Cannula-mounted Robots for Semi-autonomous Vertebroplasty: A Comparison of Piezo-based and Screw-based Inchworm Drive Designs.

Vertebral compression fractures are estimated to affect over 200 million people globally. Percutaneous vertebroplasty is a widely accepted minimally invasive treatment, but it has limitations including prolonged radiation exposure for providers and a steep learning curve. To address these challenges, we present two cannula-mounted robot designs for semi-autonomous, high-precision cannula insertion.

FastSAM3D: An Efficient Segment Anything Model for 3D Volumetric Medical Images.

Segment anything models (SAMs) are gaining attention for their zero-shot generalization capability in segmenting objects of unseen classes and in unseen domains when properly prompted. Interactivity is a key strength of SAMs, allowing users to iteratively provide prompts that specify objects of interest to refine outputs. However, to realize the interactive use of SAMs for 3D medical imaging tasks, rapid inference times are necessary.

The interpretable surgical temporal informer: explainable surgical time completion prediction.

Purpose: Predicting surgical time completion helps streamline surgical workflow and OR utilization, enhancing hospital efficacy. When time prediction is based on interventional video of the surgical site, time predictions may correlate with technical proficiency of the surgeon because skill is a useful proxy of completion time. To understand features that are predictive of surgical time in surgical site video, we develop prototype-like visual explanations, making them applicable to video sequences.

Automated Prediction of Bone Volume Removed in Mastoidectomy.

Objective: The bone volume drilled by surgeons during mastoidectomy is determined by the need to localize the position, optimize the view, and reach the surgical endpoint while avoiding critical structures. Predicting the volume of bone removed before an operation can significantly enhance surgical training by providing precise, patient-specific guidance and enable the development of more effective computer-assisted and robotic surgical interventions.

Study Design: Single institution, cross-sectional.

FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification.

Accurate segmentation of anatomical structures and pathological regions in medical images is crucial for diagnosis, treatment planning, and disease monitoring. While the Segment Anything Model (SAM) and its variants have demonstrated impressive interactive segmentation capabilities on image types not seen during training without the need for domain adaptation or retraining, their practical application in volumetric 3D medical imaging workflows has been hindered by the lack of a user-friendly interface. To address this challenge, we introduce FastSAM-3DSlicer, a 3D Slicer extension that integrates both 2D and 3D SAM models, including SAM-Med2D, MedSAM, SAM-Med3D, and FastSAM-3D.

ProtoSAM-2D: 2D Semantic Segment Anything Model with Mask-Level Prototype-Learning and Distillation.

Semantic segmentation on medical images has been marginally improved by deep learning methods with higher accuracy and efficiency in delineating anatomical structures and pathologies. However, traditional deep learning methods approaches have relied on fully supervised training using specific datasets on specific image modalities, limiting their adaptability across diverse medical imaging scenarios. The emergence of foundation models like the Segment Anything Model (SAM) has opened new avenues for interactive instance segmentation, but they lack semantic understanding, particularly in medical contexts where anatomical knowledge is important.

Automatic photoacoustic monitoring of perinatal brain hypoxia with superior sagittal sinus detection.

Significance: Despite advances in perinatal medicine over decades, perinatal hypoxic-ischemic encephalopathy (HIE) remains a significant cause of fetal cerebral palsy and can lead to other severe medical sequelae or death. Therefore, it is highly desirable to effectively detect brain hypoxia during labor and postnatally for HIE management.

Aim: We recently validated the feasibility of transcranial photoacoustic (PA) imaging for oxyhemoglobin saturation measurement at the superior sagittal sinus ( ) in the neonatal piglet brain, at which overall oxygen supply status can be reflected as a primary collective vein.

Uncertainty Quantification in Image-based 2D/3D Registration and Its Relationship with Accuracy.

Purpose: Reliable and accurate 2D/3D registration is essential for image-guided navigation and surgical robotics, enabling precise spatial alignment. This work investigates uncertainty quantification and characterization, addressing challenges specific to 2D/3D registration. Despite a few degrees of freedom (DoF), uncertainty in 2D/3D registration is difficult to estimate and interpret since it lacks the dimensional consistency in 2D/2D or 3D/3D registration.

Near-infrared beacons: tracking anatomy with biocompatible fluorescent dots for mixed reality surgical navigation.

Purpose: Mixed reality for surgical navigation is an emerging tool for precision surgery. Achieving reliable surgical guidance hinges on robust tracking of the mixed reality device relative to patient anatomy. Contemporary approaches either introduce bulky fiducials that need to be invasively attached to the anatomy or make strong assumptions about the patient remaining stationary.

Intelligent control of robotic X-ray devices using a language-promptable digital twin.

Purpose: Natural language offers a convenient, flexible interface for controlling robotic C-arm X-ray systems, making advanced functionality and controls easily accessible.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Benjamin D.

Perspectives on AI and Novel Technologies Among Older Adults, Clinicians, Payers, Investors, and Developers.

Importance: Artificial intelligence (AI) and novel technologies, such as remote sensors, robotics, and decision support algorithms, offer the potential for improving the health and well-being of older adults, but the priorities of key partners across the technology innovation continuum are not well understood.

Objective: To examine the priorities and suggested applications for AI and novel technologies for older adults among key partners.

Design, Setting, And Participants: This qualitative study comprised individual interviews using grounded theory conducted from May 24, 2023, to January 24, 2024.

ProtoSAM-3D: Interactive semantic segmentation in volumetric medical imaging via a Segment Anything Model and mask-level prototypes.

Semantic segmentation of volumetric medical images is essential for accurate delineation of anatomic structures and pathology, enabling quantitative analysis in precision medicine applications. While volumetric segmentation has been extensively studied, most existing methods require full supervision and struggle to generalize to new classes at inference time, particularly for irregular, ill-defined targets such as tumors, where fine-grained, high-salience segmentation is required. Consequently, conventional semantic segmentation methods cannot easily offer zero/few-shot generalization to segment objects of interest beyond their closed training set.

Direct Prediction of 48 Month Survival Status in Patients with Uveal Melanoma Using Deep Learning and Digital Cytopathology Images.

Background: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. The median overall survival time for patients who develop metastasis is approximately one year. In this study, we aim to leverage deep learning (DL) techniques to analyze digital cytopathology images and directly predict the 48 month survival status on a patient level.

Seamless augmented reality integration in arthroscopy: a pipeline for articular reconstruction and guidance.

Arthroscopy is a minimally invasive surgical procedure used to diagnose and treat joint problems. The clinical workflow of arthroscopy typically involves inserting an arthroscope into the joint through a small incision, during which surgeons navigate and operate largely by relying on their visual assessment through the arthroscope. However, the arthroscope's restricted field of view and lack of depth perception pose challenges in navigating complex articular structures and achieving surgical precision during procedures.

StraightTrack: Towards mixed reality navigation system for percutaneous K-wire insertion.

In percutaneous pelvic trauma surgery, accurate placement of Kirschner wires (K-wires) is crucial to ensure effective fracture fixation and avoid complications due to breaching the cortical bone along an unsuitable trajectory. Surgical navigation via mixed reality (MR) can help achieve precise wire placement in a low-profile form factor. Current approaches in this domain are as yet unsuitable for real-world deployment because they fall short of guaranteeing accurate visual feedback due to uncontrolled bending of the wire.

Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.

Background: Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retraining.

Purpose: To develop an expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI.

Methods: A Delphi consensus exercise was conducted by the ASER AI/ML expert panel between 2022-2024.

Detecting Glaucoma Worsening Using Optical Coherence Tomography Derived Visual Field Estimates.

Objective: Multiple studies have attempted to generate visual field (VF) mean deviation (MD) estimates using cross-sectional optical coherence tomography (OCT) data. However, whether such models offer any value in detecting longitudinal VF progression is unclear. We address this by developing a machine learning (ML) model to convert OCT data to MD and assessing its ability to detect longitudinal worsening.

Generative Artificial Intelligence: Enhancing Patient Education in Cardiovascular Imaging.

Cardiovascular disease (CVD) is a major cause of mortality worldwide, especially in resource-limited countries with limited access to healthcare resources. Early detection and accurate imaging are vital for managing CVD, emphasizing the significance of patient education. Generative artificial intelligence (AI), including algorithms to synthesize text, speech, images, and combinations thereof given a specific scenario or prompt, offers promising solutions for enhancing patient education.

Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian Inference.

Pelvic ring disruptions result from blunt injury mechanisms and are potentially lethal mainly due to associated injuries and massive pelvic hemorrhage. The severity of pelvic fractures in trauma victims is frequently assessed by grading the fracture according to the Tile AO/OTA classification in whole-body Computed Tomography (CT) scans. Due to the high volume of whole-body CT scans generated in trauma centers, the overall information content of a single whole-body CT scan and low manual CT reading speed, an automatic approach to Tile classification would provide substantial value, e.

Vessel-targeted compensation of deformable motion in interventional cone-beam CT.

The present standard of care for unresectable liver cancer is transarterial chemoembolization (TACE), which involves using chemotherapeutic particles to selectively embolize the arteries supplying hepatic tumors. Accurate volumetric identification of intricate fine vascularity is crucial for selective embolization. Three-dimensional imaging, particularly cone-beam CT (CBCT), aids in visualization and targeting of small vessels in such highly variable anatomy, but long image acquisition time results in intra-scan patient motion, which distorts vascular structures and tissue boundaries.

OneSLAM to map them all: a generalized approach to SLAM for monocular endoscopic imaging based on tracking any point.

Purpose: Monocular SLAM algorithms are the key enabling technology for image-based surgical navigation systems for endoscopic procedures. Due to the visual feature scarcity and unique lighting conditions encountered in endoscopy, classical SLAM approaches perform inconsistently. Many of the recent approaches to endoscopic SLAM rely on deep learning models.

An endoscopic chisel: intraoperative imaging carves 3D anatomical models.

Purpose: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging. However, surgery elicits anatomical changes not represented in the preoperative model, generating an inaccurate basis for navigation during surgery progression.

Methods: We propose a first vision-based approach to update the preoperative 3D anatomical model leveraging intraoperative endoscopic video for navigated sinus surgery where relative camera poses are known.