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Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System

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ClinicalTrials.gov Identifier: NCT04876157
Recruitment Status : Recruiting
First Posted : May 6, 2021
Last Update Posted : May 7, 2021
Sponsor:
Information provided by (Responsible Party):
National Taiwan University Hospital

Study Description
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Brief Summary:
This proposal is for an one-year project. In this project, we aim to investigate the feasibility of using AI for sonographic image interpretation. The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for Biomedical Image Segmentation) and Transfer Learning to build up the models for image recognition and validating the efficacy of the models. The purpose of Subproject 1 is to develop an image recognition system for dynamic images: pericardial effusion. After building up the model, validating the efficacy and future revision will be done. Subproject 2 comes out an image recognition system for static images: hydronephrosis. After building up the model, validating the efficacy and future revision will be done.

Condition or disease Intervention/treatment Phase
Ultrasound Image Interpretation Diagnostic Test: Artificial intelligence-aimed point-of-care ultrasound image interpretation system Not Applicable

Detailed Description:

Ultrasound is a non-invasive and non-radiated diagnostic tool in the emergency and critical care settings. In clinical practice, timely interpretation of sonographic images to facilitate decision-making is essential. However, it depends on operators' experience. As usual, it takes time for junior emergency physicians to have good diagnostic accuracy through traditional sonographic education. How to shorten the learning This proposal is for an one-year project. In this project, we aim to investigate the feasibility of using AI for sonographic image interpretation. The main project is responsible for coordination between the two sub-projects and the main project, providing image resources, and using U-Net (Convolutional Networks for Biomedical Image Segmentation) and Transfer Learning to build up the models for image recognition and validating the efficacy of the models. The purpose of Subproject 1 is to develop an image recognition system for dynamic images: pericardial effusion. After building up the model, validating the efficacy and future revision will be done. Subproject 2 comes out an image recognition system for static images: hydronephrosis. After building up the model, validating the efficacy and future revision will be done.

This pioneer study can provide two AI-assisted ultrasound image recognition systems in the real clinical conditions. They can experience of clinical applications and contribute to current medical education. Moreover, it can improve decision-making process and quality of care in the emergency and critical care units. Furthermore, the set-up models can be used in other target ultrasound image recognition in the future.

Study Design
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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 300 participants
Allocation: N/A
Intervention Model: Single Group Assignment
Masking: None (Open Label)
Primary Purpose: Diagnostic
Official Title: Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System
Actual Study Start Date : August 1, 2020
Estimated Primary Completion Date : July 31, 2021
Estimated Study Completion Date : July 31, 2021
Arms and Interventions
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Arm Intervention/treatment
Experimental: Artificial intelligence-aimed ultrasound image interpretation Diagnostic Test: Artificial intelligence-aimed point-of-care ultrasound image interpretation system
improve the sensitivity and specificity of the AI-aimed ultrasound interpretation system


Outcome Measures
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Primary Outcome Measures :
  1. sensitivity and specificity of AI interpretation [ Time Frame: 6 months ]
    increase the sensitivity and specificity of AI to interpret the ultrasound image


Eligibility Criteria
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Information from the National Library of Medicine

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Ages Eligible for Study:   20 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • patients receiving echocardiography or renal ultrasound

Exclusion Criteria:

  • patients not receiving echocardiography or renal ultrasound
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04876157


Contacts
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Contact: Wan-Ching Lien, Ph D +886-2-23123456 wanchinglien@ntu.edu.tw
Contact: Wan-Ching Lien 0988088719 dtemer17@yahoo.com.tw

Locations
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Taiwan
Wan-Ching Lien Recruiting
Taipei, None Selected, Taiwan, 100
Contact: Wan-Ching Lien    +886223123456    wanchinglien@ntu.edu.tw   
Sponsors and Collaborators
National Taiwan University Hospital
Investigators
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Principal Investigator: Wan-Ching Lien National Taiwan University Hospital
More Information
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Responsible Party: National Taiwan University Hospital
ClinicalTrials.gov Identifier: NCT04876157    
Other Study ID Numbers: 202006124RINC
First Posted: May 6, 2021    Key Record Dates
Last Update Posted: May 7, 2021
Last Verified: May 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No