AbdomenNCT05151939
Endoscopic Ultrasound (EUS) Artificial Intelligence Model for Normal Mediastinal and Abdominal Strictures Assessment
An observational study of Abdomen and Mediastinum, run by Instituto Ecuatoriano de Enfermedades Digestivas at 1 site in Ecuador.
Trial summary
Therefore, a high number of procedures is necessary to achieve EUS competency, but interobserver agreement still varies widely. Artificial intelligence (AI) aided recognition of anatomical structures may improve the training process and inter-observer agreement. Robles-Medranda et al. Read more about this study
- Who is it for?
- Ages 18 to 79 years
- Conditions the study is for
- Abdomen, Mediastinum, Anatomic Abnormality
- What is being tested?
- Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist and Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI)
- How many people
- 60 participants
- Where
- 1 siteEcuador
- Study dates
- 1 Oct 2021to 30 Jun 2022
- 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 79 years
- All sexes
- Adult, Older adult
Study population: Adult patients with normal mediastinal and abdominal organ/anatomic strictures after imaging test and EUS assessment due to chronic dyspepsia.
Who may be able to join
- Patients with no history of the thorax and abdominal abnormalities confirmed through an imaging test requested for healthcare purposes in the last twelve months (e.g., thorax X-ray and abdominal ultrasound or thorax and abdominal CT)
- Patients who undergo EUS assessment due to chronic dyspepsia.
Who cannot join
- Morphological alteration on at least one mediastinal and abdominal organ/anatomic strictures documented through any imaging test or EUS.
- Uncontrolled coagulopathy, kidney/liver failure, or any comorbidity with a meaningful impact on cardiac risk assessment (NHYA III/IV);
- Refuse to participate in the study or to sign corresponding informed consent.
Trial locations and contacts
1 site in 1 country.
Ecuador1 site, 1 recruiting
- Recruiting
Ecuadorian Institute of Digestive Diseases
Guayaquil, Guayas, 090505
Carlos A Robles-Medranda, MD, +593989158865, carlosoakm@yahoo.es
Carlos Robles-Medranda, MD FASGE
About this study
Official title: Endoscopic Ultrasound (EUS) Assessment of Normal Mediastinal and Abdominal Organ/Anatomic Strictures Using a Novel Developed Artificial Intelligence Model
Therefore, a high number of procedures is necessary to achieve EUS competency, but interobserver agreement still varies widely. Artificial intelligence (AI) aided recognition of anatomical structures may improve the training process and inter-observer agreement. Robles-Medranda et al. developed an AI model that recognizes normal anatomical structures during linear and radial EUS evaluations. We pursue to design an external validation of our developed AI model, considering an endoscopist expert as the gold standard.
Read the detailed description
Endoscopic ultrasound (EUS) is a high-skilled procedure with a limited number of facilities available for training. Therefore, a high number of procedures is necessary to achieve competency. However, the agreement between observers varies widely. Artificial intelligence (AI) aided recognition and characterization of anatomical structures may improve the training process while improving the agreement between observers. However, developed EUS-AI models have been explicitly trained or only with disease samples or for detecting abdominal anatomical features.
In other fields as Radiation Oncology, developed AI models have been widely used. They must recognize in unison healthy and disease strictures throughout any part of the human body during the contouring. It avoids unnecessary irradiation of normal tissue. EUS-AI models not trained with healthy samples can cause an increase in false-positive cases during real-life practice. It implies potential overdiagnosis of abnormal/disease strictures. EUS-AI models not trained with samples outside
Using an automated machine learning software, Robles-Medranda et al. have previously developed a convolutional neuronal networks (CNN) AI model that recognizes the anatomical structures during linear and radial EUS evaluations (AI Works, MD Consulting group, Ecuador). To the best of our knowledge, this EUS-AI model is the first trained with EUS videos from patients without pathologies and, thus, with normal mediastinal and abdominal organ/anatomic strictures. In this second stage, we pursue to design an external validation of our developed AI model, considering an endoscopist expert as the gold standard.
- Conditions
- Abdomen, Mediastinum, Anatomic Abnormality, Strictures
- Being tested
- Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist, Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI)
- Keywords
- Endoscopic ultrasound, Artificial intelligence
- Medical subject headings
- Congenital Abnormalities, Constriction, Pathologic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopistDiagnostic test
An expert endoscopist will select a dataset of mediastinal and abdominal EUS videos (one per patient). An expert endoscopist will identify or discharge visualization of the following organs correctly: aorta, vertebral spine, aortic arch, trachea, AP window, left kidney, liver, spleen, pancreas body, pancreas tail, coeliac trunk, splenic artery, splenic vein, inferior vena cava, adrenal gland, right kidney, gallbladder, common bile duct, ampulla of Vater, portal vein.
Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI)Diagnostic test
Using the same previous dataset of mediastinal and abdominal EUS videos, the EUS-AI model will recognize the following organs: aorta, vertebral spine, aortic arch, trachea, AP window, left kidney, liver, spleen, pancreas body, pancreas tail, coeliac trunk, splenic artery, splenic vein, inferior vena cava, adrenal gland, right kidney, gallbladder, common bile duct, ampulla of Vater, portal vein. Considering each patient (and not data frame videos) as the study unit, a contingency table per each mediastinal and abdominal organ/anatomic stricture will be designed.
| Study group | Role | What they receive |
|---|---|---|
| Patients with normal mediastinal and abdominal organ/anatomic strictures | Adult patients with normal mediastinal and abdominal organ/anatomic strictures after imaging test and EUS assessment due to chronic dyspepsia. Receives: Diagnostic Test: Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist; Diagnostic Test: Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall accuracy of Endoscopic ultrasound (EUS) artificial intelligence (AI) model for identifying normal mediastinal and abdominal organ/anatomic strictures
Overall accuracy features will be calculated: sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy, and observed agreement. In addition, there will be defined the following probabilities:
- True-positive (TP): mediastinal/abdominal organ/anatomic stricture recognized by the EUS-AI model. The expert endoscopist previously correctly identified it.
- False-positive (FP): mediastinal/abdominal organ/anatomic stricture recognized by the EUS-AI model. The expert endoscopist previously correctly discharged its visualization.
- False-negative (FN): mediastinal/abdominal organ/anatomic stricture not recognized by the EUS-AI model. The expert endoscopist previously correctly identified it.
- True-negative (TN): mediastinal/abdominal organ/anatomic stricture not recognized by the EUS-AI model. The expert endoscopist previously correctly discharged its visualization.
Measured: Three months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Dec 2021
- Study start1 Oct 2021
- Primary completion30 Mar 2022 (estimated)
- Study completion30 Jun 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for AbdomenStudies 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
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Instituto Ecuatoriano de Enfermedades Digestivas
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Carlos Robles-Medranda, Principal investigator, Ecuadorian Institute of Digestive Diseases
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05151939
- Sponsor's protocol ID
- IECED-26112021
- Status last verified
- Dec 2021
- First submitted
- 26 Nov 2021
- Last updated
- 30 Dec 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Zhang J, Zhu L, Yao L, Ding X, Chen D, Wu H, Lu Z, Zhou W, Zhang L, An P, Xu B, Tan W, Hu S, Cheng F, Yu H. Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video). Gastrointest Endosc. 2020 Oct;92(4):874-885.e3. doi: 10.1016/j.gie.2020.04.071. Epub 2020 May 6.
PubMed 32387499Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Koda H, Miyano A, Fumihara D. Current status of artificial intelligence analysis for endoscopic ultrasonography. Dig Endosc. 2021 Jan;33(2):298-305. doi: 10.1111/den.13880. Epub 2020 Dec 5.
PubMed 33098123Robles-Medranda C, Oleas R, Del Valle R, Mendez JC, Alcívar-Vásquez JM, Puga-Tejada M, Lukashok H. Intelligence for real-time anatomical recognition during endoscopic ultrasound evaluation: a pilot study. Gastrointestinal Endoscopy. 2021; 93(6), AB221. https://doi.org/10.1016/J.GIE.2021.03.491
Udristoiu AL, Cazacu IM, Gruionu LG, Gruionu G, Iacob AV, Burtea DE, Ungureanu BS, Costache MI, Constantin A, Popescu CF, Udristoiu S, Saftoiu A. Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model. PLoS One. 2021 Jun 28;16(6):e0251701. doi: 10.1371/journal.pone.0251701. eCollection 2021.
PubMed 34181680Yao L, Zhang J, Liu J, Zhu L, Ding X, Chen D, Wu H, Lu Z, Zhou W, Zhang L, Xu B, Hu S, Zheng B, Yang Y, Yu H. A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound. EBioMedicine. 2021 Mar;65:103238. doi: 10.1016/j.ebiom.2021.103238. Epub 2021 Feb 24.
PubMed 33639404Tonozuka R, Mukai S, Itoi T. The Role of Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Disorders. Diagnostics (Basel). 2020 Dec 24;11(1):18. doi: 10.3390/diagnostics11010018.
PubMed 33374181Marya NB, Powers PD, Chari ST, Gleeson FC, Leggett CL, Abu Dayyeh BK, Chandrasekhara V, Iyer PG, Majumder S, Pearson RK, Petersen BT, Rajan E, Sawas T, Storm AC, Vege SS, Chen S, Long Z, Hough DM, Mara K, Levy MJ. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis. Gut. 2021 Jul;70(7):1335-1344. doi: 10.1136/gutjnl-2020-322821. Epub 2020 Oct 7.
PubMed 33028668Minoda Y, Ihara E, Komori K, Ogino H, Otsuka Y, Chinen T, Tsuda Y, Ando K, Yamamoto H, Ogawa Y. Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors. J Gastroenterol. 2020 Dec;55(12):1119-1126. doi: 10.1007/s00535-020-01725-4. Epub 2020 Sep 11.
PubMed 32918102Cazacu IM, Udristoiu A, Gruionu LG, Iacob A, Gruionu G, Saftoiu A. Artificial intelligence in pancreatic cancer: Toward precision diagnosis. Endosc Ultrasound. 2019 Nov-Dec;8(6):357-359. doi: 10.4103/eus.eus_76_19. No abstract available.
PubMed 31854344
Study at a glance
From the registry record, last updated by the sponsor on 30 Dec 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Endoscopic Ultrasound (EUS) Assessment of Normal Mediastinal and Abdominal Organ/Anatomic Strictures Using a Novel Developed Artificial Intelligence Model
- ClinicalTrials.gov ID
- NCT05151939
- Acronym / sponsor ID
- IECED-26112021
- Conditions
- Abdomen, Mediastinum, Anatomic Abnormality, Strictures
- Keywords
- Endoscopic ultrasound, Artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Instituto Ecuatoriano de Enfermedades DigestivasUniversity, hospital or other
- Study start
- 1 Oct 2021 (actual)
- Primary completion
- 30 Mar 2022 (estimated)
- Study completion
- 30 Jun 2022 (estimated)
- First posted
- 9 Dec 2021 (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.
Therefore, a high number of procedures is necessary to achieve EUS competency, but interobserver agreement still varies widely. Artificial intelligence (AI) aided recognition of anatomical structures may improve the training process and inter-observer agreement. Robles-Medranda et al. developed an AI model that recognizes normal anatomical structures during linear and radial EUS evaluations. We pursue to design an external validation of our developed AI model, considering an endoscopist expert as the gold standard.
Detailed description
Endoscopic ultrasound (EUS) is a high-skilled procedure with a limited number of facilities available for training. Therefore, a high number of procedures is necessary to achieve competency. However, the agreement between observers varies widely. Artificial intelligence (AI) aided recognition and characterization of anatomical structures may improve the training process while improving the agreement between observers. However, developed EUS-AI models have been explicitly trained or only with disease samples or for detecting abdominal anatomical features.
In other fields as Radiation Oncology, developed AI models have been widely used. They must recognize in unison healthy and disease strictures throughout any part of the human body during the contouring. It avoids unnecessary irradiation of normal tissue. EUS-AI models not trained with healthy samples can cause an increase in false-positive cases during real-life practice. It implies potential overdiagnosis of abnormal/disease strictures. EUS-AI models not trained with samples outside
Using an automated machine learning software, Robles-Medranda et al. have previously developed a convolutional neuronal networks (CNN) AI model that recognizes the anatomical structures during linear and radial EUS evaluations (AI Works, MD Consulting group, Ecuador). To the best of our knowledge, this EUS-AI model is the first trained with EUS videos from patients without pathologies and, thus, with normal mediastinal and abdominal organ/anatomic strictures. In this second stage, we pursue to design an external validation of our developed AI model, considering an endoscopist expert as the gold standard.
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with normal mediastinal and abdominal organ/anatomic strictures | – | Diagnostic Test: Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist, Diagnostic Test: Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI)Adult patients with normal mediastinal and abdominal organ/anatomic strictures after imaging test and EUS assessment due to chronic dyspepsia. |
Who may qualify
- Ages
- 18 to 79 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Adult patients with normal mediastinal and abdominal organ/anatomic strictures after imaging test and EUS assessment due to chronic dyspepsia.
Key inclusion criteria (2 listed)
- Patients with no history of the thorax and abdominal abnormalities confirmed through an imaging test requested for healthcare purposes in the last twelve months (e.g., thorax X-ray and abdominal ultrasound or thorax and abdominal CT)
- Patients who undergo EUS assessment due to chronic dyspepsia.
Key exclusion criteria (3 listed)
- Morphological alteration on at least one mediastinal and abdominal organ/anatomic strictures documented through any imaging test or EUS.
- Uncontrolled coagulopathy, kidney/liver failure, or any comorbidity with a meaningful impact on cardiac risk assessment (NHYA III/IV);
- Refuse to participate in the study or to sign corresponding informed consent.
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 |
|---|---|---|
| Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopistAn expert endoscopist will select a dataset of mediastinal and abdominal EUS videos (one per patient). An expert endoscopist will identify or discharge visualization of the following organs correctly: aorta, vertebral spine, aortic arch, trachea, AP window, left kidney, liver, spleen, pancreas body, pancreas tail, coeliac trunk, splenic artery, splenic vein, inferior vena cava, adrenal gland, right kidney, gallbladder, common bile duct, ampulla of Vater, portal vein. | Diagnostic test | Patients with normal mediastinal and abdominal organ/anatomic strictures |
| Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI)Using the same previous dataset of mediastinal and abdominal EUS videos, the EUS-AI model will recognize the following organs: aorta, vertebral spine, aortic arch, trachea, AP window, left kidney, liver, spleen, pancreas body, pancreas tail, coeliac trunk, splenic artery, splenic vein, inferior vena cava, adrenal gland, right kidney, gallbladder, common bile duct, ampulla of Vater, portal vein. Considering each patient (and not data frame videos) as the study unit, a contingency table per each mediastinal and abdominal organ/anatomic stricture will be designed. | Diagnostic test | Patients with normal mediastinal and abdominal organ/anatomic strictures |
Primary outcome
Overall accuracy of Endoscopic ultrasound (EUS) artificial intelligence (AI) model for identifying normal mediastinal and abdominal organ/anatomic strictures
Time frame: Three months
Overall accuracy features will be calculated: sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy, and observed agreement. In addition, there will be defined the following probabilities: * True-positive (TP): mediastinal/abdominal organ/anatomic stricture recognized by the EUS-AI model. The expert endoscopist previously correctly identified it. * False-positive (FP): mediastinal/abdominal organ/anatomic stricture recognized by the EUS-AI model. The expert endoscopist previously correctly discharged its visualization. * False-negative (FN): mediastinal/abdominal organ/anatomic stricture not recognized by the EUS-AI model. The expert endoscopist previously correctly identified it. * True-negative (TN): mediastinal/abdominal organ/anatomic stricture not recognized by the EUS-AI model. The expert endoscopist previously correctly discharged its visualization.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Dec 2021.
- Background: PubMed 32387499Zhang J, Zhu L, Yao L, Ding X, Chen D, Wu H, Lu Z, Zhou W, Zhang L, An P, Xu B, Tan W, Hu S, Cheng F, Yu H. Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video). Gastrointest Endosc. 2020 Oct;92(4):874-885.e3. doi: 10.1016/j.gie.2020.04.071. Epub 2020 May 6.
- Background: PubMed 33098123Kuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Koda H, Miyano A, Fumihara D. Current status of artificial intelligence analysis for endoscopic ultrasonography. Dig Endosc. 2021 Jan;33(2):298-305. doi: 10.1111/den.13880. Epub 2020 Dec 5.
- BackgroundRobles-Medranda C, Oleas R, Del Valle R, Mendez JC, Alcívar-Vásquez JM, Puga-Tejada M, Lukashok H. Intelligence for real-time anatomical recognition during endoscopic ultrasound evaluation: a pilot study. Gastrointestinal Endoscopy. 2021; 93(6), AB221. https://doi.org/10.1016/J.GIE.2021.03.491
- Background: PubMed 34181680Udristoiu AL, Cazacu IM, Gruionu LG, Gruionu G, Iacob AV, Burtea DE, Ungureanu BS, Costache MI, Constantin A, Popescu CF, Udristoiu S, Saftoiu A. Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model. PLoS One. 2021 Jun 28;16(6):e0251701. doi: 10.1371/journal.pone.0251701. eCollection 2021.
- Background: PubMed 33639404Yao L, Zhang J, Liu J, Zhu L, Ding X, Chen D, Wu H, Lu Z, Zhou W, Zhang L, Xu B, Hu S, Zheng B, Yang Y, Yu H. A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound. EBioMedicine. 2021 Mar;65:103238. doi: 10.1016/j.ebiom.2021.103238. Epub 2021 Feb 24.
- Background: PubMed 33374181Tonozuka R, Mukai S, Itoi T. The Role of Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Disorders. Diagnostics (Basel). 2020 Dec 24;11(1):18. doi: 10.3390/diagnostics11010018.
- Background: PubMed 33028668Marya NB, Powers PD, Chari ST, Gleeson FC, Leggett CL, Abu Dayyeh BK, Chandrasekhara V, Iyer PG, Majumder S, Pearson RK, Petersen BT, Rajan E, Sawas T, Storm AC, Vege SS, Chen S, Long Z, Hough DM, Mara K, Levy MJ. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis. Gut. 2021 Jul;70(7):1335-1344. doi: 10.1136/gutjnl-2020-322821. Epub 2020 Oct 7.
- Background: PubMed 32918102Minoda Y, Ihara E, Komori K, Ogino H, Otsuka Y, Chinen T, Tsuda Y, Ando K, Yamamoto H, Ogawa Y. Efficacy of endoscopic ultrasound with artificial intelligence for the diagnosis of gastrointestinal stromal tumors. J Gastroenterol. 2020 Dec;55(12):1119-1126. doi: 10.1007/s00535-020-01725-4. Epub 2020 Sep 11.
- Background: PubMed 31854344Cazacu IM, Udristoiu A, Gruionu LG, Iacob A, Gruionu G, Saftoiu A. Artificial intelligence in pancreatic cancer: Toward precision diagnosis. Endosc Ultrasound. 2019 Nov-Dec;8(6):357-359. doi: 10.4103/eus.eus_76_19. No abstract available.
- 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 principal investigator
Ecuadorian Institute of Digestive Diseases
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 Abdomen and Mediastinum, 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 2 inclusion and 3 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: status not confirmed, as of the sponsor's last update on 30 Dec 2021. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Carlos Robles-Medranda (+59342109180, carlosoakm@yahoo.es), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall accuracy of Endoscopic ultrasound (EUS) artificial intelligence (AI) model for identifying normal mediastinal and abdominal organ/anatomic strictures (time frame: Three 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 NCT05151939, which also lists 9 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 30 Dec 2021.