AI-Assisted Diagnosis for Stomach Problems

(AI-OD Trial)

Recruiting at 1 trial location

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Daniel Von Renteln

Disqualifiers: Inflammatory bowel disease, Active colitis, Hereditary CRC syndrome, Coagulopathy, ASA status >3

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 2

Duration: Approximately 17 weeks

204 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new method of using artificial intelligence (AI) during colonoscopies to diagnose small growths in the colon, known as polyps. The researchers aim to determine if AI can accurately identify these polyps, either independently or with a doctor's assistance, compared to standard methods. The trial also examines how well the AI's findings align with those of expert doctors and whether using AI can reduce costs for medical tests. Suitable candidates for this trial include individuals scheduled for an outpatient colonoscopy at the Centre Hospitalier de l'Université de Montréal, excluding those with conditions like active colitis or certain genetic syndromes. As an unphased trial, this study provides a unique opportunity to contribute to cutting-edge research that could enhance colonoscopy procedures for future patients.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. Please consult with the trial coordinators for more details.

What prior data suggests that this AI-assisted diagnosis method is safe for use in colonoscopy procedures?

Research has shown that using artificial intelligence (AI) to help find polyps during colonoscopies is generally safe and well-received. Studies indicate that AI predicts the type of polyps, assisting doctors in deciding whether to remove them.

When AI is used alongside an endoscopist, studies have found that this combination improves accuracy without adding extra risk to patients. The endoscopist, an expert in examining the digestive system, provides an additional layer of safety.

In cases where AI operates independently during the procedure, research indicates that this method is also safe and provides results as accurate as traditional methods.

Both approaches have been tested for accuracy and effectiveness in real-time situations. These studies suggest that AI-assisted systems are as safe as regular diagnostic procedures. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about AI-assisted classification for stomach problems because it has the potential to enhance the accuracy and efficiency of diagnosing diminutive colorectal polyps during colonoscopy procedures. Unlike traditional methods that rely solely on an endoscopist's judgment, this approach leverages the CAD-eye detection and classification system, which can work with or without the endoscopist's input. This dual approach not only aims to improve diagnostic precision but also helps in streamlining the procedure, potentially leading to quicker diagnoses and better patient outcomes. By utilizing advanced AI technology, this method could significantly reduce the chances of missing serrated polyps, offering a promising improvement over existing diagnostic practices.

What evidence suggests that AI-assisted diagnosis is effective for stomach problems?

Studies have shown that using AI during a colonoscopy can help find more small growths in the colon, called polyps, compared to traditional methods. One study found that AI accurately and safely identified these small polyps. However, some research suggests that AI might not always excel at detecting very tiny polyps that could become cancerous. In this trial, participants will be assigned to one of two arms: one where the AI collaborates with a doctor's input using a system called CADx, and another where the AI operates autonomously. This system might also reduce costs by decreasing the need for lab tests on these polyps. ^{4 6 7 8 9}

Who Is on the Research Team?

Daniel von Renteln, MD

Principal Investigator

Centre hospitalier de l'Université de Montréal (CHUM)

Are You a Good Fit for This Trial?

This trial is for people aged 45-80 who are having an outpatient colonoscopy at the Centre Hospitalier de l'Université de Montréal. They must understand and agree to the study by signing a consent form. It's not for those with severe health risks (ASA status >3), inflammatory bowel diseases, active colitis, blood clotting disorders, or inherited colorectal cancer syndromes.

Inclusion Criteria

You are between 45-80 years old.

You have provided a signed consent document.

You are in the process of having a colonoscopy procedure at CHUM.

Exclusion Criteria

Inflammatory Bowel Disease

American Society of Anesthesiologists (ASA) status >3

Active colitis

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

1 visit (in-person)

Treatment

Participants undergo standard colonoscopy procedures with AI-assisted optical diagnosis for diminutive colorectal polyps

Approximately 17 weeks

1 visit (in-person) per patient

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Artificial intelligence-assisted classification (CADx)

Trial Overview

The study tests if artificial intelligence can help doctors decide how often patients need colonoscopies based on real-time analysis during the procedure. This AI-assisted method will be compared to decisions made by two expert endoscopists following established guidelines.

How Is the Trial Designed?

Group I: Autonomous AI-assisted classification Experimental Treatment 1 Intervention

AI-assisted classification for diminutive polyps during a colonoscopy procedure using the CAD-eye detection and classification system, with no input from the endoscopist, for patients who agree to undergo optical diagnosis of diminutive colorectal polyps.

Group II: AI-assisted classification with endoscopist's input Experimental Treatment 1 Intervention

AI-assisted classification for diminutive polyps during a colonoscopy procedure using the CAD-eye detection and classification system, with input from the endoscopist in the case of serrated polyps, for patients who agree to undergo optical diagnosis of diminutive colorectal polyps.

Who Is Running the Clinical Trial?

Daniel Von Renteln

Lead Sponsor

Trials

1

Recruited

200+

Centre hospitalier de l'Université de Montréal (CHUM)

Lead Sponsor

Trials

389

Recruited

143,000+

Published Research Related to This Trial

The computer-aided diagnosis system demonstrated a high accuracy rate, correctly diagnosing 78.8% of acute abdominal pain cases and 70% of dyspepsia cases in a study of 205 patients from Sherbrooke, Quebec.

The system was particularly effective for diagnosing appendicitis (97% accuracy) and cholecystitis (91% accuracy), but it struggled with pancreatitis, achieving only a 25% detection rate.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-aided diagnosis of gastroenterologic diseases in Sherbrooke: preliminary report. Horrocks, JC., Devroede, G., de Dombal, FT.[2007]

In a study of 165 patients at Airedale District General Hospital, a computer-aided diagnosis system accurately predicted 83% of positive lesions found during endoscopy, including nearly all cases of gastric cancer.

The findings suggest that the computer-aided system can effectively transfer between locations and may help identify 'high-risk' patients who require more thorough investigation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Transfer of computer-aided diagnosis of dyspepsia from one geographical area to another. Horrocks, JC., Lambert, DE., McAdam, WA., et al.[2019]

The proposed artificial intelligence-based decision support system can effectively classify five subtypes of gastric cancer, achieving a class-average sensitivity of over 0.85, which enhances the accuracy of treatment planning.

AI-assisted pathologists showed significantly improved diagnostic sensitivity and reduced screening time compared to traditional human pathologists, indicating the system's potential to improve clinical outcomes in gastric cancer management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment. Oh, Y., Bae, GE., Kim, KH., et al.[2023]

In a study of 212 patients with dyspepsia, the computer-aided diagnosis achieved a high diagnostic accuracy of 92.6% after full investigation, indicating its effectiveness in identifying conditions before surgery.

The computer system demonstrated an overall diagnostic accuracy of 87.7% based on initial interviews, and it was particularly successful in distinguishing organic from functional dyspepsia, correctly categorizing 22 out of 23 patients with organic disease.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-aided diagnosis of "dyspepsia". Horrocks, JC., de Dombal, FT.[2022]

Computer-assisted systems in esogastroduodenoscopy show promising results for quality monitoring and accurately detecting gastric lesions, often matching or surpassing the performance of experienced endoscopists.

These AI systems can help determine the best treatment strategies for gastric cancer, potentially reducing unnecessary surgeries and improving patient outcomes, while ongoing clinical trials aim to enhance their real-world application.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The role of computer-assisted systems for upper-endoscopy quality monitoring and assessment of gastric lesions. Lazăr, DC., Avram, MF., Faur, AC., et al.[2023]

A novel AI system called MMP-AI was developed to automatically detect and classify protruded gastric lesions, achieving high diagnostic accuracy with AUC scores of 0.896 for GISTs, 0.890 for GILs, and 0.999 for GEPs based on data from 1,366 patients.

MMP-AI outperformed experienced endoscopists in recognizing submucosal tumors, indicating its potential to improve diagnostic accuracy and reduce variability in routine clinical practice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Multimodal Multipath Artificial Intelligence System for Diagnosing Gastric Protruded Lesions on Endoscopy and Endoscopic Ultrasonography Images. Zhu, C., Hua, Y., Zhang, M., et al.[2023]

An integrated and automated system has been developed to collect and analyze clinical and instrumental data from dyspeptic patients, which may enhance the accuracy of diagnosing dyspepsia.

The system not only focuses on dyspepsia but also shows potential for application in diagnosing absorption and hepatic problems, indicating its versatility in gastrointestinal diagnostics.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Breath Multi Analysis: a database to collect data on gastric related non invasive analysis. Giacomini, M., Peragallo, I., Bertone, S., et al.[2004]

The AI diagnostic tool 'Tango' outperformed specialist endoscopists in sensitivity for detecting early gastric cancers (EGCs), achieving 84.7% sensitivity compared to 65.8% for specialists, indicating its potential to enhance diagnostic accuracy.

Tango also showed superior sensitivity and accuracy compared to nonspecialist endoscopists, suggesting that this AI tool could significantly reduce misdiagnosis rates in early gastric cancer detection.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Performance of an artificial intelligence-based diagnostic support tool for early gastric cancers: Retrospective study. Ishioka, M., Osawa, H., Hirasawa, T., et al.[2023]

AI has the potential to significantly enhance the diagnosis and treatment of gastrointestinal conditions by improving image analysis and integrating complex data, which can lead to more accurate assessments and personalized therapies.

In managing diseases like inflammatory bowel disease and peptic ulcer bleeding, AI can predict disease outcomes, potentially increasing treatment efficacy, but there are important considerations regarding the responsibility and liability of AI in medical practice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence in gastroenterology: where are we heading? Sung, JJ., Poon, NC.[2021]

Citations

1.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC12540608/

Effectiveness of artificial intelligence-assisted colonoscopy ...

Recent studies have revealed conflicting results [76], indicating that real-time polyp assessment using CADx did not significantly enhance the ...

2.

sciencedirect.com

sciencedirect.com/science/article/pii/S1091255X23011848

Artificial Intelligence-Assisted Colonoscopy for Detection of ...

In this study, we found that AI-assisted colonoscopy could significantly increase the number of polyps detected compared with traditional colonoscopy, and the ...

3.

gastrojournal.org

gastrojournal.org/article/S0016-5085(24)00131-8/fulltext

Autonomous Artificial Intelligence vs ...

The findings suggest that optical diagnosis of diminutive polyps can be accurately and safely performed using autonomous AI. This would allow the potential for ...

4.

evidence.nejm.org

evidence.nejm.org/doi/full/10.1056/EVIDoa2200003

Real-Time Artificial Intelligence–Based Optical Diagnosis of ...

Our study indicates that real-time AI with CADx may not significantly increase the sensitivity for small neoplastic polyps. However, CADx may ...

5.

wjgnet.com

wjgnet.com/2689-7164/full/v5/i1/90574.htm

Artificial intelligence for characterization of diminutive ...

Artificial intelligence for characterization of diminutive colorectal polyps: A feasibility study comparing two computer-aided diagnosis systems.

6.

clinicaltrials.gov

clinicaltrials.gov/study/NCT06059378

Using AI-assisted Optical Polyp Diagnosis for Diminutive ...

This is a prospective study that is the first to implement resect and discard and diagnose and leave strategies in real-time practice.

7.

pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC10584286/

Artificial Intelligence–Assisted Optical Diagnosis

This article reviews the potential of artificial intelligence to enhance the accuracy of polyp diagnosis through computer-aided diagnosis (CADx).

8.

sciencedirect.com

sciencedirect.com/science/article/pii/S0016508524001318

Autonomous Artificial Intelligence vs ...

Artificial intelligence (AI)–based optical diagnosis systems (CADx) have been developed to allow pathology prediction of colorectal polyps during colonoscopies.

9.

cghjournal.org

cghjournal.org/article/S1542-3565(22)00639-5/fulltext

Artificial Intelligence Allows Leaving-In-Situ Colorectal Polyps

Artificial Intelligence (AI) could support cost-saving strategies for colonoscopy because of its accuracy in the optical diagnosis of colorectal polyps.