Publications

Visceral adipose tissue (VAT) is a key marker of both metabolic health and habitual physical activity (PA). Excess VAT is highly correlated with type 2 diabetes and insulin resistance. The mechanistic basis for this pathophysiology relates to overloading the liver with fatty acids. VAT is also a highly labile fat depot, with increased turnover stimulated by catecholamines during exercise. VAT can be measured with sophisticated imaging technologies, but can also be inferred directly from PA.We tested this relationship using National Health and Nutrition Examination Survey (NHANES) data from 2011-2014, for individuals aged 20-60 years with 7 days of accelerometry data (n=2,456 men; 2,427 women) [1]. Two approaches were used for estimating VAT from activity. The first used engineered features based on movements during gait and sleep, and then ridge regression to map summary statistics of these features into a VAT estimate. The second approach used deep neural networks trained on 24 hours of continuous accelerometry. A foundation model first mapped each 10 s frame into a high-dimensional feature vector. A transformer model then mapped each day's feature vector time series into a VAT estimate, which were averaged over multiple days. For both approaches, the most accurate estimates were obtained with the addition of covariate information about subject demographics and body measurements. The best performance was obtained by combining the two approaches, resulting in VAT estimates with correlations of r=0.86. These findings demonstrate a strong relationship between PA and VAT and, by extension, between PA and metabolic health risks.

Massive hemorrhage remains the primary cause of potentially preventable death in traumatic injuries. Monitoring hemorrhage and resuscitation accurately can improve outcomes but continues to be challenging since traditional vital signs are highly compensated by the body. Previous work has developed physiologically interpretable algorithms to assess volume status in simulated models of hemorrhage and resuscitation. In this paper, we further develop these algorithms to assess volume status in a nonhuman primate model of controlled blood loss and subsequent whole-blood resuscitation. We acquired arterial blood pressure (ABP) waveform data on 12 adult male baboons during a step-and-hold protocol for hemorrhage and a constant resuscitation rate. A gradient-boosted regression tree model trained on only the ejected-wave pulse area (EWPA) feature yielded a 19% root-mean-square-error (RMSE), 0.71 R2, and an area under the receiver operating characteristic curve of ≥ 0.9 for key operating points of volume status. The performance of this model with a single feature compares well to results reported previously from single-feature machine-learning (ML) models as well as more complex machine learning models that are difficult to interpret and computationally intensive. This study is the first investigation of these physiologically interpretable models on invasively measured ABP waveforms.

Goal: Non-compressible torso hemorrhage represents a category of lethal injuries in both civilian and military traumatically injured populations that with proper intervention, training, or technological advancements are survivable. Endovascular localization of active bleeding in the pre-hospital setting can allow faster, less invasive, and more accurate applications of life-saving interventions. In this paper, we report initial in vivo and in silico experimental results to test the feasibility of endovascular localization of hemorrhage. Methods: Endovascular pressure waveforms were acquired on five pigs with an induced aortic injury via a custom intra-aortic catheter instrumented with four pressure sensors. Pressure and velocity data were then simulated on an in silico human aortic model with the same kind of injury. Results: A decrease in pulse pressure across the injury (proximal to distal) reliably indicated the injury location to within a few centimeters. The simulated model showed a similar decrease in pulse pressure as well as an increase in velocity. Conclusions: With additional refinement, localization accuracy may be sufficient for application of a modern covered stent to stop bleeding. The simulated model results indicate relevance for humans and provide guidance for future experiments.

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.

Assessment of trauma-induced hemorrhage with ultrasound is particularly challenging outside of the clinic, where its detection is crucial. The current clinical standard for hematoma detection – the focused assessment with sonography of trauma (FAST) exam – does not aim to detect ongoing blood loss, and thus is unable to detect injuries of increasing severity. To enhance detection of active bleeding, we propose the use of ultrasound contrast agents (UCAs), together with a novel flow phantom and contrast-sensitive processing techniques, to facilitate efficient, practical characterization of internal bleeding. Within a the custom phantom, UCAs and processing techniques enabled a significant enhancement of the hemorrhage visualization (mean increase in generalized contrast-to-noise ratio of 17 %) compared to the contrast-free case over a range of flow rates up to 40 ml/min. Moreover, we have shown that the use of UCAs improves the probability of detection: the area under the receiver operating characteristic curve for a flow rate of 40 ml/min was 0.99, compared to 0.72 without contrast. We also demonstrate how additional processing of the spatial and temporal information further localizes the bleeding site. UCAs also enhanced Doppler signals over the non-contrast case. These results show that specialized nonlinear processing (NLP) pipelines together with UCAs may offer an efficient means to improve substantially the detection of slower hemorrhages and increase survival rates for trauma-induced injury in pre-hospital settings.

Purpose: Non-verbal utterances are an important tool of communication for individuals who are non- or minimally-speaking. While these utterances are typically understood by caregivers, they can be challenging to interpret by their larger community. To date, there has been little work done to detect and characterize the vocalizations produced by non- or minimally-speaking individuals. This paper aims to characterize five categories of utterances across a set of 7 non- or minimally-speaking individuals. Methods: The characterization is accomplished using a correlation structure methodology, acting as a proxy measurement for motor coordination, to localize similarities and differences to specific speech production systems. Results: We specifically find that frustrated and dysregulated utterances show similar correlation structure outputs, especially when compared to self-talk, request, and delighted utterances. We additionally witness higher complexity of coordination between articulatory and respiratory subsystems and lower complexity of coordination between laryngeal and respiratory subsystems in frustration and dysregulation as compared to self-talk, request, and delight. Finally, we observe lower complexity of coordination across all three speech subsystems in the request utterances as compared to self-talk and delight. Conclusion: The insights from this work aid in understanding of the modifications made by non- or minimally-speaking individuals to accomplish specific goals in non-verbal communication.

Private Automated Contact Tracing (PACT) was a collaborative team and effort formed during the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic. PACT's mission was to enhance contact tracing in pandemic response by designing exposure-detection functions in personal digital communication devices that have maximal public health utility while preserving privacy. This report explains and discusses the use of automated exposure notification during the COVID-19 pandemic and to provide some recommendations for those who may try to design and deploy similar technologies in future pandemics.

This paper introduces an automated post-traumatic stress disorder (PTSD) screening tool that could potentially be used as a self-assessment or inserted into routine medical visits for PTSD diagnosis and treatment. Methods: With an emotion estimation algorithm providing arousal (excited to calm) and valence (pleasure to displeasure) levels through discourse, we select regions of the acoustic signal that are most salient for PTSD detection. Our algorithm was tested on a subset of data from the DVBIC-TBICoE TBI Study, which contains PTSD Check List Civilian (PCL-C) assessment scores. Results: Speech from low-arousal and positive-valence regions provide the best discrimination for PTSD. Our model achieved an AUC (area under the curve) equal to 0.80 in detecting PCL-C ratings, outperforming models with no emotion filtering (AUC = 0.68). Conclusions: This result suggests that emotion drives the selection of the most salient temporal regions of an audio recording for PTSD detection.

The COVID-19 pandemic placed unprecedented demands on the global public health systems for disease surveillance and contact tracing. Engineers and scientists recognized that it might be possible to augment the efforts of public health teams, if a system for automated digital contact tracing could be quickly devised and deployed to the population of smartphones. The Private Automated Contact Tracing (PACT) protocol was one of several digital contact tracing proposals offered worldwide. PACT’s mission—to preserve individuals’ privacy and anonymity while enabling them to quickly alert even nearby strangers of a likely risky exposure—was adopted by Google and Apple and realized in the Exposure Notifications (EN) service and API for mobile application development. The Exposure Notifications system, like many digital proximity tools, is based on Bluetooth signal strength estimation, and keeps much of the necessary information and computation on the smartphones themselves. It implemented a decentralized approach to contact tracing: the public health authority, and other governmental authorities, cannot access the records of an individual’s encounters with others; nor is physical location used or shared by the service. Although the service is available on most modern iOS and Android devices, it is not enabled by default; the individual must opt in to use a particular region’s implementation of the service, either by installing the regional app or by enrolling through a menu of regions in the operating system settings. Likewise, individuals must affirm their consent before the service can share anonymized infection status with the regional public health authority, and alert recent close contacts. The widespread availability of Exposure Notifications through Apple and Google’s platforms has made it a de facto world standard. Determining its accuracy and effectiveness as a public health tool has been a subject of intense interest. In July 2020, CDC’s Innovative Technologies Team designated MIT LL and the PACT team as trusted technical advisors on the deployment of private automated contact tracing systems as part of its overall public health response to COVID-19. The Innovative Technologies Team sought to answer the following key question regarding automated contact tracing: Does automated contact tracing have sufficient public health value that it is worthwhile to integrate it at scale into existing and evolving manual contact tracing systems? Rapidly rising caseloads necessitated parallel-path assessment activities of most mature systems at the time. When access to the Google and Apple Exposure Notifications system became available, MIT LL focused the assessment efforts on the systems being built and deployed. There were two immediate and significant challenges to observing and quantifying the performance of the system as a whole: first, the privacy preserving design decisions of PACT and the system implementers denied access to system-level performance metrics, and second, obtaining accurate “ground truth” data about risky encounters in the population, against which to measure the detector performance, would require an unacceptable level of effort and intrusion. Therefore, MIT LL designed a set of parallel research activities to decompose the problem into components that could be assessed quantifiably (Bluetooth sensor performance, algorithm performance, user preferences and behaviors), components that could be assessed qualitatively (potential cybersecurity risks, potential for malicious use), and components that could be modeled based on current and emergent knowledge (population-level effects). The MIT LL research team conducted early assessments of the privacy and security aspects of new EN app implementations and closely reviewed the available system code exercised by the apps, before conducting a series of phone-to-phone data collections both in the laboratory and in simulated real-world conditions. The data from these experiments fed into models and visualization tools created to predict and understand the risk score output of candidate “weights and thresholds” configurations for EN, i.e., to predict the performance of the system as-built against ground truth data for distance and duration of “exposure”. The data and performance predictions from this effort helped to inform the global and local community of practice in making configuration decisions, and can help to predict the performance of future versions of similar tools, or alternative implementations of the current system. We conducted a human factors and usability review of early app user interfaces and messaging from public health, and designed a follow-on large-scale survey to investigate questions about user trust and system adoption decisions. The results of the human factors, user trust, and adoption studies were used by U.S. public health jurisdictions to make adjustments to public-facing communications, and were shared with Apple and Google to improve the user interface. Information gathered from public health experts enabled us to better understand conventional contact tracing workflows and data streams, and we incorporated that information into an agent-based model of “hybrid” contact tracing plus Exposure Notifications. We then combined it with emerging reports on vaccination, mask effectiveness, social interaction, variant transmissibility, and our own data on the sensitivity and specificity of the Bluetooth “dose” estimator, to predict system-level effects under various conditions. Finally, we helped to establish a network of Exposure Notifications “practitioners” in public health, who surfaced desirable system-level key performance indicators (implemented during 2021 and 2022, in the Exposure Notifications Private Analytics system, or ENPA). At the conclusion of the program, many of the initial conditions of the pandemic had changed. The Exposure Notifications service was available to most of the world, but had only been deployed by 28 U.S. states and territories, and had not been adopted by much of the population in those regions. High case rates during the Omicron surge (December 2021 – January 2022) and newly available ENPA data offered the first hints at calculating “real” state-level performance metrics, but those data belong to the states and many are cautious about publishing. Although Google and Apple have stated that Exposure Notifications was designed for COVID-19, and will not be maintained in its current form after the pandemic ends, the public health and engineering communities show clear interest in using the “lessons learned” from Exposure Notifications and other similar solutions to preserve the capabilities developed and prepare better systems for future public health emergencies. The intent of this report is to document the work that has been completed, as well as to inform where the work could be updated or adapted to meet future needs.

Non-compressible internal hemorrhage (NCIH) is the most common cause of death in acute non-penetrating trauma. NCIH management requires accurate hematoma localization and evaluation for ongoing bleeding for risk stratification. The current standard point-of-care diagnostic tool, the focused assessment with sonography for trauma (FAST), detects free fluid in body cavities with conventional B-mode imaging. The FAST does not assess whether bleeding is ongoing, at which location(s), and to what extent. Here, we propose contrast-enhanced ultrasound (CEUS) techniques to better identify, localize, and quantify hemorrhage. We designed and fabricated a custom hemorrhage-mimicking phantom, comprising a perforated vessel and cavity to simulate active bleeding. Lumason contrast agents (UCAs) were introduced at clinically relevant concentrations (3.5×108 bubbles/ml). Conventional and contrast pulse sequence images were captured, and post-processed with bubble localization techniques (SVD clutter filter and bubble localization). The results showed contrast pulse sequences enabled a 2.2-fold increase in the number of microbubbles detected compared with conventional CEUS imaging, over a range of flow rates, concentrations, and localization processing parameters. Additionally, particle velocimetry enabled mapping of dynamic flow within the simulated bleeding site. Our findings indicate that CEUS combined with advanced image processing may enhance visualization of hemodynamics and improve non-invasive, real-time detection of active bleeding.