This study introduces a novel framework for defining screw trajectory that utilizes PointNet—a deep neural network trained on lumbar vertebrae point clouds—to improve the manual surgical planning procedures. The conventional architecture of PointNet was modified to accommodate various vertebral orientations and predict six values, which were reconstructed into two control points that define a linear trajectory. A custom loss function was designed to align the predicted trajectory with the ground-truth trajectory. The neural networks were trained on 4284 point clouds of vertebrae, and 28 unseen point clouds were used to evaluate the model’s performance based on translational error, angular error, and clinical accuracy. For the left pedicle, the mean translational errors were 1.5 ± 0.8 mm at the entry point and 2.3 ± 1.2 mm at the target point. For the right pedicle, the mean translational errors were 1.5 ± 0.7 mm at the entry point and 2.3 ± 1.0 mm at the target point. The mean angular error was 3.5 ± 2.3° for the left pedicle and 3.9 ± 1.7° for the right pedicle. Clinically, the network generated 52 out of 56 trajectories without medial-cortical violations of the spinal canal. The trained neural network demonstrated promising technical and clinical accuracy, generating feasible screw trajectories across various vertebral orientations. Integrating a spinal segmentation network with the proposed framework could enable fully automated surgical planning in the future.
Hwang et al. (Wed,) studied this question.