Publications
Building digital twins for cardiovascular health: From principles to clinical impact
August 1, 2024
Journal Article
Published in:
J. Am. Heart Assoc., online ahead of print, 1 August 2024.
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Summary
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.
Summary
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)...
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Optimizing MobileNet algorithms for real-time vessel detection on smartphones
October 9, 2023
Conference Paper
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Proc. 2023 IEEE 19th Intl. Conf. on Body Sensor Networks, BSN, 9-11 October 2023.
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Internal bleeding due to non-compressible torso hemorrhage is the leading cause of prehospital fatalities in civilian and military trauma. A limited number of trauma surgeons are expected to be available in disaster scenarios and future large-scale combat operations. As a result, non-specialists will need to perform life-saving interventions to address internal bleeding. A first step in mitigation is ultrasound-guided central vascular access, which involves identifying a deep blood vessel in the imagery, such as the femoral vein, femoral artery, or internal jugular vein, and then placing a needle and catheter into the vessel for follow-on resuscitation. In this paper, we demonstrate machine learning algorithms for both femoral and neck vessel detection with high accuracy and real-time speed on smartphones. The algorithms are integrated with commercial ultrasound and optimized for use on low size, weight, and power devices. Coupled with custom robotics, this technology can enable rapid vascular access by non-specialist operators using a handheld platform.
Summary
Internal bleeding due to non-compressible torso hemorrhage is the leading cause of prehospital fatalities in civilian and military trauma. A limited number of trauma surgeons are expected to be available in disaster scenarios and future large-scale combat operations. As a result, non-specialists will need to perform life-saving interventions to address...
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Daily activity profiles and activity fluctuations correlate with BMI
October 9, 2023
Conference Paper
Published in:
2023 IEEE 19th Intl. Conf. on Body Sensor Networks, BSN, 9-11 October 2023.
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The rising levels of obesity have been declared a global epidemic by the World Health Organization, with obesity rates surpassing 50% in many countries. Between the late 1970s and the early 2000s in the U.S., the prevalence of obesity doubled while the prevalence of severe obesity more than tripled. One of the factors underlying the obesity epidemic is secular changes in activity patterns due to an increasingly sedentary lifestyle. A better understanding is needed of how daily activity patterns relate to obesity. In this study we use wrist-worn accelerometry from the National Health And Nutrition Examination Survey (NHANES) data set to develop a number of features that characterize daily activity profiles, as well as fluctuations in those profiles over time, and determine how those features correlate with body mass index (BMI). Using a data set of 2,882 subjects split evenly between a training and test fold, we constructed regression models that estimate BMI based on activity profiles and fluctuations. We found a correlation of r=0.47 between estimated and true BMI, resulting in detection of overweight, obese, and severely obese subjects with area under the ROC curve (AUC) of 0.69, 0.73 and 0.85. These results indicate how patterns of activity levels across daily sleep/wake cycles are associated with higher risk for obesity.
Summary
The rising levels of obesity have been declared a global epidemic by the World Health Organization, with obesity rates surpassing 50% in many countries. Between the late 1970s and the early 2000s in the U.S., the prevalence of obesity doubled while the prevalence of severe obesity more than tripled. One...
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Individualized ultrasound-guided intervention phantom development, fabrication, and proof of concept
July 24, 2023
Conference Paper
Published in:
45th Annual Intl. Conf. of the IEEE Engineering in Medicine and Biology Society, EMBC, 24-27 July 2023.
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Commercial ultrasound vascular phantoms lack the anatomic diversity required for robust pre-clinical interventional device testing. We fabricated individualized phantoms to test an artificial intelligence enabled ultrasound-guided surgical robotic system (AI-GUIDE) which allows novices to cannulate deep vessels. After segmenting vessels on computed tomography scans, vessel cores, bony anatomy, and a mold tailored to the skin contour were 3D-printed. Vessel cores were coated in silicone, surrounded in tissue-mimicking gel tailored for ultrasound and needle insertion, and dissolved with water. One upper arm and four inguinal phantoms were constructed. Operators used AI-GUIDE to deploy needles into phantom vessels. Two groin phantoms were tested due to imaging artifacts in the other two phantoms. Six operators (medical experience: none, 3; 1-5 years, 2; 5+ years, 1) inserted 27 inguinal needles with 81% (22/27) success in a median of 48 seconds. Seven operators performed 24 arm injections, without tuning the AI for arm anatomy, with 71% (17/24) success. After excluding failures due to motor malfunction and a defective needle, success rate was 100% (22/22) in the groin and 85% (17/20) in the arm. Individualized 3D-printed phantoms permit testing of surgical robotics across a large number of operators and different anatomic sites. AI-GUIDE operators rapidly and reliably inserted a needle into target vessels in the upper arm and groin, even without prior medical training. Virtual device trials in individualized 3-D printed phantoms may improve rigor of results and expedite translation.
Summary
Commercial ultrasound vascular phantoms lack the anatomic diversity required for robust pre-clinical interventional device testing. We fabricated individualized phantoms to test an artificial intelligence enabled ultrasound-guided surgical robotic system (AI-GUIDE) which allows novices to cannulate deep vessels. After segmenting vessels on computed tomography scans, vessel cores, bony anatomy, and a...
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Development of 3D-Printed Individualized Vascular Phantoms for Artificial Intelligence (AI) Enabled Interventional Device Testing
March 1, 2023
Abstract
Published in:
Journal of Vascular and Interventional Radiology 34 (3), S12.
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We developed vascular phantoms mapped from human subjects to test AI-enabled ultrasound-guided vascular cannulation. Translational device prototyping necessitates anatomically accurate models. Commercial phantoms fail to address anatomic variability. Uniformity leads to optimistic AI model and operator performance. Individualized 3D-printed vascular phantoms yield anatomically correct models optimized for AI-device testing.
Summary
We developed vascular phantoms mapped from human subjects to test AI-enabled ultrasound-guided vascular cannulation. Translational device prototyping necessitates anatomically accurate models. Commercial phantoms fail to address anatomic variability. Uniformity leads to optimistic AI model and operator performance. Individualized 3D-printed vascular phantoms yield anatomically correct models optimized for AI-device testing.
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Noninvasive monitoring of simulated hemorrhage and whole blood resuscitation
December 14, 2022
Journal Article
Published in:
Biosensors, Vol. 12, No. 12, 2022, Art. No. 1168.
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Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R2 of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model's performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement.
Summary
Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators...
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Contrast-enhanced ultrasound to detect active bleeding
October 1, 2022
Abstract
Published in:
J. Acoust. Soc. Am. 152, A280 (2022)
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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.
Summary
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...
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Transfer learning for automated COVID-19 B-line classification in lung ultrasound
July 15, 2022
Conference Paper
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44th Annual Int. Conf. of IEEE Engineering in Medicine & Biology Society (EMBC), DOI: 10.1109/EMBC48229.2022.9871894.
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Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective was to develop automated methods to classify B-lines as pathologic vs. normal. We developed transfer learning models based on ResNet networks to classify B-lines as pathologic (at least 3 B-lines per lung field) vs. normal using COVID-19 LUS data. Assessment of B-line severity on a 0-4 multi-class scale was also explored. For binary B-line classification, at the frame-level, all ResNet models pretrained with ImageNet yielded higher performance than the baseline nonpretrained ResNet-18. Pretrained ResNet-18 has the best Equal Error Rate (EER) of 9.1% vs the baseline of 11.9%. At the clip-level, all pretrained network models resulted in better Cohen's kappa agreement (linear-weighted) and clip score accuracy, with the pretrained ResNet-18 having the best Cohen's kappa of 0.815 [95% CI: 0.804-0.826], and ResNet-101 the best clip scoring accuracy of 93.6%. Similar results were shown for multi-class scoring, where pretrained network models outperformed the baseline model. A class activation map is also presented to guide clinicians in interpreting LUS findings. Future work aims to further improve the multi-class assessment for severity of B-lines with a more diverse LUS dataset.
Summary
Lung ultrasound (LUS) as a diagnostic tool is gaining support for its role in the diagnosis and management of COVID-19 and a number of other lung pathologies. B-lines are a predominant feature in COVID-19, however LUS requires a skilled clinician to interpret findings. To facilitate the interpretation, our main objective...
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Feature importance analysis for compensatory reserve to predict hemorrhagic shock
July 15, 2022
Conference Paper
Published in:
44th Annual Int. Conf. of IEEE Engineering in Medicine & Biology Society (EMBC), DOI: 10.1109/EMBC48229.2022.9871661.
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Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate on an arterial blood pressure (ABP) waveform acquired via photoplethysmography have been shown to provide an effective early indicator. However, these machine learning approaches lack physiological interpretability. In this paper, we evaluate the importance of nine ABP-derived features that provide physiological insight, using a database of 40 human subjects from a lower-body negative pressure model of progressive central hypovolemia. One feature was found to be considerably more important than any other. That feature, the half-rise to dicrotic notch (HRDN), measures an approximate time delay between the ABP ejected and reflected wave components. This delay is an indication of compensatory mechanisms such as reduced arterial compliance and vasoconstriction. For a scale of 0% to 100%, with 100% representing normovolemia and 0% representing decompensation, linear regression of the HRDN feature results in root-mean-squared error of 16.9%, R2 of 0.72, and an area under the receiver operating curve for detecting decompensation of 0.88. These results are comparable to previously reported results from the more complex black box machine learning models. Clinical Relevance- A single physiologically interpretable feature measured from an arterial blood pressure waveform is shown to be effective in monitoring for blood loss and impending hemorrhagic shock based on data from a human lower-body negative pressure model of progressive central hypolemia.
Summary
Hemorrhage is the leading cause of preventable death from trauma. Traditionally, vital signs have been used to detect blood loss and possible hemorrhagic shock. However, vital signs are not sensitive for early detection because of physiological mechanisms that compensate for blood loss. As an alternative, machine learning algorithms that operate...
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Wearable technology in extreme environments
March 14, 2022
Book Chapter
Published in:
Chapter 2 in: Cibis, T., McGregor AM, C. (eds) Engineering and Medicine in Extreme Environments. Springer, Cham. https://doi-org.ezproxy.canberra.edu.au/10.1007/978-3-030-96921-9_2
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Humans need to work in many types of extreme environments where there is a need to stay safe and even to improve performance. Examples include: medical providers treating infectious disease, people responding to other biological or chemical hazards, firefighters, astronauts, pilots, divers, and people working outdoors in extreme hot or cold temperatures. Wearable technology is ubiquitous in the consumer market but is still needed for extreme environments. For these applications, it is particularly challenging to meet requirements to be actionable, accurate, acceptable, integratable, and affordable. To provide insight into these needs and possible solutions and the technology trade-offs involved, several examples are provided. A physiological monitoring example is described for predicting and avoiding heat injury. A cognitive monitoring example is described for estimating cognitive workload, with broader applicability to a variety of conditions, such as cognitive fatigue and depression. Finally, eye tracking is considered as a promising wearable sensing modality with applications for both physiological and cognitive monitoring. Concluding thoughts are offered on the compelling need for wearable technology in the face of pandemics, wildfires, and climate change, but also for global projects that can uplift mankind, such as long-duration spaceflight and missions to Mars.
Summary
Humans need to work in many types of extreme environments where there is a need to stay safe and even to improve performance. Examples include: medical providers treating infectious disease, people responding to other biological or chemical hazards, firefighters, astronauts, pilots, divers, and people working outdoors in extreme hot or...
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