Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06540742
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-08-02
Brief Title:
Multimodal Imaging Diagnosis and Decision Aid System for Hepatic Echinococcosis Based on Image Omics and Vision Macromodel
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Multimodal Imaging Diagnosis and Decision Aid System for Hepatic Echinococcosis Based on Image Omics and Vision Macromodel
Status:
COMPLETED
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:
Hepatic echinococcosis hepatic echinococcosis is an important zoonotic disease widely existing in the agricultural and pastoral areas of northwest China The disease can be parasitic in any part of the human body and may affect multiple organs In severe cases patients will lose the ability to work At present the disease faces challenges in diagnostic accuracy specific type identification preoperative activity assessment postoperative recurrence prediction and decision evaluation of T-tube indentation This problem is particularly significant in high incidence areas with uneven distribution of medical resources and shortage of excellent imaging physicians and clinicians Our previous studies have demonstrated that the use of visual large models and imaging omics algorithms can effectively segment liver echinococcus lesions extract key features and provide clinicians with accurate and reliable diagnosis and treatment recommendations We believe that on the basis of the transformation of different medical image modes such as MRI CT and ultrasound based on a broader multicentre large data set the goal of effective identification diagnosis surgical decision support and postoperative accurate prediction of hepatic echinococcosis can be achieved We will use artificial intelligence technology solutions such as adversarial generation network vision large model image omics and decision level fusion taking into account diagnosis and treatment efficiency diagnosis and treatment automation and interpretability of diagnosis results to build a comprehensive accurate diagnosis and prognosis system for hepatic echinococcosis
Detailed Description:
1 Automatic recognitionEfficient diagnosis of hepatic Echinococcosis Development of a deep learn-based AI diagnostic tool aimed at improving the differentiation of hepatic echinococcosis from other liver diseases such as liver cysts liver abscesses and other hepatic cystic space occupying lesions The tool will utilize generative adversarial networks and polarized self-attention algorithms to effectively identify and classify hepatic echinococcosis to make up for the uneven medical resources and shortage of professional physicians in the western region
2 Differential diagnosis of specific types of hepatic echinococcosis To explore the use of multimodal imaging combined with deep learning methods to distinguish CL type CE1 type and hepatic cyst of hepatic echinococcosis This research will apply DINOv2 medical image segmentation algorithm and deep learning technology to accurately identify cases with relatively unevenly distributed and complex data
3 Prediction of postoperative recurrence of hepatic echinococcosis Transfer learning and Deep-SVDD algorithm are used to predict the risk of postoperative recurrence of hepatic echinococcosis providing an effective solution for unbalanced sample size data sets In addition by integrating the data on the medical big data platform an AI-based postoperative recurrence prediction model was established
2 Differential diagnosis of specific types of hepatic echinococcosis To explore the use of multimodal imaging combined with deep learning methods to distinguish CL type CE1 type and hepatic cyst of hepatic echinococcosis This research will apply DINOv2 medical image segmentation algorithm and deep learning technology to accurately identify cases with relatively unevenly distributed and complex data
3 Prediction of postoperative recurrence of hepatic echinococcosis Transfer learning and Deep-SVDD algorithm are used to predict the risk of postoperative recurrence of hepatic echinococcosis providing an effective solution for unbalanced sample size data sets In addition by integrating the data on the medical big data platform an AI-based postoperative recurrence prediction model was established
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None