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Phacoemulsification with Toric intraocular lens (IOL) implantation is the main surgery for curing cataracts with astigmatism. During this surgery, accurate astigmatic axis alignment of the toric IOL is crucial for the postoperative astigmatism correction effect. Automatic cyclotorsion identification through computer-assisted navigation can help guide doctors to quickly and accurately align the axis without markers. However, navigation can be easily hampered by illumination variation, instrument intervention, and inconspicuous biomarkers during surgery. Moreover, existing advanced methods are difficult to balance alignment accuracy and real-time performance. To address these challenges, we propose a toric IOL navigation framework based on learnable keypoint matching. The framework detects dense and reliable keypoint features in frames through a self-supervised lightweight neural network, and then uses semantic filtering and keypoint matching to complete the inter-frame cyclotorsion alignment estimation. During navigation, the keyframe is updated and inserted in real-time according to the matching quality to overcome accumulated alignment errors and potential abnormal matching. The framework was tested using a self-developed surgical dataset. Test results show that compared with other advanced methods, our framework achieves the lowest alignment error (0.71°) on a lowconfiguration computer, with a processing speed of 26.3 FPS, and can adapt to non-ideal surgical scenarios such as strong intervention, low texture, and weak illumination.
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