View clinical trials related to Computer-aided Diagnosis.
Filter by:Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images. Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Linked color imaging (LCI),a new endoscopy modality, creates clear and bright images by using short wavelength narrow band laser light. LCI can make red area appear redder and white areas appear whiter. Thus, it may be possible to distinguish adenoma and non-adenoma polyps based on color evaluation of LCI images. This study aimed to assess the correlation between histology results and LCI images. Moreover, the investigators conducted a pilot study to explore the clinical potential of LCI to distinguish adenoma and non-adenoma polyps and the accuracy of an automatic computer-aided diagnosis system using LCI imagine to predict histology polyps when compared to human experts physicians.