Clinical trial NCT05836246: The Development of Quantitative Ultrasound Imaging Software Platform
Official title: The Development of Artificial Intelligence (AI) Based High Performance Structural-functional and Quantitative Ultrasound Imaging Software Platform
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Chronic Liver Disease
Thyroid Disease
Benign Breast Disease
Malignant Breast Neoplasm
Acute Myocardial Infarction
Lead sponsor: Seoul National University Bundang Hospital
Sponsor class: OTHER
First posted: 2023-05-01
Enrollment: 196
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: A person who visits Seoul National University Bundang Hospital for medical treatment or consultation.

Brief summary: The goal of this observational study is to compare the image differences between conventional ultrasound and artificial intelligence-based ultrasound software in conscious adults.

The main question it aims to answer is to evaluate the effectiveness by determining that the new image analysis method is considered valid if it helps to identify more than 30% of histological characteristics.

Participants will undergo the examination using the two methods mentioned earlier after signing the consent form.


Eligibility criteria: Inclusion Criteria:

* People with heart disease, thyroid disease, breast disease, and liver disease.

Exclusion Criteria:

* Someone who has received surgery on the target organ in question.

Primary outcomes:
measure: Quantitative ultrasound information
timeFrame: 5 years
Secondary outcomes:
Other outcomes:
Locations:
city: Seongnam
country: Korea, Republic of
facility: Seoul National University Bundang Hospital
state: Gyeonggi-do
Study officials:
References:
citation: Cheng PM, Malhi HS. Transfer Learning with Convolutional Neural Networks for Classification of Abdominal Ultrasound Images. J Digit Imaging. 2017 Apr;30(2):234-243. doi: 10.1007/s10278-016-9929-2.
citation: Chi J, Walia E, Babyn P, Wang J, Groot G, Eramian M. Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network. J Digit Imaging. 2017 Aug;30(4):477-486. doi: 10.1007/s10278-017-9997-y.
citation: F. Milletari, N. Navab and S. -A. Ahmadi. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA. 2016; 565-571.
citation: Ma J, Wu F, Jiang T, Zhu J, Kong D. Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images. Med Phys. 2017 May;44(5):1678-1691. doi: 10.1002/mp.12134. Epub 2017 Apr 17.
citation: Chen H, Zheng Y, Park JH, Heng PA, Zhou SK. (2016). Iterative Multi-domain Regularized Deep Learning for Anatomical Structure Detection and Segmentation from Ultrasound Images. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. 2016; 9901.
citation: Lekadir K, Galimzianova A, Betriu A, Del Mar Vila M, Igual L, Rubin DL, Fernandez E, Radeva P, Napel S. A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound. IEEE J Biomed Health Inform. 2017 Jan;21(1):48-55. doi: 10.1109/JBHI.2016.2631401. Epub 2016 Nov 22.