Clinical trial NCT04876157: Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System
Official title: Artificial Intelligence-aimed Point-of-care Ultrasound Image Interpretation System
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Ultrasound Image Interpretation
Lead sponsor: National Taiwan University Hospital
Sponsor class: OTHER
First posted: 2021-05-06
Enrollment: 300
Sex: ALL
Minimum age (years): 20
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: Inclusion Criteria:

* patients receiving echocardiography or renal ultrasound

Exclusion Criteria:

* patients not receiving echocardiography or renal ultrasound

Primary outcomes:
measure: sensitivity and specificity of AI interpretation
timeFrame: 6 months
Secondary outcomes:
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: Wan-Ching Lien
state: None Selected
Study officials:
affiliation: National Taiwan University Hospital
name: Wan-Ching Lien
role: PRINCIPAL_INVESTIGATOR
References: