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| Name | Class |
|---|---|
| The Methodist Hospital Research Institute | OTHER |
| Baylor Saint Luke's Medical Center | UNKNOWN |
| Beth Israel Deaconess Medical Center | OTHER |
| Barra Life Medical Center, Brazil |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
| |
| 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. |
| |
| 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Detection of structures | Diagnostic 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 5 months |
| Measure | Description | Time Frame |
|---|---|---|
| Interobserver agreement | Comparison of diagnostic accuracies between Artificial intelligence (AI)-based model and both groups (expert and non-expert endoscopists) using Fleiss Kappa. | 5 months |
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Inclusion Criteria:
Exclusion Criteria:
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Expert and non-expert gastrointestinal EUS-endoscopists.
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| Name | Affiliation | Role |
|---|---|---|
| Carlos Robles-Medranda, MD FASGE | Instituto Ecuatoriano de Enfermedades Digestivas (IECED) | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| IECED | Guayaquil | Guayas | 090505 | Ecuador |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 32913147 | Background | 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. | |
| 28783919 | Background | Cho CM. Training in Endoscopy: Endoscopic Ultrasound. Clin Endosc. 2017 Jul;50(4):340-344. doi: 10.5946/ce.2017.067. Epub 2017 Jul 31. |
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| ID | Term |
|---|---|
| D005767 | Gastrointestinal Diseases |
| ID | Term |
|---|---|
| D004066 | Digestive System Diseases |
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| UNKNOWN |
| Hospital Clinico Universitario de Santiago | OTHER |
| Universitair Ziekenhuis Brussel | OTHER |
| Hospital Civil de Morelia, Michoacan | UNKNOWN |
| ELIAS Emergency University Hospital | OTHER |
| Larkin Community Hospital | OTHER |
| Carol Davila University of Medicine and Pharmacy | OTHER |
| mdconsgroup, Guayaquil, Ecuador | UNKNOWN |
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| 33804773 | Background | Finocchiaro 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. |
| 37827432 | Background | Robles-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. |