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Intelligent Evaluation and Supervision of Cataract Surgery

NCT05260775 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 344

Last updated 2022-03-02

No results posted yet for this study

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.

Conditions

  • Cataract

Interventions

OTHER

Evaluation 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

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Principal Investigators

  • Yizhi Liu, M.D., Ph.D. · Zhongshan Ophthalmic Center, Sun Yat-sen University

Eligibility

Min Age
50 Years
Max Age
100 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-01-01
Primary Completion
2021-09-30
Completion
2021-12-30

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05260775 on ClinicalTrials.gov