Intelligent Evaluation and Supervision of Cataract Surgery

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05260775

Enrollment

344

Registered

2022-03-02

Start date

2019-01-01

Completion date

2021-12-30

Last updated

2022-03-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Cataract

Keywords

cataract surgery

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.

Interventions

OTHEREvaluation test: cataract surgery steps

The development datasets were used to train the deep learning model. The validation and test group were used to optimize hyperparameters

Study design

Observational model

OTHER

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

50 Years to 100 Years

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.

Design outcomes

Primary

Measure	Time frame	Description
Accuracy	baseline	The investigators will calculate accuracy of deep learning system and compare this index between deep learning system and human doctors

Secondary

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

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026