Early Signs of Elevated Intracranial Pressure on Computed Tomography Correlate With Measured Intracranial Pressure in the Intensive Care Unit and Six-Month Outcome After Moderate to Severe Traumatic Brain Injury.

Traumatic brain injury (TBI) is a leading cause of death and disability in the United States. Early triage and treatment after TBI have been shown to improve outcome. Identifying patients at risk for increased intracranial pressure (ICP) via baseline computed tomography (CT) , however, has not been validated previously in a prospective dataset. We hypothesized that acute CT findings of elevated ICP, combined with direct ICP measurement, hold prognostic value in terms of six-month patient outcome after TBI. Data were obtained from the Progesterone for Traumatic Brain Injury, Experimental Clinical Treatment (ProTECTIII) multi-center clinical trial. Baseline CT scans for 881 participants were individually reviewed by a blinded central neuroradiologist. Five signs of elevated ICP were measured (sulcal obliteration, lateral ventricle compression, third ventricle compression, midline shift, and herniation). Associations between signs of increased ICP and outcomes (six-month functional outcome and death) were assessed. Secondary analyses of 354 patients with recorded ICP monitoring data available explored the relationships between hemorrhage phenotype/anatomic location, sustained ICP ≥20 mm Hg, and surgical intervention(s). Univariate and multi-variate logistic/linear regressions were performed; p < 0.05 is defined as statistically significant. Imaging characteristics associated with ICP in this cohort include sulcal obliteration (p = 0.029) and third ventricular compression (p = 0.039). Univariate regression analyses indicated that increasing combinations of the five defined signs of elevated ICP were associated with death, poor functional outcome, and time to death. There was also an increased likelihood of death if patients required craniotomy (odds ratio [OR] = 4.318, 95% confidence interval [1.330-16.030]) or hemicraniectomy (OR = 2.993 [1.109-8.482]). On multi-variate regression analyses, hemorrhage location was associated with death (posterior fossa, OR = 3.208 [1.120-9.188] and basal ganglia, OR = 3.079 [1.178-8.077]). Volume of hemorrhage >30 cc was also associated with increased death, OR = 3.702 [1.575-8.956]). The proportion of patient hours with sustained ICP ≥20 mm Hg, and maximum ICP ≥20 mm Hg were also directly correlated with increased death (OR = 6 4.99 [7.731-635.51]; and OR = 1.025 [1.004-1.047]), but not with functional outcome. Poor functional outcome was predicted by concurrent presence of all five radiographic signs of elevated ICP (OR = 4.44 [1.514-14.183]) and presence of frontal lobe (OR = 2.951 [1.265-7.067]), subarachnoid (OR = 2.231 [1.067-4.717]), or intraventricular (OR = 2.249 [1.159-4.508]) hemorrhage. Time to death was modulated by total patient days of elevated ICP ≥20 mm Hg (effect size = 3.424 [1.500, 5.439]) in the first two weeks of hospitalization. Sulcal obliteration and third ventricular compression, radiographic signs of elevated ICP, were significantly associated with measurements of ICP ≥20 mm Hg. These radiographic biomarkers were significantly associated with patient outcome. There is potential utility of ICP-related imaging variables in triage and prognostication for patients after moderate-severe TBI.

Prenatal Diagnosis of Craniosynostosis Using Ultrasound.

BACKGROUND: Craniosynostosis is typically diagnosed postnatally. Prenatal diagnosis would allow for improved parental counseling and facilitate timely intervention. Our purpose was to determine whether prenatal ultrasound can be used to diagnose nonsyndromic craniosynostosis. METHODS: The authors reviewed 22 prenatal ultrasounds of infants known to have nonsyndromic craniosynostosis and 22 age-matched controls. Cross-sectional images at the plane used to measure biparietal diameter were selected and cranial shape of each participant was parameterized and used to discriminate affected patients from controls. The results from quantitative shape analysis were compared with results from a blinded visual inspection alone. RESULTS: Among the 22 patients, the most common diagnosis was sagittal synostosis ( n = 11), followed by metopic synostosis ( n = 6). The average gestational age at time of ultrasound of controls and synostotic patients was 26 weeks and 6.8 days at the junction of the second and third trimesters. The controls and synostotic cases segregated into statistically different populations by their shape profiles ( p < 0.001). An automatic shape classifier using leave-one-out cross-validation correctly classified the 44 images as normal versus synostotic 85 percent of the time (sensitivity, 82 percent; specificity, 87 percent). Cephalic index was a poor indicator of sagittal synostosis (45 percent sensitivity). Visual inspection alone demonstrated only a fair level of accuracy (40 to 50 percent agreement) in identifying cases of synostosis (kappa, 0.09 to 0.23). CONCLUSIONS: Craniosynostosis can be identified on prenatal ultrasound with good sensitivity using formal shape analysis. Cephalic index and visual inspection alone performed poorly. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, II.

Volumetric White Matter Hyperintensity Ranges Correspond to Fazekas Scores on Brain MRI.

INTRODUCTION: White matter hyperintensity (WMH) is an abnormal T2 signal in the deep and subcortical white matter visualized on MRI associated with hypertension, cerebrovascular disease, and aging. The Fazekas (Fz) scoring system is a commonly used qualitative tool to assess the severity of WMH. While studies have compared Fazekas scores to other scoring methods, the comparison of Fazekas scores and volume of WMH using current semiautomated volumetric techniques has not been studied. METHODS: We reviewed MRI studies acquired at our institution between 2015 and 2017. Relative WMH was scored by one author trained in Fazekas scoring. A board certified neuroradiologist scored them independently for confirmation. Manual segmentations of WMH were completed using 3D Slicer 4.9. A 3D model was formed to quantify WMH in milliliters (mL). ANOVA tests were performed to determine the association of Fazekas scores with corresponding WMH volumes. RESULTS: Among the 198 patients in our study, WMH were visualized in 163 (Fz1: n=66; Fz2: n=49; Fz3: n=48). WMH volumes significantly differed according to Fazekas score (F = 141.1, p<0.001), with increasing WMHV associated with higher Fazekas scores: Fz1, range 0.1-8.3 mL (mean 3.7, SD 2.3); Fz2, range 6.0-17.7 mL (mean 10.8, SD 3.1); Fz3, range 14.2-77.2 mL (mean 35.2, SD 17.9); and Fz3 (excluding 11 outliers above 50 mL), 14.2-47.0 mL (mean 27.1, SD 8.9). CONCLUSION: Fazekas scores correspond with distinct ranges of WMH volume with relatively little overlap, but scores based on volumes are more efficacious. A modified Fazekas from 0-4 should be considered.

DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.

In the era of data-driven medicine, rapid access and accurate interpretation of medical images are becoming increasingly important. The DICOM Image ANalysis and Archive (DIANA) system is an open-source, lightweight, and scalable Python interface that enables users to interact with hospital Picture Archiving and Communications Systems (PACS) to access such data. In this work, DIANA functionality was detailed and evaluated in the context of retrospective PACS data retrieval and two prospective clinical artificial intelligence (AI) pipelines: bone age (BA) estimation and intra-cranial hemorrhage (ICH) detection. DIANA orchestrates activity beginning with post-acquisition study discovery and ending with online notifications of findings. For AI applications, system latency (exam completion to system report time) was quantified and compared to that of clinicians (exam completion to initial report creation time). Mean DIANA latency was 9.04 ± 3.83 and 20.17 ± 10.16 min compared to clinician latency of 51.52 ± 58.9 and 65.62 ± 110.39 min for BA and ICH, respectively, with DIANA latencies being significantly lower (p < 0.001). DIANA's capabilities were also explored and found effective in retrieving and anonymizing protected health information for "big-data" medical imaging research and analysis. Mean per-image retrieval times were 1.12 ± 0.50 and 0.08 ± 0.01 s across x-ray and computed tomography studies, respectively. The data herein demonstrate that DIANA can flexibly integrate into existing hospital infrastructure and improve the process by which researchers/clinicians access imaging repository data. This results in a simplified workflow for large data retrieval and clinical integration of AI models.

Computer-Assisted Measurement of Traumatic Brain Hemorrhage Volume Is More Predictive of Functional Outcome and Mortality than Standard ABC/2 Method: An Analysis of Computed Tomography Imaging Data from the Progesterone for Traumatic Brain Injury Experimental Clinical Treatment Phase-III Trial.

Hemorrhage volume is an important variable in emergently assessing traumatic brain injury (TBI). The most widely used method for rapid volume estimation is ABC/2, a simple algorithm that approximates lesion geometry as perfectly ellipsoid. The relative prognostic value of volume measurement based on more precise hematoma topology remains unknown. In this study, we compare volume measurements obtained using ABC/2 versus computer-assisted volumetry (CAV) for both intra- and extra-axial traumatic hemorrhages, and then quantify the association of measurements using both methods with patient outcome following moderate to severe TBI. A total of 517 computer tomography (CT) scans acquired during the Progesterone for Traumatic Brain Injury Experimental Clinical Treatment Phase-III (ProTECTIII) multi-center trial were retrospectively reviewed. Lesion volumes were measured using ABC/2 and CAV. Agreement between methods was tested using Bland-Altman analysis. Relationship of volume measurements with 6-month mortality, Extended Glasgow Outcome Scale (GOS-E), and Disability Rating Scale (DRS) were assessed using linear regression and area under the curve (AUC) analysis. In subdural hematoma (SDH) >50cm3, ABC/2 and CAV produce significantly different volume measurements (p < 0.0001), although the difference was not significant for smaller SDH or intra-axial lesions. The disparity between ABC/2 and CAV measurements varied significantly with hematoma size for both intra- and extra-axial lesions (p < 0.0001). Across all lesions, volume was significantly associated with outcome using either method (p < 0.001), but CAV measurement was a significantly better predictor of outcome than ABC/2 estimation for SDH. Among large traumatic SDH, ABC/2 significantly overestimates lesion volume compared with measurement based on precise bleed topology. CAV also offers significantly better prediction of patient functional outcofme and mortality.

Thyroid Nodule Malignancy Risk Stratification Using a Convolutional Neural Network.

This study evaluates the performance of convolutional neural networks (CNNs) in risk stratifying the malignant potential of thyroid nodules alongside traditional methods such as American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS). The data set consisted of 651 pathology-proven thyroid nodules (500 benign, 151 malignant) from 571 patients collected at a single tertiary academic medical center. Each thyroid nodule consisted of two orthogonal views (sagittal and transverse) for a total of 1,302 grayscale images. A CNN classifier was developed to identify malignancy versus benign thyroid nodules, and a nested double cross validation scheme was applied to allow for both model parameter selection and for model accuracy evaluation. All thyroid nodules were classified according to ACR TIRADS criteria and were compared with their respective CNN-generated malignancy scores. The best performing model was the MobileNet CNN ensemble with an area under the curve of 0.86 (95% confidence interval, 0.83-0.90). Thyroid nodules within the highest and lowest CNN risk strata had malignancy rates of 81.4% and 5.9%, respectively. The rate of malignancy for ACR TIRADS ranged from 0% for TR1 nodules to 60% for TR5 nodules. Convolutional neural network malignancy scores correlated well with TIRADS levels, as malignancy scores ranged from 0.194 for TR1 nodules and 0.519 for TR5 nodules. Convolutional neural networks can be trained to generate accurate malignancy risk scores for thyroid nodules. These predictive models can aid in risk stratifying thyroid nodules alongside traditional professional guidelines such as TIRADS and can function as an adjunct tool for the radiologist when identifying those patients requiring further histopathologic workup.

Objective Indirect Assessment of Transverse Ligament Competence Using Quantitative Analysis of 3-Dimensional Segmented Flexion-Extension Computed Tomography Scan.

OBJECTIVE: Assessment of transverse ligament (TL) competence in patients with suspected atlantoaxial instability is performed via indirect radiograph measurements or direct TL visualization on magnetic resonance imaging (MRI). Interpretation of these images can be limited by unique patient anatomy or imaging technique variability. We report a novel technique for evaluating TL competence using flexion-extension computed tomography (feCT) scan with 3-dimensional (3D) segmentation and quantitative analysis. METHODS: feCT scans of 11 patients were segmented to create 3D surface models. Six patients with atlantoaxial pathology were evaluated for possible instability based on clinical examination and imaging findings. The other 5 patients had no clinical or imaging evidence of atlantoaxial injury. Dynamic atlantodental interval (ADI) was calculated using point-to-point voxel changes between flexion and extension 3D models. Magnitude and direction of ADI changes were quantified and compared with available cervical spine flexion-extension radiograph and/or MRI findings. RESULTS: In the 5 patients without evidence of atlantoaxial injury, 94.3% of ADI vector changes were <3.0 mm. In the 3 patients with atlantoaxial pathology but TL competence, 92.4% of ADI vector changes were <3.0 mm. In the 3 patients with atlantoaxial pathology and TL incompetence, only 49.1% of ADI vector changes were <3.0 mm. In addition to the significant atlantoaxial subluxation in these 3 patients, there was significant rotational motion compared with the patients with an intact TL. CONCLUSIONS: 3D segmentation and quantitative analysis of feCT scan allow objective indirect assessment of TL integrity. Results are consistent with MRI findings and offer additional biomechanical information regarding the direction and distribution of atlantoaxial motion.

Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs.

PURPOSE: To develop a deep learning approach to bone age assessment based on a training set of developmentally normal pediatric hand radiographs and to compare this approach with automated and manual bone age assessment methods based on Greulich and Pyle (GP). METHODS: In this retrospective study, a convolutional neural network (trauma hand radiograph-trained deep learning bone age assessment method [TDL-BAAM]) was trained on 15 129 frontal view pediatric trauma hand radiographs obtained between December 14, 2009, and May 31, 2017, from Children's Hospital of New York, to predict chronological age. A total of 214 trauma hand radiographs from Hasbro Children's Hospital were used as an independent test set. The test set was rated by the TDL-BAAM model as well as a GP-based deep learning model (GPDL-BAAM) and two pediatric radiologists (radiologists 1 and 2) using the GP method. All ratings were compared with chronological age using mean absolute error (MAE), and standard concordance analyses were performed. RESULTS: The MAE of the TDL-BAAM model was 11.1 months, compared with 12.9 months for GPDL-BAAM (P = .0005), 14.6 months for radiologist 1 (P < .0001), and 16.0 for radiologist 2 (P < .0001). For TDL-BAAM, 95.3% of predictions were within 24 months of chronological age compared with 91.6% for GPDL-BAAM (P = .096), 86.0% for radiologist 1 (P < .0001), and 84.6% for radiologist 2 (P < .0001). Concordance was high between all methods and chronological age (intraclass coefficient > 0.93). Deep learning models demonstrated a systematic bias with a tendency to overpredict age for younger children versus radiologists who showed a consistent mean bias. CONCLUSION: A deep learning model trained on pediatric trauma hand radiographs is on par with automated and manual GP-based methods for bone age assessment and provides a foundation for developing population-specific deep learning algorithms for bone age assessment in modern pediatric populations.Supplemental material is available for this article.© RSNA, 2020See also the commentary by Halabi in this issue.

Towards Placental Surface Vasculature Exploration in Virtual Reality.

We present a case study evaluating the potential for interactively identifying placental surface blood vessels using magnetic resonance imaging (MRI) scans in virtual reality (VR) environments. We visualized the MRI data using direct volume rendering in a high-fidelity CAVE-like VR system, allowing medical professionals to identify relevant placental vessels directly from volume visualizations in the VR system, without prior vessel segmentation. Participants were able to trace most of the observable vascular structure, and consistently identified blood vessels down to diameters of 1 mm, an important requirement in diagnosing vascular diseases. Qualitative feedback from our participants suggests that our VR visualization is easy to understand and allows intuitive data exploration, but complex user interactions remained a challenge. Using these observations, we discuss implications and requirements for spatial tracing user interaction methods in VR environments. We believe that VR MRI visualizations are the next step towards effective surgery planning for prenatal diseases.

Efficacy of Computed Tomography Utilization in the Assessment of Acute Traumatic Brain Injury in Adult and Pediatric Emergency Department Patients.

BACKGROUND: Computed tomography (CT) is commonly used to assess traumatic brain injury (TBI) in the emergency department (ED). Radiologists at a Level 1 trauma center implemented a novel tool, the RADiology CATegorization (RADCAT) system, to communicate injuries to clinicians in real time. Using this categorization system, we aimed to determine the rates of positive head CTs among pediatric and adult ED patients evaluated for TBI. METHODS: We performed a retrospective analysis of all patients who received a head CT to assess for TBI. We classified head CTs using the RADCAT tool. On a 5-point scale, scores of 3 or less are considered normal or routine. Scores of 4-5 are considered high priority, representing findings such as intracranial bleeding. RESULTS: Of the 5,341 head CT's obtained during the study period, 992 (18.5%) had high priority results (scores 4-5). A large number of pediatric studies, 30.8%, were positive for high priority results. Among the adult population, 18.0 % contained high priority results. CONCLUSION: The pediatric population had a higher rate of high priority reads among those undergoing non- contrast head CT for TBI compared to adult patients.

Longitudinal MRI findings in patient with SLC25A12 pathogenic variants inform disease progression and classification.

Aspartate-glutamate carrier 1 (AGC1) is one of two exchangers within the malate-aspartate shuttle. AGC1 is encoded by the SLC25A12 gene. Three patients with pathogenic variants in SLC25A12 have been reported in the literature. These patients were clinically characterized by neurodevelopmental delay, epilepsy, hypotonia, cerebral atrophy, and hypomyelination; however, there has been discussion in the literature as to whether this hypomyelination is primary or secondary to a neuronal defect. Here we report a 12-year-old patient with variants in SLC25A12 and magnetic resonance imaging (MRI) at multiple ages. Novel compound heterozygous, recessive variants in SLC25A12 were identified: c.1295C>T (p.A432V) and c.1447-2_1447-1delAG. Clinical presentation is characterized by severe intellectual disability, nonambulatory, nonverbal status, hypotonia, epilepsy, spastic quadriplegia, and a happy disposition. The serial neuroimaging findings are notable for cerebral atrophy with white matter involvement, namely, early hypomyelination yet subsequent progression of myelination. The longitudinal MRI findings are most consistent with a leukodystrophy of the leuko-axonopathy category, that is, white matter abnormalities that are most suggestive of mechanisms that result from primary neuronal defects. We present here the first case of a patient with compound heterozygous variants in SLC25A12, including brain MRI findings, in the oldest individual reported to date with this neurogenetic condition.

Generalizable Inter-Institutional Classification of Abnormal Chest Radiographs Using Efficient Convolutional Neural Networks.

Our objective is to evaluate the effectiveness of efficient convolutional neural networks (CNNs) for abnormality detection in chest radiographs and investigate the generalizability of our models on data from independent sources. We used the National Institutes of Health ChestX-ray14 (NIH-CXR) and the Rhode Island Hospital chest radiograph (RIH-CXR) datasets in this study. Both datasets were split into training, validation, and test sets. The DenseNet and MobileNetV2 CNN architectures were used to train models on each dataset to classify chest radiographs into normal or abnormal categories; models trained on NIH-CXR were designed to also predict the presence of 14 different pathological findings. Models were evaluated on both NIH-CXR and RIH-CXR test sets based on the area under the receiver operating characteristic curve (AUROC). DenseNet and MobileNetV2 models achieved AUROCs of 0.900 and 0.893 for normal versus abnormal classification on NIH-CXR and AUROCs of 0.960 and 0.951 on RIH-CXR. For the 14 pathological findings in NIH-CXR, MobileNetV2 achieved an AUROC within 0.03 of DenseNet for each finding, with an average difference of 0.01. When externally validated on independently collected data (e.g., RIH-CXR-trained models on NIH-CXR), model AUROCs decreased by 3.6-5.2% relative to their locally trained counterparts. MobileNetV2 achieved comparable performance to DenseNet in our analysis, demonstrating the efficacy of efficient CNNs for chest radiograph abnormality detection. In addition, models were able to generalize to external data albeit with performance decreases that should be taken into consideration when applying models on data from different institutions.

Comparative Analysis of Emergency Medical Service Provider Workload During Simulated Out-of-Hospital Cardiac Arrest Resuscitation Using Standard Versus Experimental Protocols and Equipment.

INTRODUCTION: Protocolized automation of critical, labor-intensive tasks for out-of-hospital cardiac arrest (OHCA) resuscitation may decrease Emergency Medical Services (EMS) provider workload. A simulation-based assessment method incorporating objective and self-reported metrics was developed and used to quantify workloads associated with standard and experimental approaches to OHCA resuscitation. METHODS: Emergency Medical Services-Basic (EMT-B) and advanced life support (ALS) providers were randomized into two-provider mixed-level teams and fitted with heart rate (HR) monitors for continuous HR and energy expenditure (EE) monitoring. Subjects' resting salivary α-amylase (sAA) levels were measured along with Borg perceived exertion scores and multidimensional workload assessments (NASA-TLX). Each team engaged in the following three OHCA simulations: (1) baseline simulation in standard BLS/ALS roles; (2) repeat simulation in standard roles; and then (3) repeat simulation in reversed roles, ie, EMT-B provider performing ALS tasks. Control teams operated with standard state protocols and equipment; experimental teams used resuscitation-automating devices and accompanying goal-directed algorithmic protocol for simulations 2 and 3. Investigators video-recorded resuscitations and analyzed subjects' percent attained of maximal age-predicted HR (%mHR), EE, sAA, Borg, and NASA-TLX measurements. RESULTS: Ten control and ten experimental teams completed the study (20 EMT-Basic; 1 EMT-Intermediate, 8 EMT-Cardiac, 11 EMT-Paramedic). Median %mHR, EE, sAA, Borg, and NASA-TLX scores did not differ between groups at rest. Overall multivariate analyses of variance did not detect significant differences; univariate analyses of variance for changes in %mHR, Borg, and NASA-TLX from resting state detected significant differences across simulations (workload reductions in experimental groups for simulations 2 and 3). CONCLUSIONS: A simulation-based OHCA resuscitation performance and workload assessment method compared protocolized automation-assisted resuscitation with standard response. During exploratory application of the assessment method, subjects using the experimental approach appeared to experience reduced levels of physical exertion and perceived workload than control subjects.

Exploratory Application of Augmented Reality/Mixed Reality Devices for Acute Care Procedure Training.

INTRODUCTION: Augmented reality (AR), mixed reality (MR), and virtual reality devices are enabling technologies that may facilitate effective communication in healthcare between those with information and knowledge (clinician/specialist; expert; educator) and those seeking understanding and insight (patient/family; non-expert; learner). Investigators initiated an exploratory program to enable the study of AR/MR use-cases in acute care clinical and instructional settings. METHODS: Academic clinician educators, computer scientists, and diagnostic imaging specialists conducted a proof-of-concept project to 1) implement a core holoimaging pipeline infrastructure and open-access repository at the study institution, and 2) use novel AR/MR techniques on off-the-shelf devices with holoimages generated by the infrastructure to demonstrate their potential role in the instructive communication of complex medical information. RESULTS: The study team successfully developed a medical holoimaging infrastructure methodology to identify, retrieve, and manipulate real patients' de-identified computed tomography and magnetic resonance imagesets for rendering, packaging, transfer, and display of modular holoimages onto AR/MR headset devices and connected displays. Holoimages containing key segmentations of cervical and thoracic anatomic structures and pathology were overlaid and registered onto physical task trainers for simulation-based "blind insertion" invasive procedural training. During the session, learners experienced and used task-relevant anatomic holoimages for central venous catheter and tube thoracostomy insertion training with enhanced visual cues and haptic feedback. Direct instructor access into the learner's AR/MR headset view of the task trainer was achieved for visual-axis interactive instructional guidance. CONCLUSION: Investigators implemented a core holoimaging pipeline infrastructure and modular open-access repository to generate and enable access to modular holoimages during exploratory pilot stage applications for invasive procedure training that featured innovative AR/MR techniques on off-the-shelf headset devices.

Improving Ultrasound Detection of Uterine Adenomyosis Through Computational Texture Analysis.

The purpose of our study was to determine if a textural analysis metric can be implemented to improve diagnosis of adenomyosis by ultrasound.We retrospectively identified 38 patients with a magnetic resonance imaging (MRI) diagnosis of uterine adenomyosis that also had a pelvic ultrasound within 6 months. We also identified 50 normal pelvic ultrasound examinations confirmed by a normal pelvic MRI within 6 months as a control group. A region of interest (ROI) was subsequently placed on the study population ultrasound image corresponding to the area of adenomyosis on MRI. An ROI was placed in the area of the junctional zone in the normal controls. The abnormal and normal ROIs were then compared against trained normal and abnormal distributions to determine the success rate, sensitivity, specificity, and negative and positive predictive values of our computer metric. The ultrasound reports performed before MRI were also reviewed to determine the radiologist correct/incorrect interpretation rate for comparison with our textural analysis metric.Using a training population of 50 normal ultrasound examinations (confirmed with a normal MRI) and 38 abnormal ultrasound examinations (MRI confirmed adenomyosis), we had an overall 75% (66/88 accurately diagnosed) success rate with a sensitivity, specificity, and negative and positive predictive values of 70%, 79%, 73%, and 76%, respectively (P < .0001). The sensitivity and false-negative rate of the initial ultrasound interpretation were 26% (10/38) and 74% (28/38), respectively.

Computational modeling of 915 MHz microwave ablation: Comparative assessment of temperature-dependent tissue dielectric models.

PURPOSE: The objective of this study is to develop a computational model for simulating 915 MHz microwave ablation (MWA), and verify the simulation predictions of transient temperature profiles against experimental measurements. Due to the limited experimental data characterizing temperature-dependent changes of tissue dielectric properties at 915 MHz, we comparatively assess two temperature-dependent approaches of modeling of dielectric properties: model A- piecewise linear temperature dependencies based on existing, but limited, experimental data, and model B- similar to model A, but augmented with linear decrease in electrical conductivity above 95 °C, as guided by our experimental measurements. METHODS: The finite element method was used to simulate MWA procedures in liver with a clinical 915 MHz ablation applicator. A coupled electromagnetic-thermal solver incorporating temperature-dependent tissue biophysical properties of liver was implemented. Predictions of the transient temperature profiles and ablation zone dimensions for both model A and model B were compared against experimental measurements in ex vivo bovine liver tissue. Broadband dielectric properties of tissue within different regions of the ablation zone were measured and reported at 915 MHz and 2.45 GHz. RESULTS: Model B yielded peak tissue temperatures in closer agreement with experimental measurements, attributed to the inclusion of decrease in electrical conductivity at elevated temperature. The simulated transverse diameters of the ablation zone predicted by both models were greater than experimental measurements, which may be in part due to the lack of a tissue shrinkage model. At both considered power levels, predictions of transverse ablation zone diameters were in closer agreement with measurements for model B (max. discrepancy of 5 mm at 60 W, and 3 mm at 30 W), compared to model A (max. discrepancy of 9 mm at 60 W, and 6 mm at 30 W). Ablation zone lengths with both models were within 2 mm at 30 W, but overestimated by up to 10 mm at 60 W. CONCLUSIONS: The inclusion of decreased electrical conductivity above 95 °C, implemented with model B as guided by our experimental measurements, may be a good approach for approximating the dynamic changes that occur during MWA at 915 MHz. Although a step toward more effectively modeling MWA at 915 MHz, further investigation of the transition in dielectric properties with temperature and tissue shrinkage, especially at high temperatures is needed for more accurate simulations.

Experimental measurement of microwave ablation heating pattern and comparison to computer simulations.

INTRODUCTION: For computational models of microwave ablation (MWA), knowledge of the antenna design is necessary, but the proprietary design of clinical applicators is often unknown. We characterised the specific absorption rate (SAR) during MWA experimentally and compared to a multi-physics simulation. METHODS: An infrared (IR) camera was used to measure SAR during MWA within a split ex vivo liver model. Perseon Medical's short-tip (ST) or long-tip (LT) MWA antenna were placed on top of a tissue sample (n = 6), and microwave power (15 W) was applied for 6 min, while intermittently interrupting power. Tissue surface temperature was recorded via IR camera (3.3 fps, 320 × 240 resolution). SAR was calculated intermittently based on temperature slope before and after power interruption. Temperature and SAR data were compared to simulation results. RESULTS: Experimentally measured SAR changed considerably once tissue temperatures exceeded 100 °C, contrary to simulation results. The ablation zone diameters were 1.28 cm and 1.30 ± 0.03 cm (transverse), and 2.10 cm and 2.66 ± -0.22 cm (axial), for simulation and experiment, respectively. The average difference in temperature between the simulation and experiment were 5.6 °C (ST) and 6.2 °C (LT). Dice coefficients for 1000 W/kg SAR iso-contour were 0.74 ± 0.01 (ST) and 0.77 (± 0.03) (LT), suggesting good agreement of SAR contours. CONCLUSION: We experimentally demonstrated changes in SAR during MWA ablation, which were not present in simulation, suggesting inaccuracies in dielectric properties. The measured SAR may be used in simplified computer simulations to predict tissue temperature when the antenna geometry is unknown.

Development and Application of a Clinical Microsystem Simulation Methodology for Human Factors-Based Research of Alarm Fatigue.

OBJECTIVES: (1) To develop a clinical microsystem simulation methodology for alarm fatigue research with a human factors engineering (HFE) assessment framework and (2) to explore its application to the comparative examination of different approaches to patient monitoring and provider notification. BACKGROUND: Problems with the design, implementation, and real-world use of patient monitoring systems result in alarm fatigue. A multidisciplinary team is developing an open-source tool kit to promote bedside informatics research and mitigate alarm fatigue. METHOD: Simulation, HFE, and computer science experts created a novel simulation methodology to study alarm fatigue. Featuring multiple interconnected simulated patient scenarios with scripted timeline, "distractor" patient care tasks, and triggered true and false alarms, the methodology incorporated objective metrics to assess provider and system performance. Developed materials were implemented during institutional review board-approved study sessions that assessed and compared an experimental multiparametric alerting system with a standard monitor telemetry system for subject response, use characteristics, and end-user feedback. RESULTS: A four-patient simulation setup featuring objective metrics for participant task-related performance and response to alarms was developed along with accompanying structured HFE assessment (questionnaire and interview) for monitor systems use testing. Two pilot and four study sessions with individual nurse subjects elicited true alarm and false alarm responses (including diversion from assigned tasks) as well as nonresponses to true alarms. In-simulation observation and subject questionnaires were used to test the experimental system's approach to suppressing false alarms and alerting providers. CONCLUSIONS: A novel investigative methodology applied simulation and HFE techniques to replicate and study alarm fatigue in controlled settings for systems assessment and experimental research purposes.

Simulation-based Randomized Comparative Assessment of Out-of-Hospital Cardiac Arrest Resuscitation Bundle Completion by Emergency Medical Service Teams Using Standard Life Support or an Experimental Automation-assisted Approach.

INTRODUCTION: Effective resuscitation of out-of-hospital cardiac arrest (OHCA) patients is challenging. Alternative resuscitative approaches using electromechanical adjuncts may improve provider performance. Investigators applied simulation to study the effect of an experimental automation-assisted, goal-directed OHCA management protocol on EMS providers' resuscitation performance relative to standard protocols and equipment. METHODS: Two-provider (emergency medical technicians (EMT)-B and EMT-I/C/P) teams were randomized to control or experimental group. Each team engaged in 3 simulations: baseline simulation (standard roles); repeat simulation (standard roles); and abbreviated repeat simulation (reversed roles, i.e., basic life support provider performing ALS tasks). Control teams used standard OHCA protocols and equipment (with high-performance cardiopulmonary resuscitation training intervention); for second and third simulations, experimental teams performed chest compression, defibrillation, airway, pulmonary ventilation, vascular access, medication, and transport tasks with goal-directed protocol and resuscitation-automating devices. Videorecorders and simulator logs collected resuscitation data. RESULTS: Ten control and 10 experimental teams comprised 20 EMT-B's; 1 EMT-I, 8 EMT-C's, and 11 EMT-P's; study groups were not fully matched. Both groups suboptimally performed chest compressions and ventilations at baseline. For their second simulations, control teams performed similarly except for reduced on-scene time, and experimental teams improved their chest compressions (P=0.03), pulmonary ventilations (P<0.01), and medication administration (P=0.02); changes in their performance of chest compression, defibrillation, airway, and transport tasks did not attain significance against control teams' changes. Experimental teams maintained performance improvements during reversed-role simulations. CONCLUSION: Simulation-based investigation into OHCA resuscitation revealed considerable variability and improvable deficiencies in small EMS teams. Goal-directed, automation-assisted OHCA management augmented select resuscitation bundle element performance without comprehensive improvement.

Physical modeling of microwave ablation zone clinical margin variance.

PURPOSE: The objective of this study is to measure through simulation the impact of (1) heterogeneity of biophysical parameters in tumor vs healthy tissue, (2) applicator placement relative to the tumor, and (3) proximity to large blood vessels on microwave ablation (MWA) treatment effect area. This will help identify the biophysical properties that have the greatest impact on improving clinical modeling of MWA procedures. METHODS: The authors' approach was to develop two-compartment models with variable tissue properties and simulate MWA procedures performed in liver with Perseon Medical's 915 MHz short-tip applicator. Input parameters for the dielectric and thermal properties considered in this study were based on measurements for healthy and malignant (primary or metastatic) liver tissue previously reported in the literature. Compartment 1 (C1) represented normal, fatty, or cirrhotic liver, and compartment 2 (C2) represented a primary hepatocellular carcinoma tumor sample embedded within C1. To evaluate the sensitivity to tissue parameters, a range of clinically relevant tissue properties were simulated. To evaluate the impact of MWA antenna position, the authors simulated various tumor perfusion models with the antenna shifted 5 mm anteriorly and posteriorly. To evaluate the effect of local vasculature, the authors simulated an additional heat sink of various diameters and distances from the tumor. Dice coefficient statistics were used to evaluate ablation zone effects from these local heat sinks. RESULTS: Models showed less than 11% of volume variability (1 cm(3) increase) in ablation treatment effect region when accounting for the difference in relative permittivity and electrical conductivity between malignant and healthy liver tissue. There was a 27% increase in volume when simulating thermal conductivity of fatty liver disease versus the baseline simulation. The ablation zone volume increased more than 36% when simulating cirrhotic surrounding liver tissue. Antenna placement relative to the tumor had minimal sensitivity to the absolute size of the treatment effect area, with less than 1.5 mm variation. However, when considering the overlap between the ablation zone and the ideal clinical margin when the antenna was displaced 5 mm anteriorly and posteriorly, there was approximately a 6 mm difference in the margins. Dice coefficient statistics showed as much as an 11% decrease in the ablation margin due to the presence of vessel heat sinks within the model. CONCLUSIONS: The results from simulating the variance in malignant tissue thermal and electrical properties will help guide better approximations for MWA treatments. The results suggest that assuming malignant and healthy liver tissues have similar dielectric properties is a reasonable first approximation. Antenna placement relative to the tumor has minimal impact on the absolute size of the ablation zone, yet it does cause relevant variation between desired treatment margin and ablation zone. Blood vessel cooling, especially hepatic vessels close to the region of interest, may be a significant factor to consider in treatment planning. Further data need to be collected for assessing treatment planning utility of modeling MWA in this context.