Objective To develop a high-accuracy, better-safety and low-cost cervical pedicle locator system for guiding cervical pedicle screw placement. Methods Cervical pedicle screw locator system was made of stainless steel. Ten cervical specimens from voluntary donation were divided into two groups according to compatibil ity design: control group inwhich 60 screws were planted into C2-7 by free hand; and experimental group in which 60 screws were planted into C2-7 under the guidance of three-dimensional locator system. The condition of screw insertion was observed and the accuracy was evaluated by the integrity of pedicle walls. Results In the control group, 32 screws (53.33%) were placed inside the pedicles and 28 (46.67%) were outside; 9 screws (15.00%) led to nerve root injury, 5 screws (8.33%) caused vertebral artery injury and no spinal cord injury occurred; and the qual ification ratio of screw insertion was 76.67% (excellent 32, fair 14, poor 14). While in the experimental group, 54 screws (90.00%) were placed inside the pedicles and 6 (10.00%) were outside; 1 screw (1.67%) caused vertebral artery injury and no nerve root injury and spinal cord injury occurred; and the qual ification ratio of screw insertion was 98.33% (excellent 54, fair 5, poor 1). There was significant difference between the two groups (P lt; 0.05). Conclusion Cervical pedicle screw locator system has the advantages of easy manipulation, high accuracy of screw placement and low cost. With further study, it can be appl ied to the cl inical.
To address the current issues of data imbalance and scarcity in photoplethysmography (PPG) data for type 2 diabetes mellitus (T2DM) prediction, this study proposes an improved conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP). The algorithm integrated gated recurrent unit (GRU) networks and self-attention mechanisms to construct a generator, aiming to produce high-quality PPG signals. Various data augmentation methods, including the improved CWGAN-GP, were employed to expand the PPG dataset, and multiple classifiers were applied for T2DM prediction analysis. Experimental results showed that the model trained on data generated by the improved CWGAN-GP achieved the optimal prediction performance. The highest accuracy reached 0.895 0, and compared with other data enhancement methods, this approach exhibited significant advantages in terms of precision and F1-score. The generated data notably enhances the accuracy and generalization ability of T2DM prediction models, providing a more reliable technical basis for non-invasive early T2DM screening based on PPG signals.