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

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.

Study Overview

• Status: Recruiting
• Conditions: Ultrasound Image Interpretation
• Intervention / Treatment: Diagnostic test: Artificial intelligence-aimed point-of-care ultrasound image interpretation system

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 Type: Interventional
• Enrollment (Estimated): 300
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Wan-Ching Lien, Ph D; Phone Number: +886-2-23123456; Email: wanchinglien@ntu.edu.tw
• Study Contact Backup: Name: Wan-Ching Lien; Phone Number: 0988088719; Email: dtemer17@yahoo.com.tw

Study Locations

• Taiwan
  • None Selected
    • Taipei, None Selected, Taiwan, 100
      • Recruiting
      • Wan-Ching Lien
      • Contact: Wan-Ching Lien; Phone Number: +886223123456; Email: wanchinglien@ntu.edu.tw

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 20 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• patients receiving echocardiography or renal ultrasound

Exclusion Criteria:

• patients not receiving echocardiography or renal ultrasound

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / 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

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
sensitivity and specificity of AI interpretation	increase the sensitivity and specificity of AI to interpret the ultrasound image	6 months

Collaborators and Investigators

• Sponsor: National Taiwan University Hospital
• Investigators: Principal Investigator: Wan-Ching Lien, National Taiwan University Hospital

Study record dates

Study Major Dates

• Study Start (Actual): August 1, 2020
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026

Study Registration Dates

• First Submitted: May 2, 2021
• First Submitted That Met QC Criteria: May 2, 2021
• First Posted (Actual): May 6, 2021

Study Record Updates

• Last Update Posted (Estimated): September 19, 2025
• Last Update Submitted That Met QC Criteria: September 15, 2025
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Other Study ID Numbers: 202006124RINC

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No