Ignite Creation Date:
2024-10-26 @ 3:37 PM
Last Modification Date:
2024-10-26 @ 3:37 PM
Study NCT ID:
NCT06540846
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-08-02
Brief Title:
Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Deep Learning for Histopathological Classification and Prognostication of Gynaecologic Smooth Muscle Tumours
Status:
RECRUITING
Status Verified Date:
2024-08
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:
STUMP
Brief Summary:
Smooth muscle tumors of the uterus that do not fit the diagnostic criteria of benignity such as leiomyomas or malignancy such as leiomyosarcomas are called STUMP smooth muscle tumor of uncertain malignant potential A potential solution to this problem could be the application of predictive models using artificial intelligence AI to aid in the histopathological classification and prognosis of gynecological smooth muscle tumors Deep learning using convolutional neural networks represents a specific class of machine learning in which predictive models are trained by considering small groups of pixels in digital images and iteratively identifying salient features In this study we aim to develop deep learning models capable of accurately subclassifying and predicting the prognosis of gynecological smooth muscle tumors based on histopathological features of hematoxylin and eosin HE slides The aim is to develop a diagnostic and prognostic algorithm to help pathologists better classify and diagnose uterine smooth muscle tumors and predict their clinical course
Detailed Description:
None
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