Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep...

Area of Science:

• Biomedical Engineering
• Optical Imaging
• Artificial Intelligence

Background:

• Global population aging increases age-related diseases like cancer, straining healthcare systems.
• Early cancer detection is crucial for reducing morbidity and improving patient outcomes.
• Current endoscopic approaches require enhancement for improved gastrointestinal cancer diagnostics.

Purpose of the Study:

• To review emerging technologies for next-generation endoscopy systems.
• To explore the potential of biomimetic vision, non-linear optical microscopies, and Deep Learning in cancer diagnostics.
• To discuss the translation of these technologies into clinical practice for enhanced gastrointestinal imaging.

Main Methods:

• Review of prominent endoscopic approaches for gastric cancer diagnostics.
• Analysis of advancements in biomimetic vision (compound eye cameras).
• Exploration of non-linear optical microscopies for label-free in vivo tissue characterization.
• Integration of Deep Learning for intelligent automation in diagnostics.

Main Results:

• Biomimetic vision offers enhanced lesion detection capabilities.
• Non-linear optical microscopies enable label-free in vivo tissue characterization.
• Deep Learning facilitates intelligent automation for gastrointestinal cancer diagnosis.

Conclusions:

• Emerging technologies promise to significantly enhance gastrointestinal cancer detection, characterization, and confirmation.
• Addressing challenges in translation is key to realizing next-generation endoscopic systems.
• A future integrated system could revolutionize gastrointestinal imaging and diagnostics.