 13C-urea breath test (which were defined as HP infertion) were assigned to the case group ( n=485), and those with negative results to the control group ( n=548). Gastroscopic images of various mucosal features indicating HP positive and negative, as well as the gastroscopic images of HP positive and negative cases were randomly assigned to the training set, validation set and test set with at 8∶1∶1. An artificial intelligence-assisted diagnosis system for identifying HP infection was developed based on convolutional neural network (CNN) and long short-term memory network (LSTM). In the system, CNN can identify and extract mucosal features of endoscopic images of each patient, generate feature vectors, and then LSTM receives feature vectors to comprehensively judge HP infection status. The diagnostic performance of the system was evaluated by sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC). Results:The diagnostic accuracy of this system for nodularity, atrophy, intestinal metaplasia, xanthoma, diffuse redness + spotty redness, mucosal swelling + enlarged fold + sticky mucus and HP negative features was 87.5% (14/16), 74.1% (83/112), 90.0% (45/50), 88.0% (22/25), 63.3% (38/60), 80.1% (238/297) and 85.7% (36 /42), respectively. The sensitivity, specificity, accuracy and AUC of the system for predicting HP infection was 89.6% (43/48), 61.8% (34/55), 74.8% (77/103), and 0.757, respectively. The diagnostic accuracy of the system was equivalent to that of endoscopist in diagnosing HP infection under white light (74.8% VS 72.1%, χ2=0.246, P=0.620). Conclusion:The system developed in this study shows noteworthy ability in evaluating HP status, and can be used to assist endoscopists to diagnose HP infection.
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Objective:To compare the ability of deep convolutional neural network-crop (DCNN-C) and deep convolutional neural network-whole (DCNN-W), 2 artificial intelligence systems based on different training methods to dignose early gastric cancer (EGC) diagnosis under magnifying image-enhanced endoscopy (M-IEE).Methods:The images and video clips of EGC and non-cancerous lesions under M-IEE under narrow band imaging or blue laser imaging mode were retrospectively collected in the Endoscopy Center of Renmin Hospital of Wuhan University, for the training set and test set for DCNN-C and DCNN-W. The ability of DCNN-C and DCNN-W in EGC identity in image test set were compared. The ability of DCNN-C, DCNN-W and 3 senior endoscopists (average performance) in EGC identity in video test set were also compared. Paired Chi-squared test and Chi-squared test were used for statistical analysis. Inter-observer agreement was expressed as Cohen′s Kappa statistical coefficient (Kappa value).Results:In the image test set, the accuracy, sensitivity, specificity and positive predictive value of DCNN-C in EGC diagnosis were 94.97%(1 133/1 193), 97.12% (202/208), 94.52% (931/985), and 78.91%(202/256), respectively, which were higher than those of DCNN-W(86.84%, 1 036/1 193; 92.79%, 193/208; 85.58%, 843/985 and 57.61%, 193/335), and the differences were statistically significant ( χ2=4.82, 4.63, 61.04 and 29.69, P=0.028, =0.035, <0.001 and <0.001). In the video test set, the accuracy, specificity and positive predictive value of senior endoscopists in EGC diagnosis were 67.67%, 60.42%, and 53.37%, respectively, which were lower than those of DCNN-C (93.00%, 92.19% and 87.18%), and the differences were statistically significant ( χ2=20.83, 16.41 and 11.61, P<0.001, <0.001 and =0.001). The accuracy, specificity and positive predictive value of DCNN-C in EGC diagnosis were higher than those of DCNN-W (79.00%, 70.31% and 64.15%, respectively), and the differences were statistically significant ( χ2=7.04, 8.45 and 6.18, P=0.007, 0.003 and 0.013). There were no significant differences in accuracy, specificity and positive predictive value between senior endoscopists and DCNN-W in EGC diagnosis (all P>0.05). The sensitivity of senior endoscopists, DCNN-W and DCNN-C in EGC diagnosis were 80.56%, 94.44%, and 94.44%, respectively, and the differences were not statistically significant (all P>0.05). The results of the agreement analysis showed that the agreement between senior endoscopists and the gold standard was fair to moderate (Kappa=0.259, 0.532, 0.329), the agreement between DCNN-W and the gold standard was moderate (Kappa=0.587), and the agreement between DCNN-C and the gold standard was very high (Kappa=0.851). Conclusion:When the training set is the same, the ability of DCNN-C in EGC diagnosis is better than that of DCNN-W and senior endoscopists, and the diagnostic level of DCNN-W is equivalent to that of senior endoscopists.
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Objective:To analyze the expression of circular RNA circ_0008274 in cetuximab-resistant colorectal cancer cells using bioinformatics technology and to explore its involvement in the development of cetuximab resistance.Methods:Five concentrations of cetuximab (10, 50, 100, 150, 200 nmol/L) were set. Cetuximab-resistant cells DiFi-R and Caco-2-R were screened out and established by concentration increasing method using colorectal cancer cells DiFi and Caco-2. The expression of circ_0008274 in DiFi-R and Caco-2-R cells was detected by reverse transcription-polymerase chain reaction(RT-PCR). The interaction and regulation between circ_0008274 and microRNA(miR)-140-3p were analyzed by double-luciferase reporter assay. The highly expressed gene SMARCC1 related to cetuximab resistance was determined by Western blotting. Circ_0008274 in DiFi-R and Caco-2-R cells were knocked out with small interfering RNA si-circ_0008274 transfection. After knock out, the differences in the colony formation and cell proliferation in DiFi-R and Caco-2-R cells were compared. MiR-140-3p mimic and blank control miR were transfected into DiFi-R and Caco-2-R cells. After transfection the difference in cell proliferation between transfected with miR-140-3p mimic and blank control miR in DiFi-R and Caco-2-R cells were analyzed. After Caco-2-R cell was knocked out with si-circ_0008274, the changes of SMARCC1 protein expression rescued by pcDNA3.1 SMARCC1 and cell viability were analyzed. The tumor specimens of 15 colorectal cancer patients hospitalized in Renmin Hospital of Wuhan University from March 2019 to August 2020 were included. According to the treatment effect, the patients were divided into sensitive group (11 cases) and drug-resistant group (4 cases). The relative expression levels of circ_0008274, downstream SMARCC1and miR-140-3p in colorectal cancer tissues in the two groups were detected by RT-PCR. Independent sample t test was used for statistical analysis. Results:The level of circ_0008274 in DiFi-R cells was 2.33±0.12 times of that of DiFi cells, while the level in Caco-2-R was (2.92±0.42) times of that of Caco-2 cells, and the differences were statistically significant ( t=19.97 and 7.80, both P<0.05). The results of double-luciferase reporter showed that after miR-140-3p mimic combined with wild-type circ_0008274, the relative fluorescence intensity was lower than before (0.28±0.04 vs. 1.00±0.00), and the difference was statistically significant ( t=-30.71, P=0.001). The expression of SMARCC1 protein in DiFi-R and Caco-2-R cells was significantly increased, the expression at protein level was higher than that of DiFi and Caco-2 cells (2.22±0.36 vs. 0.61±0.17, 0.85±0.11 vs. 0.35±0.08), and the differences were statistically significant ( t=6.23 and 6.32, both P<0.01). After circ_0008274 was knocked out, the numbers of colony formation of DiFi-R and Caco-2-R cells were both lower than those of before knockout (36.67±4.04 vs. 66.00±9.54, 17.35±4.04 vs. 52.33±8.02), the relative active cell ratios after interventing by 10, 50, 100, 150 and 200 nmol/L cetuximab were also lower than those of before knockout (DiFi-R cells: (73.75±2.75)% vs. (88.10±2.48)%, (56.50±6.66)% vs. (75.15±6.03)%, (35.75±5.32)% vs. (59.63±6.67)%, (24.25±3.30)% vs. (52.40±6.71)%, (6.25±2.75)% vs. (48.60±5.38)%; Caco-2-R cells: (63.74±5.25)% vs. (85.76±4.79)%, (56.50±4.20)% vs.(83.50±3.90)%, (46.00±2.94)% vs. (80.00±6.05)%, (35.30±5.56)% vs. (68.30±4.57)%, (12.25±7.37)% vs. (62.40±7.51)%), and the differences were statistically significant ( t=4.90, 6.71, -7.75, -4.16, -5.60, -7.53, -14.02, -6.19, -8.33, -10.10, -9.17 and -9.56, all P<0.01). After transfecting with miR-140-3p mimic, the relative active cell ratios of DiFi-R and Caco-2-R cells interventing by 10, 50, 100, 150 and 200 nmol/L cetuximab were both lower than those transfected with blank control miR (DiFi-R cells: (71.55±4.97)% vs. (85.90±2.66)%, (51.58±3.91)% vs. (74.95±6.35)%, (41.23±8.84)% vs. (58.43±7.05)%, (28.60±5.26)% vs. (53.75±5.65)%, (18.90±5.13)% vs. (51.30±3.30)%; Caco-2-R cells: (61.75±2.22)% vs. (90.10±1.41)%, (53.25±4.17)% vs. (86.18±2.69)%, (46.38±4.55)% vs. (77.75±6.70)%, (36.10±8.76)% vs. (70.15±4.18)%, (24.25±2.63)% vs. (65.10±7.62)%), and the differences were statistically significant ( t=-5.09, -6.47, -3.05, -6.28, -10.30, -21.48, -12.83, -8.01, -6.79 and -10.12, all P<0.01). After circ_0008274 was knocked out, the SMARCC1 protein level of Caco-2-R cells rescued by pcDNA3.1 SMARCC1 was higher than that of before rescue (0.63±0.19 vs. 0.09±0.03), and the relative active cell ratios after interventing by 10, 50, 100, 150 and 200 nmol/L cetuximab were also higher than that of before rescue ((93.10±3.56)% vs. (83.83±3.97)%, (83.28±4.26)% vs. (60.90±7.02)%, (61.83±2.12)% vs. (50.10±5.59)%, (53.20±3.74)% vs. (40.50±3.42)%, (46.20±4.08)% vs. (30.80±4.82)%), and the differences were statistically significant( t=3.55, 3.52, 5.44, 3.87, 4.64 and 4.88, all P<0.01). The relative expression levels of circ_0008274 and downstream SMARCC1 of colon cancer tissues in the drug-resistant group were higher than those in the sensitive group (6.45±1.32 vs. 2.26±1.39, 12.53±1.60 vs. 3.82±1.56), and the relative expression level of miR-140-3p was lower than that in the sensitive group (3.91±1.25 vs. 7.43±2.23), and the differences were statistically significant ( t=5.22, 9.51, -2.93, all P<0.01). Conclusions:Circular RNA circ_0008274 is highly expressed in colorectal cancer tissues and cetuximab resistant cells, interacts and inhibits miR-140-3p expression, up-regulates SMARCC1, and participates in the occurrence of cetuximab resistance. PcDNA3.1 SMARCC1 rescue can block the sensitization effect of si-circ_0008274 on cetuximab, and can significantly increase cetuximab resistance of colorectal cancer cells.
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Objective:To assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI).Methods:M-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice, with and without AI. The influence of the system on endoscopists′ performance was assessed.Results:Without AI assistance, accuracy, sensitivity, and specificity of endoscopists′ diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy ( P=0.302) and sensitivity ( P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists ( P=0.002). Conclusion:AI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.
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Objective To study and analyze the risk factors in children with type 1 diabetes and formulate preventive health measures. Methods A total of 112 children with type 1 diabetes treated in our hospital from January 2017 to October 2019 were selected as the type 1 diabetes group, and 50 healthy children who underwent physical examination during the same period were selected as the control group. Multifactor logistic regression analysis was used to analyze predisposing factors of type 1 diabetes in children, and preventive health measures was proposed. Results The results of multivariate logistic regression analysis indicated that maternal age, passive smoking during pregnancy, milk feeding time, and children's respiratory infections were independent risk factors for children with type 1 diabetes (OR: 6404, 6.903, 6.417, 8.256, P <0.05). Conclusion Maternal age, passive smoking during pregnancy, milk addition time, and children's respiratory infections were independent risk factors for children with type 1 diabetes. Strengthening health education, breastfeeding as soon as possible, and preventing respiratory infections can help reduce the incidence of children with type 1 diabetes.
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Kawasaki disease (KD) is an acute systemic vasculitis that primarily affects young children between 6 months and 4 years old. Coronary arteritis is an important clinical feature of KD because it is associated with aneurysms and thromboembolic events that can lead to severe complications, even sudden death. To date, the etiology and pathogenesis of Kawasaki disease has not been understood completely. In this paper, we will review the recent advances in epidemiology, etiology, pathogenesis and genetic susceptibility of Kawasa-ki disease.