Type B Aortic Dissection CTA Collection with True and False Lumen Expert Annotations for the Development of AI-based Algorithms.

Aortic dissections (ADs) are serious conditions of the main artery of the human body, where a tear in the inner layer of the aortic wall leads to the formation of a new blood flow channel, named false lumen. ADs affecting the aorta distally to the left subclavian artery are classified as a Stanford type B aortic dissection (type B AD). This is linked to substantial morbidity and mortality, however, the course of the disease for the individual case is often unpredictable. Computed tomography angiography (CTA) is the gold standard for the diagnosis of type B AD. To advance the tools available for the analysis of CTA scans, we provide a CTA collection of 40 type B AD cases from clinical routine with corresponding expert segmentations of the true and false lumina. Segmented CTA scans might aid clinicians in decision making, especially if it is possible to fully automate the process. Therefore, the data collection is meant to be used to develop, train and test algorithms.

Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.

Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .

The axillary vein puncture for implantable cardiac defibrillator implantation: 14 years of experience. Analysis of the results.

BACKGROUND: Axillary vein puncture (AVP) is a valid alternative to Subclavan vein puncture for leads insertion in cardiac implantable electronic device implantation, that may reduce acute and delayed complications. Very few data are available about ICD recipients. A simplified AVP technique is described. METHODS: All the patients who consecutively underwent "de novo" ICD implantation, from March 2006 to December 2020 at the University of Verona, were considered. Leads insertion was routinely performed through an AVP, according to a simplified technique. Outcome and complications have been retrospectively analyzed. RESULTS: The study population consisted of 1711 consecutive patients. Out of 1711 patients, 38 (2.2%) were excluded because they were implanted with Medtronic Sprint Fidelis lead. Out of 1673 ICD implantations, 963 (57.6%) were ICD plus cardiac resynchronization therapy, 434 (25.9%) were dual-chamber defibrillators, and 276 (16.5%) were single-chamber defibrillators, for a total of 3879 implanted leads. The AVP success rate was 99.4%. Acute complications occurred in 7/1673 (0.42%) patients. Lead failure (LF) occurred in 20/1673 (1.19%) patients. Comparing the group of patients with lead failure with the group without LF, the presence of three leads inside the vein was significantly associated with LF, and the multivariate analysis confirmed three leads in place as an independent predictor of LF. CONCLUSION: AVP, according to our simplified technique, is safe, effective, has a high success rate, and a very low complication rate. The incidence of LF was exceptionally low. The advantages of AVP are maintained over time in a population of ICD recipients.

Geodetic imaging of magma ascent through a bent and twisted dike during the Tajogaite eruption of 2021 (La Palma, Canary Islands).

On Sept. 19th, 2021, the largest historical eruption on the island of La Palma began, which had a significant scientific, social, and economic impact. The 2021 Tajogaite eruption was characterised by short precursors, lasting only 8 days. The seismicity started on Sept. 11th with a westward and upward migration of hypocenters. Permanent GNSS stations started recording deformation on Sept. 12th on the island's western side, which reached more than 15 cm just before the eruption. After the eruption onset, the ground deformation increased, reaching a maximum on Sept. 22nd and showing a nearly steady deflation trend in the following months. To better understand the dynamics of the eruption, we exploited a joint dataset of GNSS and Sentinel-1 SBAS time series along both ascending and descending orbits. To obtain the geometry of the causative source of the ground deformation, we combined the result of a preliminary non-linear inversion and the precise location of the seismicity. The resulting geometry of the source is that of a twisted dike bending eastward. We performed inverse modelling to obtain the spatiotemporal kinematics of the opening function of the dike. The forward modelling has been realised using a 3D finite-element approach considering the island's topography. Our findings reveal a close correspondence between the magmatic intrusion and pre-eruptive seismicity. The ascent of the magma occurred along two branches, and the rheology of a previously identified ductile layer strongly affected the magma propagation process. Finally, we found evidence of an early shallow deformation, which we interpret as the effect of ascending hydrothermal fluids. Our findings highlight the need for advanced modelling to understand pre-eruptive processes in basaltic volcanoes.

3D surgical instrument collection for computer vision and extended reality.

The availability of computational hardware and developments in (medical) machine learning (MML) increases medical mixed realities' (MMR) clinical usability. Medical instruments have played a vital role in surgery for ages. To further accelerate the implementation of MML and MMR, three-dimensional (3D) datasets of instruments should be publicly available. The proposed data collection consists of 103, 3D-scanned medical instruments from the clinical routine, scanned with structured light scanners. The collection consists, for example, of instruments, like retractors, forceps, and clamps. The collection can be augmented by generating likewise models using 3D software, resulting in an inflated dataset for analysis. The collection can be used for general instrument detection and tracking in operating room settings, or a freeform marker-less instrument registration for tool tracking in augmented reality. Furthermore, for medical simulation or training scenarios in virtual reality and medical diminishing reality in mixed reality. We hope to ease research in the field of MMR and MML, but also to motivate the release of a wider variety of needed surgical instrument datasets.

Sparse convolutional neural network for high-resolution skull shape completion and shape super-resolution.

Traditional convolutional neural network (CNN) methods rely on dense tensors, which makes them suboptimal for spatially sparse data. In this paper, we propose a CNN model based on sparse tensors for efficient processing of high-resolution shapes represented as binary voxel occupancy grids. In contrast to a dense CNN that takes the entire voxel grid as input, a sparse CNN processes only on the non-empty voxels, thus reducing the memory and computation overhead caused by the sparse input data. We evaluate our method on two clinically relevant skull reconstruction tasks: (1) given a defective skull, reconstruct the complete skull (i.e., skull shape completion), and (2) given a coarse skull, reconstruct a high-resolution skull with fine geometric details (shape super-resolution). Our method outperforms its dense CNN-based counterparts in the skull reconstruction task quantitatively and qualitatively, while requiring substantially less memory for training and inference. We observed that, on the 3D skull data, the overall memory consumption of the sparse CNN grows approximately linearly during inference with respect to the image resolutions. During training, the memory usage remains clearly below increases in image resolution-an [Formula: see text] increase in voxel number leads to less than [Formula: see text] increase in memory requirements. Our study demonstrates the effectiveness of using a sparse CNN for skull reconstruction tasks, and our findings can be applied to other spatially sparse problems. We prove this by additional experimental results on other sparse medical datasets, like the aorta and the heart. Project page at https://github.com/Jianningli/SparseCNN .

Automated cross-sectional view selection in CT angiography of aortic dissections with uncertainty awareness and retrospective clinical annotations.

Surveillance imaging of patients with chronic aortic diseases, such as aneurysms and dissections, relies on obtaining and comparing cross-sectional diameter measurements along the aorta at predefined aortic landmarks, over time. The orientation of the cross-sectional measuring planes at each landmark is currently defined manually by highly trained operators. Centerline-based approaches are unreliable in patients with chronic aortic dissection, because of the asymmetric flow channels, differences in contrast opacification, and presence of mural thrombus, making centerline computations or measurements difficult to generate and reproduce. In this work, we present three alternative approaches - INS, MCDS, MCDbS - based on convolutional neural networks and uncertainty quantification methods to predict the orientation (ϕ,θ) of such cross-sectional planes. For the monitoring of chronic aortic dissections, we show how a dataset of 162 CTA volumes with overall 3273 imperfect manual annotations routinely collected in a clinic can be efficiently used to accomplish this task, despite the presence of non-negligible interoperator variabilities in terms of mean absolute error (MAE) and 95% limits of agreement (LOA). We show how, despite the large limits of agreement in the training data, the trained model provides faster and more reproducible results than either an expert user or a centerline method. The remaining disagreement lies within the variability produced by three independent expert annotators and matches the current state of the art, providing a similar error, but in a fraction of the time.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

In-Hospital Mortality After Massive Transfusion and Motorcycle Trauma May Predict Highest Injury Severity in a Rural Level I Trauma System.

The leading cause of preventable traumatic death is uncontrolled bleeding. This study aimed to better identify those most likely to experience in-hospital mortality with increasing injury severity scoring (ISS). This is a single-center study of Trauma Registry data, from July 3, 2016, to February 24, 2022. The inclusion criteria were based upon age (≥18 years) and in-hospital mortality. 546 patients (mean age 58) were included in the analysis. There were several significant associations with increasing ISS among those who experienced in-hospital mortality, which included a rising shock index ratio, activation of the massive transfusion protocol, and, most notably, motorcycle trauma. This research reiterates the importance of the "Stop the Bleed" campaign as vital for training laypersons in the life-saving technique for hemorrhage control.

High Grade Intestinal Obstruction From Internal Hernia Beneath an Intraperitoneal External Iliac Artery.

Acute small bowel obstruction (SBO) is a common cause of emergency department visits in the United States, and it accounts for approximately 20% of emergency surgical operations.1 Its etiology is divided into intrinsic luminal obstruction or extrinsic compression of the bowel.2 Among the causes of SBO, by far the most common is intraperitoneal adhesions due to previous abdominal surgeries, which comprises about 60-70% of the cases.2 The abdominal cavity is subdivided into the peritoneal cavity and the retroperitoneal cavity; the division is marked by a thin covering of parietal peritoneum that encases all the intraperitoneal structures. Here, we present a rare case of an acute small bowel obstruction secondary to exposure of the retroperitoneal external iliac artery from a surgical procedure 20 years prior to presentation.

Predicting Complicated Mild Traumatic Brain Injury in Adolescent Trauma to Enhance Clinical Decisions in Imaging.

BACKGROUND: The Pediatric Emergency Care Applied Research Network (PECARN) traumatic brain injury algorithm is used to identify children at low risk of clinically significant traumatic brain injuries to reduce computed tomography (CT) exposure. Adapting PECARN rules based on population-specific risk stratification has been suggested to improve diagnostic accuracy. OBJECTIVE: This study sought to identify center-specific patient variables, beyond PECARN rules, that may enhance the identification of patients requiring neuroimaging. METHODS: This single-center, retrospective cohort study was conducted from July 1, 2016, to July 1, 2020, in a Southwestern U.S. Level II pediatric trauma center. The inclusion criteria were adolescents (10-15 years), Glasgow Coma Scale (13-15), with a confirmed mechanical blow to the head. Patients without a head CT were excluded. Logistic regression was performed to identify additional complicated mild traumatic brain injury predictor variables beyond the PECARN. RESULTS: There were 136 patients studied; 21 (15%) presented with a complicated mild traumatic brain injury. Relative to motorcycle collision or all-terrain vehicle trauma (odds ratio [OR] 211.75, 95% confidence interval, CI [4.51, 9931.41], p < .001), an unspecified mechanism (OR 42.0, 95% CI [1.30, 1350.97], p = .03) and consult activation (OR 17.44, 95% CI [1.75, 173.31], p = .01) were significantly associated with complicated mild traumatic brain injury. CONCLUSIONS: We identified additional factors associated with complex mild traumatic brain injury, including motorcycle collision and all-terrain vehicle trauma, unspecified mechanism, and consult activation that are not in the PECARN imaging decision rule. Adding these variables may aid in determining the need for appropriate CT scanning.

Adult Traumatic Brain Injury and Likelihood of Routine Discharge: Do Comorbidities Matter?

The current literature demonstrates an association between both size and presence of traumatic brain injury (TBI) and its effects on mortality, however it does not readily address the morbidity and associated functional outcomes of those who survive. We hypothesize that the likelihood of discharge to home decreases with advancement of age in the presence of TBI. This is a single-center study of Trauma Registry data, inclusive years July 1, 2016 to October 31, 2021. The inclusion criteria was based upon age (≥40 years), and ICD10 diagnosis of a TBI. Disposition to home without services was the dependent variable. 2031 patients were included in the analysis. We hypothesized correctly that the likelihood of discharge to home decreases (by 6%) with advancement of age (per year) in the presence of intracranial hemorrhage.

Ambulance Response in Eight Minutes or Less: Are Comorbidities a Factor.

A recommended emergency medical services ambulance response time to a medical emergency is within eight minutes for at least 90% of calls. This study aimed to evaluate scene times for rural education and outreach to improve the quality of trauma care. This is a single-center study of Trauma Registry data from July 1, 2016 to February 28, 2022. The inclusion criteria were based upon age (≥18 years). A logistic regression was performed to identify predictor variables on the likelihood that an adult trauma patient will experience scene times greater than eight minutes. 19 321 patients were included in the analysis; 7233 (37%) experienced an elapsed scene time within eight minutes. This research identified an opportunity to improve rural trauma team response time, which is only reaching 37% of the patient population within eight minutes. Prehospital cardiac arrest and unique pre-existing comorbidities may play a role in extended response times by EMS.

Predicting Early Versus Late In-Hospital Mortality in the Trauma Population.

This study aimed to evaluate non-survivors who were admitted to a level I trauma center but later died, in terms of predicting who would expire early vs late. This is a single-center study of Trauma Registry data, from July 3, 2016, to February 24, 2022. The inclusion criteria were based upon age (≥18 years) and in-hospital mortality. 546 patients (mean age 58) were included in the analysis. Trauma patients who may experience an earlier death were those with increasing injury severity scores, activation of massive transfusion protocol, comorbid advanced directive limiting care, COPD, personality disorder, and ED death location. Patients were more likely to experience later in-hospital mortality, including those with increasing ICU stays, and comorbid dementia.

In-Hospital Mortality after Massive Transfusion and Motorcycle Trauma may Predict Highest Injury Severity in a Rural Level I Trauma System.

The leading cause of preventable traumatic death is uncontrolled bleeding. This study aimed to better identify those most likely to experience in-hospital mortality with increasing injury severity scoring (ISS). This is a single-center study of Trauma Registry data, from July 3, 2016, to February 24, 2022. The inclusion criteria were based upon age (≥18 years) and in-hospital mortality. 546 patients (mean age 58) were included in the analysis. There were several significant associations with increasing ISS among those who experienced in-hospital mortality, which included a rising shock index ratio, activation of the massive transfusion protocol, and, most notably, motorcycle trauma. This research reiterates the importance of the "Stop the Bleed" campaign as vital for training laypersons in the life-saving technique for hemorrhage control.

Can Pre-Hospital Medical Management Predict In-Hospital Mortality in Trauma?

The current literature demonstrates an association between both size and presence of TBI and its effects on mortality; however, it does not readily address the morbidity and associated functional outcomes of those who survive. We hypothesize that the likelihood of discharge to home decreases with advancement of age in the presence of TBI. This is a single-center study of trauma registry data, inclusive years July 1, 2016, to October 31, 2021. The inclusion criteria was based upon age (≥40 years), and ICD10 diagnosis of a TBI. Disposition to home without services was the dependent variable. 2031 patients were included in the analysis. We hypothesized correctly that the likelihood of discharge to home decreases (by 6%) with advancement of age (per year) in the presence of intracranial hemorrhage.

Traumatic Brain Injury Under Triage Risk: A Rural Trauma System Experience.

BACKGROUND: The process of interfacility transfer may cause a delay in the necessary medical treatment, which could lead to poor outcomes and increased mortality rates. The ACS-COT considers an acceptable under triage rate of <5%. The aim of this research was to identify the likelihood of under triage among transferred-in traumatic brain injury (TBI) patients. METHODS: This is a single-center study of Trauma Registry data, from July 1, 2016, to October 31, 2021. The inclusion criteria were based upon age (≥40 years), ICD10 diagnosis of TBI, and interfacility transfer. Under triage using the Cribari matrix method was the dependent variable. A logistic regression was performed to identify additional predictor variables on the likelihood that an adult TBI trauma patient experienced under triage. RESULTS: 878 patients were included in the analysis; 168 (19%) experienced an under triage. The logistic regression model was statistically significant (N = 837, P < .01). In addition, several significant increases in odds for under triage were identified, which included increasing injury severity score (ISS; OR 1.40, P < .01), increasing AIS head region (OR 6.19, P < .01), and personality disorders (OR 3.61, P = .02). In addition, a reduction in odds in TBI adult trauma under triage is the comorbidity of anticoagulant therapy (OR .25, P < .01). CONCLUSIONS: The likelihood of under triage in the adult TBI trauma population is associated with increasing AIS head injuries and increasing ISS and among those with mental health comorbidities. This evidence and additional protective factors, such as patients on anticoagulant therapy, may aid in education and outreach efforts to reduce under triage among the regional referring centers.

MADIA - Meteorological variables for agriculture: A dataset for the Italian area.

The MADIA gridded dataset provides the dekadal series of the main agro-meteorological variables derived from ERA5 hourly surface data, across Italy for the period 1981-2021, and their respective 1981-2010 and 1991-2020 climate normals, also including absolute minimum and maximum and the main quantiles. Temporal and spatial resolutions are 10-day and 0.25 degrees respectively and the dataset is annotated with standard metadata. The dataset was obtained by: (1) estimating the daily time series of minimum, average and maximum air temperature, minimum and maximum air relative humidity, wind speed, surface solar radiation downwards, precipitation and reference evapotranspiration according to the FAO Penman-Monteith method; (2) summarising them to 10-day series as accumulated values for precipitation and evapotranspiration and mean values for the other variables. The MADIA dataset is provided in both NetCDF and csv format. A complementary vector file is provided which reports for every cell the fractions covered of the total area of each administrative unit considered to derive statistics for Italy on the European Nomenclature of Territorial Units for Statistics levels (NUTS 2 and 3). Other potential dataset reuses are the estimation of bioclimatic indices and statistical downscaling of climate scenarios.

The HoloLens in medicine: A systematic review and taxonomy.

The HoloLens (Microsoft Corp., Redmond, WA), a head-worn, optically see-through augmented reality (AR) display, is the main player in the recent boost in medical AR research. In this systematic review, we provide a comprehensive overview of the usage of the first-generation HoloLens within the medical domain, from its release in March 2016, until the year of 2021. We identified 217 relevant publications through a systematic search of the PubMed, Scopus, IEEE Xplore and SpringerLink databases. We propose a new taxonomy including use case, technical methodology for registration and tracking, data sources, visualization as well as validation and evaluation, and analyze the retrieved publications accordingly. We find that the bulk of research focuses on supporting physicians during interventions, where the HoloLens is promising for procedures usually performed without image guidance. However, the consensus is that accuracy and reliability are still too low to replace conventional guidance systems. Medical students are the second most common target group, where AR-enhanced medical simulators emerge as a promising technology. While concerns about human-computer interactions, usability and perception are frequently mentioned, hardly any concepts to overcome these issues have been proposed. Instead, registration and tracking lie at the core of most reviewed publications, nevertheless only few of them propose innovative concepts in this direction. Finally, we find that the validation of HoloLens applications suffers from a lack of standardized and rigorous evaluation protocols. We hope that this review can advance medical AR research by identifying gaps in the current literature, to pave the way for novel, innovative directions and translation into the medical routine.

A Statistical Approach for the Integration of Multi-Temporal InSAR and GNSS-PPP Ground Deformation Measurements.

Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between -4.86 mm/yr and -23.58 mm/yr and -9.50 and -27.77 mm/yr for InSAR and GNSS, respectively.