Neoplasms

All Conditions

Symptoms and General Pathology

Adenoma

Polyps

Adenomatous Polyps

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

A standard colonoscopy will be performed according to the standard of routine care. All optically diagnosed polyps will be removed and sent to the CHUM pathology laboratory for histopathological evaluation according to institutional standards. The AI system will capture video of the procedure in real time, and provide additional information about polypectomy procedures.

The AI system will capture the live video of the procedure and the AI feedbackwill be shown on a second screen installed next to the regular endoscopy screen. Screen A will show the regular endoscopy image and screen B will show the regular endoscopy image together with the areas that might harbor a polyp and the information to help the polypectomy.

Computer-aided polypectomy decision support by Artificial Intelligence

Daniel von Renteln

Quality components of colonoscopy include the detection and complete removal of colorectal polyps, which are precursors to CRC. However, endoscopic ablation may be incomplete, posing a risk for the development of "interval cancers". The investigators propose to develop a solution based on artificial intelligence (AI) (CADp computer-aided decision support polypectomy) to solve this problem.This research project aims to develop CADp, a computer decision support solution (CDS) for the ablation of colorectal polyps from 1 to 20 mm.

This research project aims to develop CADp, a computer-based decision support (CDS) solution for the removal of colorectal polyps ranging from 1-20 mm. The investigators will use a video and image dataset of polypectomy procedures to train the CADp model; thus, it can provide real-time overlaid video feedback for polypectomy procedures based on five specific metrics: 1) estimation of polyp size; 2) prediction of morphology and histology; 3) suggestion of an appropriate resection accessory and technical approach based on the characteristics, size, and histology of the polyp according to current guidelines; 4) image overlay, based on semantic image segmentation technology, showing the extent of the lesion and suggestion of an appropriate resection margin contour around the polyp to ensure its complete removal; 5) post-resection analysis to identify any remnant polyp tissue or insufficient resection margin that may increase this risk.

The investigators will collect a set of images and video data from live polypectomy procedures to leverage recent advances in AI technology to train deep learning models. This dataset will be obtained prospectively from a cohort of adults (ages 45-80) undergoing screening, diagnostic, or surveillance colonoscopies. To train the CADp solution, the investigators will obtain the corresponding completeness of resection status using the yield of post-resection margin biopsies. The dataset will be divided into two groups, the training, and the CADp test, respectively.

prospective, multi-endoscopist, single center, clinical study at tertiary referral center (CHUM)

Development of a Computer-aided Polypectomy Decision Support

accuracy with which the CADp system predicts completeness of polypectomy in the test set with the reference standard for completeness being determined by the histology of post-polypectomy margin biopsies; if free from any polyp tissue (adenomatous, serrated or hyperplastic), the resection will be considered complete. If remnant polyp tissue is detected in any one or more of the margin biopsies the resection is deemed incomplete

We will evaluate the agreement between the different subjective and objective ways of assessing the completeness of the polypectomy : evaluation of margins (presence or not, measurement of margins) by endoscopists self-assessment, and by expert consensus.

Evaluation of the concordance of data on polyp size, extension of margins around the polyp, quality of resection between clinical data (endoscopists' self-assessment and experts' assessments) and CADp prediction.

Based on the results and comparison of the different assessment methods, we will perform sensitivity analyses to assess the validity and robustness of the choice of primary outcome.

Centre hospitalier de l'Université de Montréal (CHUM)

Pathological Conditions, Anatomical

Colonoscopies will be performed according to the standard of care. Patients will undergo colonoscopies using CAD-eye (CADe and CADx) and Scale-eye. All optically diagnosed (CADx) polyps will be removed and sent to the CHUM pathology laboratory for histopathological evaluation according to institutional standards. CADx will be combined with human input for final diagnosis.

Real time polyp detection using a standard colonoscopy without CAD eye and without CADe. Patients will undergo a standard colonoscopy. Polyp size measurement will be assessed visually (phase 1 and 2) or with instruments such as snare or forceps (phase 3 or later) and polyp classification will be done by CADx without further input by the endoscopist. All optically diagnosed polyps will be removed and sent to the CHUM pathology laboratory for histopathological evaluation according to institutional standards.

Detection and classification and size measurement of polyp by Artificial Intelligence (CADeye) and Scale Eye This study will be conducted in multiple phases: phase 1 (pilot phase, n=40 polyps, about 60 patients) to evaluate the feasibility of applying Scale Eye in real-time practice, training endoscopists to work with CADe and CADx and determining the sample size and reference standard to evaluate SCALE-EYE during the second phase of the study (randomized controlled trial), phase 2 will evaluate the performance of SCALE-EYE and CAD-eye in real-time practice. The sample size for phase 2 (RCT comparing visual with scasle eye) will be based on the pilot data allowing to compare relative accuracy of size measurement with scale eye versus visual size estimation. After completion of phase 2 later phases will use instruments such as snare and/or forceps as reference measurement devices compared to relative accuracy of measurement when using scale eye.

Detection and classification and size measurement of polyp by Artificial Intelligence (Cad eye) and the Scale Eye

The investigators hypothesize that the clinical implementation of an AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps during colonoscopy. The purpose of this prospective clinical cohort study is to evaluate the performance of the SCALE-EYE virtual scale for measuring polyp size when used during live colonoscopies. The investigators also wish to evaluate CAD-eye for detection and classification of polyp histology. It is hypothesized that CAD-eye and SCALE-EYE can function in real-time practice with high accuracy.

Detailed Description:

This study will be conducted in several phases: phase 1 will evaluate the feasibility of using CAD-eye in real-time practice, training endoscopists to work with this platform, obtaining preliminary data on relative size measurement accuracy when using scale eye, testing feasibility of fresh polyp specimen size measurement and determining the sample size and reference standard for evaluating SCALE-EYE during the subsequent phases of the study in randomized controlled trials. Phase 2 will evaluate relative size measurement accuracy of SCALE-EYE compared to visual size estimation and CADe/CADx performance in a randomized controlled trial. Scale-eye will further be validated through video-based analysis comparing different size measurement methods (scale eye, snare, forceps, visual assessment) with different groups of endoscopists (experts, staff gastroenterologists, trainees evaluating videos showing video sequences that show the polyps either with scale eye, visual or instrument information. Furthermore, fresh specimen size measurement and pathology-based size (including different ways to prepare specimens for pathology sectioning) will be evaluated for agreement between size measurement methods and tissue shrinkage effects. Phase 3 will evaluate relative size measurement accuracy of SCALE-EYE compared to snare-based size estimation.

Eligible patients will be adults (aged 45 to 80 years) referred to CHUM for diagnostic, screening or surveillance colonoscopy. Patients will undergo colonoscopies using CAD-eye, and the size of all polyps detected will be estimated by different subjective and objective methods. All polyp size measurements will be videorecorded. Video recordings with different measurement instruments (forceps, snare, scale eye, visual assessment) will be assessed with different groups of endoscopists (experts, staff gastroenterologists, trainees) for size measurements through online video presentations in a randomized order. The main results of this study are to evaluate the relative accuracy of SCALE-EYE for polyp size estimation and the adenoma detection rates, sensitivity, specificity, NPV for CAD-eye for polyp detection and classification. We will also calculate other characteristics of CAD-eye and SCALE-EYE tests, the rate of divergence and/or agreement between subjective and objective methods of polyp size estimation, the agreement between surveillance intervals based on optical diagnosis by CAD-eye and pathological findings, and the false positive rate and latency for polyp detection.

prospective, multi-endoscopist, single center

The randomization would be concealed. The patient will be blind to the allocated groups. The endoscopist will be blind to the allocated groups until the beginning of the procedure. The pathologists will be blinded to the polyp size measurements by the endoscopists, the SCALE-EYE, or other methods.

Evaluation of CAD-EYE/SCALE-EYE for Detection, Classification, and Measurement of Colorectal Polyps: a Prospective Study

Performance Evaluation of CAD-EYE and SCALE-EYE for Detection, Classification, and Measurement of Colorectal Polyps: a Prospective Study

to compare the relative accuracies of SCALE-EYE size measurement, visual size estimation or size estimation based on a standard polypectomy snare for colorectal polyps during real-time colonoscopies with measurement of viable specimens by a Vernier caliper as reference standard. Relative accuracy is defined as "1-\[(ScaleEye measurement - gold standard measurement)/gold standard measurement\] x100". Scale: 0-100. Higher score is a better outcome.

compare the adenoma detection rate between the CAD-eye-assisted colonoscopy and standard colonoscopy (i.e., detecting adenomas only by endoscopists)

evaluate the accuracy, sensitivity, specificity, positive and negative predictive value of CADx for classifying 1-5 mm polyp histology into neoplastic and non-neoplastic compared with the histopathology outcomes as the reference standard

calculate the agreement and discrepancy between SCALE-EYE and other measurement methods

compare in video-based assessments with different endoscopists (expert level, staff GI, surgeons, trainees the relative size measurements accuracy of scale eye with visual and instrument based (snare and/or forceps) size measurement accuracy

evaluate the sensitivity of the CAD-eye in combination with adjunct methods for real-time detecting colorectal polyps compared with standard colonoscopy

to evaluate the proportion of polyps detected by the CAD-eye in combination with adjunct methods

compare the accuracy, sensitivity, specificity, positive and negative predictive value of CADx for classifying 1-5 mm polyp histology to that of using human decision based on CADx suggestion

calculate the proportion of 1-5 mm polyps, CADx can not suggest histology (in the intervention arm, please refer to the randomization section), while endoscopists can suggest histology with high confidence, or vice versa

evaluate diagnostic uncertainty of the current CADx module when predicting the pathology of colorectal polyps

evaluation of the relative accuracy of 3D scanned polyps compared to measurement of viable specimens by a Vernier caliper as reference standard. Relative accuracy is defined as "1-\[(ScaleEye measurement - gold standard measurement)/gold standard measurement\] x100". Scale: 0-100. Higher score is a better outcome.

evaluation of the relative accuracy of histologic size measurement compared to measurement of viable specimens by a Vernier caliper as reference standard. Relative accuracy is defined as "1-\[(ScaleEye measurement - gold standard measurement)/gold standard measurement\] x100". Scale: 0-100. Higher score is a better outcome.

A standard colonoscopy will be performed according to the standard of routine care. All optically diagnosed polyps will be removed and sent to the CHUM pathology laboratory for histopathological evaluation according to institutional standards. The AI system will capture video of the procedure in real time, and provide additional information on the detection of polyps, follow-up and prediction of pathology. The full-length colonoscopy videos will be annotated for the exact time of the identification of the anatomical landmarks, polyps, also for polyp- and procedural-related characteristics.

The AI system will capture the live video of the procedure and the AI feedback (polyp detection, tracking, and pathology prediction) will be shown on a second screen installed next to the regular endoscopy screen. Screen A will show the regular endoscopy image and screen B will show the regular endoscopy image together with the areas that might harbor a polyp or the information to predict pathology

Polyps detection by Artificial Intelligence

The investigators hypothesize that the clinical implementation of a deep learning AI system is an optimal tool to monitor, audit and improve the detection and classification of polyps and other anatomical landmarks during colonoscopy. The objectives of this study are to generate preliminary data to evaluate the effectiveness of AI-assisted colonoscopy on: a) the rate of detection of adenomas; b) the automatic detection of the anatomical landmarks (i.e., ileocecal valve and appendiceal orifice).

In this trial, the investigators aim to evaluate the followings:

1. the accuracy of automatic detection of important anatomical landmarks (i.e., ileocecal valve, appendiceal orifice);
2. the accuracy of automatic detection of polyps/adenomas (PDR/ADR);

Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps

Efficacy of AI assisted colonoscopy to detect the proportion of patients with at least 1 polyp. Polyp detection rate with an AI.

Compare of the automatic detection of the ileocecal valve, appendiceal orifice, and the automatic calculation of the withdrawal time with manual detection

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Development of a Computer-aided Polypectomy Decision Support	NCT04811937	Adenomatous Pol...	Computer-aided ...	45 Years - 80 Years	Centre hospitalier de l'Université de Montréal (CHUM)
Evaluation of CAD-EYE/SCALE-EYE for Detection, Classification, and Measurement of Colorectal Polyps: a Prospective Study	NCT05236790	Colorectal Poly... Colorectal Aden...	Detection and c...	45 Years - 80 Years	Centre hospitalier de l'Université de Montréal (CHUM)
Evaluation of CAD-EYE/SCALE-EYE for Detection, Classification, and Measurement of Colorectal Polyps: a Prospective Study	NCT05236790	Colorectal Poly... Colorectal Aden...	Detection and c...	45 Years - 80 Years	Centre hospitalier de l'Université de Montréal (CHUM)
Artificial Intelligence for Real-time Detection and Monitoring of Colorectal Polyps	NCT04586556	Adenomatous Pol...	Polyps detectio...	45 Years - 80 Years	Centre hospitalier de l'Université de Montréal (CHUM)

Spots Global Cancer Trial Database for polyps classification