Gastrointestinal DiseasesNCT06279546
Artificial Intelligence vs Endoscopist Identification in EUS Normal Anatomy
An observational study of Gastrointestinal Diseases, run by Instituto Ecuatoriano de Enfermedades Digestivas at 1 site in Ecuador.
Trial summary
Endoscopic ultrasound (EUS) visual impression is operator-dependant and can hinder diagnostic accuracy, especially in less experienced endoscopists. The implementation of artificial intelligence can potentially mitigate operator dependency and interpretation variability, helping or improving the overall accuracy. Read more about this study
- Who is it for?
- Ages 18 to 99 years
- Conditions the study is for
- Gastrointestinal Diseases
- What is being tested?
- Detection of structures
- How many people
- 30 participants
- Where
- 1 siteEcuador
- Study dates
- 1 May 2023to 26 Jan 2024
- How long will I take part?
- 7 Days
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Expert and non-expert gastrointestinal EUS-endoscopists.
Who may be able to join
- Expert gastrointestinal EUS-endoscopists.
- Non-expert gastrointestinal endoscopists training for EUS.
- Patients with chronic dyspepsia without other findings.
- Patients with previous CT images or upper digestive endoscopy reporting no other findings.
- Patients requiring EUS for surveillance due to family history of pancreatic cancer without findings on MRI.
Who cannot join
- Internet connection less than 100 MBs per second.
- Patients with abnormal structures or with visible lesions.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Comparative Evaluation of Artificial Intelligence and Endoscopists´ Accuracy in Endoscopic Ultrasound for Identifying Normal Anatomical Structures: A Multi-institutional, Cross-sectional Study
Endoscopic ultrasound (EUS) visual impression is operator-dependant and can hinder diagnostic accuracy, especially in less experienced endoscopists. The implementation of artificial intelligence can potentially mitigate operator dependency and interpretation variability, helping or improving the overall accuracy.
The investigators therefore aim to compare diagnostic accuracy between artificial intelligence (AI)-based model and the endoscopists when identifying normal anatomical structures in EUS-procedures.
Read the detailed description
EUS is an operator dependent procedure where accuracy depends on experience and skills. Nowadays, EUS-training can be achieved by a formal fellowship training in a center for 6-24 months or an informal training through didactic sessions with a short hands-on experience. However, parameters for a correct and complete learning experience measurement are yet to be defined. The implementation of artificial intelligence on EUS can potentially mitigate the operator-dependent variable and improve diagnostic accuracy.
Therefore, detection of normal anatomical structures on a separate basis using an AI-based model, expert and non-expert endoscopists to determine where the AI would be most helpful.
The investigators aim to compare the diagnostic accuracy of the AI-based model with the endoscopists identification of normal anatomical structures in EUS procedures.
- Conditions
- Gastrointestinal Diseases
- Being tested
- Detection of structures
- Keywords
- EUS, AI, Gastrointestinal
- Medical subject headings
- Gastrointestinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Detection of structuresDiagnostic test
Pre-recorded videos, cropped according to the different windows (mediastinal, gastric, duodenal) will be analyzed by the AIWorks-EUS model and endoscopists on different times for recognition of the different normal anatomical structures.
| Study group | Role | What they receive |
|---|---|---|
| AI-based model | AIWorks-EUS Convolutional Neural Network version 2 (CNNv2) (mdconsgroup, Guayaquil, Ecuador) applied on pre-recorded videos for the detection of normal anatomical structures. Receives: Diagnostic Test: Detection of structures | |
| Expert endoscopists | Endoscopists with >190 EUS procedures per year, including 75 pancreatobiliary and mucosal cancer staging procedures each, 40 subepithelial cases; and 50 cases of EUS-fine needle aspiration (FNA) (25 of them being pancreatic cases); following the American Society for Gastrointestinal Endoscopy (ASGE) recommendations. Receives: Diagnostic Test: Detection of structures | |
| Non-expert endoscopists | Endoscopists with <190 EUS procedures per year, including 75 pancreatobiliary and mucosal cancer staging procedures each, 40 subepithelial cases; and 50 cases of EUS-FNA (25 of them being pancreatic cases); following the American Society for Gastrointestinal Endoscopy (ASGE) recommendations. Receives: Diagnostic Test: Detection of structures |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic accuracy
The true positive, true negative, false positive and false negative based on detection of anatomical structures according to the an external expert endoscopist as gold-standard.
Measured: 5 months
Secondary outcomes (1)
Interobserver agreement
Comparison of diagnostic accuracies between Artificial intelligence (AI)-based model and both groups (expert and non-expert endoscopists) using Fleiss Kappa.
Measured: 5 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Feb 2024
- Study start1 May 2023
- Primary completion1 Oct 2023
- Study completion26 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Gastrointestinal DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 30 participants (actual)
- Target duration
- 7 Days
Sponsor and investigators
- Lead sponsor
- Instituto Ecuatoriano de Enfermedades Digestivas
- Sponsor type
- University, hospital or other
- Collaborators
- The Methodist Hospital Research Institute, Baylor Saint Luke's Medical Center, Beth Israel Deaconess Medical Center, Barra Life Medical Center, Brazil, Hospital Clinico Universitario de Santiago, Universitair Ziekenhuis Brussel, Hospital Civil de Morelia, Michoacan, ELIAS Emergency University Hospital, Larkin Community Hospital, Carol Davila University of Medicine and Pharmacy, mdconsgroup, Guayaquil, Ecuador
- Responsible party
- Principal investigator, Carlos Robles-Medranda, Instituto Ecuatoriano de Enfermedades Digestivas
- Investigators
- Carlos Robles-Medranda, MD FASGE, Principal investigator, Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06279546
- Sponsor's protocol ID
- IECED-12345
- Status last verified
- Feb 2024
- First submitted
- 16 Feb 2024
- Last updated
- 28 Feb 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Han C, Nie C, Shen X, Xu T, Liu J, Ding Z, Hou X. Exploration of an effective training system for the diagnosis of pancreatobiliary diseases with EUS: A prospective study. Endosc Ultrasound. 2020 Sep-Oct;9(5):308-318. doi: 10.4103/eus.eus_47_20.
PubMed 32913147Cho CM. Training in Endoscopy: Endoscopic Ultrasound. Clin Endosc. 2017 Jul;50(4):340-344. doi: 10.5946/ce.2017.067. Epub 2017 Jul 31.
PubMed 28783919Finocchiaro M, Cortegoso Valdivia P, Hernansanz A, Marino N, Amram D, Casals A, Menciassi A, Marlicz W, Ciuti G, Koulaouzidis A. Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives. Cancers (Basel). 2021 Mar 20;13(6):1427. doi: 10.3390/cancers13061427.
PubMed 33804773Robles-Medranda C, Baquerizo-Burgos J, Puga-Tejada M, Del Valle R, Mendez JC, Egas-Izquierdo M, Arevalo-Mora M, Cunto D, Alcivar-Vasquez J, Pitanga-Lukashok H, Tabacelia D. Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos). Gastrointest Endosc. 2024 Feb;99(2):271-279.e2. doi: 10.1016/j.gie.2023.10.028. Epub 2023 Oct 10.
PubMed 37827432
Study at a glance
From the registry record, last updated by the sponsor on 28 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparative Evaluation of Artificial Intelligence and Endoscopists´ Accuracy in Endoscopic Ultrasound for Identifying Normal Anatomical Structures: A Multi-institutional, Cross-sectional Study
- ClinicalTrials.gov ID
- NCT06279546
- Acronym / sponsor ID
- IECED-12345
- Conditions
- Gastrointestinal Diseases
- Keywords
- EUS, AI, Gastrointestinal
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 30 participants (actual)
- Lead sponsor
- Instituto Ecuatoriano de Enfermedades DigestivasUniversity, hospital or other
- Collaborators
- The Methodist Hospital Research Institute, Baylor Saint Luke's Medical Center, Beth Israel Deaconess Medical Center, Barra Life Medical Center, Brazil, Hospital Clinico Universitario de Santiago, Universitair Ziekenhuis Brussel, Hospital Civil de Morelia, Michoacan, ELIAS Emergency University Hospital, Larkin Community Hospital, Carol Davila University of Medicine and Pharmacy, mdconsgroup, Guayaquil, Ecuador
- Study start
- 1 May 2023 (actual)
- Primary completion
- 1 Oct 2023 (actual)
- Study completion
- 26 Jan 2024 (actual)
- First posted
- 28 Feb 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Endoscopic ultrasound (EUS) visual impression is operator-dependant and can hinder diagnostic accuracy, especially in less experienced endoscopists. The implementation of artificial intelligence can potentially mitigate operator dependency and interpretation variability, helping or improving the overall accuracy.
The investigators therefore aim to compare diagnostic accuracy between artificial intelligence (AI)-based model and the endoscopists when identifying normal anatomical structures in EUS-procedures.
Detailed description
EUS is an operator dependent procedure where accuracy depends on experience and skills. Nowadays, EUS-training can be achieved by a formal fellowship training in a center for 6-24 months or an informal training through didactic sessions with a short hands-on experience. However, parameters for a correct and complete learning experience measurement are yet to be defined. The implementation of artificial intelligence on EUS can potentially mitigate the operator-dependent variable and improve diagnostic accuracy.
Therefore, detection of normal anatomical structures on a separate basis using an AI-based model, expert and non-expert endoscopists to determine where the AI would be most helpful.
The investigators aim to compare the diagnostic accuracy of the AI-based model with the endoscopists identification of normal anatomical structures in EUS procedures.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Target follow-up
- 7 Days
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| AI-based model | – | Diagnostic Test: Detection of structuresAIWorks-EUS Convolutional Neural Network version 2 (CNNv2) (mdconsgroup, Guayaquil, Ecuador) applied on pre-recorded videos for the detection of normal anatomical structures. |
| Expert endoscopists | – | Diagnostic Test: Detection of structuresEndoscopists with >190 EUS procedures per year, including 75 pancreatobiliary and mucosal cancer staging procedures each, 40 subepithelial cases; and 50 cases of EUS-fine needle aspiration (FNA) (25 of them being pancreatic cases); following the American Society for Gastrointestinal Endoscopy (ASGE) recommendations. |
| Non-expert endoscopists | – | Diagnostic Test: Detection of structuresEndoscopists with <190 EUS procedures per year, including 75 pancreatobiliary and mucosal cancer staging procedures each, 40 subepithelial cases; and 50 cases of EUS-FNA (25 of them being pancreatic cases); following the American Society for Gastrointestinal Endoscopy (ASGE) recommendations. |
Who may qualify
- Ages
- 18 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Expert and non-expert gastrointestinal EUS-endoscopists.
Key inclusion criteria (5 listed)
- Expert gastrointestinal EUS-endoscopists.
- Non-expert gastrointestinal endoscopists training for EUS.
- Patients with chronic dyspepsia without other findings.
- Patients with previous CT images or upper digestive endoscopy reporting no other findings.
- Patients requiring EUS for surveillance due to family history of pancreatic cancer without findings on MRI.
Key exclusion criteria (2 listed)
- Internet connection less than 100 MBs per second.
- Patients with abnormal structures or with visible lesions.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Detection of structuresPre-recorded videos, cropped according to the different windows (mediastinal, gastric, duodenal) will be analyzed by the AIWorks-EUS model and endoscopists on different times for recognition of the different normal anatomical structures. | Diagnostic test | AI-based model, Expert endoscopists, Non-expert endoscopists |
Primary outcome
Diagnostic accuracy
Time frame: 5 months
The true positive, true negative, false positive and false negative based on detection of anatomical structures according to the an external expert endoscopist as gold-standard.
1 secondary outcome
Interobserver agreement
Time frame: 5 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Feb 2024.
- Background: PubMed 32913147Han C, Nie C, Shen X, Xu T, Liu J, Ding Z, Hou X. Exploration of an effective training system for the diagnosis of pancreatobiliary diseases with EUS: A prospective study. Endosc Ultrasound. 2020 Sep-Oct;9(5):308-318. doi: 10.4103/eus.eus_47_20.
- Background: PubMed 28783919Cho CM. Training in Endoscopy: Endoscopic Ultrasound. Clin Endosc. 2017 Jul;50(4):340-344. doi: 10.5946/ce.2017.067. Epub 2017 Jul 31.
- Background: PubMed 33804773Finocchiaro M, Cortegoso Valdivia P, Hernansanz A, Marino N, Amram D, Casals A, Menciassi A, Marlicz W, Ciuti G, Koulaouzidis A. Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives. Cancers (Basel). 2021 Mar 20;13(6):1427. doi: 10.3390/cancers13061427.
- Background: PubMed 37827432Robles-Medranda C, Baquerizo-Burgos J, Puga-Tejada M, Del Valle R, Mendez JC, Egas-Izquierdo M, Arevalo-Mora M, Cunto D, Alcivar-Vasquez J, Pitanga-Lukashok H, Tabacelia D. Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos). Gastrointest Endosc. 2024 Feb;99(2):271-279.e2. doi: 10.1016/j.gie.2023.10.028. Epub 2023 Oct 10.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Carlos Robles-Medranda, MD FASGE principal investigator
Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastrointestinal Diseases, run by Instituto Ecuatoriano de Enfermedades Digestivas at 1 site in Ecuador.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 28 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Ecuador. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy (time frame: 5 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06279546, which also lists 4 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Feb 2024.