Endoscopic Ultrasound (EUS) Artificial Intelligence Model for Normal Mediastinal and Abdominal Strictures Assessment

NCT05151939 | Unknown DMC

• 1 other identifier: IECED-26112021
• Study Type: observational
• Target: 60
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Abdomen; Mediastinum; Anatomic Abnormality; Strictures

Keywords

Endoscopic ultrasound; Artificial intelligence

Outcome Measures

Primary Outcomes (1)

• 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.

  Three months

Study Arms (1)

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.

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)

Interventions

Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist DIAGNOSTIC_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.

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) 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.

Patients with normal mediastinal and abdominal organ/anatomic strictures

Eligibility Criteria

• Age: 18 Years - 79 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Adult patients with normal mediastinal and abdominal organ/anatomic strictures after imaging test and EUS assessment due to chronic dyspepsia.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Instituto Ecuatoriano de Enfermedades Digestivas: lead

Study Sites (1)

Ecuadorian Institute of Digestive Diseases

Guayaquil, Guayas, 090505, Ecuador

RECRUITING

Related Publications (9)

• 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.

  PMID: 32387499 BACKGROUND

• Kuwahara 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.

  PMID: 33098123 BACKGROUND

• Robles-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

• 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.

  PMID: 34181680 BACKGROUND

• Yao 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.

  PMID: 33639404 BACKGROUND

• Tonozuka 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.

  PMID: 33374181 BACKGROUND

• Marya 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.

  PMID: 33028668 BACKGROUND

• Minoda 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.

  PMID: 32918102 BACKGROUND

• Cazacu 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.

  PMID: 31854344 BACKGROUND

MeSH Terms

Conditions

Congenital Abnormalities; Constriction, Pathologic

Interventions

Endosonography

Condition Hierarchy (Ancestors)

Congenital, Hereditary, and Neonatal Diseases and Abnormalities; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Ultrasonography; Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Carlos Robles-Medranda

  Ecuadorian Institute of Digestive Diseases

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Carlos Robles-Medranda

CONTACT

+59342109180; carlosoakm@yahoo.es

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 26, 2021
• First Posted: December 9, 2021
• Study Start: October 1, 2021
• Primary Completion: March 30, 2022
• Study Completion: June 30, 2022
• Last Updated: December 30, 2021

Record last verified: 2021-12

Data Sharing

• IPD Sharing: Will not share

Locations

EC (1)