Adequacy of manual endoscopic inspection of the upper and lower gastrointestinal mucosa is operator-dependent: it is common knowledge that the likelihood of finding lesions and the degree of cancer prevention of manual endoscopic procedures is dependent on the skillset and effort by endoscopists. Recent developments in artificial intelligence allow measurement of skillset and effort during actual endoscopic procedures. Many endoscopic features, representing skillset and effort, such as clarity of image, absence of stool, looking sideways, performing retroflexion and obtaining all possible mucosal views, can be classified or counted, and the results presented in real-time to endoscopists and stored at the end of the procedure as an automated, objective report with representative images documenting adequacy of inspection. Real-time feedback provides endoscopists the option, when measurements suggest inadequate or incomplete inspection, to change technique or repeat and expand inspection. However, responding to real-time feedback and obtaining best possible measurements or all possible mucosal views do not equate to careful inspection; endoscopists may be focused on obtaining best measurements instead of inspecting the mucosa. Therefore, prospective studies with long-term follow-up will be required to determine whether artificial intelligence driven real-time feedback will lead not only to better intra-procedural measurements but also to improved patient outcome, for example, a decrease in gastrointestinal cancer incidence and mortality.
|Original language||English (US)|
|Number of pages||9|
|Journal||Techniques and Innovations in Gastrointestinal Endoscopy|
|State||Published - Apr 2020|
Bibliographical noteFunding Information:
Financial support: This work was supported by grant DK106130 from the NIDDK and the University of Minnesota.Conflicts of interest: Piet de Groen has a financial interest in EndoMetric INC, a company that analyzes colonoscopy video streams for features of quality.
Financial support: This work was supported by grant DK106130 from the NIDDK and the University of Minnesota .
© 2019 Elsevier Inc.
- Artificial intelligence
- Deep learning
- Mucosal inspection
- Real-time feedback