Enhancing Heart Failure Care: Deep Learning-Based Activity Classification in Left Ventricular Assist Device Patients.

Accurate activity classification is essential for the advancement of closed-loop control for left ventricular assist devices (LVADs), as it provides necessary feedback to adapt device operation to the patient's current state. Therefore, this study aims at using deep neural networks (DNNs) to precisely classify activity for these patients. Recordings from 13 LVAD patients were analyzed, including heart rate, LVAD flow, and accelerometer data, classifying activities into six states: active, inactive, lying, sitting, standing, and walking. Both binary and multiclass classifiers have been trained to distinguish between active and inactive states and to discriminate the remaining categories. The models were refined by testing several architectures, including recurrent and convolutional layers, optimized via hyperparameter search. Results demonstrate that integrating LVAD flow, heart rate, and accelerometer data leads to the highest accuracy in both binary and multiclass classification. The optimal architectures featured two and three bidirectional long short-term memory layers for binary and multiclass classifications, respectively, achieving accuracies of 91% and 84%. In this study, the potential of DNNs has been proven for providing a robust method for activity classification that is vital for the effective closed-loop control of medical devices in cardiac care.

Development and assessment of case-specific physical and augmented reality simulators for intracranial aneurysm clipping.

BACKGROUND: Microsurgical clipping is a delicate neurosurgical procedure used to treat complex Unruptured Intracranial Aneurysms (UIAs) whose outcome is dependent on surgeon's experience. Simulations are emerging as excellent complements to standard training, but their adoption is limited by the realism they provide. The aim of this study was to develop and validate a microsurgical clipping simulator platform. METHODS: Physical and holographic simulators of UIA clipping have been developed. The physical phantom consisted of a 3D printed hard skull and five (n = 5) rapidly interchangeable, perfused and fluorescence compatible 3D printed aneurysm silicone phantoms. The holographic clipping simulation included a real-time finite-element-model of the aneurysm sac, allowing interaction with a virtual clip and its occlusion. Validity, usability, usefulness and applications of the simulators have been assessed through clinical scores for aneurysm occlusion and a questionnaire study involving 14 neurosurgical residents (R) and specialists (S) for both the physical (p) and holographic (h) simulators by scores going from 1 (very poor) to 5 (excellent). RESULTS: The physical simulator allowed to replicate successfully and accurately the patient-specific anatomy. UIA phantoms were manufactured with an average dimensional deviation from design of 0.096 mm and a dome thickness of 0.41 ± 0.11 mm. The holographic simulation executed at 25-50 fps allowing to gain unique insights on the anatomy and testing of the application of several clips without manufacturing costs. Aneurysm closure in the physical model evaluated by fluorescence simulation and post-operative CT revealed Raymond 1 (full) occlusion respectively in 68.89% and 73.33% of the cases. For both the simulators content validity, construct validity, usability and usefulness have been observed, with the highest scores observed in clip selection usefulness Rp=4.78, Sp=5.00 and Rh=4.00, Sh=5.00 for the printed and holographic simulators. CONCLUSIONS: Both the physical and the holographic simulators were validated and resulted usable and useful in selecting valid clips and discarding unsuitable ones. Thus, they represent ideal platforms for realistic patient-specific simulation-based training of neurosurgical residents and hold the potential for further applications in preoperative planning.

Bone-Mimetic Osteon Microtopographies on Poly-ε-Caprolactone Enhance the Osteogenic Potential of Human Mesenchymal Stem Cells.

The attributes of implant surfaces are pivotal for successful osseointegration. Among surface engineering strategies, microtopography stands out as a promising approach to promote early cellular interactions. This study aims to design and craft a novel biomimetic osteon-like surface modification and to compare its impact on human mesenchymal stem cells (hMSCs) with four established topographies: blank, inverted pyramids, protrusions, and grooves. Poly-ε-caprolactone samples are fabricated using 2-photon-polymerization and soft lithography, prior to analysis via scanning electron microscopy (SEM), water contact angle (WCA), and protein adsorption assays. Additionally, cellular responses including cell attachment, proliferation, morphology, cytoskeletal organization, and osteogenic differentiation potential are evaluated. SEM confirms the successful fabrication of microtopographies, with minimal effect on WCA and protein adsorption. Cell attachment experiments demonstrate a significant increase on the osteon-like structure, being three times higher than on the blank. Proliferation assays indicate a fourfold increase with osteon-like microtopography compared to the blank, while ALP activity is notably elevated with osteon-like microtopography at days 7 (threefold increase over blank) and 14 (fivefold increase over blank). In conclusion, the novel biomimetic osteon-like structure demonstrates favorable responses from hMSCs, suggesting potential for promoting successful implant integration in vivo.

Mechanical properties of additively manufactured lattice structures composed of zirconia and hydroxyapatite ceramics.

Ceramic lattices hold great potential for bone scaffolds to facilitate bone regeneration and integration of native tissue with medical implants. While there have been several studies on additive manufacturing of ceramics and their osseointegrative and osteoconductive properties, there is a lack of a comprehensive examination of their mechanical behavior. Therefore, the aim of this study was to assess the mechanical properties of different additively manufactured ceramic lattice structures under different loading conditions and their overall ability to mimic bone tissue properties. Eleven different lattice structures were designed and manufactured with a porosity of 80% using two materials, hydroxyapatite (HAp) and zirconium dioxide (ZrO2). Six cell-based lattices with cubic and hexagonal base, as well as five Voronoi-based lattices were considered in this study. The samples were manufactured using lithography-based ceramic additive manufacturing and post-processed thermally prior to mechanical testing. Cell-based lattices with cubic and hexagonal base, as well as Voronoi-based lattices were considered in this study. The lattices were tested under four loading conditions: compression, four-point bending, shear and tension. The manufacturing process of the different ceramics leads to different deviations of the lattice geometry, hence, the elastic properties of one structure cannot be directly inferred from one material to another. ZrO2 lattices prove to be stiffer than HAp lattices of the same designed structure. The Young's modulus for compression of ZrO2 lattices ranges from 2 to 30GPa depending on the used lattice design and for HAp 200MPa to 3.8GPa. The expected stability, the load where 63.2% of the samples are expected to be destroyed, of the lattices ranges from 81 to 553MPa and for HAp 6 to 42MPa. For the first time, a comprehensive overview of the mechanical properties of various additively manufactured ceramic lattice structures is provided. This is intended to serve as a reference for designers who would like to expand the design capabilities of ceramic implants that will lead to an advancement in their performance and ability to mimic human bone tissue.

Preoperative anatomical landmarks and longitudinal HeartMate 3 pump position in X-rays: Relevance for adverse events.

BACKGROUND: Left ventricular assist device (LVAD) malposition has been linked to hemocompatibility-related adverse events (HRAEs). This study aimed to identify preoperative anatomical landmarks and postoperative pump position, associated with HRAEs during LVAD support. METHODS: Pre- and postoperative chest X-ray measures (≤14 days pre-implantation, first postoperative standing, 6, 12, 18, and 24 months post-implantation) were analyzed for their association with HRAEs over 24 months in 33 HeartMate 3 (HM3) patients (15.2% female, age 66 (9.5) years). RESULTS: HM3 patients with any HRAE showed significantly lower preoperative distances between left ventricle and thoracic outline (dLVT) (25.3 ± 10.2 mm vs. 40.3 ± 15.5 mm, p = 0.004). A ROC-derived cutoff dLVT ≤ 29.2 mm provided 85.7% sensitivity and 72.2% specificity predicting any HRAE during HM3 support (76.2% (>29.2 mm) vs. 16.7% (≤29.2 mm) freedom from HRAE, p < 0.001) and significant differences in cardiothoracic ratio (0.58 ± 0.04 vs. 0.62 ± 0.04, p = 0.045). Postoperative X-rays indicated lower pump depths in patients with ischemic strokes (9.1 ± 16.2 mm vs. 38.0 ± 18.5 mm, p = 0.007), reduced freedom from any neurological event (pump depth ≤ 28.7 mm: 45.5% vs. 94.1%, p = 0.004), and a significant correlation between pump depth and inflow cannula angle (r = 0.66, p < 0.001). Longitudinal changes were observed in heart-pump width (F(4,60) = 5.61, p < 0.001). CONCLUSION: Preoperative X-ray markers are associated with postoperative HRAE occurrence. Applying this knowledge in clinical practice may enhance risk stratification, guide therapy optimization, and improve HM3 recipient management.

Design and optimization of a novel patient-specific subperiosteal implant additively manufactured in yttria-stabilized zirconia.

OBJECTIVE: To design a patient-specific subperiosteal implant for a severely atrophic maxillary ridge using yttria-stabilized additively manufactured zirconia (3YSZ) and evaluate its material properties by applying topology optimization (TO) to replace bulk material with a lattice structure. MATERIALS: A contrast-based segmented skull model from anonymized computed tomography data of a patient was used for the initial anatomical design of the implant for the atrophic maxillary ridge. The implant underwent finite element analysis (FEA) and TO under different occlusal load-bearing conditions. The resulting implant designs, in bulk material and lattice, were evaluated via in-silico tensile tests and 3D printed. RESULTS: The workflow produced two patient-specific subperiosteal designs: a) an anatomically precise bulk implant, b) a TO lattice implant. In-silico tensile tests revealed that the Young's modulus of yttria-stabilized zirconia is 205 GPa for the bulk material and 83.3 GPa for the lattice. Maximum principal stresses in the implant were 61.14 MPa in bulk material and 278.63 MPa in lattice, both tolerable, indicating the redesigned implant can withstand occlusal forces of 125-250 N per abutment. Furthermore, TO achieved a 13.10 % mass reduction and 208.71 % increased surface area, suggesting improved osteointegration potential. SIGNIFICANCE: The study demonstrates the planning and optimization of ceramic implant topology. A further iteration of the implant was successfully implanted in a patient-named use case, employing the same fabrication process and parameters.

Minimal Detectable Bone Fracture Gaps in CT Images and Digital Three-Dimensional (3D) Radii Models.

Knowledge of the minimal detectable bone fracture gap is essential in three-dimensional (3D) models, particularly in pre-operative planning of osteosynthesis to avoid overlooking gaps. In this study, defined incisions and bony displacements ranging from 100 to 400 µm were created in diaphyseal radii in 20 paired forearm specimens and verified with light microscopy. The specimens were scanned utilizing different computed tomography (CT) technologies/scanners, specimen positionings, scan protocols, image segmentations, and processing protocols. Inter- and intra-operator variabilities were reported as coefficient kappa. In CT images, fracture gaps of 100 µm and bone lamellae of 300 µm and 400 µm width were identified at a rate of 80 to 100%, respectively, independent of the investigated settings. In contrast, only 400µm incisions and bony displacements were visible in digital 3D models, with detection rates dependent on CT technology, image segmentation, and post-processing algorithm. 3D bone models based on state-of-the-art CT imaging can reliably visualize clinically relevant bone fracture gap sizes. However, verification of fractures to be surgically addressed should be verified with the original CT image series.

Effect of hot water maceration, rehydration, and soft tissue presence on 3D geometry of bone.

PURPOSE: In forensic medicine, maceration is often essential for examining bone surfaces, serving purposes such as identifying cut marks, making geometric measurements, and determining the victim's age. While hot water maceration removes soft tissue effectively, it is known to cause bone surface shrinkage. This raises the question of whether this effect is permanent or if it can be partially reversed through rehydration, considering the presence of soft tissue. METHODS: Computed tomography (CT) scans were conducted on the radii of 20 paired human anatomic forearm specimens. Subsequently, the radii were extracted, macerated in 60 °C water, CT-scanned in an air environment, rehydrated, re-implanted into the forearms, and CT-scanned again. RESULTS: Maceration resulted in a mean shrinkage of 0.12 mm on the outer bone surface. This shrinkage was nearly fully recoverable for the diaphysis after rehydration and accounting for soft tissue surrounding the bone. In contrast, the epiphysis showed permanent shrinkage, likely due to the loss of small bone fragments. Analysis of the inner bone surface indicated a smaller effect, but with significant standard deviations, especially for the epiphysis, possibly related to the less well-defined nature of the inner bone surface. CONCLUSION: The epiphyseal surface of hot water-macerated bone will, on average, be approximately 0.15 mm deflated and cannot retain the original surface. On the other hand, the diaphyseal surface is less affected and can be nearly completely restored after rehydration and accounting for soft tissue surrounding the bone.

Importance of cardiac-synchronized vagus nerve stimulation parameters on the provoked chronotropic response for different levels of cardiac innervation.

Introduction: The influence of vagus nerve stimulation (VNS) parameters on provoked cardiac effects in different levels of cardiac innervation is not well understood yet. This study examines the effects of VNS on heart rate (HR) modulation across a spectrum of cardiac innervation states, providing data for the potential optimization of VNS in cardiac therapies. Materials and Methods: Utilizing previously published data from VNS experiments on six sheep with intact innervation, and data of additional experiments in five rabbits post bilateral rostral vagotomy, and four isolated rabbit hearts with additionally removed sympathetic influences, the study explored the impact of diverse VNS parameters on HR. Results: Significant differences in physiological threshold charges were identified across groups: 0.09 ± 0.06 µC for intact, 0.20 ± 0.04 µC for vagotomized, and 9.00 ± 0.75 µC for isolated hearts. Charge was a key determinant of HR reduction across all innervation states, with diminishing correlations from intact (r = 0.7) to isolated hearts (r = 0.44). An inverse relationship was observed for the number of pulses, with its influence growing in conditions of reduced innervation (intact r = 0.11, isolated r = 0.37). Frequency and stimulation delay showed minimal correlations (r < 0.17) in all conditions. Conclusion: Our study highlights for the first time that VNS parameters, including stimulation intensity, pulse width, and pulse number, crucially modulate heart rate across different cardiac innervation states. Intensity and pulse width significantly influence heart rate in innervated states, while pulse number is key in denervated states. Frequency and delay have less impact impact across all innervation states. These findings suggest the importance of customizing VNS therapy based on innervation status, offering insights for optimizing cardiac neuromodulation.

Characterization, number, and spatial organization of nerve fibers in the human cervical vagus nerve and its superior cardiac branch.

BACKGROUND: Electrical stimulation of the vagus nerve (VN) is a therapy for epilepsy, obesity, depression, and heart diseases. However, whole nerve stimulation leads to side effects. We examined the neuroanatomy of the mid-cervical segment of the human VN and its superior cardiac branch to gain insight into the side effects of VN stimulation and aid in developing targeted stimulation strategies. METHODS: Nerve specimens were harvested from eight human body donors, then subjected to immunofluorescence and semiautomated quantification to determine the signature, quantity, and spatial distribution of different axonal categories. RESULTS: The right and left cervical VN (cVN) contained a total of 25,489 ± 2781 and 23,286 ± 3164 fibers, respectively. Two-thirds of the fibers were unmyelinated and one-third were myelinated. About three-quarters of the fibers in the right and left cVN were sensory (73.9 ± 7.5 % versus 72.4 ± 5.6 %), while 13.2 ± 1.8 % versus 13.3 ± 3.0 % were special visceromotor and parasympathetic, and 13 ± 5.9 % versus 14.3 ± 4.0 % were sympathetic. Special visceromotor and parasympathetic fibers formed clusters. The superior cardiac branches comprised parasympathetic, vagal sensory, and sympathetic fibers with the left cardiac branch containing more sympathetic fibers than the right (62.7 ± 5.4 % versus 19.8 ± 13.3 %), and 50 % of the left branch contained sensory and sympathetic fibers only. CONCLUSION: The study indicates that selective stimulation of vagal sensory and motor fibers is possible. However, it also highlights the potential risk of activating sympathetic fibers in the superior cardiac branch, especially on the left side.

Two-photon lithography for customized microstructured surfaces and their influence on wettability and bacterial load.

BACKGROUND: Device-related bacterial infections account for a large proportion of hospital-acquired infections. The ability of bacteria to form a biofilm as a protective shield usually makes treatment impossible without removal of the implant. Topographic surfaces have attracted considerable attention in studies seeking antibacterial properties without the need for additional antimicrobial substances. As there are still no valid rules for the design of antibacterial microstructured surfaces, a fast, reproducible production technique with good resolution is required to produce test surfaces and to examine their effectiveness with regard to their antibacterial properties. METHODS: In this work various surfaces, flat and with microcylinders in different dimensions (flat, 1, 3 and 9 µm) with a surface area of 7 × 7 mm were fabricated with a nanoprinter using two-photon lithography and evaluated for their antibiofilm effect. The microstructured surfaces were cultured for 24 h with different strains of Pseudomonas aeruginosa and Staphylococcus aureus to study bacterial attachment to the patterned surfaces. In addition, surface wettability was measured by a static contact angle measurement. RESULTS: Contact angles increased with cylinder size and thus hydrophobicity. Despite the difference in wettability, Staphylococcus aureus was not affected by the microstructures, while for Pseudomonas aeruginosa the bacterial load increased with the size of the cylinders, and compared to a flat surface, a reduction in bacteria was observed for one strain on the smallest cylinders. CONCLUSIONS: Two-photon lithography allowed rapid and flexible production of microcylinders of different sizes, which affected surface wettability and bacterial load, however, depending on bacterial type and strain.

Accuracy Analysis of 3D Bone Fracture Models: Effects of Computed Tomography (CT) Imaging and Image Segmentation.

The introduction of three-dimensional (3D) printed anatomical models has garnered interest in pre-operative planning, especially in orthopedic and trauma surgery. Identifying potential error sources and quantifying their effect on the model dimensional accuracy are crucial for the applicability and reliability of such models. In this study, twenty radii were extracted from anatomic forearm specimens and subjected to osteotomy to simulate a defined fracture of the distal radius (Colles' fracture). Various factors, including two different computed tomography (CT) technologies (energy-integrating detector (EID) and photon-counting detector (PCD)), four different CT scanners, two scan protocols (i.e., routine and high dosage), two different scan orientations, as well as two segmentation algorithms were considered to determine their effect on 3D model accuracy. Ground truth was established using 3D reconstructions of surface scans of the physical specimens. Results indicated that all investigated variables significantly impacted the 3D model accuracy (p < 0.001). However, the mean absolute deviation fell within the range of 0.03 ± 0.20 to 0.32 ± 0.23 mm, well below the 0.5 mm threshold necessary for pre-operative planning. Intra- and inter-operator variability demonstrated fair to excellent agreement for 3D model accuracy, with an intra-class correlation (ICC) of 0.43 to 0.92. This systematic investigation displayed dimensional deviations in the magnitude of sub-voxel imaging resolution for all variables. Major pitfalls included missed or overestimated bone regions during the segmentation process, necessitating additional manual editing of 3D models. In conclusion, this study demonstrates that 3D bone fracture models can be obtained with clinical routine scanners and scan protocols, utilizing a simple global segmentation threshold, thereby providing an accurate and reliable tool for pre-operative planning.

Measured and simulated mechanical properties of additively manufactured matrix-inclusion multimaterials fabricated by material jetting.

Modern additive manufacturing enables the simultaneous processing of different materials during the printing process. While multimaterial 3D printing allows greater freedom in part design, the prediction of the mix-material properties becomes challenging. One type of multimaterials are matrix-inclusion composites, where one material contains inclusions of another material. Aim of this study was to develop a method to predict the uniaxial Young's modulus and Poisson's ratio of material jetted matrix-inclusion composites by a combination of simulations and experimental data.Fifty samples from commercially available materials in their pure and matrix-inclusion mixed forms, with cubic inclusions, have been fabricated using material jetting and mechanically characterized by uniaxial tensile tests. Multiple simulation approaches have been assessed and compared to the measurement results in order to find and validate a method to predict the multimaterials' properties. Optical coherence tomography and microscopy was used to characterize the size and structure of the multimaterials, compared to the design.The materials exhibited Young's moduli in the range of 1.4 GPa to 2.5 GPa. The multimaterial mixtures were never as stiff as the weighted volume average of the primary materials (up to [Formula: see text] softer for 45% RGD8530-DM inclusions in VeroClear matrix). Experimental data could be predicted by finite element simulations by considering a non-ideal contact stiffness between matrix and inclusion ([Formula: see text] for RGD8530-DM, [Formula: see text] for RGD8430-DM), and geometries of the printed inclusions that deviated from the design (rounded edge radii of [Formula: see text]m). Not considering this would lead to a difference of the estimation result of up to [Formula: see text]MPa (44%), simulating an inclusion volume fraction of 45% RGD8530-DM.Prediction of matrix-inclusion composites fabricated by multimaterial jetting printing, is possible, however, requires a priori knowledge or additional measurements to characterize non-ideal contact stiffness between the components and effective printed geometries, precluding therefore a simple multimaterial modelling.

Decoding cardiac reinnervation from cardiac autonomic markers: A mathematical model approach.

BACKGROUND: Although cardiac autonomic markers (CAMs) are commonly used to assess cardiac reinnervation in heart-transplant patients, their relationship to the degree of sympathetic and vagal cardiac reinnervation is not well understood yet. To study this relationship, we applied a mathematical model of the cardiovascular system and its autonomic control. METHODS: By simulating varying levels of sympathetic and vagal efferent sinoatrial reinnervation, we analyzed the induced changes in CAMs including resting heart rate (HR), bradycardic and tachycardic HR response to Valsalva maneuver, root mean square of successive differences between normal heartbeats (RMSSD), low-frequency (LF), high-frequency (HF), and total spectral power (TSP). RESULTS: For assessment of vagal cardiac reinnervation levels >20%, resting HR (ρ = 0.99, p < 0.05), RMSSD (ρ = 0.97, p < 0.05), and TSP (ρ = 0.96, p < 0.05) may be equally suitable as HF-power (ρ = 0.97, p < 0.05). To assess sympathetic reinnervation, LF/HF ratio (ρ = 0.87, p < 0.05) and tachycardic response to Valsalva maneuver (ρ = 0.9, p < 0.05) may be more suitable than LF-power (ρ = 0.77, p < 0.05). CONCLUSIONS: Our model reports mechanistic relationships between CAMs and levels of efferent autonomic sinoatrial reinnervation. The results indicate differences in the suitability of these markers to assess vagal and sympathetic reinnervation. Although our analysis is purely conceptual, the developed model can help to gain important insights into the genesis of CAMs and their relationship to efferent sinoatrial reinnervation and, thus, provide indications for clinical study evaluation.

Dimensional accuracy and precision and surgeon perception of additively manufactured bone models: effect of manufacturing technology and part orientation.

BACKGROUND: Additively manufactured (AM) anatomical bone models are primarily utilized for training and preoperative planning purposes. As such, they must meet stringent requirements, with dimensional accuracy being of utmost importance. This study aimed to evaluate the precision and accuracy of anatomical bone models manufactured using three different AM technologies: digital light processing (DLP), fused deposition modeling (FDM), and PolyJetting (PJ), built in three different part orientations. Additionally, the study sought to assess surgeons' perceptions of how well these models mimic real bones in simulated osteosynthesis. METHODS: Computer-aided design (CAD) models of six human radii were generated from computed tomography (CT) imaging data. Anatomical models were then manufactured using the three aforementioned technologies and in three different part orientations. The surfaces of all models were 3D-scanned and compared with the original CAD models. Furthermore, an anatomical model of a proximal femur including a metastatic lesion was manufactured using the three technologies, followed by (mock) osteosynthesis performed by six surgeons on each type of model. The surgeons' perceptions of the quality and haptic properties of each model were assessed using a questionnaire. RESULTS: The mean dimensional deviations from the original CAD model ranged between 0.00 and 0.13 mm with maximal inaccuracies < 1 mm for all models. In surgical simulation, PJ models achieved the highest total score on a 5-point Likert scale ranging from 1 to 5 (with 1 and 5 representing the lowest and highest level of agreement, respectively), (3.74 ± 0.99) in the surgeons' perception assessment, followed by DLP (3.41 ± 0.99) and FDM (2.43 ± 1.02). Notably, FDM was perceived as unsuitable for surgical simulation, as the material melted during drilling and sawing. CONCLUSIONS: In conclusion, the choice of technology and part orientation significantly influenced the accuracy and precision of additively manufactured bone models. However, all anatomical models showed satisfying accuracies and precisions, independent of the AM technology or part orientation. The anatomical and functional performance of FDM models was rated by surgeons as poor.

An evaluation of physical and augmented patient-specific intracranial aneurysm simulators on microsurgical clipping performance and skills: a randomized controlled study.

OBJECTIVE: In the era of flow diversion, there is an increasing demand to train neurosurgeons outside the operating room in safely performing clipping of unruptured intracranial aneurysms. This study introduces a clip training simulation platform for residents and aspiring cerebrovascular neurosurgeons, with the aim to visualize peri-aneurysm anatomy and train virtual clipping applications on the matching physical aneurysm cases. METHODS: Novel, cost-efficient techniques allow the fabrication of realistic aneurysm phantom models and the additional integration of holographic augmented reality (AR) simulations. Specialists preselected suitable and unsuitable clips for each of the 5 patient-specific models, which were then used in a standardized protocol involving 9 resident participants. Participants underwent four sessions of clip applications on the models, receiving no interim training (control), a video review session (video), or a video review session and holographic clip simulation training (video + AR) between sessions 2 and 3. The study evaluated objective microsurgical skills, which included clip selection, number of clip applications, active simulation time, wrist tremor analysis during simulations, and occlusion efficacy. Aneurysm occlusions of the reference sessions were assessed by indocyanine green videoangiography, as well as conventional and photon-counting CT scans. RESULTS: A total of 180 clipping procedures were performed without technical complications. The measurements of the active simulation times showed a 39% improvement for all participants. A median of 2 clip application attempts per case was required during the final session, with significant improvement observed in experienced residents (postgraduate year 5 or 6). Wrist tremor improved by 29% overall. The objectively assessed aneurysm occlusion rate (Raymond-Roy class 1) improved from 76% to 80% overall, even reaching 93% in the extensively trained cohort (video + AR) (p = 0.046). CONCLUSIONS: The authors introduce a newly developed simulator training platform combining physical and holographic aneurysm clipping simulators. The development of exchangeable, aneurysm-comprising housings allows objective radio-anatomical evaluation through conventional and photon-counting CT scans. Measurable performance metrics serve to objectively document improvements in microsurgical skills and surgical confidence. Moreover, the different training levels enable a training program tailored to the cerebrovascular trainees' levels of experience and needs.

Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges.

Training deep neural network classifiers for electrocardiograms (ECGs) requires sufficient data. However, imbalanced datasets pose a major problem for the training process and hence data augmentation is commonly performed. Generative adversarial networks (GANs) can create synthetic ECG data to augment such imbalanced datasets. This review aims at identifying the present literature concerning synthetic ECG signal generation using GANs to provide a comprehensive overview of architectures, quality evaluation metrics, and classification performances. Thirty publications from the years 2019 to 2022 were selected from three separate databases. Nine publications used a quality evaluation metric neglecting classification, eleven performed a classification but omitted a quality evaluation metric, and ten publications performed both. Twenty different quality evaluation metrics were observed. Overall, the classification performance of databases augmented with synthetically created ECG signals increased by 7 % to 98 % in accuracy and 6 % to 97 % in sensitivity. In conclusion, synthetic ECG signal generation using GANs represents a promising tool for data augmentation of imbalanced datasets. Consistent quality evaluation of generated signals remains challenging. Hence, future work should focus on the establishment of a gold standard for quality evaluation metrics for GANs.

Medical 3D printing with polyjet technology: effect of material type and printing orientation on printability, surface structure and cytotoxicity.

Due to its high printing resolution and ability to print multiple materials simultaneously, inkjet technology has found wide application in medicine. However, the biological safety of 3D-printed objects is not always guaranteed due to residues of uncured resins or support materials and must therefore be verified. The aim of this study was to evaluate the quality of standard assessment methods for determining the quality and properties of polyjet-printed scaffolds in terms of their dimensional accuracy, surface topography, and cytotoxic potential.Standardized 3D-printed samples were produced in two printing orientations (horizontal or vertical). Printing accuracy and surface roughness was assessed by size measurements, VR-5200 3D optical profilometer dimensional analysis, and scanning electron microscopy. Cytotoxicity tests were performed with a representative cell line (L929) in a comparative laboratory study. Individual experiments were performed with primary cells from clinically relevant tissues and with a Toxdent cytotoxicity assay.Dimensional measurements of printed discs indicated high print accuracy and reproducibility. Print accuracy was highest when specimens were printed in horizontal direction. In all cytotoxicity tests, the estimated mean cell viability was well above 70% (p < 0.0001) regardless of material and printing direction, confirming the low cytotoxicity of the final 3D-printed objects.

An in vitro model to study suction events by a ventricular assist device: validation with clinical data.

Introduction: Ventricular assist devices (LVADs) are a valuable therapy for end-stage heart failure patients. However, some adverse events still persist, such as suction that can trigger thrombus formation and cardiac rhythm disorders. The aim of this study is to validate a suction module (SM) as a test bench for LVAD suction detection and speed control algorithms. Methods: The SM consists of a latex tube, mimicking the ventricular apex, connected to a LVAD. The SM was implemented into a hybrid in vitro-in silico cardiovascular simulator. Suction was induced simulating hypovolemia in a profile of a dilated cardiomyopathy and of a restrictive cardiomyopathy for pump speeds ranging between 2,500 and 3,200 rpm. Clinical data collected in 38 LVAD patients were used for the validation. Clinical and simulated LVAD flow waveforms were visually compared. For a more quantitative validation, a binary classifier was used to classify simulated suction and non-suction beats. The obtained classification was then compared to that generated by the simulator to evaluate the specificity and sensitivity of the simulator. Finally, a statistical analysis was run on specific suction features (e.g., minimum impeller speed pulsatility, minimum slope of the estimated flow, and timing of the maximum slope of the estimated flow). Results: The simulator could reproduce most of the pump waveforms observed in vivo. The simulator showed a sensitivity and specificity and of 90.0% and 97.5%, respectively. Simulated suction features were in the interquartile range of clinical ones. Conclusions: The SM can be used to investigate suction in different pathophysiological conditions and to support the development of LVAD physiological controllers.

Optimizing efficiency in the creation of patient-specific plates through field-driven generative design in maxillofacial surgery.

Field driven design is a novel approach that allows to define through equations geometrical entities known as implicit bodies. This technology does not rely upon conventional geometry subunits, such as polygons or edges, rather it represents spatial shapes through mathematical functions within a geometrical field. The advantages in terms of computational speed and automation are conspicuous, and well acknowledged in engineering, especially for lattice structures. Moreover, field-driven design amplifies the possibilities for generative design, facilitating the creation of shapes generated by the software on the basis of user-defined constraints. Given such potential, this paper suggests the possibility to use the software nTopology, which is currently the only software for field-driven generative design, in the context of patient-specific implant creation for maxillofacial surgery. Clinical scenarios of applicability, including trauma and orthognathic surgery, are discussed, as well as the integration of this new technology with current workflows of virtual surgical planning. This paper represents the first application of field-driven design in maxillofacial surgery and, although its results are very preliminary as it is limited in considering only the distance field elaborated from specific points of reconstructed anatomy, it introduces the importance of this new technology for the future of personalized implant design in surgery.