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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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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| 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. |
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| 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Evaluation test: cataract surgery steps | Other | The development datasets were used to train the deep learning model. The validation and test group were used to optimize hyperparameters |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy | The investigators will calculate accuracy of deep learning system and compare this index between deep learning system and human doctors | baseline |
| Measure | Description | Time Frame |
|---|---|---|
| kappa | Cohen's kappa coefficient was calculated to assess the agreement between the grades given by human doctors and DeepSurgery | baseline |
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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.
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Participants who had senile cataracts and phacoemulsification and IOL implantation in the Zhongshan Ophthalmic Centre (ZOC, Guangzhou, Guangdong, China) and Shenzhen Eye Hospital (Shenzhen, Guangdong, China)
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| Name | Affiliation | Role |
|---|---|---|
| Yizhi Liu, M.D., Ph.D. | Zhongshan Ophthalmic Center, Sun Yat-sen University | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Zhognshan Ophthalmic Center, Sun Yat-sen University | Guangzhou | Guangdong | 510060 | China |
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| ID | Term |
|---|---|
| D002386 | Cataract |
| ID | Term |
|---|---|
| D007905 | Lens Diseases |
| D005128 | Eye Diseases |
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