Artificial neural networks(ANNs)are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields.In recent years,there has been a sharp increase in research concerning AN...Artificial neural networks(ANNs)are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields.In recent years,there has been a sharp increase in research concerning ANNs in gastrointestinal(GI)diseases.This state-of-the-art technique exhibits excellent performance in diagnosis,prognostic prediction,and treatment.Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements.However,the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice.In this review,we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists.Existing limitations and future directions are also proposed to optimize ANN’s clinical potential.In consideration of barriers to interdisciplinary knowledge,sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.展开更多
Upper gastrointestinal(GI)cancers are the leading cause of cancer-related deaths worldwide.Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital...Upper gastrointestinal(GI)cancers are the leading cause of cancer-related deaths worldwide.Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy.However,unlike GI cancers,precancerous lesions in the upper GI tract can be subtle and difficult to detect.Artificial intelligence techniques,especially deep learning algorithms with convolutional neural networks,might help endoscopists identify the precancerous lesions and reduce interobserver variability.In this review,a systematic literature search was undertaken of the Web of Science,PubMed,Cochrane Library and Embase,with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract.The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized.The challenges and recommendations targeting this field are comprehensively analyzed for future research.展开更多
While cholangiocarcinoma represents only about 3%of all gastrointestinal tumors,it has a dismal survival rate,usually because it is diagnosed at a late stage.The utilization of Artificial Intelligence(AI)in medicine i...While cholangiocarcinoma represents only about 3%of all gastrointestinal tumors,it has a dismal survival rate,usually because it is diagnosed at a late stage.The utilization of Artificial Intelligence(AI)in medicine in general,and in gastroenterology has made gigantic steps.However,the application of AI for biliary disease,in particular for cholangiocarcinoma,has been sub-optimal.The use of AI in combination with clinical data,cross-sectional imaging(computed tomography,magnetic resonance imaging)and endoscopy(endoscopic ultrasound and cholangioscopy)has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options,leading to a transformation in the prognosis of this feared disease.In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.展开更多
Esophageal cancer remains as one of the top ten causes of cancer-related death in the United States.The primary risk factor for esophageal adenocarcinoma is the presence of Barrett’s esophagus(BE).Currently,identific...Esophageal cancer remains as one of the top ten causes of cancer-related death in the United States.The primary risk factor for esophageal adenocarcinoma is the presence of Barrett’s esophagus(BE).Currently,identification of early dysplasia in BE patients requires an experienced endoscopist performing a diagnostic endoscopy with random 4-quadrant biopsies taken every 1-2 cm using appropriate surveillance intervals.Currently,there is significant difficulty for endoscopists to distinguish different forms of dysplastic BE as well as early adenocarcinoma due to subtleties in mucosal texture and color.This obstacle makes taking multiple random biopsies necessary for appropriate surveillance and diagnosis.Recent advances in artificial intelligence(AI)can assist gastroenterologists in identifying areas of likely dysplasia within identified BE and perform targeted biopsies,thus decreasing procedure time,sedation time,and risk to the patient along with maximizing potential biopsy yield.Though using AI represents an exciting frontier in endoscopic medicine,recent studies are limited by selection bias,generalizability,and lack of robustness for universal use.Before AI can be reliably employed for BE in the future,these issues need to be fully addressed and tested in prospective,randomized trials.Only after that is achieved,will the benefit of AI in those with BE be fully realized.展开更多
The application of artificial intelligence(AI),especially machine learning or deep learning(DL),is advancing at a rapid pace.The need for increased accuracy at endoscopic visualisation of the gastrointestinal(GI)tract...The application of artificial intelligence(AI),especially machine learning or deep learning(DL),is advancing at a rapid pace.The need for increased accuracy at endoscopic visualisation of the gastrointestinal(GI)tract is also growing.Convolutional neural networks(CNNs)are one such model of DL,which have been used for endoscopic image analysis,whereby computer-aided detection and diagnosis of GI pathology can be carried out with increased scrupulousness.In this article,we briefly focus on the framework of the utilisation of CNNs in GI endoscopy along with a short review of a few published AI-based articles in the last 4 years.展开更多
Gastric cancer(GC)is the fifth most common cancer in the world,and at present,esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC.Convolutional neural networks(CNNs)...Gastric cancer(GC)is the fifth most common cancer in the world,and at present,esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC.Convolutional neural networks(CNNs)are a type of deep learning model and have been widely used for image analysis.This paper reviews the application and prospects of CNNs in detecting and classifying GC,aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies.展开更多
Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the nee...Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis,prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias(class imbalance) have the possibility of overestimating the accuracy,external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification,prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability.Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.展开更多
基金National Natural Science Foundation of China,No.81773135 and No.82073192。
文摘Artificial neural networks(ANNs)are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields.In recent years,there has been a sharp increase in research concerning ANNs in gastrointestinal(GI)diseases.This state-of-the-art technique exhibits excellent performance in diagnosis,prognostic prediction,and treatment.Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements.However,the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice.In this review,we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists.Existing limitations and future directions are also proposed to optimize ANN’s clinical potential.In consideration of barriers to interdisciplinary knowledge,sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
基金The Science and Technology Development Fund,Macao SAR,No.0021/2019/A.
文摘Upper gastrointestinal(GI)cancers are the leading cause of cancer-related deaths worldwide.Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy.However,unlike GI cancers,precancerous lesions in the upper GI tract can be subtle and difficult to detect.Artificial intelligence techniques,especially deep learning algorithms with convolutional neural networks,might help endoscopists identify the precancerous lesions and reduce interobserver variability.In this review,a systematic literature search was undertaken of the Web of Science,PubMed,Cochrane Library and Embase,with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract.The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized.The challenges and recommendations targeting this field are comprehensively analyzed for future research.
文摘While cholangiocarcinoma represents only about 3%of all gastrointestinal tumors,it has a dismal survival rate,usually because it is diagnosed at a late stage.The utilization of Artificial Intelligence(AI)in medicine in general,and in gastroenterology has made gigantic steps.However,the application of AI for biliary disease,in particular for cholangiocarcinoma,has been sub-optimal.The use of AI in combination with clinical data,cross-sectional imaging(computed tomography,magnetic resonance imaging)and endoscopy(endoscopic ultrasound and cholangioscopy)has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options,leading to a transformation in the prognosis of this feared disease.In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.
文摘Esophageal cancer remains as one of the top ten causes of cancer-related death in the United States.The primary risk factor for esophageal adenocarcinoma is the presence of Barrett’s esophagus(BE).Currently,identification of early dysplasia in BE patients requires an experienced endoscopist performing a diagnostic endoscopy with random 4-quadrant biopsies taken every 1-2 cm using appropriate surveillance intervals.Currently,there is significant difficulty for endoscopists to distinguish different forms of dysplastic BE as well as early adenocarcinoma due to subtleties in mucosal texture and color.This obstacle makes taking multiple random biopsies necessary for appropriate surveillance and diagnosis.Recent advances in artificial intelligence(AI)can assist gastroenterologists in identifying areas of likely dysplasia within identified BE and perform targeted biopsies,thus decreasing procedure time,sedation time,and risk to the patient along with maximizing potential biopsy yield.Though using AI represents an exciting frontier in endoscopic medicine,recent studies are limited by selection bias,generalizability,and lack of robustness for universal use.Before AI can be reliably employed for BE in the future,these issues need to be fully addressed and tested in prospective,randomized trials.Only after that is achieved,will the benefit of AI in those with BE be fully realized.
文摘The application of artificial intelligence(AI),especially machine learning or deep learning(DL),is advancing at a rapid pace.The need for increased accuracy at endoscopic visualisation of the gastrointestinal(GI)tract is also growing.Convolutional neural networks(CNNs)are one such model of DL,which have been used for endoscopic image analysis,whereby computer-aided detection and diagnosis of GI pathology can be carried out with increased scrupulousness.In this article,we briefly focus on the framework of the utilisation of CNNs in GI endoscopy along with a short review of a few published AI-based articles in the last 4 years.
基金The Key Project for Social Development of Yangzhou,No.YZ2020069.
文摘Gastric cancer(GC)is the fifth most common cancer in the world,and at present,esophagogastroduodenoscopy is recognized as an acceptable method for the screening and monitoring of GC.Convolutional neural networks(CNNs)are a type of deep learning model and have been widely used for image analysis.This paper reviews the application and prospects of CNNs in detecting and classifying GC,aiming to introduce a computer-aided diagnosis system and to provide evidence for subsequent studies.
文摘Artificial intelligence(AI) using deep-learning(DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis,prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias(class imbalance) have the possibility of overestimating the accuracy,external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification,prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability.Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
