Sagittal support rather than medial cortical support matters in geriatric intertrochanteric fracture: A finite element analysis study.

Hip fracture, increasing exponentially with age, is osteoporosis's most severe clinical consequence. Intertrochanteric fracture, one of the main types of hip fracture, is associated with higher mortality and morbidity. The current research hotspots lay in improving the treatment effect and optimizing the secondary stability after intertrochanteric fracture surgery. Cortex buttress reduction is a widely accepted method for treating intertrochanteric fracture by allowing the head-neck fragment to slide and rigidly contact the femoral shaft's cortex. Medial cortical support is considered a more effective option in treating young patients. However, osteo-degenerations features, including bone weakness and cortical thickness thinning, affect the performance of cortex support in geriatric intertrochanteric fracture treatment. Literature focusing on the age-specific difference in cortex performance in the fractured hip is scarce. We hypothesized that this osteo-19 degenerative feature affects the performance of cortex support in treating intertrochanteric fractures between the young and the elderly. We established twenty models for the old and the young with intertrochanteric fractures and performed static and dynamic simulations under one-legged stance and walking cycle conditions. The von Mises stress and displacement on the femur, proximal femoral nail anti-rotation (PFNA) implant, fracture plane, and the cutting volume of cancellous bone of the femur were compared. It was observed that defects in the anterior and posterior cortical bone walls significantly increase the stress on the PFNA implant, the displacement of the fracture surface, and cause a greater volume of cancellous bone to be resected. We concluded that ensuring the integrity and alignment of the anterior and posterior cortical bones is essential for elderly patients, and sagittal support is recommended. This finding suggests that the treatment method for intertrochanteric fracture may differ, considering the patient's age difference.

Fast and precise collision detection for detailed and complex physiological structures.

BACKGROUND AND OBJECTIVES: Virtual reality has been proved indispensable in computer-assisted surgery, especially for surgical planning, and simulation systems. Collision detection is an essential part of surgery simulators and its accuracy and computational efficiency play a decisive role in the fidelity of simulations. Nevertheless, current collision detection methods in surgical simulation and planning struggle to meet precise requirements, especially for detailed and complex physiological structures. To address this, the primary objective of this study was to develop a new algorithm that enables fast and precise collision detection to facilitate the improvement of the realism of virtual reality surgical procedures. METHODS: The method consists of two main parts, bounding spheres formation and two-level collision detection. A specified surface subdivision method is devised to reduce the radius of basic bounding spheres formed by circumcenters of underlying triangles. The spheres are then clustered and adjusted to obtain a compact personalized hierarchy whose position is updated in real time during surgical simulation, followed by two-level collision detection. Triangular facets with collision potential through interaction between hierarchies and then accurate results are obtained by means of precise detection phase. The effectiveness of the algorithm was evaluated in various models and surgical scenarios and was compared with prior relevant implementations. RESULTS: Results on multiple models demonstrated that the method can generate a personalized hierarchy with fewer and smaller bounding spheres for tight wrapping. Simulation experiments proved that the proposed approach is significantly superior to comparable methods under the premise of error-free detection, even for severe model-model collision. CONCLUSIONS: The algorithm proposed through this study enables higher numerical efficiency and detection accuracy, which is capable of significantly enlarging the fidelity/realism of haptic simulators and surgical planning methods.

An explainable machine learning method for assessing surgical skill in liposuction surgery.

PURPOSE: Surgical skill assessment has received growing interest in surgery training and quality control due to its essential role in competency assessment and trainee feedback. However, the current assessment methods rarely provide corresponding feedback guidance while giving ability evaluation. We aim to validate an explainable surgical skill assessment method that automatically evaluates the trainee performance of liposuction surgery and provides visual postoperative and real-time feedback. METHODS: In this study, machine learning using a model-agnostic interpretable method based on stroke segmentation was introduced to objectively evaluate surgical skills. We evaluated the method on liposuction surgery datasets that consisted of motion and force data for classification tasks. RESULTS: Our classifier achieved optimistic accuracy in clinical and imitation liposuction surgery models, ranging from 89 to 94%. With the help of SHapley Additive exPlanations (SHAP), we deeply explore the potential rules of liposuction operation between surgeons with variant experiences and provide real-time feedback based on the ML model to surgeons with undesirable skills. CONCLUSION: Our results demonstrate the strong abilities of explainable machine learning methods in objective surgical skill assessment. We believe that the machine learning model based on interpretive methods proposed in this article can improve the evaluation and training of liposuction surgery and provide objective assessment and training guidance for other surgeries.