Abstract
Artificial intelligence (AI) and its application in medicine has grown large interest. Within gastrointestinal (GI) endoscopy, the field of colonoscopy and polyp detection is the most investigated, however, upper GI follows the lead. Since endoscopy is performed by humans, it is inherently an imperfect procedure. Computer-aided diagnosis may improve its quality by helping prevent missing lesions and supporting optical diagnosis for those detected. An entire evolution in AI systems has been established in the last decades, resulting in optimization of the diagnostic performance with lower variability and matching or even outperformance of expert endoscopists. This shows a great potential for future quality improvement of endoscopy, given the outstanding diagnostic features of AI. With this narrative review, we highlight the potential benefit of AI to improve overall quality in daily endoscopy and describe the most recent developments for characterization and diagnosis as well as the recent conditions for regulatory approval.
Highlights
In the last decades there is a growing attention for artificial intelligence (AI) in biomedical sciences
Several factors influence the adenoma detection rate (ADR) of an endoscopist, so Artificial intelligence (AI) might be an additional tool to improve the ADR.[8]. To improve this scattered quality landscape, AI is expected to support all endoscopists and improve overall daily performances and quality. In this physician – engineer co-authored narrative review article we summarize the literature of AI in both upper and lower GI endoscopy and how AI can improve quality of daily endoscopy
In this review we highlighted the potential benefit of AI to improve overall quality in daily endoscopy
Summary
In the last decades there is a growing attention for artificial intelligence (AI) in biomedical sciences. The same sensitivity and specificity results have been recently shown in CADe models for detection based on NBI images by two other groups.[38,39] CADx models for the diagnosis of the invasion depth of early gastric cancer have been developed by Kubota et al and improved by Zhu et al, resulting higher sensitivity and specificity than those achieved with endoscopists’ visual inspection.[40,41] three research groups recently developed and validated their deep learning models for recognition of gastric deformities (ulcers, cancer, polyps, erosions).[42,43,44]
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Schedule a callMore From: Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
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