The dynamic stress environment in the hip joint is thought to contribute to pain and osteoarthritis (OA) in people with Legg-Calvé-Perthes disease (LCPD) deformity, but is poorly understood, limiting clinical management options. The objective of this study was to develop and evaluate a patient-specific biomechanical "digital twin" model of LCPD to predict chondrolabral shear stress in dynamic and static loading scenarios.

Minimally invasive surgical approaches are currently the standard of care for men with prostate cancer, presenting higher rates of erectile function preservation. With these laparoscopic techniques, there is an increasing amount of data and information available. Adaptive systems can play an important role, acting as an intelligent information filter, assuring that all the available information can become useful for the procedure and not overwhelming for the surgeon. Standardizing and structuring the surgical workflow are key requirements for such smart assistants to recognize the different surgical steps through context information about the environment. This work aims to do a detailed characterization of a laparoscopic radical prostatectomy procedure, focusing on the formalization of medical expert knowledge, via surgical process modeling.

Surgical video review is essential for minimally invasive surgical training, but manual annotation of surgical steps is time-consuming and limits scalability. We propose a weakly supervised pre-training framework that leverages unannotated or heterogeneously labeled surgical videos to improve automated surgical step recognition.

Accurate prediction of overall survival (OS) in glioblastoma patients is critical for advancing personalized treatments and improving clinical trial design. Conventional radiomics approaches rely on manually engineered features, which limit their ability to capture complex, high-dimensional imaging patterns. This study employs a deep learning architecture to process MRI data for automated glioma segmentation and feature extraction, leveraging high-level representations from the encoder's latent space.

Virtual reality (VR) has attracted attention in healthcare for many promising applications including pre-surgical planning. Currently, there exists a critical gap in comprehension of the impact of VR on physicians' thinking. Self-reported data from surveys and metrics based on confidence and task completion may not yield sufficiently detailed understanding of the complex decision making and cognitive load experienced by surgeons during VR-based pre-surgical planning.

Prostate cancer is a prevalent malignant tumor in men, and accurate diagnosis and personalized treatment rely on multimodal imaging, such as MRI and TRUS. However, differences in imaging mechanisms and prostate deformation due to ultrasound probe compression pose significant challenges for high-quality registration between the two modalities.

This article reports on the development and feasibility testing of an MR-safe robotic needle driver. The needle driver is pneumatically actuated and designed for automatic insertion and extraction of needles along a straight trajectory within the MRI scanner.

Automatic recognition of surgical workflows is a complex yet essential task of context-aware systems in the operating room. However, achieving high accuracy in phase recognition remains a challenge due to the complexity of surgical procedures. While recent deep learning models have made significant progress, individual models often exhibit limitations-some may excel at capturing spatial features, while others are better at modeling temporal dependencies or handling class imbalance.

The recognition of surgical instrument-tissue interactions can enhance the surgical workflow analysis, improve automated safety systems and enable skill assessment in minimally invasive surgery. However, current deep learning methods for surgical instrument-tissue interaction recognition often rely on static images or coarse temporal sampling, limiting their ability to capture rapid surgical dynamics. Therefore, this study systematically investigates the impact of incorporating fine-grained temporal context into deep learning models for interaction recognition.

Artificial intelligence (AI) is rapidly transforming diagnostic and interventional radiology, supported by accelerating regulatory approvals and clinical adoption. Despite progress, integration varies across modalities and procedures. This study is a structured narrative review of four representative workflows-MRI and CT screening, coronary stenting, and liver cryoablation-to quantify automation readiness, accuracy gains, and efficiency improvements. The novelty lies in comparing diagnostic and interventional domains to highlight distinct maturity levels and future opportunities for AI-driven workflow optimization and clinical value creation.

Temporomandibular joint (TMJ) prosthesis implantation is an effective procedure for treating temporomandibular joint disorders. Traditionally, preoperative planning for TMJ surgery has been conducted manually by experienced surgeons, which often results in longer operating time and less reliable prosthesis placement. This study proposes an automated surgical planning algorithm for TMJ prosthesis implantation that calculates the optimal position for prosthesis placement.

Distinguishing idiopathic normal pressure hydrocephalus (iNPH) from progressive supranuclear palsy (PSP) presents a clinical challenge due to overlapping clinical symptoms such as gait disturbances and cognitive decline. This study presents a novel multi-scale deep learning framework that integrates global and local magnetic resonance imaging (MRI) features using a mixture of experts (MoE) mechanism, enhancing diagnostic accuracy and minimizing interobserver variability.

Teamwork is fundamental to medical practice and relies on seamless collaboration among professionals with different tasks. Integrating robotic systems into this environment demands smooth interactions. Human action recognition, which infers a person's state without explicit input, can support this. We focus on handovers between medical staff, using the actions as implicit cues for robotic assistance to replace the giving party in such scenarios.

For diffusion MRI (dMRI) parameter estimation, machine-learning approaches have shown promising results so far including the synthetic Q-space learning (synQSL) based on regressor training with only synthetic data. In this study, we aimed at the development of a new method named synthetic X-Q space learning (synXQSL) to improve robustness and investigated the basic characteristics.

Radiotherapy treats cancers through precise delivery of radiation to target volumes. Radiotherapy treatment plans, prescribing the delivery of therapeutic radiation, are presently created primarily from clinical experience and application of clinical protocols through trial-and-error rather than standardized quantitative methods. We developed an informatics infrastructure and decision support system to assist during treatment plan creation by providing access to applicable retrospective radiotherapy cases.

Bayesian networks (BNs) are valuable for clinical decision support due to their transparency and interpretability. However, BN modelling requires considerable manual effort. This study explores how integrating large language models (LLMs) with retrieval-augmented generation (RAG) can improve BN modelling by increasing efficiency, reducing cognitive workload, and ensuring accuracy.

System calibration, including hand-robot and robot-world calibration, is an essential step that directly influences the location accuracy of surgical robots. Conventional calibration methods for orthognathic robot systems (ORSs) face significant challenges in handling irregularly shaped end tools, leading to manual intervention and compromised accuracy. Therefore, an automatic method has been proposed to improve the calibration efficiency and accuracy of ORSs.

Model-guided medicine (MGM) represents a paradigm shift in clinical practice, emphasizing the integration of computational models to support diagnosis, therapy planning and individualized patient care. The general and/or specific domain models, on which recommendations, decisions or actions of these systems are based, should reflect in their model identity certificate (MIC) the level of model relevance, truthfulness and transparency.

This study proposes DMCIE (diffusion model with concatenation of inputs and errors) to enhance binary brain tumor segmentation from multimodal MRI scans. Accurate voxel-wise tumor localization remains challenging due to variability in tumor size, shape, and imaging conditions, impacting clinical diagnosis and treatment planning.

Coronary artery disease is a major global cause of morbidity and mortality, especially in obstructive CAD patients. Precise segmentation of coronary arteries and atherosclerotic plaques is essential for effective treatment. However, no previous study has addressed the joint segmentation of these two within a unified framework, which motivates our work.

While related studies have explored robotic forceps adaptations for narrow surgical workspaces, most have focused on horizontal needle driving, with limited research on optimizing robotic forceps configurations for vertical needle driving in pediatric choledochojejunostomy. Moreover, the impact of inter-joint distance adjustments on motion volume and obstructed value for vertical needle driving remains unclear, necessitating further investigation. We aimed to evaluate the effect of inter-joint distances in robotic forceps on a needle driving task that simulated vertical needle driving in a choledochojejunostomy for congenital biliary dilatation in children using a virtual reality simulator.