Abdomen, Anatomic Abnormality, Mediastinum, Strictures
Conditions
Keywords
Endoscopic ultrasound, Artificial intelligence
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.
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.
Interventions
DIAGNOSTIC_TESTIdentification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist
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.
DIAGNOSTIC_TESTRecognition 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.
Sponsors
Instituto Ecuatoriano de Enfermedades Digestivas
Study design
Observational model
CASE_ONLY
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 79 Years
Inclusion criteria
* 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.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Overall accuracy of Endoscopic ultrasound (EUS) artificial intelligence (AI) model for identifying normal mediastinal and abdominal organ/anatomic strictures | 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. |
Countries
Ecuador
Contacts
Primary ContactCarlos Robles-Medranda
Outcome results
None listed