Ignite Creation Date:
2024-05-06 @ 4:06 PM
Last Modification Date:
2024-10-26 @ 2:04 PM
Study NCT ID:
NCT04876157
Status:
RECRUITING
Last Update Posted:
2023-07-17
First Post:
2021-05-02
Brief Title:
Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System
Sponsor:
National Taiwan University Hospital
Organization:
National Taiwan University Hospital
Study Overview
Official Title:
Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System
Status:
RECRUITING
Status Verified Date:
2024-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None