StethAid: A Digital Auscultation Platform for Pediatrics.

(1) Background: Mastery of auscultation can be challenging for many healthcare providers. Artificial intelligence (AI)-powered digital support is emerging as an aid to assist with the interpretation of auscultated sounds. A few AI-augmented digital stethoscopes exist but none are dedicated to pediatrics. Our goal was to develop a digital auscultation platform for pediatric medicine. (2) Methods: We developed StethAid-a digital platform for artificial intelligence-assisted auscultation and telehealth in pediatrics-that consists of a wireless digital stethoscope, mobile applications, customized patient-provider portals, and deep learning algorithms. To validate the StethAid platform, we characterized our stethoscope and used the platform in two clinical applications: (1) Still's murmur identification and (2) wheeze detection. The platform has been deployed in four children's medical centers to build the first and largest pediatric cardiopulmonary datasets, to our knowledge. We have trained and tested deep-learning models using these datasets. (3) Results: The frequency response of the StethAid stethoscope was comparable to those of the commercially available Eko Core, Thinklabs One, and Littman 3200 stethoscopes. The labels provided by our expert physician offline were in concordance with the labels of providers at the bedside using their acoustic stethoscopes for 79.3% of lungs cases and 98.3% of heart cases. Our deep learning algorithms achieved high sensitivity and specificity for both Still's murmur identification (sensitivity of 91.9% and specificity of 92.6%) and wheeze detection (sensitivity of 83.7% and specificity of 84.4%). (4) Conclusions: Our team has created a technically and clinically validated pediatric digital AI-enabled auscultation platform. Use of our platform could improve efficacy and efficiency of clinical care for pediatric patients, reduce parental anxiety, and result in cost savings.

A Smartphone application to provide real-time cardiopulmonary resuscitation quality feedback.

BACKGROUND: Quality of cardiopulmonary resuscitation (CPR) contributes significantly to outcomes. Key determinants of CPR quality pertaining to chest compressions are compression rate, compression depth, duration of interruptions, and chest recoil. Several studies have demonstrated that real-time audiovisual feedback improves CPR quality. We hypothesize that a mobile application using sensor data from built-in accelerometers in smartphones can provide accurate chest compression quality feedback in real time. This study aims to develop and validate an application for smartphone which can provide real-time audiovisual and haptic feedback on determinants of CPR quality. METHODS: A mobile application was developed to detect the compression depth and compression rate in real time using data captured from a smartphone's intrinsic accelerometer. The mobile device was placed on an adult manikin's chest at the point of compressions. In a simulated environment, data obtained using the application was compared directly to data obtained from a validated standard CPR quality tool. RESULTS: CPR quality parameters were obtained from the application and industry standard for 60, 30s-long sessions. Bland-Altman plot analysis for compression depth showed agreement between the app measurements and standard within ±4 mm (<10% error). The interclass correlation for agreement in the measurement of compression count was 0.92 (95% CI: 0.88-0.95), indicative of very strong agreement. CONCLUSIONS: Smart device applications using acceleration sensor data derived from smart phones can accurately provide real-time CPR quality feedback. With further development and validation, they can provide a ubiquitously available CPR feedback tool valuable for out-of-hospital arrests and in-hospital arrests in under-privileged areas.

Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG.

Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture-the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system's latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.

Rapid Quality Assessment of Nonrigid Image Registration Based on Supervised Learning.

When preprocedural images are overlaid on intraprocedural images, interventional procedures benefit in that more structures are revealed in intraprocedural imaging. However, image artifacts, respiratory motion, and challenging scenarios could limit the accuracy of multimodality image registration necessary before image overlay. Ensuring the accuracy of registration during interventional procedures is therefore critically important. The goal of this study was to develop a novel framework that has the ability to assess the quality (i.e., accuracy) of nonrigid multimodality image registration accurately in near real time. We constructed a solution using registration quality metrics that can be computed rapidly and combined to form a single binary assessment of image registration quality as either successful or poor. Based on expert-generated quality metrics as ground truth, we used a supervised learning method to train and test this system on existing clinical data. Using the trained quality classifier, the proposed framework identified successful image registration cases with an accuracy of 81.5%. The current implementation produced the classification result in 5.5 s, fast enough for typical interventional radiology procedures. Using supervised learning, we have shown that the described framework could enable a clinician to obtain confirmation or caution of registration results during clinical procedures.

Developing a clinically feasible personalized medicine approach to pediatric septic shock.

RATIONALE: Using microarray data, we previously identified gene expression-based subclasses of septic shock with important phenotypic differences. The subclass-defining genes correspond to adaptive immunity and glucocorticoid receptor signaling. Identifying the subclasses in real time has theranostic implications, given the potential for immune-enhancing therapies and controversies surrounding adjunctive corticosteroids for septic shock. OBJECTIVES: To develop and validate a real-time subclassification method for septic shock. METHODS: Gene expression data for the 100 subclass-defining genes were generated using a multiplex messenger RNA quantification platform (NanoString nCounter) and visualized using gene expression mosaics. Study subjects (n = 168) were allocated to the subclasses using computer-assisted image analysis and microarray-based reference mosaics. A gene expression score was calculated to reduce the gene expression patterns to a single metric. The method was tested prospectively in a separate cohort (n = 132). MEASUREMENTS AND MAIN RESULTS: The NanoString-based data reproduced two septic shock subclasses. As previously, one subclass had decreased expression of the subclass-defining genes. The gene expression score identified this subclass with an area under the curve of 0.98 (95% confidence interval [CI95] = 0.96-0.99). Prospective testing of the subclassification method corroborated these findings. Allocation to this subclass was independently associated with mortality (odds ratio = 2.7; CI95 = 1.2-6.0; P = 0.016), and adjunctive corticosteroids prescribed at physician discretion were independently associated with mortality in this subclass (odds ratio = 4.1; CI95 = 1.4-12.0; P = 0.011). CONCLUSIONS: We developed and tested a gene expression-based classification method for pediatric septic shock that meets the time constraints of the critical care environment, and can potentially inform therapeutic decisions.

Software-based PET-MR image coregistration: combined PET-MRI for the rest of us!

BACKGROUND: With the introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI), a new imaging option to acquire multimodality images with complementary anatomical and functional information has become available. Compared with hybrid PET/computed tomography (CT), hybrid PET/MRI is capable of providing superior anatomical detail while removing the radiation exposure associated with CT. The early adoption of hybrid PET/MRI, however, has been limited. OBJECTIVE: To provide a viable alternative to the hybrid PET/MRI hardware by validating a software-based solution for PET-MR image coregistration. MATERIALS AND METHODS: A fully automated, graphics processing unit-accelerated 3-D deformable image registration technique was used to align PET (acquired as PET/CT) and MR image pairs of 17 patients (age range: 10 months-21 years, mean: 10 years) who underwent PET/CT and body MRI (chest, abdomen or pelvis), which were performed within a 28-day (mean: 10.5 days) interval. MRI data for most of these cases included single-station post-contrast axial T1-weighted images. Following registration, maximum standardized uptake value (SUVmax) values observed in coregistered PET (cPET) and the original PET were compared for 82 volumes of interest. In addition, we calculated the target registration error as a measure of the quality of image coregistration, and evaluated the algorithm's performance in the context of interexpert variability. RESULTS: The coregistration execution time averaged 97±45 s. The overall relative SUVmax difference was 7% between cPET-MRI and PET/CT. The average target registration error was 10.7±6.6 mm, which compared favorably with the typical voxel size (diagonal distance) of 8.0 mm (typical resolution: 0.66 mm × 0.66 mm × 8 mm) for MRI and 6.1 mm (typical resolution: 3.65 mm × 3.65 mm × 3.27 mm) for PET. The variability in landmark identification did not show statistically significant differences between the algorithm and a typical expert. CONCLUSION: We have presented a software-based solution that achieves the many benefits of hybrid PET/MRI scanners without actually needing one. The method proved to be accurate and potentially clinically useful.

Time to Computerized Tomography Scan, Age, and Mortality in Acute Stroke.

BACKGROUND: Time to computerized tomography (CT) is important to institute appropriate and timely hyperacute management in stroke. We aimed to evaluate mortality outcomes in relation to age and time to CT scan. METHODS: We used routinely collected data in 8 National Health Service trusts in East of England between September 2008 and April 2011. Stroke cases were prospectively identified and confirmed. Odds ratios (ORs) for unadjusted and adjusted models for age categories (<65, 65-74, 75-84, and ≥85 years) as well as time to CT categories (<90 minutes, ≥90 to <180 minutes, ≥180 minutes to 24 hours, and >24 hours) and in-hospital and early (<7 days) mortality outcomes were calculated. RESULTS: Of the 7693 patients (mean age 76.1 years, 50% male) included, 1151 (16%) died as inpatients and 336 (4%) died within 7 days. Older patients and those admitted from care home had a significantly longer time from admission until CT (P < .001). Patients who had earlier CT scans were admitted to stroke units more frequently (P < .001) but had higher in-patient (P < .001) and 7-day mortality (P < .001). Whereas older age was associated with increased odds of mortality outcomes, longer time to CT was associated with significantly reduced mortality within 7 days (corresponding ORs for the above time periods were 1.00, .61 [95% confidence interval {CI}: .39-.95], .39 [.24-.64], and .16 [.08-.33]) and in-hospital mortality (ORs 1.00, .86 [.64-1.15], .57 [.42-.78] and .71 [.52-.98]). CONCLUSIONS: Older age was associated with a significantly longer time to CT. However, using CT scan time as a benchmarking tool in stroke may have inherent limitations and does not appear to be a suitable quality marker.

Stereoscopic augmented reality for laparoscopic surgery.

BACKGROUND: Conventional laparoscopes provide a flat representation of the three-dimensional (3D) operating field and are incapable of visualizing internal structures located beneath visible organ surfaces. Computed tomography (CT) and magnetic resonance (MR) images are difficult to fuse in real time with laparoscopic views due to the deformable nature of soft-tissue organs. Utilizing emerging camera technology, we have developed a real-time stereoscopic augmented-reality (AR) system for laparoscopic surgery by merging live laparoscopic ultrasound (LUS) with stereoscopic video. The system creates two new visual cues: (1) perception of true depth with improved understanding of 3D spatial relationships among anatomical structures, and (2) visualization of critical internal structures along with a more comprehensive visualization of the operating field. METHODS: The stereoscopic AR system has been designed for near-term clinical translation with seamless integration into the existing surgical workflow. It is composed of a stereoscopic vision system, a LUS system, and an optical tracker. Specialized software processes streams of imaging data from the tracked devices and registers those in real time. The resulting two ultrasound-augmented video streams (one for the left and one for the right eye) give a live stereoscopic AR view of the operating field. The team conducted a series of stereoscopic AR interrogations of the liver, gallbladder, biliary tree, and kidneys in two swine. RESULTS: The preclinical studies demonstrated the feasibility of the stereoscopic AR system during in vivo procedures. Major internal structures could be easily identified. The system exhibited unobservable latency with acceptable image-to-video registration accuracy. CONCLUSIONS: We presented the first in vivo use of a complete system with stereoscopic AR visualization capability. This new capability introduces new visual cues and enhances visualization of the surgical anatomy. The system shows promise to improve the precision and expand the capacity of minimally invasive laparoscopic surgeries.

Scapholunate Ligament Rupture and Coincident Fracture Proximal Pole Scaphoid Presenting Late with Osteonecrosis: A Rare Case Report with Unique Management.

CASE: A 29-year-old young active man with ununited necrosed proximal fifth of scaphoid with chronic scapholunate ligament disruption was managed by excision of proximal pole fragment and interosseous scapholunate reconstruction using modified Brunelli triple ligament tenodesis technique with satisfying outcome at 6 months and return to sports instructor job by the end of 1 year. CONCLUSION: Meticulous understanding and algorithmic itemwise approach of injury components can lead to optimal management of complex unstable wrist injuries such as scapholunate dissociation. To the best of our knowledge, this is the first report on excision of proximal pole of scaphoid coupled with scapholunate ligament reconstruction.

Assessing the Brainstem Auditory Evoked Potentials in Subjects with Family History of Hypertension.

Background: Hypertension (HTN) has a genetic predisposition and it also impairs microcirculation, thereby, affecting the well vascularized structures like the brainstem and causing changes in Brainstem Auditory Evoked Potentials (BAEPs). Purpose: To find out the usefulness of BAEPs as a screening tool in apparently healthy individuals with a family history of HTN. Methods: One hundred and ten volunteers, aged 17 to 23 years, were enrolled in the study as participants with proper consent. After excluding the subjects with existing diseases or co-morbidities (e.g. diabetes, HTN, schizophrenia, neuropathy, etc.), those on ototoxic or neurotoxic drugs, a preliminary physical examination was performed, following which BAEPs were recorded with a proper device. Statistical analysis is done with SPSS 2016 software using the chi-square test. Results: A consistent distortion in the inter-peak latency of III-V waves is noted when a family history of HTN is present in either parent or maternal grandparents. Other statistically significant findings are present in V/I% (HTN in mother), wave I (HTN in paternal grandfather), wave III (HTN in maternal grandfather), and inter-peak latency I-V (HTN in maternal grandmother). Conclusion: BAEP may be used as a screening tool in individuals with a family history of HTN with supportive evidence from further studies in the near future.

The SOAR stroke score predicts inpatient and 7-day mortality in acute stroke.

BACKGROUND AND PURPOSE: An accurate prognosis is useful for patients, family, and service providers after acute stroke. METHODS: We validated the Stroke subtype, Oxfordshire Community Stroke Project Classification, Age, and prestroke Rankin stroke score in predicting inpatient and 7-day mortality using data from 8 National Health Service hospital trusts in the Anglia Stroke and Heart Clinical Network between September 2008 and April 2011. RESULTS: A total of 3547 stroke patients (ischemic, 92%) were included. An incremental increase of inpatient and 7-day mortality was observed with increase in Stroke subtype, Oxfordshire Community Stroke Project Classification, Age, and prestroke Rankin stroke score. Using a cut-off of ≥3, the area under the receiver operator curves values for inpatient and 7-day mortality were 0.80 and 0.82, respectively. CONCLUSIONS: A simple score based on 4 easily obtainable variables at the point of care may potentially help predict early stroke mortality.

Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation.

Digital stethoscopes can enable the development of integrated artificial intelligence (AI) systems that can remove the subjectivity of manual auscultation, improve diagnostic accuracy, and compensate for diminishing auscultatory skills. Developing scalable AI systems can be challenging, especially when acquisition devices differ and thus introduce sensor bias. To address this issue, a precise knowledge of these differences, i.e., frequency responses of these devices, is needed, but the manufacturers often do not provide complete device specifications. In this study, we reported an effective methodology for determining the frequency response of a digital stethoscope and used it to characterise three common digital stethoscopes: Littmann 3200, Eko Core, and Thinklabs One. Our results show significant inter-device variability in that the frequency responses of the three studied stethoscopes were distinctly different. A moderate intra-device variability was seen when comparing two separate units of Littmann 3200. The study highlights the need for normalisation across devices for developing successful AI-assisted auscultation and provides a technical characterisation approach as a first step to accomplish it.

Assessing the Effect of Augmented Reality on Procedural Outcomes During Ultrasound-Guided Vascular Access.

OBJECTIVE: Augmented reality devices are increasingly accepted in health care, though most applications involve education and pre-operative planning. A novel augmented reality ultrasound application, HoloUS, was developed for the Microsoft HoloLens 2 to project real-time ultrasound images directly into the user's field of view. In this work, we assessed the effect of using HoloUS on vascular access procedural outcomes. METHODS: A single-center user study was completed with participants with (N = 22) and without (N = 12) experience performing ultrasound-guided vascular access. Users completed a venipuncture and aspiration task a total of four times: three times on study day 1, and once on study day 2 between 2 and 4 weeks later. Users were randomized to use conventional ultrasound during either their first or second task and the HoloUS application at all other times. Task completion time, numbers of needle re-directions, head adjustments and needle visualization rates were recorded. RESULTS: For expert users, task completion time was significantly faster using HoloUS (11.5 s, interquartile range [IQR] = 6.5-23.5 s vs. 18.5 s, IQR = 11.0-36.5 s; p = 0.04). The number of head adjustments was significantly lower using the HoloUS app (1.0, IQR = 0.0-1.0 vs. 3.0, IQR = 1.0-5.0; p < 0.0001). No significant differences were identified in other measured outcomes. CONCLUSION: This is the first investigation of augmented reality-based ultrasound-guided vascular access using the second-generation HoloLens. It demonstrates equivalent procedural efficiency and accuracy, with favorable usability, ergonomics and user independence when compared with traditional ultrasound techniques.

Isotropic-nematic phase transition in the Lebwohl-Lasher model from density of states simulations.

Density of states Monte Carlo simulations have been performed to study the isotropic-nematic (IN) transition of the Lebwohl-Lasher model for liquid crystals. The IN transition temperature was calculated as a function of system size using expanded ensemble density of states simulations with histogram reweighting. The IN temperature for infinite system size was obtained by extrapolation of three independent measures. A subsequent analysis of the kinetics in the model showed that the transition occurs via spinodal decomposition through aggregation of clusters of liquid crystal molecules.

HoloUS: Augmented reality visualization of live ultrasound images using HoloLens for ultrasound-guided procedures.

PURPOSE: Microsoft HoloLens is a pair of augmented reality (AR) smart glasses that could improve the intraprocedural visualization of ultrasound-guided procedures. With the wearable HoloLens headset, an ultrasound image can be virtually rendered and registered with the ultrasound transducer and placed directly in the practitioner's field of view. METHODS: A custom application, called HoloUS, was developed using the HoloLens and a portable ultrasound machine connected through a wireless network. A custom 3D-printed case with an AR-pattern for the ultrasound transducer permitted ultrasound image tracking and registration. Voice controls on the HoloLens supported the scaling and movement of the ultrasound image as desired. The ultrasound images were streamed and displayed in real-time. A user study was performed to assess the effectiveness of the HoloLens as an alternative display platform. Novices and experts were timed on tasks involving targeting simulated vessels using a needle in a custom phantom. RESULTS: Technical characterization of the HoloUS app was conducted using frame rate, tracking accuracy, and latency as performance metrics. The app ran at 25 frames/s, had an 80-ms latency, and could track the transducer with an average reprojection error of 0.0435 pixels. With AR visualization, the novices' times improved by 17% but the experts' times decreased slightly by 5%, which may reflect the experts' training and experience bias. CONCLUSION: The HoloUS application was found to enhance user experience and simplify hand-eye coordination. By eliminating the need to alternately observe the patient and the ultrasound images presented on a separate monitor, the proposed AR application has the potential to improve efficiency and effectiveness of ultrasound-guided procedures.

Hyperacute stroke thrombolysis via telemedicine: a multicentre study of performance, safety and clinical efficacy.

OBJECTIVES: Timely thrombolysis of ischaemic stroke improves functional recovery, yet its delivery nationally is challenging due to shortages in the stroke specialist workforce and large geographical areas. One solution is remote stroke specialist input to regional centres via telemedicine. This study evaluates the usage and key metrics of performance of the East of England Stroke Telemedicine Partnership-the largest telestroke service in the UK-in providing hyperacute stroke care. DESIGN: Prospective observational study. SETTING: The East of England Stroke Telemedicine Partnership provides a horizontal 'hubless' model of out-of-hours hyperacute stroke care to a population of 6.2 million across a 7500 square mile semirural region. PARTICIPANTS: All (2709) telestroke consultations between 1 January 2014 and 31 December 2019. MAIN OUTCOME MEASURES: Thrombolysis decision, pre-thrombolysis and post-thrombolysis stroke severity (National Institutes of Health Stroke Scale, NIHSS), haemorrhagic complications, and hyperacute pathway timings. RESULTS: Over the period, 1149 (42.4%) individuals were thrombolysed. Thrombolysis rates increased from 147/379 (38.8%) in 2014 to 225/490 (45.9%) in 2019. Median (IQR) pre-thrombolysis NIHSS was 10 (6-17), reducing to 6 (2-14) 24-hour post-thrombolysis (p<0.001). Post-thrombolysis haemorrhage occurred in 27 cases (2.3%). Over the period, median (IQR) door-to-needle time reduced from 85 (65-108) min to 68 (55-97.5) min (p<0.01), driven by improved imaging-to-needle times from 52.5 (38-72.25) min to 42 (30.5-62.5) min (p<0.01). However, the same period saw an increase in median onset-to-hospital arrival time from 77.5 (60-109.25) min to 95 (70-135) min (p<0.001). CONCLUSIONS: The results from this large hyperacute telestroke cohort indicate two important points for clinical practice. First, telemedicine via a hubless horizontal model provides a clinically effective and safe method for delivering hyperacute stroke thrombolysis. Second, improved door-to-needle times were offset by a concerning rise in prehospital timings. These findings indicate that although telemedicine may benefit in-hospital hyperacute stroke care, improvements across the whole stroke pathway are essential.

Automated identification of innocent Still's murmur using a convolutional neural network.

Background: Still's murmur is the most prevalent innocent heart murmur of childhood. Auscultation is the primary clinical tool to identify this murmur as innocent. Whereas pediatric cardiologists routinely perform this task, primary care providers are less successful in distinguishing Still's murmur from the murmurs of true heart disease. This results in a large number of children with a Still's murmur being referred to pediatric cardiologists. Objectives: To develop a computer algorithm that can aid primary care providers to identify the innocent Still's murmur at the point of care, to substantially decrease over-referral. Methods: The study included Still's murmurs, pathological murmurs, other innocent murmurs, and normal (i.e., non-murmur) heart sounds of 1,473 pediatric patients recorded using a commercial electronic stethoscope. The recordings with accompanying clinical diagnoses provided by a pediatric cardiologist were used to train and test the convolutional neural network-based algorithm. Results: A comparative analysis showed that the algorithm using only the murmur sounds recorded at the lower left sternal border achieved the highest accuracy. The developed algorithm identified Still's murmur with 90.0% sensitivity and 98.3% specificity for the default decision threshold. The area under the receiver operating characteristic curve was 0.943. Conclusions: Still's murmur can be identified with high accuracy with the algorithm we developed. Using this approach, the algorithm could help to reduce the rate of unnecessary pediatric cardiologist referrals and use of echocardiography for a common benign finding.

Evaluation of stroke services in Anglia Stroke Clinical Network to examine the variation in acute services and stroke outcomes.

BACKGROUND: Stroke is the third leading cause of death in developed countries and the leading cause of long-term disability worldwide. A series of national stroke audits in the UK highlighted the differences in stroke care between hospitals. The study aims to describe variation in outcomes following stroke and to identify the characteristics of services that are associated with better outcomes, after accounting for case mix differences and individual prognostic factors. METHODS/DESIGN: We will conduct a cohort study in eight acute NHS trusts within East of England, with at least one year of follow-up after stroke. The study population will be a systematically selected representative sample of patients admitted with stroke during the study period, recruited within each hospital. We will collect individual patient data on prognostic characteristics, health care received, outcomes and costs of care and we will also record relevant characteristics of each provider organisation. The determinants of one year outcome including patient reported outcome will be assessed statistically with proportional hazards regression models. Self (or proxy) completed EuroQol (EQ-5D) questionnaires will measure quality of life at baseline and follow-up for cost utility analyses. DISCUSSION: This study will provide observational data about health service factors associated with variations in patient outcomes and health care costs following hospital admission for acute stroke. This will form the basis for future RCTs by identifying promising health service interventions, assessing the feasibility of recruiting and following up trial patients, and provide evidence about frequency and variances in outcomes, and intra-cluster correlation of outcomes, for sample size calculations. The results will inform clinicians, public, service providers, commissioners and policy makers to drive further improvement in health services which will bring direct benefit to the patients.

Hybrid electromagnetic-ArUco tracking of laparoscopic ultrasound transducer in laparoscopic video.

Purpose: The purpose of this work was to develop a new method of tracking a laparoscopic ultrasound (LUS) transducer in laparoscopic video by combining the hardware [e.g., electromagnetic (EM)] and the computer vision-based (e.g., ArUco) tracking methods. Approach: We developed a special tracking mount for the imaging tip of the LUS transducer. The mount incorporated an EM sensor and an ArUco pattern registered to it. The hybrid method used ArUco tracking for ArUco-success frames (i.e., frames where ArUco succeeds in detecting the pattern) and used corrected EM tracking for the ArUco-failure frames. The corrected EM tracking result was obtained by applying correction matrices to the original EM tracking result. The correction matrices were calculated in previous ArUco-success frames by comparing the ArUco result and the original EM tracking result. Results: We performed phantom and animal studies to evaluate the performance of our hybrid tracking method. The corrected EM tracking results showed significant improvements over the original EM tracking results. In the animal study, 59.2% frames were ArUco-success frames. For the ArUco-failure frames, mean reprojection errors for the original EM tracking method and for the corrected EM tracking method were 30.8 pixel and 10.3 pixel, respectively. Conclusions: The new hybrid method is more reliable than using ArUco tracking alone and more accurate and practical than using EM tracking alone for tracking the LUS transducer in the laparoscope camera image. The proposed method has the potential to significantly improve tracking performance for LUS-based augmented reality applications.

Live augmented reality: a new visualization method for laparoscopic surgery using continuous volumetric computed tomography.

BACKGROUND: Current laparoscopic images are rich in surface detail but lack information on deeper structures. This report presents a novel method for highlighting these structures during laparoscopic surgery using continuous multislice computed tomography (CT). This has resulted in a more accurate augmented reality (AR) approach, termed "live AR," which merges three-dimensional (3D) anatomy from live low-dose intraoperative CT with live images from the laparoscope. METHODS: A series of procedures with swine was conducted in a CT room with a fully equipped laparoscopic surgical suite. A 64-slice CT scanner was used to image the surgical field approximately once per second. The procedures began with a contrast-enhanced, diagnostic-quality CT scan (initial CT) of the liver followed by continuous intraoperative CT and laparoscopic imaging with an optically tracked laparoscope. Intraoperative anatomic changes included user-applied deformations and those from breathing. Through deformable image registration, an intermediate image processing step, the initial CT was warped to align spatially with the low-dose intraoperative CT scans. The registered initial CT then was rendered and merged with laparoscopic images to create live AR. RESULTS: Superior compensation for soft tissue deformations using the described method led to more accurate spatial registration between laparoscopic and rendered CT images with live AR than with conventional AR. Moreover, substitution of low-dose CT with registered initial CT helped with continuous visualization of the vasculature and offered the potential of at least an eightfold reduction in intraoperative X-ray dose. CONCLUSIONS: The authors proposed and developed live AR, a new surgical visualization approach that merges rich surface detail from a laparoscope with instantaneous 3D anatomy from continuous CT scanning of the surgical field. Through innovative use of deformable image registration, they also demonstrated the feasibility of continuous visualization of the vasculature and considerable X-ray dose reduction. This study provides motivation for further investigation and development of live AR.