ABSTRACT
Objective:To evaluate the impact of artificial intelligence (AI) system on the diagnosis rate of precancerous state of gastric cancer.Methods:A single center self-controlled study was conducted under the premise that such factors were controlled as mainframe and model of the endoscope, operating doctor, season and climate, and pathology was taken as the gold standard. The diagnosis rate of precancerous state of gastric cancer, including atrophic gastritis (AG) and intestinal metaplasia (IM) in traditional gastroscopy (from September 1, 2019 to November 30, 2019) and AI assisted endoscopy (from September 1, 2020 to November 15, 2020) in the Eighth Hospital of Wuhan was statistically analyzed and compared, and the subgroup analysis was conducted according to the seniority of doctors.Results:Compared with traditional gastroscopy, AI system could significantly improve the diagnosis rate of AG [13.3% (38/286) VS 7.4% (24/323), χ2=5.689, P=0.017] and IM [33.9% (97/286) VS 26.0% (84/323), χ2=4.544, P=0.033]. For the junior doctors (less than 5 years of endoscopic experience), AI system had a more significant effect on the diagnosis rate of AG [11.9% (22/185) VS 5.8% (11/189), χ2=4.284, P=0.038] and IM [30.3% (56/185) VS 20.6% (39/189), χ2=4.580, P=0.032]. For the senior doctors (more than 10 years of endoscopic experience), although the diagnosis rate of AG and IM increased slightly, the difference was not statistically significant. Conclusion:AI system shows the potential to improve the diagnosis rate of precancerous state of gastric cancer, especially for junior endoscopists, and to reduce missed diagnosis of early gastric cancer.
ABSTRACT
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.
ABSTRACT
The outbreak of corona virus disease (COVID-19) has raised concerns among dentists to develop strategies to prevent infection of dental equipment, materials, and patients during an epidemic period. Strategies following the National Laws and Standards of China and local standards of several provinces for controlling cross-infection and instituting protective measures for medical staff in dental clinics during an epidemic period are discussed. A proposal is put forth for dental clinics that will face similar situations in the future. Further research is warranted to address potential problems that will be encountered under such dire circumstances.