This article analyses the literature regarding the value of computer-assisted systems in esogastroduodenoscopy-quality monitoring and the assessment of gastric lesions.Current data show promising results in upper-endo...This article analyses the literature regarding the value of computer-assisted systems in esogastroduodenoscopy-quality monitoring and the assessment of gastric lesions.Current data show promising results in upper-endoscopy quality control and a satisfactory detection accuracy of gastric premalignant and malignant lesions,similar or even exceeding that of experienced endoscopists.Moreover,artificial systems enable the decision for the best treatment strategies in gastriccancer patient care,namely endoscopic vs surgical resection according to tumor depth.In so doing,unnecessary surgical interventions would be avoided whilst providing a better quality of life and prognosis for these patients.All these performance data have been revealed by numerous studies using different artificial intelligence(AI)algorithms in addition to white-light endoscopy or novel endoscopic techniques that are available in expert endoscopy centers.It is expected that ongoing clinical trials involving AI and the embedding of computer-assisted diagnosis systems into endoscopic devices will enable real-life implementation of AI endoscopic systems in the near future and at the same time will help to overcome the current limits of the computer-assisted systems leading to an improvement in performance.These benefits should lead to better diagnostic and treatment strategies for gastric-cancer patients.Furthermore,the incorporation of AI algorithms in endoscopic tools along with the development of large electronic databases containing endoscopic images might help in upper-endoscopy assistance and could be used for telemedicine purposes and second opinion for difficult cases.展开更多
文摘This article analyses the literature regarding the value of computer-assisted systems in esogastroduodenoscopy-quality monitoring and the assessment of gastric lesions.Current data show promising results in upper-endoscopy quality control and a satisfactory detection accuracy of gastric premalignant and malignant lesions,similar or even exceeding that of experienced endoscopists.Moreover,artificial systems enable the decision for the best treatment strategies in gastriccancer patient care,namely endoscopic vs surgical resection according to tumor depth.In so doing,unnecessary surgical interventions would be avoided whilst providing a better quality of life and prognosis for these patients.All these performance data have been revealed by numerous studies using different artificial intelligence(AI)algorithms in addition to white-light endoscopy or novel endoscopic techniques that are available in expert endoscopy centers.It is expected that ongoing clinical trials involving AI and the embedding of computer-assisted diagnosis systems into endoscopic devices will enable real-life implementation of AI endoscopic systems in the near future and at the same time will help to overcome the current limits of the computer-assisted systems leading to an improvement in performance.These benefits should lead to better diagnostic and treatment strategies for gastric-cancer patients.Furthermore,the incorporation of AI algorithms in endoscopic tools along with the development of large electronic databases containing endoscopic images might help in upper-endoscopy assistance and could be used for telemedicine purposes and second opinion for difficult cases.
