A team at Zhongshan Ophthalmic Center develops MetaS, an AI-driven system that evaluates and selects ideal capsulorhexis from 17,538 cataract surgery videos, extracts digital features via Mask R-CNN and InceptionResNetV2, and guides surgeons with a calibrated lens caliper or real-time overlay. They also demonstrate autonomous robot-assisted capsulorhexis in porcine eyes, boosting precision and consistency.
Key points
MetaS evaluation module (InceptionResNetV2) classifies capsulorhexis quality with AUC >0.96 across ideal, acceptable, and poor categories.
Feature extraction via Mask R-CNN identifies ideal capsulorhexis path (radius=0.58×limbus radius; diameter 5.15–5.39 mm) with circularity 0.98 and off-center <0.30 mm.
Digital guidance with a scale-engraved lens caliper and GhostNet-FPN overlay raises ideal capsulorhexis rate to 85% and enables autonomous robot-assisted capsulorhexis in porcine eyes.
Why it matters:
This AI-driven digitalization standardizes critical surgical steps, reducing variability and paving the way for autonomous precision in ophthalmic interventions.
Q&A
What is capsulorhexis?
How does MetaS evaluate capsulorhexis quality?
What role does Mask R-CNN play in MetaS?
How does the lens caliper assist surgeons?
How is autonomous robot-assisted capsulorhexis achieved?
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Academy
AI-Driven Surgical Guidance
AI-driven surgical guidance combines real-time imaging, advanced algorithms, and feedback tools to assist surgeons in performing precise operations. In cataract surgery, accurate creation of a circular opening in the lens capsule—known as capsulorhexis—is critical for implant stability and patient vision. Traditional manual techniques rely on the surgeon’s experience and visual judgment, leading to variability.
MetaS, an AI platform, employs convolutional neural networks (CNNs) and instance segmentation to analyze thousands of cataract surgery videos. The system’s evaluation module (InceptionResNetV2) classifies capsule openings by quality, while a Mask R-CNN extraction pipeline measures anatomical landmarks, calculates an optimal incision path (radius = 0.58 × limbus radius), and derives metrics such as opening diameter (5.15–5.39 mm) and centration. These digital features are delivered via two methods:
- Lens Caliper Assistance: A modified intraocular instrument marked with a 0.1 mm scale lets surgeons place key markers on the lens surface, guiding manual capsulorhexis along the ideal circle.
- Real-Time Overlay: A GhostNet-FPN model tracks ocular structures under the microscope and projects green keypoints indicating the target path, allowing surgeons to follow a live guide.
Both approaches increase the rate of ideal, centered capsulorhexis, minimizing risks like lens decentration and postoperative complications. This technology exemplifies how AI can standardize critical surgical steps and expand precision medicine.
Robotic Surgery in Ophthalmology
Robotic surgery uses mechanical arms controlled by software to perform tasks with high stability and accuracy. In ophthalmology, where millimeter-scale precision is mandatory, integrating AI-extracted targets with robotic platforms offers the potential for autonomous procedures. The MetaS team demonstrates this by guiding a diathermy capsulorhexis tip on a hybrid parallel-serial micromanipulator.
After entering the eye through a corneal incision, the robot's trajectory is planned based on anatomical measurements (S = r/sin(a) – R), impedance control ensures safe tissue contact, and a remote center of motion (RCM) preserves instrument pivoting. The system executes a circular diathermy incision autonomously, creating a precise 5.3 mm opening in ex vivo porcine eyes. This task-level autonomy (LoA 2) highlights steps toward fully automated intraocular surgery, promising reduced variability and enhanced reproducibility for aging populations requiring delicate ophthalmic interventions.
