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Intraoperative Gastroscopy for Tumor Localization in Laparoscopic Surgery for Gastric Adenocarcinoma

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Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms.

Ryosuke Kikuchi¹, Kazuaki Okamoto¹, Tsuyoshi Ozawa^2,3

¹Department of Surgical Oncology, Faculty of Medicine, The University of Tokyo, Tokyo, Japan.

|July 28, 2024

Summary

Artificial intelligence (AI) systems enhance gastrointestinal endoscopy by improving the detection and diagnosis of neoplasms. These computer-aided detection and diagnosis tools show expert-level performance, aiding endoscopists and reducing missed lesions.

Keywords:

Artificial intelligence Colorectal polyps Deep learning Esophageal cancer Gastric cancer

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Area of Science:

Gastroenterology
Medical Imaging
Artificial Intelligence

Background:

Deep learning-based AI is increasingly used in medical fields, particularly in gastroenterology for gastrointestinal (GI) endoscopy.
Computer-aided detection/diagnosis (CAD) systems assist endoscopists in identifying and differentiating GI neoplasms.
AI systems for colorectal polyps are in clinical practice, and CAD systems for upper GI neoplasms are available in Asian countries.

Purpose of the Study:

To review recent studies on endoscopic AI systems for GI neoplasms.
To focus on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps.
To evaluate the performance and utility of AI in endoscopic diagnostics.

Main Methods:

Review of recent articles on endoscopic AI systems.
Analysis of meta-analyses and randomized controlled trials (RCTs) for CADe and CADx systems.
Focus on diagnostic performance metrics such as sensitivity and specificity.

Main Results:

CADe systems for ESCC and EAC show high sensitivities (91.2%, 93.1%) and specificities (80%, 86.9%).
CADe systems for GC achieve high sensitivity and specificity (around 90%), with RCTs demonstrating reduced miss rates.
CADx systems for GC show expert-level performance, and AI systems for colorectal polyps improve detection rates and differentiation accuracy.

Conclusions:

Endoscopic AI systems generally perform better than nonexpert endoscopists and are comparable to expert endoscopists.
AI tools can significantly reduce the risk of overlooking GI lesions.
These systems hold promise for enhancing diagnostic accuracy in GI endoscopy.