Fig. 2: Performances of feature extraction and digitalization modules of MetaS.
From: Digitalization of surgical features improves surgical accuracy via surgeon guidance and robotization
a The evaluation module of MetaS was trained to classify capsulorhexis into ideal, acceptable, or poor using the InceptionResNetV2 algorithm. b ROC curves for the classification of capsulorhexis with our ensemble method. AUC = area under the receiver operating characteristic curve. ROC = receiver operating characteristic. c The Mask R-CNN was used to establish the extraction module of MetaS. According to the ratio of the area of the CL before and after capsulorhexis SCL1/SCL2, the area of the CO, SCO2, was shifted and scaled into the frame before the capsulorhexis, and its area was measured as SCO1. By calculating the area ratio of SCO1/ SCL1 in all ideal capsulorhexis operations, the radius (RC) of ideal CO could be deduced as the radius of the limbus (RL) × 0.58. d The extraction module of MetaS was used to extract the specific features of ideal capsulorhexis using images, and found that the diameter of ideal CO falls within the range of 5.15–5.39 mm. e Ideal CO should be concentric with the pupil and the CL. f The off-center distance of ideal CO should be less than 0.30 mm.
