In Chinese journal of cancer research = Chung-kuo yen cheng yen chiu
White-light endoscopy with tissue biopsy is the gold standard interface for diagnosing gastric neoplastic lesions. However, misdiagnosis of lesions is a challenge because of operator variability and learning curve issues. These issues have not been resolved despite the introduction of advanced imaging technologies, including narrow band imaging, and confocal laser endomicroscopy. To ensure consistently high diagnostic accuracy among endoscopists, artificial intelligence (AI) has recently been introduced to assist endoscopists in the diagnosis of gastric neoplasia. Current endoscopic AI systems for endoscopic diagnosis are mostly based upon interpretation of endoscopic images. In real-life application, the image-based AI system remains reliant upon skilful operators who will need to capture sufficiently good quality images for the AI system to analyze. Such an ideal situation may not always be possible in routine practice. In contrast, non-image-based AI is less constraint by these requirements. Our group has recently developed an endoscopic Raman fibre-optic probe that can be delivered into the gastrointestinal tract via the working channel of any endoscopy for Raman measurements. We have also successfully incorporated the endoscopic Raman spectroscopic system with an AI system. Proof of effectiveness has been demonstrated in in vivo studies using the Raman endoscopic system in close to 1,000 patients. The system was able to classify normal gastric tissue, gastric intestinal metaplasia, gastric dysplasia and gastric cancer, with diagnostic accuracy of >85%. Because of the excellent correlation between Raman spectra and histopathology, the Raman-AI system can provide optical diagnosis, thus allowing the endoscopists to make clinical decisions on the spot. Furthermore, by allowing non-expert endoscopists to make real-time decisions as well as expert endoscopists, the system will enable consistency of care.
Ho Khek Yu
2022-Oct-30
Raman spectroscopy, artificial intelligence, diagnosis, gastric cancer
