Intelligent Evaluation and Supervision of Cataract Surgery
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 |
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Study Design
Arms and Interventions
Arm | Intervention/Treatment |
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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. |
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
- 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
- kappa [baseline]
Cohen's kappa coefficient was calculated to assess the agreement between the grades given by human doctors and DeepSurgery
Eligibility Criteria
Criteria
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 | |
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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.- CS-2022