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: | Zhaomei Geng |
| Institution: | Sun Yat-sen University |
| Department: | School of Mathematics |
| Country: | |
| Proposed Analysis: | The innovative training project is titled "Application of Deep Learning Algorithms based on Medical Imaging in the Classification, Diagnosis, and Survival Prediction of Mental Disorders". The main objective of the project is to develop an accurate and effective classification and diagnosis system for mental disorders using deep learning algorithms based on medical imaging data such as MRI and PET scans. The project also aims to predict survival outcomes using the developed model. The primary areas of the research include medical imaging-based diagnosis and classification of mental disorders, multi-modal data-based diagnosis and classification of mental disorders, and medical imaging-based survival analysis and development prediction. To achieve these objectives, we plan to calculate classification criteria for mental disorders and assess their accuracy, improve the accuracy of classification using multi-modal data fusion technology, and predict future survival outcomes based on disease diagnosis. The novelty of the research lies in the use of multi-modal data fusion technology and imaging genomics for predicting neurological disorders. |
| Additional Investigators | |
| Investigator's Name: | Minghao Luo |
| Proposed Analysis: | The innovative training project is titled "Application of Deep Learning Algorithms based on Medical Imaging in the Classification, Diagnosis, and Survival Prediction of Mental Disorders". The main objective of the project is to develop an accurate and effective classification and diagnosis system for mental disorders using deep learning algorithms based on medical imaging data such as MRI and PET scans. The project also aims to predict survival outcomes using the developed model. The primary areas of the research include medical imaging-based diagnosis and classification of mental disorders, multi-modal data-based diagnosis and classification of mental disorders, and medical imaging-based survival analysis and development prediction. To achieve these objectives, we plan to calculate classification criteria for mental disorders and assess their accuracy, improve the accuracy of classification using multi-modal data fusion technology, and predict future survival outcomes based on disease diagnosis. The novelty of the research lies in the use of multi-modal data fusion technology and imaging genomics for predicting neurological disorders. |
| Investigator's Name: | Yini Lu |
| Proposed Analysis: | The innovative training project is titled "Application of Deep Learning Algorithms based on Medical Imaging in the Classification, Diagnosis, and Survival Prediction of Mental Disorders". The main objective of the project is to develop an accurate and effective classification and diagnosis system for mental disorders using deep learning algorithms based on medical imaging data such as MRI and PET scans. The project also aims to predict survival outcomes using the developed model. The primary areas of the research include medical imaging-based diagnosis and classification of mental disorders, multi-modal data-based diagnosis and classification of mental disorders, and medical imaging-based survival analysis and development prediction. To achieve these objectives, we plan to calculate classification criteria for mental disorders and assess their accuracy, improve the accuracy of classification using multi-modal data fusion technology, and predict future survival outcomes based on disease diagnosis. The novelty of the research lies in the use of multi-modal data fusion technology and imaging genomics for predicting neurological disorders. |
| Investigator's Name: | Siqi Wu |
| Proposed Analysis: | The innovative training project is titled "Application of Deep Learning Algorithms based on Medical Imaging in the Classification, Diagnosis, and Survival Prediction of Mental Disorders". The main objective of the project is to develop an accurate and effective classification and diagnosis system for mental disorders using deep learning algorithms based on medical imaging data such as MRI and PET scans. The project also aims to predict survival outcomes using the developed model. The primary areas of the research include medical imaging-based diagnosis and classification of mental disorders, multi-modal data-based diagnosis and classification of mental disorders, and medical imaging-based survival analysis and development prediction. To achieve these objectives, we plan to calculate classification criteria for mental disorders and assess their accuracy, improve the accuracy of classification using multi-modal data fusion technology, and predict future survival outcomes based on disease diagnosis. The novelty of the research lies in the use of multi-modal data fusion technology and imaging genomics for predicting neurological disorders. |
