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 .

Accuracy analysis of fMRI and MEG activations determined by intraoperative mapping.

OBJECTIVE: By looking at how the accuracy of preoperative brain mapping methods vary according to differences in the distance from the activation clusters used for the analysis, the present study aimed to elucidate how preoperative functional neuroimaging may be used in such a way that maximizes the mapping accuracy. METHODS: The eloquent function of 19 patients with a brain tumor or cavernoma was mapped prior to resection with both functional MRI (fMRI) and magnetoencephalography (MEG). The mapping results were then validated using direct cortical stimulation mapping performed immediately after craniotomy and prior to resection. The subset of patients with equivalent MEG and fMRI tasks performed for motor (n = 14) and language (n = 12) were evaluated as both individual and combined predictions. Furthermore, the distance resulting in the maximum accuracy, as evaluated by the J statistic, was determined by plotting the sensitivities and specificities against a linearly increasing distance threshold. RESULTS: fMRI showed a maximum mapping accuracy at 5 mm for both motor and language mapping. MEG showed a maximum mapping accuracy at 40 mm for motor and 15 mm for language mapping. At the standard 10-mm distance used in the literature, MEG showed a greater specificity than fMRI for both motor and language mapping but a lower sensitivity for motor mapping. Combining MEG and fMRI showed a maximum accuracy at 15 mm and 5 mm-MEG and fMRI distances, respectively-for motor mapping and at a 10-mm distance for both MEG and fMRI for language mapping. For motor mapping, combining MEG and fMRI at the optimal distances resulted in a greater accuracy than the maximum accuracy of the individual predictions. CONCLUSIONS: This study demonstrates that the accuracy of language and motor mapping for both fMRI and MEG is heavily dependent on the distance threshold used in the analysis. Furthermore, combining MEG and fMRI showed the potential for increased motor mapping accuracy compared to when using the modalities separately.Clinical trial registration no.: NCT01535430 (clinicaltrials.gov).

More fatal all-terrain vehicle crashes occur on the roadway than off: increased risk-taking characterises roadway fatalities.

BACKGROUND: All-terrain vehicles (ATVs) have steadily increased in popularity, size and speed, characteristics that likely contribute to the alarming rise in ATV-related fatalities. One potentially high-risk activity is riding on the road. OBJECTIVES: To compare fatal ATV crashes that occur on the roadway and off, to more fully understand factors that contribute to fatalities at each location. METHODS: Fatality data from the US Consumer Product Safety Commission (CPSC) were used for descriptive and comparative analyses. Multivariate logistic regression analysis was performed to determine relative risk. RESULTS: Over 60% of all fatalities (1985-2009) resulted from roadway crashes. After 1998, roadway fatalities increased at over twice the rate of off-road fatalities. Roadway crashes were more likely than off-road crashes to involve multiple fatalities, carrying passengers, alcohol use, collisions and head injuries. Roadway victims were less likely to be helmeted than off-road victims. Passengers and operators with passengers were also less likely to be helmeted than operators riding alone. Helmeted victims were half as likely to suffer a head injury. CONCLUSIONS: Fatal roadway crashes were more likely than off-road crashes to involve risk-taking behaviours (eg, carrying passengers) that could exacerbate the inherent difficulty of operating ATVs on roadway surfaces. Higher crash forces from greater speed, and lower use of protective equipment, may also have contributed to higher roadway mortality rates. Eliminating non-essential ATV road use may be an effective way to reduce ATV-related fatalities. This will likely require a substantial investment in rider education and better enforcement of ATV road use restriction laws.

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.

Structural Brain Imaging Predicts Individual-Level Task Activation Maps Using Deep Learning.

Accurate individual functional mapping of task activations is a potential tool for biomarker discovery and is critically important for clinical care. While structural imaging does not directly map task activation, we hypothesized that structural imaging contains information that can accurately predict variations in task activation between individuals. To this end, we trained a convolutional neural network to use structural imaging (T1-weighted, T2-weighted, and diffusion tensor imaging) to predict 47 different functional MRI task activation volumes across seven task domains. The U-Net model was trained on 591 subjects and then subsequently tested on 122 unrelated subjects. The predicted activation maps correlated more strongly with their actual maps than with the maps of the other test subjects. An ablation study revealed that a model using the shape of the cortex alone or the shape of the subcortical matter alone was sufficient to predict individual-level differences in task activation maps, but a model using the shape of the whole brain resulted in markedly decreased performance. The ablation study also showed that the additional information provided by the T2-weighted and diffusion tensor imaging strengthened the predictions as compared to using the T1-weighted imaging alone. These results indicate that structural imaging contains information that is predictive of inter-subject variability in task activation mapping and that cortical folding patterns, as well as microstructural features, could be a key component to linking brain structure to brain function.

The Association of Fractal Dimension with Vascularity and Clinical Outcomes in Glioblastoma.

BACKGROUND: Growing evidence indicates fractal analysis (FA) has potential as a computational tool to assess tumor microvasculature in glioblastoma (GBM). As fractal parameters of microvasculature have shown to be reliable quantitative biomarkers in brain tumors, there has been similar success in measuring the architecture of tumor tissue using FA in other tumor types. However, evaluating fractal parameters of tissue structure in relation to the microvasculature has not yet been implemented in GBM. We aimed to assess the utility of this methodology in quantifying structural characteristics of GBM cytoarchitecture and vascularity by correlating fractal parameters with gene expression. METHODS: Formalin-fixed paraffin-embedded specimens were retrospectively collected from 43 patients following resection of a newly diagnosed GBM; 4 normal brain specimens were obtained from epilepsy surgeries as controls. Tumor samples were processed using FA employing a software-based box-counting method algorithm and custom messenger RNA expression assays. Fractal parameters were then correlated with clinical features, outcomes, and a panel of 92 genes associated with vascularity and angiogenesis. RESULTS: Statistical analysis demonstrated that fractal-based indices were not adequate parameters for distinction of GBM cytoarchitecture compared with normal brain specimens. Correlation analysis of our gene expression findings suggested that hematoxylin and eosin-based FA may have adequate sensitivity to detect associations with vascular gene expression. CONCLUSIONS: The combination of neuropathological assessment and histology does not provide optimized data for FA in GBM. However, an association between FA and gene expression in GBM of genes pertaining to cytoarchitecture and angiogenesis warrants further investigation.

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design.

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi.

A preliminary study of a novel emergency department nursing triage simulation for research applications.

BACKGROUND: Studying the effect on functioning of the emergency department of disasters with a potential impact on staff members themselves usually involves table top and simulated patient exercises. Computerized virtual reality simulations have the potential to configure a variety of scenarios to determine likely staff responses and how to address them without intensive utilization of resources. To decide whether such studies are justified, we determined whether a novel computer simulation has the potential to serve as a valid and reliable model of on essential function in a busy ED. METHODS: Ten experienced female ED triage nurses (mean age 51) mastered navigating a virtual reality model of triage of 4 patients in an ED with which they were familiar, after which they were presented in a testing session with triage of 6 patients whose cases were developed using the Emergency Severity Index to represent a range of severity and complexity. Attitudes toward the simulation, and perceived workload in the simulation and on the job, were assessed with questionnaires and the NASA task load index. Z-scores were calculated for data points reflecting subject actions, the time to perform them, patient prioritization according to severity, and the importance of the tasks. Data from questionnaires and scales were analyzed with descriptive statistics and paired t tests using SPSS v. 21. Microsoft Excel was used to compute a correlation matrix for all standardized variables and all simulation data. RESULTS: Nurses perceived their work on the simulation task to be equivalent to their workload on the job in all aspects except for physical exertion. Although they were able to work with written communications with the patients, verbal communication would have been preferable. Consistent with the workplace, variability in performance during triage reflected subject skill and experience and was correlated with comfort with the task. Time to perform triage corresponded to the time required in the ED and virtual patients were prioritized appropriately according to severity. CONCLUSIONS: This computerized simulation appears to be a reasonable accurate proxy for ED triage. If future studies of this kind of simulation with a broader range of subjects that includes verbal communication between virtual patients and subjects and interactions of multiple subjects, supports the initial impressions, the virtual ED could be used to study the impact of disaster scenarios on staff functioning.

Consultation times in emergency telemedicine using realtime videoconferencing.

We reviewed 2135 consecutive emergency teleconsultations, which were received at an academic emergency department from state correctional facilities. During the 52-week study period, an average of 5.8 video-consultations per day were performed. A total of 1522 consultations (71%) had complete start and end consultation times, and were included in the analysis. Of these, 923 were managed primarily by emergency medicine residents and physician assistants, while the remaining 599 were managed by attending physicians alone. Following consultation, the disposition of the patients included 940 who were transported to the emergency department, 351 who were discharged to the general facility population and 193 who were admitted to the local infirmary. Overall, 38% of patients avoided a journey to the emergency department. The average consultation time was 17 min (95% confidence interval [CI], 10-24). The average consultation time for residents and physician assistants was 16 min (95% CI, 8-24) and it was 19 min (95% CI, 11-27) for attending physicians. Consultation time for patients not transported to the emergency room was 21 min (95% CI, 13-29), while for patients transferred to an emergency department, consultation time was 15 min (95% CI, 9-21). These results may assist in planning the workforce requirements for emergency department-based telemedicine services.

Utilization of virtual reality for endotracheal intubation training.

Tracheal intubation is performed for urgent airway control in injured patients. Current methods of training include working on cadavers and manikins, which lack the realism of a living human being. Work in this field has been limited due to the complex nature of simulating in real-time, the interactive forces and deformations which occur during an actual patient intubation. This study addressed the issue of intubation training in an attempt to bridge the gap between actual and virtual patient scenarios. The haptic device along with the real-time performance of the simulator give it both visual and physical realism. The three-dimensional viewing and interaction available through virtual reality make it possible for physicians, pre-hospital personnel and students to practice many endotracheal intubations without ever touching a patient. The ability for a medical professional to practice a procedure multiple times prior to performing it on a patient will both enhance the skill of the individual while reducing the risk to the patient.

Off-highway vehicle parks: combining environment, knowledge, and enforcement for all-terrain vehicle injury prevention.

The number of off-highway vehicle (OHV) parks continues to grow to meet the recreational needs of ATV enthusiasts and the increasing popularity of the vehicle. Little is known about how OHV park regulations and enforcement affect ATV safety among their users. This study was designed to determine whether there were differences in crash mechanisms and/or compliance with ATV safety laws and regulations when comparing off-road ATV crashes inside and outside state OHV parks. Relative to outside the parks, a smaller percentage of park victims were under the age of sixteen, a lower percentage were passengers, and a dramatically higher percentage were helmeted. Mean injury severity scores were not different inside and outside the parks, but 5% of outside victims had severe brain injuries, as compared to no park victims. Overall, park victims exhibited better compliance with ATV safety laws and regulations and suffered less severe brain injury outcomes. However, park crashes involved more jump-related injuries, suggesting that additional approaches are needed to improve park safety. These findings support the hypothesis that riding environments with safety regulations and effective enforcement can promote safe behaviors and may prevent injuries.

The role of the independent clinical laboratory in new assay development and commercialization.

Most would agree that these are exciting times in the field of laboratory medicine. As the body of scientific knowledge expands and research activities, such as those catalyzed by the sequencing of the human genome, bring us closer to the promise of personalized medicine, the clinical laboratory industry will have increasing opportunities to partner with owners of intellectual property to develop and commercialize new diagnostic tests. The large, independent clinical laboratories are particularly well positioned to commercialize important new tests, with their broad market penetration, infrastructure, and the scale to run esoteric tests cost-effectively.

The success of emergency telemedicine at the State University of New York at Buffalo.

The Telemedicine Program at the State University of New York at Buffalo, School of Medicine, and the Erie County Medical Center (UB/ECMC) represents a "quiet success story" for telemedicine development. Though a relatively young program starting in 1992, it demonstrates steady and consistent progress in the areas of clinical services, education and research as it continues to develop as a comprehensive university telemedicine program. The Emergency Telemedicine program provided 2,294 consultations in 2001. Other services included clinics in Gastroenterology, Infectious Disease (HIV/AIDS), Psychiatry, Orthopedic Hand Surgery, Dermatology, and Neurology. The UB/ECMC program has sponsored regular education activities with a focus on internet protocol (IP) videoconferencing for grand rounds in emergency medicine and traffic injury research, nursing education, distance precepting of nurse practitioner students on clinical rotations, Megaconference I and II presentations, and the continuing exploration of advanced tools for educational presentation over the internet. The program's research activities have focused on portable, roll-about telemedicine systems and virtual palpation using a data glove.

Predicted utilization of emergency medical services telemedicine in decreasing ambulance transports.

OBJECTIVE: To determine predicted utilization, decrease in ambulance transports, and target population for emergency medical services (EMS) if telemedicine capabilities were available to the medic units in the field. METHODS: A retrospective chart review of 345 consecutive ambulance transports to four hospitals (Level I urban trauma center, urban tertiary care center, children's hospital and suburban community hospital) was performed by a panel of three board-certified emergency medicine physicians experienced and credentialed in emergency telemedicine. They independently reviewed the emergency department (ED) and EMS records and were asked to determine whether patients required ambulance transport for evaluation or whether disposition could be made following paramedic and emergency physician assessment via telemedicine. A five-point Likert scale was used to grade feasibility of telemedicine disposition (definitely yes, probably yes, maybe, probably no, definitely no). Other variables analyzed included age, sex, race, chief complaint, phone, private medical doctor, and call location by patient zip code, call site, and receiving hospital. RESULTS: In 14.7% of cases (6% definitely yes and 8.7% probably yes), disposition could be made without transport using telemedicine. The age range for eliminating transport was 2 weeks through 92 years, with mean age of 26.6 years. Under the age of 50 years, 46 out of 238 patients (19.3%) could have possibly been managed by telemedicine. CONCLUSION: Use of EMS telemedicine could result in an approximately 15% decrease in ambulance transports when it alone is added to the prehospital care provider's armamentarium. Emphasis for implementation should be placed on younger patients and an identified subset of chief complaints conducive to management using telemedicine.