Intraoperative patient-specific volumetric reconstruction and 3D visualization for laparoscopic liver surgery.

Despite the benefits of minimally invasive surgery, interventions such as laparoscopic liver surgery present unique challenges, like the significant anatomical differences between preoperative images and intraoperative scenes due to pneumoperitoneum, patient pose, and organ manipulation by surgical instruments. To address these challenges, a method for intraoperative three-dimensional reconstruction of the surgical scene, including vessels and tumors, without altering the surgical workflow, is proposed. The technique combines neural radiance field reconstructions from tracked laparoscopic videos with ultrasound three-dimensional compounding.

Cardiac anatomic digital twins: findings from a single national centre.

Aims: New three-dimensional cardiac visualization technologies are increasingly employed for anatomic digital twins in pre-operative planning. However, the role and influence of extended reality (virtual, augmented, or mixed) within heart team settings remain unclear. We aimed to assess the impact of mixed reality visualization of the intracardiac anatomy on surgical decision-making in patients with complex heart defects.

Comparing assisting technologies for proficiency in cardiac morphology: 3D printing and mixed reality versus CT slice images for morphological understanding of congenital heart defects by medical students.

Learning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students' understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience.

HoloPatch: improving intracardiac patch fit through holographically modelled templates.

Aims: Structural heart defects, including congenital ventricular septal defect closure or intracardiac rerouting, frequently require surgical reconstruction using hand-cut patch materials. Digitally modelled patch templates may improve patch fit and reduce outflow tract obstruction, residual defect risk, and conduction system damage. In this study, we benchmarked mixed-reality and a desktop application against a digitalized model of a real implanted patch.

Monte Carlo-based rendering of 3D echocardiography for mixed reality-guided atrial septal puncture positioning.

Catheter-based intervention procedures contain complex maneuvers, and they are often performed using fluoroscopic guidance assisted by 2D and 3D echocardiography viewed on a flat screen that inherently limits depth perception. Emerging mixed reality (MR) technologies, combined with advanced rendering techniques, offer potential enhancement in depth perception and navigational support. The study aims to evaluate a MR-based guidance system for the atrial septal puncture (ASP) procedure utilizing a phantom anatomical model.

Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation.

Objective: Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data.

Methods: We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates.

Hybrid optical-vision tracking in laparoscopy: accuracy of navigation and ultrasound reconstruction.

Introduction: The use of laparoscopic and robotic liver surgery is increasing. However, it presents challenges such as limited field of view and organ deformations. Surgeons rely on laparoscopic ultrasound (LUS) for guidance, but mentally correlating ultrasound images with pre-operative volumes can be difficult.

Automated structuring of gait data for analysis purposes - A deep learning pilot example.

Clinical gait analysis can help diagnose ambulatory children with cerebral palsy and provide treatment recommendations. This group represents the largest group of children with gait problems. Currently, the workflow for 3D gait analysis involves a complex process of collecting motion capture data and other types of data, analyzing the collected data, and creating an expert knowledge-based assessment.

Ascending with ultrasound: telementored eFAST in flight-a feasibility study.

Purpose: Teleultrasound uses telecommunication technologies to transmit ultrasound images from a remote location to an expert who guides the acquisition of images and interprets them in real time. Multiple studies have demonstrated the feasibility of teleultrasound. However, its application during helicopter flight using long-term evolution (LTE) for streaming has not been studied.

Real-time Remote Expert-guided Echocardiography by Medical Students.

Background: Echocardiography is a highly specialised examination performed by experienced healthcare professionals. These experienced healthcare professionals may not be available to patients during all hours in rural healthcare facilities. Remote-guided echocardiography could improve the availability of specialised care for patients living in rural areas.

Detection of inflow obstruction in left ventricular assist devices by accelerometer: A porcine model study.

Background: Left ventricular assist devices (LVAD) provide circulatory blood pump support for severe heart failure patients. Pump inflow obstructions may lead to stroke and pump malfunction. We aimed to verify in vivo that gradual inflow obstructions, representing prepump thrombosis, are detectable by a pump-attached accelerometer, where the routine use of pump power (P) is deficient.

Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation.

Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging.

Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting.

Pulse arrival time variation as a non-invasive marker of acute response to cardiac resynchronization therapy.

Aims: Successful cardiac resynchronization therapy (CRT) shortens the pre-ejection period (PEP) which is prolonged in the left bundle branch block (LBBB). In a combined animal and patient study, we investigated if changes in the pulse arrival time (PAT) could be used to measure acute changes in PEP during CRT implantation and hence be used to evaluate acute CRT response non-invasively and in real time.

Methods And Results: In six canines, a pulse transducer was attached to a lower limb and PAT was measured together with left ventricular (LV) pressure by micromanometer at baseline, after induction of LBBB and during biventricular pacing.

Detection of inflow obstruction in left ventricular assist devices by accelerometer: An in vitro study.

Inflow obstruction in left ventricular assist devices (LVAD) may lead to embolic stroke and pump malfunction. We investigated if an accelerometer detected graded LVAD inflow obstructions. Detection performances were compared to the current continuous surveillance routine based on the pump power consumption (P).

Evaluation of clinical applicability of automated liver parenchyma segmentation of multi-center magnetic resonance images.

Purpose: Automated algorithms for liver parenchyma segmentation can be used to create patient-specific models (PSM) that assist clinicians in surgery planning. In this work, we analyze the clinical applicability of automated deep learning methods together with level set post-processing for liver segmentation in contrast-enhanced T1-weighted magnetic resonance images.

Methods: UNet variants with/without attention gate, multiple loss functions, and level set post-processing were used in the workflow.

Generic surgical process model for minimally invasive liver treatment methods.

Surgical process modelling is an innovative approach that aims to simplify the challenges involved in improving surgeries through quantitative analysis of a well-established model of surgical activities. In this paper, surgical process model strategies are applied for the analysis of different Minimally Invasive Liver Treatments (MILTs), including ablation and surgical resection of the liver lesions. Moreover, a generic surgical process model for these differences in MILTs is introduced.

Evaluation and Comparison of Target Registration Error in Active and Passive Optical Tracking Systems.

Optical tracking systems combined with imaging modalities such as computed tomography and magnetic reso-nance imaging are important parts of image guided surgery systems. By determining the location and orientation of sur-gical tools relative to a patient's reference system, tracking systems assist surgeons during the planning and execution of image guided procedures. Therefore, knowledge of the tracking system-induced error is of great importance.

Towards Autonomous Robotic Biopsy-Design, Modeling and Control of a Robot for Needle Insertion of a Commercial Full Core Biopsy Instrument.

This paper presents the design, control, and experimental evaluation of a novel fully automated robotic-assisted system for the positioning and insertion of a commercial full core biopsy instrument under guidance by ultrasound imaging. The robotic system consisted of a novel 4 Degree of freedom (DOF) add-on robot for the positioning and insertion of the biopsy instrument that is attached to a UR5-based teleoperation system with 6 DOF. The robotic system incorporates the advantages of both freehand and probe-guided biopsy techniques.

Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions.

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound.

Tracking Early Systolic Motion for Assessing Acute Response to Cardiac Resynchronization Therapy in Real Time.

An abnormal systolic motion is frequently observed in patients with left bundle branch block (LBBB), and it has been proposed as a predictor of response to cardiac resynchronization therapy (CRT). Our goal was to investigate if this motion can be monitored with miniaturized sensors feasible for clinical use to identify response to CRT in real time. Motion sensors were attached to the septum and the left ventricular (LV) lateral wall of eighteen anesthetized dogs.

Automatic Detection of Aortic Valve Events Using Deep Neural Networks on Cardiac Signals From Epicardially Placed Accelerometer.

Background: Miniaturized accelerometers incorporated in pacing leads attached to the myocardium, are used to monitor cardiac function. For this purpose functional indices must be extracted from the acceleration signal. A method that automatically detects the time of aortic valve opening (AVO) and aortic valve closure (AVC) will be helpful for such extraction.

Morphology of lentic and lotic tadpoles from Madagascar.

Background: The morphology of anuran larvae is suggested to differ between species with tadpoles living in standing (lentic) and running (lotic) waters. To explore which character combinations within the general tadpole morphospace are associated with these habitats, we studied categorical and metric larval data of 123 (one third of which from lotic environments) Madagascan anurans.

Results: Using univariate and multivariate statistics, we found that certain combinations of fin height, body musculature and eye size prevail either in larvae from lentic or lotic environments.

A Recurrent Neural-Network-Based Real-Time Dynamic Model for Soft Continuum Manipulators.

This paper introduces and validates a real-time dynamic predictive model based on a neural network approach for soft continuum manipulators. The presented model provides a real-time prediction framework using neural-network-based strategies and continuum mechanics principles. A time-space integration scheme is employed to discretize the continuous dynamics and decouple the dynamic equations for translation and rotation for each node of a soft continuum manipulator.