SCH: EAGER: RUI: Collaborative Research: A novel 3D image predictive model for osteoarthritis disease
SCH: EAGER: RUI: Collaborative Research: A novel 3D image predictive model for osteoarthritis disease
批准号:
1723420
负责人:
Juan Shan
金额:
$20.81万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-15 至 2022-08-31
中文摘要
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英文摘要
Knee osteoarthritis (OA) affects 10% of older adults and is a major cause of work absence, early retirement and joint replacement. Knee OA is a disease characterized by deterioration of the cartilage in the knee. Using current technology, it is hard to predict how fast or how much deterioration will take place because cartilage loss is a slow and gradual process and can only be detected through medical images. This project will explore a novel 3D image model that can predict the accurate change of knee cartilage, to facilitate early detection and personal treatment for OA. If successful, the project could benefit 35 million people in the United States by reducing the high economic cost related to OA treatment, and improving the quality of life for these people. The PIs plan to disseminate the research to local medical communities and design a new course to involve undergraduate students into the research. The novel 3D image predictive model should have a wide variety of imaging applications. The goal of this project is to explore a novel 3D-information-fusion mechanism for medical imaging and a novel 3D image-to-image predictive model using deep neural networks as the core. The project will integrate cartilage information from MRI sequences. To handle size differences and perform image registration, a universal coordinate system will be defined to form a continuous and complete representation of the cartilage plane. Using the coordinate system, deep neural networks will be trained to learn the underlying correlation between the 3D cartilage maps. Unlike the traditional image-to-single-value prediction, the model will make image-to-image prediction; that is, from a current 3D cartilage map to a future 3D cartilage map, for different lengths of time (2, 4, 6, and 8 years respectively), leveraging a large imaging database. Finally, the team will construct the future 3D knee models from the cartilage maps to display the trajectory of cartilage change in a 3D view.
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Knee osteoarthritis severity level classification using whole knee cartilage damage Index and ANN
使用全膝软骨损伤指数和人工神经网络对膝骨关节炎严重程度进行分类
DOI:
10.1145/3278576.3278585
发表时间:
2018
期刊:
Systems and Engineering Technologies
影响因子:
--
作者:
[Du, Yaodong, Shan, Juan, Almajalid, Rania, Zhang, Ming]
通讯作者:
Zhang, Ming
FINGER JOINT SEGMENTATION USING MACHINE LEARNING AND MINIMIZED TRAINING SET
使用机器学习和最小化训练集进行手指关节分割
DOI:
10.1016/j.joca.2022.02.118
发表时间:
2022
期刊:
Osteoarthritis and Cartilage
影响因子:
7
作者:
[Wang, Y., Zhang, M., Cheung, T., Guida, C., Ren, R., Shan, J.]
通讯作者:
Shan, J.
DOI:
10.1109/icmla52953.2021.00240
发表时间:
2021
期刊:
IEEE 20th International Conference on Machine Learning and Applications
影响因子:
--
作者:
[Guida, Carmine, Zhang, Ming, Blackadar, Jordan, Yang, Zilong, Driban, Jeffrey B., Duryea, Jeffrey, Schaefer, Lena, Eaton, Charles B., McAlindon, Timothy, Shan, Juan]
通讯作者:
Shan, Juan
Automatic Hand Segmentation from Hand X-rays Using Minimized Training Samples and Machine Learning Models
使用最小化训练样本和机器学习模型根据手部 X 射线自动分割手部
DOI:
--
发表时间:
2021
期刊:
Arthritis rheumatology
影响因子:
--
作者:
[Yang, Z., Shan, J., Guida, C., Blackadar, J., Cheung, T, Driban, J, McAlindon, T, Zhang, M]
通讯作者:
Zhang, M
Bone Marrow Lesion Segmentation Using Synthetic Data and Deep Learning Models
使用合成数据和深度学习模型进行骨髓病变分割
DOI:
--
发表时间:
2021
期刊:
Arthritis rheumatology
影响因子:
--
作者:
[Michaely B., Zhang M., Shan, J.]
通讯作者:
Shan, J.
共 13 条
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