There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Yumeng Tang |
| Institution: | Tianjin University |
| Department: | School of Precision Instrument and Opto-Electronic |
| Country: | |
| Proposed Analysis: | Objectives: The aim of this project is to develop a deep learning-based classification model that can assist in the accurate and timely diagnosis of osteoarthritis using MicroCT images. Data: The dataset for this project will comprise of MicroCT images of joints from patients with osteoarthritis and those without. The images will be acquired from multiple medical institutions and will be anonymized to ensure patient privacy. The data will be divided into training, validation, and testing sets to train and evaluate the performance of the classification model. Methodology: A convolutional neural network (CNN), which has been optimized for image classification tasks, will be employed as the classification model in this project. The CNN will be trained on the labeled MicroCT images to learn the features that differentiate healthy joints from those affected by osteoarthritis. The model will be optimized using backpropagation and the Adam optimizer, and the performance will be evaluated on the validation set. The final model will be tested on the testing set to assess its accuracy, precision, recall, and F1-score. Expected Outcomes: We expect that the proposed deep learning-based classification model will be able to accurately classify MicroCT images of joints as healthy or arthritic. We anticipate that the model will achieve high accuracy, precision, recall, and F1-score, indicating its potential for clinical use as an assistive tool in the diagnosis of osteoarthritis. Conclusion: In conclusion, this project aims to develop a deep learning-based classification model that can assist in the accurate and timely diagnosis of osteoarthritis using MicroCT images. We believe that this project has the potential to improve the accuracy and efficiency of osteoarthritis diagnosis, leading to better patient outcomes. |
| Additional Investigators |
