Ignite Creation Date:
2024-05-06 @ 7:58 PM
Last Modification Date:
2024-10-26 @ 3:17 PM
Study NCT ID:
NCT06204926
Status:
RECRUITING
Last Update Posted:
2024-06-28
First Post:
2024-01-03
Brief Title:
Diagnostic Efficacy of CNN in Predicting Intraoperative Complications and Postoperative Outcomes in SMILE
Sponsor:
Second Affiliated Hospital of Nanchang University
Organization:
Second Affiliated Hospital of Nanchang University
Study Overview
Official Title:
Diagnostic Efficacy of Convolutional Neural Network Based Algorithm in Predicting Intraoperative Complications and Postoperative Outcomes in Small-incision Lenticule Extraction
Status:
RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
To evaluate the diagnostic efficiency of the neural network in predicting complications of Small Incision Lenticule Extraction in a multi-center cross-sectional study
Detailed Description:
The primary cause of global visual impairment currently is refractive error and Small Incision Lenticule Extraction SMILE using femtosecond laser for corneal stromal lenticule extraction can alter the refractive power However complications such as opaque bubble layer OBL negative pressure detachment and black spots may arise during the SMILE laser scanning process due to individual differences in corneal characteristics significantly affecting the normal course of surgery and postoperative recovery Experienced docters can often predict intraoperative complications based on scan images patient cooperation and other factors but the learning curve is relatively long At present artificial intelligence has achieved the accuracy comparable to human physicians in the interpretation of medical imaging of many different diseasesPreviously we have trained a deep convolutional neural network for predicting intraoperative complications in SMILE procedures The current multi-center study is designed to evaluate the efficacy of the convolutional neural network based algorithm in predicting intraoperative complications and to assess its utility in the real world
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None