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Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases. [ABSTRACT FROM AUTHOR]

Elbow computerized tomography (CT) scans have been widely applied for describing elbow morphology. To enhance the objectivity and efficiency of clinical diagnosis, an automatic method to recognize, segment, and reconstruct elbow joint bones is proposed in this study. The method involves three steps: initially, the humerus, ulna, and radius are automatically recognized based on the anatomical features of the elbow joint, and the prompt boxes are generated. Subsequently, elbow MedSAM is obtained through transfer learning, which accurately segments the CT images by integrating the prompt boxes. After that, hole-filling and object reclassification steps are executed to refine the mask. Finally, three-dimensional (3D) reconstruction is conducted seamlessly using the marching cube algorithm. To validate the reliability and accuracy of the method, the images were compared to the masks labeled by senior surgeons. Quantitative evaluation of segmentation results revealed median intersection over union (IoU) values of 0.963, 0.959, and 0.950 for the humerus, ulna, and radius, respectively. Additionally, the reconstructed surface errors were measured at 1.127, 1.523, and 2.062 mm, respectively. Consequently, the automatic elbow reconstruction method demonstrates promising capabilities in clinical diagnosis, preoperative planning, and intraoperative navigation for elbow joint diseases.

With the development of artificial intelligence technologies, spine-surgery robots have gradually been applied in clinical practice, and they have exhibited favorable development prospects. Force perception technology can be used to obtain the milling force, quantify the tactile sensation of a surgeon, and provide feedback or suggestions to the surgeon and robot for safe milling. In this study, a robotic system is proposed to measure the vertebral lamina milling force by using an ultrasonic bone scalpel to realize a safe milling strategy. The developed bone recognition model based on the backpropagation neural network is suitable for robot-assisted vertebral lamina milling using the milling delamination and recognition algorithm analysis. The model uses the characteristic milling force, milling speed, milling depth, and ultrasonic scalpel power as inputs to determine whether milling has reached the inner cortical bone to recognize and judge bone layers. The verification experiment on live animals showed that this model could accurately determine a safe milling endpoint. In general, this recognition model can significantly improve the safety and reliability of robot-assisted laminectomy and has significant translational prospects.

The article presents a study on the genetic algorithm (GA) approach on the segmentation of cortical and trabecular bone tissue in computed tomography (CT) scans. It states that the researchers identified the cortical and trabecular tissues in bone scans and they applied the procedure in GA, which correct the correct image processing filters. It concludes that GA method can successfully be use to distinguish cortical and trabecular tissues in CT images.