RF system calibration for global Q matrix determination

The use of multiple transmission channels (known as Parallel Transmission, or PTx) provides increased control of the MRI signal formation process. This extra flexibility comes at a cost of uncertainty of the power deposited in the patient under examination: the electric fields produced by each transmitter can interfere in such a way to lead to excessively high heating. Although it is not possible to determine local heating, the global Q matrix (which allows the whole-body Specific Absorption Rate (SAR) to be known for any PTx pulse) can be measured in-situ by monitoring the power incident upon and reflected by each transmit element during transmission. Recent observations have shown that measured global Q matrices can be corrupted by losses between the coil array and location of power measurement. In this work we demonstrate that these losses can be accounted for, allowing accurate global Q matrix measurement independent of the location of the power measurement devices

The effect of isometric exercise training on arterial stiffness: A randomized crossover controlled study

Isometric exercise training (IET) is an effective intervention for the management of resting blood pressure (BP). However, the effects of IET on arterial stiffness remain largely unknown. Eighteen unmedicated physically inactive participants were recruited. Participants were randomly allocated in a cross-over design to 4 weeks of home-based wall squat IET and control period, separated by a 3-week washout period. Continuous beat-to-beat hemodynamics, including early and late systolic (sBP 1 and sBP 2, respectively) and diastolic blood pressure (dBP) were recorded for a period of 5 min and waveforms were extracted and analyzed to acquire the augmentation index (AIx) as a measure of arterial stiffness. sBP 1 (-7.7 ± 12.8 mmHg, p = 0.024), sBP 2 (-5.9 ± 9.9 mmHg, p = 0.042) and dBP (-4.4 ± 7.2 mmHg, p = 0.037) all significantly decreased following IET compared to the control period. Importantly, there was a significant reduction in AIx following IET (-6.6 ± 14.5%, p = 0.02) compared to the control period. There were also adjacent significant reductions in total peripheral resistance (-140.7 ± 65.8 dynes·cm-5, p = 0.042) and pulse pressure (-3.8 ± 4.2, p = 0.003) compared to the control period. This study demonstrates an improvement in arterial stiffness following a short-term IET intervention. These findings have important clinical implications regarding cardiovascular risk. Mechanistically, these results suggest that reductions in resting BP following IET are induced via favorable vascular adaptations, although the intricate details of such adaptations are not yet clear

Contrastive learning for view classification of echocardiograms

Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep learning networks that are trained to predict disease or detect image features. However, such models are extremely data-hungry and training requires labelling of many thousands of images by experienced clinicians. Here we propose the use of contrastive learning to mitigate the labelling bottleneck. We train view classification models for imbalanced cardiac ultrasound datasets and show improved performance for views/classes for which minimal labelled data is available. Compared to a naïve baseline model, we achieve an improvement in F1 score of up to 26% in those views while maintaining state-of-the-art performance for the views with sufficiently many labelled training observations

Anti-decubitus bed mattress may interfere with cerebrovascular pressure reactivity measures due to induced ICP and ABP cyclic peaks

Severe traumatic brain injury (TBI) patients are monitored with continuous arterial blood pressure (ABP) and intracranial pressure (ICP). The pressure reactivity index (PRx) is a frequently used correlation coefficient between ABP and ICP to inform clinicians at the bedside about trends in global cerebrovascular pressure regulation status. We present an unexpected influence of cyclic anti-decubitus mattress inflations and deflations on invasive ICP, ABP and PRx calculations in our TBI patients. This might affect autoregulation guided management. In our database, 23% (9/39) of the patients show recurrent peaks in the monitoring signals. We hypothesize that these peaks are caused by (a combination) of hydrostatic change, local (cervical) compression and/or incorrect sensor zeroing due to positional changes induced by the anti-decubitus mattress. This warrants further investigation by the manufacturer and exploration of data filters

Are we ready for Optimal CPP-oriented management of TBI patients?

Objective: Monitoring cerebral autoregulation (CA) is important for TBI patients, 1 as impaired CA correlates with poor outcome. 2 3 Today, automated algorithms allow to assess CPP for which autoregulation is best preserved (CPPopt) continuously and present it at the bedside 5 6 . Individualising CPP treatment using CPPopt is attractive and this has been recognised in published guidelines. However there are no specifications for its use clinically and it has therefore never been prospectively evaluated 1 . Numerous logistic, technical, feasibility and safety questions remain before the idea of selecting individual CPP treatment targets based on the state of CA 4 can be incorporated into clinical practice. How far are we from strict guidelines on the incorporation of this methodology into TBI protocols? Design: Literature review Subjects: Methods: Systematic review Results: The feasibility of CPPot-guided therapy has only been evaluated retrospectively and in non-clinical ways, whereas no studies exist on its safety. A prospective investigation of CPPopt-guided therapy has been initiated with ‘CppOpt Guided Therapy: Assessment of Target Effectiveness’ (COGiTATE), a multicenter randomized trial assessing feasibility and safety of a continuous CA monitoring-based therapy in adult TBI patients. Conclusions: COGiTATE seems to be the first step to define the physiological effect of targeting CPPopt and should pave the way toward establishing the exact protocol of CPPopt-oriented therapy and the phase III study. References: 1. Carney N, Totten AM, OʼReilly C, et al. Guidelines for the Management of Severe Traumatic Brain Injury, Fourth Edition. Neurosurgery. 2016;80(1):1. doi:10.1227/NEU.0000000000001432. 2. Hlatky R, Furuya Y, Valadka AB, et al. Dynamic autoregulatory response after severe head injury. J Neurosurg. 2002;97(5):1054-1061. doi:10.3171/jns.2002.97.5.1054. 3. Czosnyka M, Czosnyka Z, Smielewski P. Pressure reactivity index: journey through the past 20 years. doi:10.1007/s00701-017-3310-1. 4. Steiner LA, Czosnyka M, Piechnik SK, et al. Continuous monitoring of cerebrovascular pressure reactivity allows determination of optimal cerebral perfusion pressure in patients with traumatic brain injury. Crit Care Med. 2002;30(4):733-738. http://www.ncbi.nlm.nih.gov/pubmed/11940737. Accessed December 28, 2017. 5. Aries MJH, Czosnyka M, Budohoski KP, et al. Continuous determination of optimal cerebral perfusion pressure in traumatic brain injury*. Crit Care Med. 2012. doi:10.1097/CCM.0b013e3182514eb6. 6. Liu X, Maurits NM, Aries MJH, et al. Monitoring of Optimal Cerebral Perfusion Pressure in Traumatic Brain Injured Patients Using a Multi-Window Weighting Algorithm. J Neurotrauma. 2017;34(22):3081-3088. doi:10.1089/neu.2017.5003. 7. Depreitere B, Güiza F, Berghe G Van Den, et al. Pressure autoregulation monitoring and cerebral perfusion pressure target recommendation in patients with severe traumatic brain injury based on minute-by-minute monitoring data. J Neurosurg. 2014;120(120):1451-1457. doi:10.3171/2014.3.JNS131500. 8. Güiza F, Meyfroidt G, Piper I, et al. Cerebral Perfusion Pressure Insults and Associations with Outcome in Adult Traumatic Brain Injury. doi:10.1089/neu.2016.4807. 9. Oshorov A V, Savin IA, Goriachev AS, Popugaev KA, Potapov AA, Gavrilov AG. [The first experience in monitoring the cerebral vascular autoregulation in the acute period of severe brain injury]. Anesteziol Reanimatol. (2):61-64. http://www.ncbi.nlm.nih.gov/pubmed/18540464. Accessed January 1, 2018. 10. Donnelly J, Czosnyka M, Adams H, et al. Individualizing Thresholds of Cerebral Perfusion Pressure Using Estimated Limits of Autoregulation. Crit Care Med. 2017;45(9):1464-1471. doi:10.1097/CCM.0000000000002575. 11. Dias C, Silva MJ, Pereira E, et al. Optimal Cerebral Perfusion Pressure Management at Bedside: A Single-Center Pilot Study. Neurocrit Care. 2015;23(1):92-102. doi:10.1007/s12028-014-0103-8. 12. Jaeger M, Dengl M, J&#252, Schuhmann MU. Effects of cerebrovascular pressure reactivity-guided optimization of cerebral perfusion pressure on brain tissue oxygenation after traumatic brain injury*. Crit Care Med. 2010;38(5):1343-1347. doi:10.1097/ccm.0b013e3181d45530

Modeling Brain–Heart Crosstalk Information in Patients with Traumatic Brain Injury

Publisher Copyright: © 2021, The Author(s).Background: Traumatic brain injury (TBI) is an extremely heterogeneous and complex pathology that requires the integration of different physiological measurements for the optimal understanding and clinical management of patients. Information derived from intracranial pressure (ICP) monitoring can be coupled with information obtained from heart rate (HR) monitoring to assess the interplay between brain and heart. The goal of our study is to investigate events of simultaneous increases in HR and ICP and their relationship with patient mortality. Methods: In our previous work, we introduced a novel measure of brain–heart interaction termed brain–heart crosstalks (ctnp), as well as two additional brain–heart crosstalks indicators [mutual information (mict) and average edge overlap (ωct)] obtained through a complex network modeling of the brain–heart system. These measures are based on identification of simultaneous increase of HR and ICP. In this article, we investigated the relationship of these novel indicators with respect to mortality in a multicenter TBI cohort, as part of the Collaborative European Neurotrauma Effectiveness Research in TBI high-resolution work package. Results: A total of 226 patients with TBI were included in this cohort. The data set included monitored parameters (ICP and HR), as well as laboratory, demographics, and clinical information. The number of detected brain–heart crosstalks varied (mean 58, standard deviation 57). The Kruskal–Wallis test comparing brain–heart crosstalks measures of survivors and nonsurvivors showed statistically significant differences between the two distributions (p values: 0.02 for mict, 0.005 for ctnp and 0.006 for ωct). An inverse correlation was found, computed using the point biserial correlation technique, between the three new measures and mortality: − 0.13 for ctnp (p value 0.04), − 0.19 for ωct (p value 0.002969) and − 0.09 for mict (p value 0.1396). The measures were then introduced into the logistic regression framework, along with a set of input predictors made of clinical, demographic, computed tomography (CT), and lab variables. The prediction models were obtained by dividing the original cohort into four age groups (16–29, 30–49, 50–65, and 65–85 years of age) to properly treat with the age confounding factor. The best performing models were for age groups 16–29, 50–65, and 65–85, with the deviance of ratio explaining more than 80% in all the three cases. The presence of an inverse relationship between brain–heart crosstalks and mortality was also confirmed. Conclusions: The presence of a negative relationship between mortality and brain–heart crosstalks indicators suggests that a healthy brain–cardiovascular interaction plays a role in TBI.Peer reviewe

Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration

PURPOSE: Neovascular age-related macular degeneration (nAMD) is a major global cause of blindness. Whilst anti-vascular endothelial growth factor (anti-VEGF) treatment is effective, response varies considerably between individuals. Thus, patients face substantial uncertainty regarding their future ability to perform daily tasks. In this study, we evaluate the performance of an automated machine learning (AutoML) model which predicts visual acuity (VA) outcomes in patients receiving treatment for nAMD, in comparison to a manually coded model built using the same dataset. Furthermore, we evaluate model performance across ethnic groups and analyse how the models reach their predictions. METHODS: Binary classification models were trained to predict whether patients' VA would be 'Above' or 'Below' a score of 70 one year after initiating treatment, measured using the Early Treatment Diabetic Retinopathy Study (ETDRS) chart. The AutoML model was built using the Google Cloud Platform, whilst the bespoke model was trained using an XGBoost framework. Models were compared and analysed using the What-if Tool (WIT), a novel model-agnostic interpretability tool. RESULTS: Our study included 1631 eyes from patients attending Moorfields Eye Hospital. The AutoML model (area under the curve [AUC], 0.849) achieved a highly similar performance to the XGBoost model (AUC, 0.847). Using the WIT, we found that the models over-predicted negative outcomes in Asian patients and performed worse in those with an ethnic category of Other. Baseline VA, age and ethnicity were the most important determinants of model predictions. Partial dependence plot analysis revealed a sigmoidal relationship between baseline VA and the probability of an outcome of 'Above'. CONCLUSION: We have described and validated an AutoML-WIT pipeline which enables clinicians with minimal coding skills to match the performance of a state-of-the-art algorithm and obtain explainable predictions

Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral

Importance: Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets. // Objective: To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models. // Design, Setting, and Participants: This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images from patients of a private Egyptian medical retina clinic (Egypt [n = 210]). An AI model was trained to classify referable diabetic retinopathy as an exemplar use case. Messidor-2 images were assigned adjudicated labels available on Kaggle; 4 images were deemed ungradable and excluded, leaving 1744 images. A total of 300 images randomly selected from the EyePACS data set were independently relabeled by 3 blinded retina specialists using the International Classification of Diabetic Retinopathy protocol for diabetic retinopathy grade and diabetic macular edema presence; 19 images were deemed ungradable, leaving 281 images. Data analysis was performed from February 1 to February 28, 2021. // Exposures: Using public data sets, a teacher model was trained with labeled images using supervised learning. Next, the resulting predictions, termed pseudolabels, were used on an unlabeled public data set. Finally, a student model was trained with the existing labeled images and the additional pseudolabeled images. Main Outcomes and Measures: The analyzed metrics for the models included the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The Fisher exact test was performed, and 2-tailed P values were calculated for failure case analysis. // Results: For the internal validation data sets, AUROC values for performance ranged from 0.886 to 0.939 for the teacher model and from 0.916 to 0.951 for the student model. For external validation of automated ML model performance, AUROC values and accuracy were 0.964 and 93.3% for the teacher model, 0.950 and 96.7% for the student model, and 0.890 and 94.3% for the manually coded bespoke model, respectively. // Conclusions and Relevance: These findings suggest that self-training using automated ML is an effective method to increase both model performance and generalizability while decreasing the need for costly expert labeling. This approach advances the democratization of AI by enabling clinicians without coding expertise or access to large, well-labeled private data sets to develop their own AI models

Cerebrovascular reactivity is not associated with therapeutic intensity in adult traumatic brain injury: a CENTER-TBI analysis.

BACKGROUND: Impaired cerebrovascular reactivity in adult traumatic brain injury (TBI) is known to be associated with poor outcome. However, there has yet to be an analysis of the association between the comprehensively assessed intracranial hypertension therapeutic intensity level (TIL) and cerebrovascular reactivity. METHODS: Using the Collaborative European Neuro Trauma Effectiveness Research in TBI (CENTER-TBI) high-resolution intensive care unit (ICU) cohort, we derived pressure reactivity index (PRx) as the moving correlation coefficient between slow-wave in ICP and mean arterial pressure, updated every minute. Mean daily PRx, and daily % time above PRx of 0 were calculated for the first 7 days of injury and ICU stay. This data was linked with the daily TIL-Intermediate scores, including total and individual treatment sub-scores. Daily mean PRx variable values were compared for each TIL treatment score via mean, standard deviation, and the Mann U test (Bonferroni correction for multiple comparisons). General fixed effects and mixed effects models for total TIL versus PRx were created to display the relation between TIL and cerebrovascular reactivity. RESULTS: A total of 249 patients with 1230 ICU days of high frequency physiology matched with daily TIL, were assessed. Total TIL was unrelated to daily PRx. Most TIL sub-scores failed to display a significant relationship with the PRx variables. Mild hyperventilation (p < 0.0001), mild hypothermia (p = 0.0001), high levels of sedation for ICP control (p = 0.0001), and use vasopressors for CPP management (p < 0.0001) were found to be associated with only a modest decrease in mean daily PRx or % time with PRx above 0. CONCLUSIONS: Cerebrovascular reactivity remains relatively independent of intracranial hypertension therapeutic intensity, suggesting inadequacy of current TBI therapies in modulating impaired autoregulation. These findings support the need for investigation into the molecular mechanisms involved, or individualized physiologic targets (ICP, CPP, or Co2) in order to treat dysautoregulation actively.EU 7th Framewor