4.7 Review

Advances in optical gastrointestinal endoscopy: a technical review

Journal

MOLECULAR ONCOLOGY
Volume 15, Issue 10, Pages 2580-2599

Publisher

WILEY
DOI: 10.1002/1878-0261.12792

Keywords

gastrointestinal tract; machine learning; molecular probe; optical endoscopy

Categories

Funding

  1. National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health [R21EB023431]

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Optical endoscopy is crucial for diagnosing and treating GI malignancies. Recent advancements in the field include novel optical imaging technologies, molecular probes, and AI algorithms, which show promise in improving detection and diagnosis of early cancers and precancerous lesions.
Optical endoscopy is the primary diagnostic and therapeutic tool for management of gastrointestinal (GI) malignancies. Most GI neoplasms arise from precancerous lesions; thus, technical innovations to improve detection and diagnosis of precancerous lesions and early cancers play a pivotal role in improving outcomes. Over the last few decades, the field of GI endoscopy has witnessed enormous and focused efforts to develop and translate accurate, user-friendly, and minimally invasive optical imaging modalities. From a technical point of view, a wide range of novel optical techniques is now available to probe different aspects of light-tissue interaction at macroscopic and microscopic scales, complementing white light endoscopy. Most of these new modalities have been successfully validated and translated to routine clinical practice. Herein, we provide a technical review of the current status of existing and promising new optical endoscopic imaging technologies for GI cancer screening and surveillance. We summarize the underlying principles of light-tissue interaction, the imaging performance at different scales, and highlight what is known about clinical applicability and effectiveness. Furthermore, we discuss recent discovery and translation of novel molecular probes that have shown promise to augment endoscopists' ability to diagnose GI lesions with high specificity. We also review and discuss the role and potential clinical integration of artificial intelligence-based algorithms to provide decision support in real time. Finally, we provide perspectives on future technology development and its potential to transform endoscopic GI cancer detection and diagnosis.

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