Clinical trial · Observational
The Development of Quantitative Ultrasound Imaging Software Platform
The Development of Artificial Intelligence (AI) Based High Performance Structural-functional and Quantitative Ultrasound Imaging Software Platform
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (5)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Acute Myocardial Infarction | — | UNRESOLVED | — |
| Benign Breast Disease | — | UNRESOLVED | — |
| Chronic Liver Disease | — | UNRESOLVED | — |
| Malignant Breast Neoplasm | Malignant Breast Neoplasm | ONTOLOGY_EXACT | 0.98 |
| Thyroid Disease | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Quantitative ultrasound information
- timeFrame
- 5 years
- description
- Quantitative ultrasound images of heart, thyroid, and breast disease
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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.
References
Publications (6)
- RESULTCheng 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. PMID 27896451
- RESULTChi 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. PMID 28695342
- RESULTF. 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.
- RESULTMa 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. PMID 28186630
- RESULTChen 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.
- RESULTLekadir 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. PMID 27893402