Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Confirm that cOlon Neoplasm Finely can be IDENTIfied by AI without overLooking; CONFIDENTIAL study

ABSTRACT: Primary outcome(s): Comparison of overall adenoma lesion detection rate (ADR) between endoscopists and Artificial Intelligence

DISEASE(S): Suspected Colorectal Tumor (including Colorectal Adenoma)

PROVIDER: 2661402 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

ACCESS DATA

Json Xml

Similar Datasets

Impact of AI on Trainee ADR

Project description:Adenoma detection rate (ADR) is a validated quality metric for colonoscopy with higher ADR correlated with improved colorectal cancer outcomes. Artificial intelligence (AI) can automatically detect polyps on the video monitor which may allow endoscopists in training to improve their ADR. Objective and Purpose of the study: Measure the effect of AI in a prospective, randomized manner to determine its impact on ADR of Gastroenterology trainees.

| 2736199 | ecrin-mdr-crc

Adenoma Detection Rate Using AI System in China

Project description:The primary aim of this study is - to explore the usefulness of Artificial Intelligence system in colonoscopy on adenoma detection rate (ADR). Other aims include to explore the data below when Artificial Intelligence is used. Mean adenomas detected per procedure, MAP Proximal Adenoma detection rate, pADR Polyp detection rate, PDR Proximal polyp detection rate, pPDR Mean polyps detected per procedure, MPP Withdrawal time, WT Cecal intubation rate, CIR Cecal intubation time, CIT

| 2297159 | ecrin-mdr-crc

A Prospective Randomized Study Comparing the Adenoma Detection Yield of SC, AI and Combined AI and G-EYE

Project description:This is a multicenter, 3-arm, randomized, open-label study. Subjects referred to colonoscopy for screening or surveillance will be randomized in a 2:5:5 into one of the following arms: (i) Standard Colonoscopy (ii) Artificial Intelligence Aided Colonoscopy (GI Genius) (iii) Combined Artificial Intelligence Aided Colonoscopy (GI Genius) and G-EYE Balloon Aided Colonoscopy

| 2717868 | ecrin-mdr-crc

Real-time use of artificial intelligence (CAD EYE) in the colorectal cancer surveillance of Lynch syndrome patients (CADLYII) – an international multicenter trial

Project description:Interventions: Group 1: Standard high-definition white-light endoscopy (HD-WLE) in Lynch syndrome patients during colorectal cancer surveillance Group 2: Standard high-definition white-light endoscopy (HD-WLE) and additional real-time use of artificial intelligence (CADEYE Fujifilm) in Lynch syndrome patients during colorectal cancer surveillance Primary outcome(s): Comparison of the adenoma detection rate (ADR) during high-definition white-light endoscopy (HD-WLE) with versus without real-time automated detection (CAD EYE Fujifilm) Study Design: Allocation: Randomized controlled study; Masking: Open (masking not used); Control: active; Assignment: parallel; Study design purpose: diagnostic

| 72537 | ecrin-mdr-crc

Development and validation of AI/ML derived splice-switching oligonucleotides

Project description:Splice-switching oligonucleotides (SSOs) are antisense compounds that act directly on pre-mRNA to modulate alternative splicing (AS). This study demonstrates the value that artificial intelligence/machine learning (AI/ML) provides for the identification of functional, verifiable, and therapeutic SSOs. We trained XGboost tree models using splicing factor (SF) pre-mRNA binding profiles and spliceosome assembly information to identify modulatory SSO binding sites on pre-mRNA. Using Shapley and out-of-bag analyses we also predicted the identity of specific SFs whose binding to pre-mRNA is blocked by SSOs. This step adds considerable transparency to AI/ML-driven drug discovery and informs biological insights useful in further validation steps. We applied this approach to previously established functional SSOs to retrospectively identify the SFs likely to regulate those events. We then took a prospective validation approach using a novel target in triple negative breast cancer (TNBC), NEDD4L exon 13 (NEDD4Le13). Targeting NEDD4Le13 with an AI/ML-designed SSO decreased the proliferative and migratory behavior of TNBC cells via downregulation of the TGFβ pathway. Overall, this study illustrates the ability of AI/ML to extract actionable insights from RNA-seq data.

2024-04-25 | GSE215917 | GEO

Capsule Endoscopy Delivery at sCale Through Enhanced AI anaLysis Study

Project description:The CESCAIL study tests the use of Artificial Intelligence (AI) on a video taken from a minimally-invasive imaging device, to improve efficiency and accuracy of the detection of polyps, which are little outgrowths within the lining of the bowel.

| 71513 | ecrin-mdr-crc

Adenoma Detection Rate in Artificial Intelligence-assisted Colonoscopy

Project description:The goal of this cluster randomized multicenter controlled clinical trial (RCT) is to investigate whether a combined real time computer-aided polyp detection (CADe) and computer-aided polyp characterization (CADx) system (GI Genius, Medtronic) can increase the adenoma detection rate (ADR) and reduce the performance variability among endoscopists. Participants will be randomized (1:1) to either receive an AI-assisted colonoscopy (AIC) or a conventional colonoscopy (CC). If there is a comparison group: Researchers will compare the AIC-group and the CC-group to see if AIC can increase the ADR significantly.

| 9267 | ecrin-mdr-crc

Adenoma Miss rate in Artificial Intelligence-Based versus Conventional Colonoscopy, A Prospective Randomized Trial

Project description:Interventions: Use the Artificial Intelligence Colonoscopy first to CRC screening, if the polyp or adenoma was seen, remove it After that, use the conventional colonoscopy to screening again for detection the polyp or adenoma,Use the Conventional colonoscopy first to CRC screening, if the polyp or adenoma was seen, remove it After that, use the Artificial intelligence colonoscopy to screening again for detection the polyp or adenoma;Active Comparator Device,Active Comparator Device;CADe-Conventional,Conventional-CADe Primary outcome(s): Adenoma Miss Rate the day colonoscopy the number adenoma detected at second colonoscopy divided by the total number of lesions detected at first and second colonoscopy Study Design: Randomized

| 75207 | ecrin-mdr-crc

The validation study of detectability and diagnostic accuracy of AI-aided endoscopic diagnosis system for colonoscopy

Project description:Interventions: Detection and diagnosis support for colorectal lesions to doctors by AAE system 1.Standard colonoscopy-first-group: Standard colonoscopy is performed first, followed by colonoscopy under the support of the AAE system 2.AAE-first-group: Colonoscopy under the support of the AAE system is performed first, followed by normal colonoscopy All colorectal polyps detected in any group are removed;D003113;Artificial Intelligence/Detection and diagnosis support Primary outcome(s): Adenoma miss rate Study Design: randomized controlled trial, single blind, no treatment control/standard of care control, parallel assignment, diagnostic purpose

| 2614654 | ecrin-mdr-crc

Retrospective Evaluation of 3 Colonic Adenoma Detection Strategies

Project description:Retrospective study, single blind (patient), allowing a posteriori clinical data collection of 90 patients during their passage to the ambulatory endoscopy circuit, to consider 3 groups and thus to deduce a colonic adenoma detection rate for each arm : * Colonoscopy Only Group * Artificial intelligence only group (IA GI GENIUS alone) * Endoscopic Cap and Artificial Intelligence Group (endoscopy cap associated with the GI GENIUS IA System)

| 2392394 | ecrin-mdr-crc

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data