Intelligent Evaluation and Supervision of Cataract Surgery

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

344 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-01-01

Study Completion Date

2021-12-30

Brief Summary

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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.

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Detailed Description

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Conditions

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Cataract

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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Development Dataset

12 cataract surgery steps including(1) main incision formation, (2) side incision formation, (3) ophthalmic viscoelastic device (OVD) injection, (4) capsulorrhexis formation, (5) hydrodissection, (6) phaco, (7) cortical material removal, (8) intraocular lens (IOL) implantation, (9) OVD removal, (10) IOL centration and (11) wound closure through corneal hydration, and (12) idle phases.

Evaluation test: cataract surgery steps

Intervention Type OTHER

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

Validation Dataset

12 cataract surgery steps including(1) main incision formation, (2) side incision formation, (3) ophthalmic viscoelastic device (OVD) injection, (4) capsulorrhexis formation, (5) hydrodissection, (6) phaco, (7) cortical material removal, (8) intraocular lens (IOL) implantation, (9) OVD removal, (10) IOL centration and (11) wound closure through corneal hydration, and (12) idle phases.

Evaluation test: cataract surgery steps

Intervention Type OTHER

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

Test Dataset

12 cataract surgery steps including(1) main incision formation, (2) side incision formation, (3) ophthalmic viscoelastic device (OVD) injection, (4) capsulorrhexis formation, (5) hydrodissection, (6) phaco, (7) cortical material removal, (8) intraocular lens (IOL) implantation, (9) OVD removal, (10) IOL centration and (11) wound closure through corneal hydration, and (12) idle phases.

Evaluation test: cataract surgery steps

Intervention Type OTHER

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

Interventions

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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

Intervention Type OTHER

Eligibility Criteria

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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.

Minimum Eligible Age

50 Years

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No