Intelligent Evaluation and Supervision of Cataract Surgery

Completed

• Conditions: Cataract

• Registration Number: NCT05260775
• Lead Sponsor: Sun Yat-sen University

Brief Summary

Research purpose: intelligent identification and evaluation of cataract surgery steps Research methods: A total of 9 items (such as gender, age, visual acuity, etc.) were extracted from the surgical videos of senile cataract patients and the clinical data recorded by the electronic medical record system. The machine learning algorithm 3D-CNN was applied to identify the 11 steps in cataract surgery and the pictures (blank pictures) without instrument manipulation on the eyeball during the operation. Six key cataract surgery steps were scored using deep learning algorithms (probability smoothing window and softmax). We employ precision, precision, recall, and F1-score to evaluate the model's performance for recognizing surgical steps. To evaluate the reliability of the model's scoring of surgical steps, we used a human-machine comparison method to calculate the agreement (kappa value) between machine and expert scores.

Detailed Description

Not available

Study Timeline

• Study Start: 2019-01-01
• Primary Completion: 2021-09-30
• Study Completion: 2021-12-30
• Status Verified: 2022-02
• Study First Submitted: 2022-02-27
• Study First Submitted QC: 2022-02-27
• Last Update Submitted: 2022-02-27
• Study First Posted: 2022-03-02
• Last Update Posted: 2022-03-02

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 344

Inclusion Criteria

-Videos of phacoemulsification and IOL implantation for senile cataracts will be included

Exclusion Criteria

-The peak signal-to-noise ratio (PSNR) is utilized to assess whether a video was blurred. If the PSNR of a video was less than 20 decibels (dBs), the whole video was discarded.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy	baseline	The investigators will calculate accuracy of deep learning system and compare this index between deep learning system and human doctors

Secondary Outcome Measures

Name	Time	Method
kappa	baseline	Cohen's kappa coefficient was calculated to assess the agreement between the grades given by human doctors and DeepSurgery

Trial Locations

Locations (1)

Zhognshan Ophthalmic Center, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China