analysis of new endoscopic features and variable stiffness in colonoscopy: prospective randomised trial

Conditions: Colorectal Adenoma; Colorectal Adenomatous Polyp

Summary: the aim of the present study is to develop and evaluate a computer-based methods for automated and improved detection and classification of different colorectal lesions, especially polyps. for this purpose first, pit pattern and vascularization features of up to 1000 polyps with a size of 10 mm or smaller will be detected and stored in our web based picture database made by a zoom bli colonoscopy. these polyps are going to be imaged and subsequently removed for histological analysis. the polyp images are analyzed by a newly developed deep learning computer algorithm. the results of the deep learning automatic classification (sensitivity, specificity, negative predictive value, positive predictive value and accuracy) are compared to those of human observers, who were blinded to the histological gold standard. in a second approach we are planning to use lci of the colon, rather than the usual white light. here, we will determine, whether this technique could improve the detection of flat neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and serrated polyps. the polyps are called serrated because of their appearance under the microscope after they have been removed. they tend to be located up high in the colon, far away from the rectum. they have been definitely shown to be a type of precancerous polyp and it is possible that using lci will make it easier to see them, as they can be quite difficult to see with standard white light.

computer-based classification and differentiation of colorectal polyps using blue light imaging (bli) purpose recent studies have shown that optical chromoendoscopy with narrow-band imaging (nbi) of fuji intelligent color enhancement (fice) is a powerful diagnostic tool for the differentiation between neoplastic and non-neoplastic colorectal polyps. linked color imaging (lci) and blue laser imaging (bli) are two new imaging systems used in endoscopy which are recently developed. bli was developed to compensate for the limitations of nbi. bli shows a bright image of the digestive mucosa, enabling the detailed visualization of both the microstructure and microvasculature. the eluxeo™ endoscopic system powered by fujifilm's unique 4-led (light-emitting diode) multi light™ technology sets a new standard in light intensity and electronic chromoendoscopy imaging. by combining four different wavelengths and the specific application of intensified from light spectra created by the integrated light source, this technology allows to easily switch between the three imaging modes white light, blue light imaging (bli) and linked colour imaging (lci). blue light imaging (bli) is a new system for image-enhanced electronic chromoendoscopy, since the 410 nm led visualizes vascular microarchitecture, similar to narrow band imaging, and a 450 nm provides white light by excitation. according to three recently published reports, the diagnostic ability of polyp characterization using blue light imaging compares favorably with narrow band imaging. no published data are available to date regarding computer assisted polyp characterization with blue light imaging. the aim of the present study is to develop and evaluate a computer-based method for automated classification of small colorectal polyps on the basis of pit pattern and vascularization features. in this prospective study up to 1000 polyps with a size of 10 mm or smaller should be detected and stored in our web based picture database made by a zoom bli colonoscopy. these polyps were imaged and subsequently removed for histological analysis. the polyp images were analyzed by a newly developed deep learning computer algorithm. the proposed computer-based method consists of several steps: picture annotation, preprocessing, vessel segmentation, feature extraction and classification, parameterization, and finally train and test of the multiple neural layer algorithms. the results of the deep learning automatic classification (sensitivity, specificity, negative predictive value, positive predictive value and accuracy) were compared to those of human observers, who were blinded to the histological gold standard. condition colorectal polyps with a size less then 10 mm study type: observational study design: observational model: cohort time perspective: prospective official title: computer-based classification and differentiation of colorectal polyps using fujifilm blue light imaging (bli) linked color imaging (lci) and magnifying blue laser imaging (bli) are two new imaging systems used in endoscopy which are recently developed. the newly developed lci system (fujifilm co.) creates clear and bright endoscopic images by using short-wavelength narrow-band laser light combined with white laser light on the basis of bli technology. lci makes red areas appear redder and white areas appear whiter. thus, it is easier to recognize a slight difference in color of the mucosa. the aim the present study to determine if using lci of the colon, rather than the usual white light on the colon, will improve the detection of flat neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and serrated polyps. the polyps are called serrated because of their appearance under the microscope after they have been removed. they tend to be located up high in the colon, far away from the rectum. they have been definitely shown to be a type of precancerous polyp and it is possible that using lci will make it easier to see them, as they can be quite difficult to see with standard white light.

Eligibility criteria:
inclusion criteria: * the patient must sign, understand and provide written consent for the procedure. * undergoing colonoscopy at our endoscopy unit for any indication in propofol deep sedation * intact colon and rectum * asa (american society of anesthesiology) risk class 1, 2 or 3 exclusion criteria: * patients with inflammatory bowel disease; * patients with poor bowel preparation; (boston score \<4) * female patients with pregnancy; * patients with mechanical bowel obstruction; * patients with diverticulitis or toxic megacolon; * patients with a history of radiation therapy to abdomen or pelvis; * patients with a history of severe cardiovascular, pulmonary, liver or renal disease and high asa (\>3) risk of propofol sedation; * personal history of coagulation disorders or use of anticoagulants; * patients who are currently enrolled in another clinical investigation in which the intervention might compromise the safety of the patient's participation in this study.

Keywords: blue light imaging linked color imaging optical histology adenoma detection rate variable stiffness deep learning