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

Not Applicable

Recruiting

• Conditions: Ultrasound Image Interpretation
• Interventions: Diagnostic Test: Artificial intelligence-aimed point-of-care ultrasound image interpretation system

• Registration Number: NCT04876157
• Lead Sponsor: National Taiwan University Hospital

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.

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 Timeline

• Study Start: 2020-08-01
• Study First Submitted: 2021-05-02
• Study First Submitted QC: 2021-05-02
• Study First Posted: 2021-05-06
• Status Verified: 2024-08
• Last Update Submitted: 2024-08-16
• Last Update Posted: 2024-08-19
• Primary Completion: 2025-12-31
• Study Completion: 2025-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 300

Inclusion Criteria

• patients receiving echocardiography or renal ultrasound

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Exclusion Criteria

• patients not receiving echocardiography or renal ultrasound

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Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
Artificial intelligence-aimed ultrasound image interpretation	Artificial intelligence-aimed point-of-care ultrasound image interpretation system	-

Primary Outcome Measures

Name	Time	Method
sensitivity and specificity of AI interpretation	6 months	increase the sensitivity and specificity of AI to interpret the ultrasound image

Trial Locations

Locations (1)

Wan-Ching Lien; 🇨🇳 Taipei, None Selected, Taiwan