Artificial intelligence (AI) devices for diagnostic endoscopic procedures have been launched increasingly during the past years. AI tools are mainly based upon computer programs being able to automatically detect or characterize pathological structures in endoscopic pictures or videos. Big data science requires that huge amounts of data (e.g. endoscopic pictures) are available in order to create such computer programs. Many endoscopic research fields are currently influenced by AI approaches. Among these automated polyp detection in the colon can be regarded as the most developed concept. Clinical studies have already proven that automated polyp detection devices are capable of increasing adenoma detection. However, further AI application systems have not been investigated broadly. Among them, automated, computer-based characterization of colorectal polyps or Barrett metaplasia are the most important ones. The establishment and clinical testing of these approaches will of major interest for endoscopic science in the coming years.
AI research is an emerging field in endoscopy. However, in clinical practice, AI is still at a very early stage of development. Both technical and user-related problems exist with AI applications. For example, regarding automated polyp detection it is unclear whether AI leads to a prolongation of withdrawal time during colonoscopy. Further applications e.g. AI solutions for Barrett's esophagus are poorly studied. Although data volume is increasing there is a lack of studies dealing with the applicability and clinical impact of AI devices in endoscopy. In this Research Topic we seek papers addressing clinical problems especially at the stage of clinical application in endoscopy.
The aim of this Research Topic is to collect high-quality papers on AI research in the field of endoscopy. The planned issue should be clinically oriented which means that mere computer science contributions are not desired. All kinds of papers (case reports, original contributions, reviews) dealing with clinical aspects of AI in endoscopy are welcome. If possible, we would like to present at least one original contribution in this collection. The most relevant AI topics are currently (i) polyp characterization in the colon, (ii) detection or characterization of neoplastic Barrett's esophagus and (iii) diagnostic yield in small bowel capsule endoscopy. However, all other endoscopy AI topics are to be welcomed. We would like to motivate leading researchers to present their data in our Research Topic.
Topic Editor W.K. is co-founder and Chief Medical Officer of Docbot. Topic Editor T.R. received speaking fees from the following companies: Pentax Medical, Olympus Medical, Mauna Kea Technologies, Medtronic and Takeda Pharmaceuticals.
Topic Editors P.K. and B.W. have no competing interest in regards to the Research Topic subject.
Artificial intelligence (AI) devices for diagnostic endoscopic procedures have been launched increasingly during the past years. AI tools are mainly based upon computer programs being able to automatically detect or characterize pathological structures in endoscopic pictures or videos. Big data science requires that huge amounts of data (e.g. endoscopic pictures) are available in order to create such computer programs. Many endoscopic research fields are currently influenced by AI approaches. Among these automated polyp detection in the colon can be regarded as the most developed concept. Clinical studies have already proven that automated polyp detection devices are capable of increasing adenoma detection. However, further AI application systems have not been investigated broadly. Among them, automated, computer-based characterization of colorectal polyps or Barrett metaplasia are the most important ones. The establishment and clinical testing of these approaches will of major interest for endoscopic science in the coming years.
AI research is an emerging field in endoscopy. However, in clinical practice, AI is still at a very early stage of development. Both technical and user-related problems exist with AI applications. For example, regarding automated polyp detection it is unclear whether AI leads to a prolongation of withdrawal time during colonoscopy. Further applications e.g. AI solutions for Barrett's esophagus are poorly studied. Although data volume is increasing there is a lack of studies dealing with the applicability and clinical impact of AI devices in endoscopy. In this Research Topic we seek papers addressing clinical problems especially at the stage of clinical application in endoscopy.
The aim of this Research Topic is to collect high-quality papers on AI research in the field of endoscopy. The planned issue should be clinically oriented which means that mere computer science contributions are not desired. All kinds of papers (case reports, original contributions, reviews) dealing with clinical aspects of AI in endoscopy are welcome. If possible, we would like to present at least one original contribution in this collection. The most relevant AI topics are currently (i) polyp characterization in the colon, (ii) detection or characterization of neoplastic Barrett's esophagus and (iii) diagnostic yield in small bowel capsule endoscopy. However, all other endoscopy AI topics are to be welcomed. We would like to motivate leading researchers to present their data in our Research Topic.
Topic Editor W.K. is co-founder and Chief Medical Officer of Docbot. Topic Editor T.R. received speaking fees from the following companies: Pentax Medical, Olympus Medical, Mauna Kea Technologies, Medtronic and Takeda Pharmaceuticals.
Topic Editors P.K. and B.W. have no competing interest in regards to the Research Topic subject.
