NCT05836246 - Clinical Trial References

Phase 1: Researchers test a new drug or treatment in a small group of people for the first time to evaluate its safety, determine a safe dosage range, and identify side effects.
Phase 2: The drug or treatment is given to a larger group of people to see if it is effective and to further evaluate its safety.
Phase 3: The drug or treatment is given to large groups of people to confirm its effectiveness, monitor side effects, compare it to commonly used treatments, and collect information that will allow the drug or treatment to be used safely.
Phase 4: Studies are done after the drug or treatment has been marketed to gather information on the drug's effect in various populations and any side effects associated with long-term use.

Citation(s)

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
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.
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.
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.
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):
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.

The Development of Artificial Intelligence (AI) Based High Performance Structural-functional and Quantitative Ultrasound Imaging Software Platform

Clinical trial NCT05836246 - References