Intelligent Evaluation and Supervision of Cataract Surgery
Status
Conditions
Treatments
Details and patient eligibility
About
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.
Enrollment
Sex
Ages
Volunteers
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.
Trial design
344 participants in 3 patient groups
Trial contacts and locations
Loading...
Data sourced from clinicaltrials.gov
Clinical trials
Research sites
Resources
Legal