Intelligent Evaluation and Supervision of Cataract Surgery

• Sponsor: Sun Yat-sen University (Other)
• Overall Status: Completed
• CT.gov ID: NCT05260775
• Collaborator: (none)
• Enrollment: 344
• Location: 1
• Actual Duration (Months): 35.9
• Patients Per Site Per Month: 9.6

Study Details

Study Description

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.

Condition or Disease	Intervention/Treatment	Phase
Cataract	Other: Evaluation test: cataract surgery steps

Study Design

• Study Type: Observational
• Actual Enrollment: 344 participants
• Observational Model: Other
• Time Perspective: Retrospective
• Official Title: Intelligent Evaluation and Supervision of Cataract Surgery
• Actual Study Start Date: Jan 1, 2019
• Actual Primary Completion Date: Sep 30, 2021
• Actual Study Completion Date: Dec 30, 2021

Arms and Interventions

Arm	Intervention/Treatment
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.	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
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.	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
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.	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

Outcome Measures

Primary Outcome Measures

1. 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

1. kappa [baseline]

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

Eligibility Criteria

Criteria

• Ages Eligible for Study: 50 Years to 100 Years
• Sexes Eligible for Study: All

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.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Zhognshan Ophthalmic Center, Sun Yat-sen University	Guangzhou	Guangdong	China	510060

Sponsors and Collaborators

• Sun Yat-sen University

Investigators

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

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Haotian Lin, principal investigator, Sun Yat-sen University
• ClinicalTrials.gov Identifier: NCT05260775
• Other Study ID Numbers: CS-2022
• First Posted: Mar 2, 2022
• Last Update Posted: Mar 2, 2022
• Last Verified: Feb 1, 2022

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by Haotian Lin, principal investigator, Sun Yat-sen University: cataract surgery
• Additional relevant MeSH terms: Cataract

Study Results

No Results Posted as of Mar 2, 2022