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

NCT ID: NCT04876157

Last Updated: 2025-09-19

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

300 participants

Study Classification

INTERVENTIONAL

Study Start Date

2020-08-01

Study Completion Date

2026-12-31

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Ultrasound Image Interpretation

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Artificial intelligence-aimed ultrasound image interpretation

Group Type EXPERIMENTAL

Artificial intelligence-aimed point-of-care ultrasound image interpretation system

Intervention Type DIAGNOSTIC_TEST

improve the sensitivity and specificity of the AI-aimed ultrasound interpretation system

Interventions

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Artificial intelligence-aimed point-of-care ultrasound image interpretation system

improve the sensitivity and specificity of the AI-aimed ultrasound interpretation system

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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Inclusion Criteria

* patients receiving echocardiography or renal ultrasound

Exclusion Criteria

* patients not receiving echocardiography or renal ultrasound
Minimum Eligible Age

20 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Taiwan University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Wan-Ching Lien

Role: PRINCIPAL_INVESTIGATOR

National Taiwan University Hospital

Locations

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Wan-Ching Lien

Taipei, None Selected, Taiwan

Site Status RECRUITING

Countries

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Taiwan

Central Contacts

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Wan-Ching Lien, Ph D

Role: CONTACT

+886-2-23123456

Wan-Ching Lien

Role: CONTACT

0988088719

Facility Contacts

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Wan-Ching Lien

Role: primary

+886223123456

Other Identifiers

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202006124RINC

Identifier Type: -

Identifier Source: org_study_id

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