Ignite Creation Date:
2024-10-26 @ 3:39 PM
Last Modification Date:
2024-10-26 @ 3:39 PM
Study NCT ID:
NCT06583109
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-09-01
Brief Title:
Prediction Model of Treatment Efficacy for Age-related Macular Degeneration Based on Multi-source Imaging Modalities
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Establishment and Application of Prediction Model of Treatment Efficacy for Age-related Macular Degeneration Based on Multi-source Imaging Modalities
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-09
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Age-related macular degeneration AMD is one of the main causes of blindness in the elderly population Intraocular injection of anti-VEGF drugs for neovascular AMD nAMD is the main treatment method at present However patients have different responses to anti-VEGF therapy and some patients do not respond well to short - and long-term treatment
In this study a retrospective study was adopted to collate and analyze the clinical data and imaging data of nAMD in the past and to extract the imaging features from the multimodal modalities before and after treatment for deep learning and to evaluate and quantify the clinical features and to construct two multi-source feature models for predicting the short-term and long-term prognosis of nAMD patients By verifying the accuracy of the model to predict the curative effect the classification efficiency of the above characteristic models was compared and the optimal model was selected Its clinical application value was evaluated by calibration curve and decision curve In addition patients with poor treatment response in the study cohort were retrospectively analyzed and the efficacy and safety of the combination of other treatment options in the actual clinic were analyzed The purpose of this study is to provide scientific basis for early prediction dynamic monitoring and optimization of overall treatment strategies for nAMD
In this study a retrospective study was adopted to collate and analyze the clinical data and imaging data of nAMD in the past and to extract the imaging features from the multimodal modalities before and after treatment for deep learning and to evaluate and quantify the clinical features and to construct two multi-source feature models for predicting the short-term and long-term prognosis of nAMD patients By verifying the accuracy of the model to predict the curative effect the classification efficiency of the above characteristic models was compared and the optimal model was selected Its clinical application value was evaluated by calibration curve and decision curve In addition patients with poor treatment response in the study cohort were retrospectively analyzed and the efficacy and safety of the combination of other treatment options in the actual clinic were analyzed The purpose of this study is to provide scientific basis for early prediction dynamic monitoring and optimization of overall treatment strategies for nAMD
Detailed Description:
None
Study Oversight
Has Oversight DMC:
Is a FDA Regulated Drug?:
Is a FDA Regulated Device?:
Is an Unapproved Device?:
Is a PPSD?:
Is a US Export?:
Is an FDA AA801 Violation?: