Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine.

Digital twins in precision medicine provide tailored health recommendations by simulating patient-specific trajectories and interventions. We examine the critical role of Verification, Validation, and Uncertainty Quantification (VVUQ) for digital twins in ensuring safety and efficacy, with examples in cardiology and oncology. We highlight challenges and opportunities for developing personalized trial methodologies, validation metrics, and standardizing VVUQ processes.

In Vivo Deformation of the Human Basilar Artery.

An estimated 6.8 million people in the United States have an unruptured intracranial aneurysms, with approximately 30,000 people suffering from intracranial aneurysms rupture each year. Despite the development of population-based scores to evaluate the risk of rupture, retrospective analyses have suggested the limited usage of these scores in guiding clinical decision-making.

Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.

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.

National trends in the endovascular and surgical treatment of idiopathic intracranial hypertension.

Background: The pattern of surgical treatments for Idiopathic Intracranial Hypertension (IIH) in the United States is not well-studied, specifically the trend of utilizing endovascular venous stenting (EVS) as an emerging technique.

Methods: In this cross-sectional study, we aimed to explore the national trend of utilizing different procedures for the treatment of IIH including EVS, Optic Nerve Sheath Fenestration (ONSF), and CSF Shunting, with a focus on novel endovascular procedures. Moreover, we explored rates of 90-day readmission and length of hospital stay following different procedures, besides the effects of sociodemographic and clinical parameters on procedure choice.

Designing an interprofessional dementia specialty clinic: Conceptualization and evaluation of a patient-centered model.

The need for blueprints to design specialty care interprofessional collaboration (IPC) models is urgent, given the expanding aging population and current challenges in dementia diagnosis and treatment. We describe key steps creating an interprofessional outpatient dementia specialty clinic, efforts to sustain the model, and evaluation of interprofessional effectiveness and clinician satisfaction. The conception for the Comprehensive Memory Center was informed by qualitative research methodologies including focus groups, interviews, and literature reviews.

Technology use and subjective cognitive concerns in older adults.

Objectives: How technology impacts the day to day cognitive functioning of older adults is a matter of some debate. On the one hand, the use of technologies such as smartphones and social media, may lead to more subjective cognitive concerns (SCC) by promoting distractibility and reliance on devices to perform memory tasks. However, continued digital engagement in older adults may also be related to better cognitive functioning.

Automatic Segmentation and Quantitative Assessment of Stroke Lesions on MR Images.

Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size.

Internet-Enabled Behaviors in Older Adults During the Pandemic: Patterns of Use, Psychosocial Impacts, and Plans for Continued Utilization.

In response to social distancing measures during the COVID-19 pandemic, there was a need to increase the frequency of internet enabled behaviors (IEBs). To date, little is known about how the pandemic impacted IEBs in older adults, a population that has historically been linked to lower digital literacy and utilization. We administered an online survey between April and July 2021 to 298 adults who were over age 50 (mean age = 73 years; 93.

Optimizing Emergency Stroke Transport Strategies Using Physiological Models.

Background And Purpose: The criteria for choosing between drip and ship and mothership transport strategies in emergency stroke care is widely debated. Although existing data-driven probability models can inform transport decision-making at an epidemiological level, we propose a novel mathematical, physiologically derived framework that provides insight into how patient characteristics underlying infarct core growth influence these decisions.

Methods: We represent the physiology of time-dependent infarct core growth within an ischemic penumbra as an exponential function with consideration to rate-determining collateral blood flow.

Predicting apneic events in preterm infants using cardio-respiratory and movement features.

Background And Objective: Preterm neonates are prone to episodes of apnea, bradycardia and hypoxia (ABH) that can lead to neurological morbidities or even death. There is broad interest in developing methods for real-time prediction of ABH events to inform interventions that prevent or reduce their incidence and severity. Using advances in machine learning methods, this study develops an algorithm to predict ABH events.

Characterizing the performance of emergency medical transport time metrics in a residentially segregated community.

Objective: To derive and characterize the performance of various metrics of emergency transport time in assessing for sociodemographic disparities in the setting of residential segregation. Secondarily to characterize racial disparities in emergency transport time of suspected stroke patients in Austin, Texas.

Data Sources: We used a novel dataset of 2518 unique entries with detailed spatial and temporal information on all suspected stroke transports conducted by a public emergency medical service in Central Texas between 2010 and 2018.

Importance of Community Impact as the Fourth Academic Mission: A Qualitative Study.

Most US medical schools have 3 primary missions: education, research, and clinical service. Recently there have been calls for a fourth primary mission focused on improving health in their surrounding communities. To date, few medical schools have done so.

Effect of spontaneous movement on respiration in preterm infants.

New Findings: What is the central question of this study? The respiratory centres in the brainstem that control respiration receive inputs from various sources, including proprioceptors in muscles and joints and suprapontine centres, which all affect limb movements. What is the effect of spontaneous movement on respiration in preterm infants? What is the main finding and its importance? Apnoeic events tend to be preceded by movements. These activity bursts can cause respiratory instability that leads to an apnoeic event.

Falling off a limit cycle using phase-agnostic stimuli: Applications to clinical oscillopathies.

For over a century, physiological studies have shown that precisely timed pulses can switch off a biological oscillator. This empiric finding has shaped our mechanistic understanding of how perturbations start, stop, and reset biological oscillators and has led to treatments that suppress pathological oscillations using electrical pulses given within specified therapeutic phase windows. Here, we present evidence, using numerical simulations of models of epileptic seizures and reentrant tachycardia, that the phase window can be opened to the entire cycle using novel complex stimulus waveforms.

Falling off a limit cycle using phase-agnostic stimuli: Definitions and conceptual framework.

Nearly a half-century of biomedical research has revealed methods and mechanisms by which an oscillator with bistable limit cycle kinetics can be stopped using critical stimuli applied at a specific phase. Is it possible to construct a stimulus that stops oscillation regardless of the phase at which the stimulus is applied? Using a radial isochron clock model, we demonstrate the existence of such stimulus waveforms, which can take on highly complex shapes but with a surprisingly simple mechanism of rhythm suppression. The perturbation, initiated at any phase of the limit cycle, first corrals the oscillator to a narrow range of new phases, then drives the oscillator to its phase singularity.

Automated movement detection reveals features of maturation in preterm infants.

Nearly 10% of all births in the United States are preterm. Preterm birth is a major risk for developmental neuromotor disorders. Early characterization of a future developmental outcome is necessary to design early interventions.

The Internal Superior Laryngeal Nerve in Humans: Evidence for Pure Sensory Function.

Objectives: To determine if the internal branch of the superior laryngeal nerve (iSLN) provides direct motor innervation to the interarytenoid muscle, a laryngeal adductor critical for airway protection. We studied the iSLN-evoked motor response in the interarytenoid and other laryngeal muscles. If the iSLN is purely sensory, there will be no detectable short latency motor response upon supramaximal stimulation, indicating the absence of a direct efferent conduction path.

Applying Machine Learning Algorithms for Automatic Detection of Swallowing from Sound.

Despite the severe consequences of dysfunctional swallowing, there is no simple method of monitoring swallowing outside of clinical settings. People who cannot swallow cannot eat safely, resulting in profound changes in quality of life and risk of death from aspiration pneumonia. A non-invasive swallowing detector may have widespread impact in both clinical care and research.

Explaining Deep Classification of Time-Series Data with Learned Prototypes.

The emergence of deep learning networks raises a need for explainable AI so that users and domain experts can be confident applying them to high-risk decisions. In this paper, we leverage data from the latent space induced by deep learning models to learn stereotypical representations or "prototypes" during training to elucidate the algorithmic decision-making process. We study how leveraging prototypes effect classification decisions of two dimensional time-series data in a few different settings: (1) electrocardiogram (ECG) waveforms to detect clinical bradycardia, a slowing of heart rate, in preterm infants, (2) respiration waveforms to detect apnea of prematurity, and (3) audio waveforms to classify spoken digits.

Sensory dysphagia: A case series and proposed classification of an under recognized swallowing disorder.

Background: Although sensory feedback is a vital regulator of deglutition, it is not comprehensively considered in the standard dysphagia evaluation. Difficulty swallowing secondary to sensory loss may be termed "sensory dysphagia" and may account for cases receiving diagnoses of exclusion, like functional or idiopathic dysphagia.

Methods And Results: Three cases of idiopathic dysphagia were suspected to have sensory dysphagia.

Optimizing stimulus waveforms for suppressing epileptic activity reveals a counterbalancing mechanism.

Electrical stimulation is used to treat drug- resistant epilepsy, and excessive stimulation can lead to adverse effects for patients. In this article, we use an extrema featured stochastic search algorithm to find energy-efficient stimulus waveforms that suppress seizure activity in two different computational models of epilepsy. We infer general principles that may provide insight into future design of energy efficient stimulus for epilepsy treatments.

Bayesian Online Changepoint Detection Of Physiological Transitions.

Transition dynamics between two states can help elucidate the behavior of sequential events in physiological signals. By detecting transitions between healthy and pathological states within individual patients, we can help clinicians focus attention on critical transitions, to either preemptively treat adverse events or to detect changes resulting from treatments. We introduce a novel application of singlepoint Bayesian online changepoint detection to predict clinical state transitions, and apply this framework to detecting pathological transitions in preterm infants with episodes of apnea and bradycardia.