Cardiovascular disease is a deadly global health crisis that carries a substantial financial burden. Innovative treatment and management of cardiovascular disease straddles medicine, personalized hemodynamic modeling, machine learning, and modern imaging to help improve patient outcomes and reduce the economic impact. Hemodynamic modeling offers a non-invasive method to provide clinicians with new pre- and post- procedural metrics and aid in the selection of treatment options. Medical imaging is an integral part in clinical workflows for understanding and managing cardiac disease and interventions. Coupling machine learning with modeling, and cardiovascular imaging, provides faster modeling, improved data fidelity, and an enhanced understanding and earlier detection of cardiovascular anomalies, leading to the development of patient-specific diagnostic and predictive tools for characterizing and assessing cardiovascular outcomes. Herein, we provide a scoping review of translational hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. We particularly focus on providing an intuitive understanding of each of these approaches and their ability to support decision making during important clinical milestones.

There is a need for improved understanding of how different cerebrovascular reactivity (CVR) protocols affect vascular cross-sectional area (CSA) to reduce error in CVR calculations when measures of vascular CSA are not feasible. In human participants, we delivered ∼±4 mm Hg end-tidal partial pressure of CO₂ (P_ETCO₂) relative to baseline through controlled delivery, and measured changes in middle cerebral artery (MCA) CSA (7 Tesla magnetic resonance imaging (MRI)), blood velocity (transcranial Doppler and Phase contrast MRI), and calculated CVR based on a 3-minute steady-state (+4 mm Hg P_ETCO₂) and a ramp (−3 to +4 mm Hg of P_ETCO₂). We observed that (1) the MCA did not dilate during the ramp protocol (slope for CSA across time P > 0.05; R² = 0.006), but did dilate by ∼7% during steady-state hypercapnia (P < 0.05); and (2) MCA blood velocity CVR was not different between ramp and steady-state hypercapnia protocols (ramp: 3.8 ± 1.7 vs. steady-state: 4.0 ± 1.6 cm/s/mm Hg), although calculated MCA blood flow CVR was ∼40% greater during steady-state hypercapnia than during ramp (P < 0.05) with the discrepancy due to MCA CSA changes during steady-state hypercapnia. We propose that a ramp model, across a delta of −3 to +4 mm Hg P_ETCO₂, may provide an alternative approach to collecting CVR measures in young adults with transcranial Doppler when CSA measures are not feasible. Novelty: We optimized a magnetic resonance imaging sequence to measure dynamic middle cerebral artery (MCA) cross-sectional area (CSA). A ramp model of hypercapnia elicited similar MCA blood velocity reactivity as the steady-state model while maintaining MCA CSA.

This study evaluated ratings of vocal strain and perceived listening effort by normal hearing participants while listening to speech samples produced by talkers with adductor spasmodic dysphonia (AdSD). In addition, objective listening effort was measured through concurrent pupillometry to determine whether listening to disordered voices changed arousal as a result of emotional state or cognitive load. Recordings of the second sentence of the “Rainbow Passage” produced by talkers with varying degrees of AdSD served as speech stimuli. Twenty naïve young adult listeners perceptually evaluated these stimuli on the dimensions of vocal strain and listening effort using two separate visual analogue scales. While making the auditory-perceptual judgments, listeners’ pupil characteristics were objectively measured in synchrony with the presentation of each voice stimulus. Data analyses revealed moderate-to-high inter- and intra-rater reliability. A significant positive correlation was found between the ratings of vocal strain and listening effort. In addition, listeners displayed greater peak pupil dilation (PPD) when listening to more strained and effortful voice samples. Findings from this study suggest that when combined with an auditory-perceptual task, non-volitional physiologic changes in pupil response may serve as an indicator of listening and cognitive effort or arousal.

Global cerebrovascular reactivity to hypercapnia is lower across nine intracranial arteries in individuals with osteoarthritis (OA) compared to those without OA. As well, middle cerebral artery flow ...

We conducted a scoping review to map the rapidly evolving landscape of wearable and ambient sensing technologies for monitoring people with dementia across home and institutional settings. We analyzed empirical sensing studies (2015–2025) to identify and inform future technical and human-centered design requirements. Five key implementation principles emerge: 1) human-centered design involving all stakeholders to augment rather than replace caregivers; 2) personalized, adaptable solutions that support autonomy across settings and severity levels instead of standardized approaches; 3) integration with existing workflows with adequate training and support; 4) proactive privacy and consent considerations, especially for ambient monitoring of residents and caregivers; and 5) cost-effective, ethical, equitable, scalable solutions with quantifiable outcomes. This article identifies gaps, trends, and opportunities for developing sensing systems that address the complex challenges while enhancing automation and autonomy in dementia care.