Development and validation of a deep-learning model to predict 10-year atherosclerotic cardiovascular disease risk from retinal images using the UK Biobank and EyePACS 10K datasets.

Background: Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death globally, and early detection of high-risk individuals is essential for initiating timely interventions. The authors aimed to develop and validate a deep learning (DL) model to predict an individual's elevated 10-year ASCVD risk score based on retinal images and limited demographic data. Methods: The study used 89,894 retinal fundus images from 44,176 UK Biobank participants (96% non-Hispanic White, 5% diabetic) to train and test the DL model. The DL model was developed using retinal images plus age, race/ethnicity, and sex at birth to predict an individual's 10-year ASCVD risk score using the pooled cohort equation (PCE) as the ground truth. This model was then tested on the US EyePACS 10K dataset (5.8% non-Hispanic White, 99.9% diabetic), composed of 18,900 images from 8969 diabetic individuals. Elevated ASCVD risk was defined as a PCE score of ≥7.5%. Results: In the UK Biobank internal validation dataset, the DL model achieved an area under the receiver operating characteristic curve of 0.89, sensitivity 84%, and specificity 90%, for detecting individuals with elevated ASCVD risk scores. In the EyePACS 10K and with the addition of a regression-derived diabetes modifier, it achieved sensitivity 94%, specificity 72%, mean error -0.2%, and mean absolute error 3.1%. Conclusion: This study demonstrates that DL models using retinal images can provide an additional approach to estimating ASCVD risk, as well as the value of applying DL models to different external datasets and opportunities about ASCVD risk assessment in patients living with diabetes.

The Echocardiographic Evaluation of the Right Heart: Current and Future Advances.

PURPOSE OF REVIEW: To discuss physiologic and methodologic advances in the echocardiographic assessment of right heart (RH) function, including the emergence of artificial intelligence (AI) and point-of-care ultrasound. RECENT FINDINGS: Recent studies have highlighted the prognostic value of right ventricular (RV) longitudinal strain, RV end-systolic dimensions, and right atrial (RA) size and function in pulmonary hypertension and heart failure. While RA pressure is a central marker of right heart diastolic function, the recent emphasis on venous excess imaging (VExUS) has provided granularity to the systemic consequences of RH failure. Several methodological advances are also changing the landscape of RH imaging including post-processing 3D software to delineate the non-longitudinal (radial, anteroposterior, and circumferential) components of RV function, as well as AI segmentation- and non-segmentation-based quantification. Together with recent guidelines and advances in AI technology, the field is shifting from specific RV functional metrics to integrated RH disease-specific phenotypes. A modern echocardiographic evaluation of RH function should focus on the entire cardiopulmonary venous unit-from the venous to the pulmonary arterial system. Together, a multi-parametric approach, guided by physiology and AI algorithms, will help define novel integrated RH profiles for improved disease detection and monitoring.

Personalized digital behaviour interventions increase short-term physical activity: a randomized control crossover trial substudy of the MyHeart Counts Cardiovascular Health Study.

Aims: Physical activity is associated with decreased incidence of the chronic diseases associated with aging. We previously demonstrated that digital interventions delivered through a smartphone app can increase short-term physical activity. Methods and results: We offered enrolment to community-living iPhone-using adults aged ≥18 years in the USA, UK, and Hong Kong who downloaded the MyHeart Counts app. After completion of a 1-week baseline period, e-consented participants were randomized to four 7-day interventions. Interventions consisted of: (i) daily personalized e-coaching based on the individual's baseline activity patterns, (ii) daily prompts to complete 10 000 steps, (iii) hourly prompts to stand following inactivity, and (iv) daily instructions to read guidelines from the American Heart Association (AHA) website. After completion of one 7-day intervention, participants subsequently randomized to the next intervention of the crossover trial. The trial was completed in a free-living setting, where neither the participants nor investigators were blinded to the intervention. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in a modified intention-to-treat analysis (modified in that participants had to complete 7 days of baseline monitoring and at least 1 day of an intervention to be included in analyses). This trial is registered with ClinicalTrials.gov, NCT03090321. Conclusion: Between 1 January 2017 and 1 April 2022, 4500 participants consented to enrol in the trial (a subset of the approximately 50 000 participants in the larger MyHeart Counts study), of whom 2458 completed 7 days of baseline monitoring (mean daily steps 4232 ± 73) and at least 1 day of one of the four interventions. Personalized e-coaching prompts, tailored to an individual based on their baseline activity, increased step count significantly (+402 ± 71 steps from baseline, P = 7.1⨯10-8). Hourly stand prompts (+292 steps from baseline, P = 0.00029) and a daily prompt to read AHA guidelines (+215 steps from baseline, P = 0.021) were significantly associated with increased mean daily step count, while a daily reminder to complete 10 000 steps was not (+170 steps from baseline, P = 0.11). Digital studies have a significant advantage over traditional clinical trials in that they can continuously recruit participants in a cost-effective manner, allowing for new insights provided by increased statistical power and refinement of prior signals. Here, we present a novel finding that digital interventions tailored to an individual are effective in increasing short-term physical activity in a free-living cohort. These data suggest that participants are more likely to react positively and increase their physical activity when prompts are personalized. Further studies are needed to determine the effects of digital interventions on long-term outcomes.

Detection of Atrial Fibrillation in a Large Population Using Wearable Devices: The Fitbit Heart Study.

BACKGROUND: Morbidity from undiagnosed atrial fibrillation (AF) may be preventable with early detection. Many consumer wearables contain optical photoplethysmography (PPG) sensors to measure pulse rate. PPG-based software algorithms that detect irregular heart rhythms may identify undiagnosed AF in large populations using wearables, but minimizing false-positive detections is essential. METHODS: We performed a prospective remote clinical trial to examine a novel PPG-based algorithm for detecting undiagnosed AF from a range of wrist-worn devices. Adults aged ≥22 years in the United States without AF, using compatible wearable Fitbit devices and Android or iOS smartphones, were included. PPG data were analyzed using a novel algorithm that examines overlapping 5-minute pulse windows (tachograms). Eligible participants with an irregular heart rhythm detection (IHRD), defined as 11 consecutive irregular tachograms, were invited to schedule a telehealth visit and were mailed a 1-week ambulatory ECG patch monitor. The primary outcome was the positive predictive value of the first IHRD during ECG patch monitoring for concurrent AF. RESULTS: A total of 455 699 participants enrolled (median age 47 years, 71% female, 73% White) between May 6 and October 1, 2020. IHRDs occurred for 4728 (1%) participants, and 2070 (4%) participants aged ≥65 years during a median of 122 (interquartile range, 110-134) days at risk for an IHRD. Among 1057 participants with an IHRD notification and subsequent analyzable ECG patch monitor, AF was present in 340 (32.2%). Of the 225 participants with another IHRD during ECG patch monitoring, 221 had concurrent AF on the ECG and 4 did not, resulting in an IHRD positive predictive value of 98.2% (95% CI, 95.5%-99.5%). For participants aged ≥65 years, the IHRD positive predictive value was 97.0% (95% CI, 91.4%-99.4%). CONCLUSIONS: A novel PPG software algorithm for wearable Fitbit devices exhibited a high positive predictive value for concurrent AF and identified participants likely to have AF on subsequent ECG patch monitoring. Wearable devices may facilitate identifying individuals with undiagnosed AF. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04380415.

Prospective validation of smartphone-based heart rate and respiratory rate measurement algorithms.

Background: Measuring vital signs plays a key role in both patient care and wellness, but can be challenging outside of medical settings due to the lack of specialized equipment. Methods: In this study, we prospectively evaluated smartphone camera-based techniques for measuring heart rate (HR) and respiratory rate (RR) for consumer wellness use. HR was measured by placing the finger over the rear-facing camera, while RR was measured via a video of the participants sitting still in front of the front-facing camera. Results: In the HR study of 95 participants (with a protocol that included both measurements at rest and post exercise), the mean absolute percent error (MAPE) ± standard deviation of the measurement was 1.6% ± 4.3%, which was significantly lower than the pre-specified goal of 5%. No significant differences in the MAPE were present across colorimeter-measured skin-tone subgroups: 1.8% ± 4.5% for very light to intermediate, 1.3% ± 3.3% for tan and brown, and 1.8% ± 4.9% for dark. In the RR study of 50 participants, the mean absolute error (MAE) was 0.78 ± 0.61 breaths/min, which was significantly lower than the pre-specified goal of 3 breaths/min. The MAE was low in both healthy participants (0.70 ± 0.67 breaths/min), and participants with chronic respiratory conditions (0.80 ± 0.60 breaths/min). Conclusions: These results validate the accuracy of our smartphone camera-based techniques to measure HR and RR across a range of pre-defined subgroups.

Wearables, telemedicine, and artificial intelligence in arrhythmias and heart failure: Proceedings of the European Society of Cardiology Cardiovascular Round Table.

Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

Ultra-selective carbon nanotubes for photoacoustic imaging of inflamed atherosclerotic plaques.

Disruption of vulnerable atherosclerotic plaques often leads to myocardial infarction and stroke, the leading causes of morbidity and mortality in the United States. A diagnostic method that detects high-risk atherosclerotic plaques at early stages could prevent these sequelae. The abundance of immune cells in the arterial wall, especially inflammatory Ly-6Chi monocytes and foamy macrophages, is indicative of plaque inflammation, and may be associated with plaque vulnerability. Hence, we sought to develop a new method that specifically targets these immune cells to offer clinically-relevant diagnostic information about cardiovascular disease. We combine ultra-selective nanoparticle targeting of Ly-6Chi monocytes and foamy macrophages with clinically-viable photoacoustic imaging (PAI) in order to precisely and specifically image inflamed plaques ex vivo in a mouse model that mimics human vulnerable plaques histopathologically. Within the plaques, high-dimensional single-cell flow cytometry (13-parameter) showed that our nanoparticles were almost-exclusively taken up by the Ly-6Chi monocytes and foamy macrophages that heavily infiltrate plaques. PAI identified inflamed atherosclerotic plaques that display ~6-fold greater signal compared to controls (P<0.001) six hours after intravenous injection of ultra-selective carbon nanotubes, with in vivo corroboration via optical imaging. Our highly selective strategy may provide a targeted, non-invasive imaging strategy to accurately identify and diagnose inflamed atherosclerotic lesions.

Smartphone-Based VO2max Measurement With Heart Snapshot in Clinical and Real-world Settings With a Diverse Population: Validation Study.

BACKGROUND: Maximal oxygen consumption (VO2max) is one of the most predictive biometrics for cardiovascular health and overall mortality. However, VO2max is rarely measured in large-scale research studies or routine clinical care because of the high cost, participant burden, and requirement for specialized equipment and staff. OBJECTIVE: To overcome the limitations of clinical VO2max measurement, we aim to develop a digital VO2max estimation protocol that can be self-administered remotely using only the sensors within a smartphone. We also aim to validate this measure within a broadly representative population across a spectrum of smartphone devices. METHODS: Two smartphone-based VO2max estimation protocols were developed: a 12-minute run test (12-MRT) based on distance measured by GPS and a 3-minute step test (3-MST) based on heart rate recovery measured by a camera. In a 101-person cohort, balanced across age deciles and sex, participants completed a gold standard treadmill-based VO2max measurement, two silver standard clinical protocols, and the smartphone-based 12-MRT and 3-MST protocols in the clinic and at home. In a separate 120-participant cohort, the video-based heart rate measurement underlying the 3-MST was measured for accuracy in individuals across the spectrum skin tones while using 8 different smartphones ranging in cost from US $99 to US $999. RESULTS: When compared with gold standard VO2max testing, Lin concordance was pc=0.66 for 12-MRT and pc=0.61 for 3-MST. However, in remote settings, the 12-MRT was significantly less concordant with the gold standard (pc=0.25) compared with the 3-MST (pc=0.61), although both had high test-retest reliability (12-MRT intraclass correlation coefficient=0.88; 3-MST intraclass correlation coefficient=0.86). On the basis of the finding that 3-MST concordance was generalizable to remote settings whereas 12-MRT was not, the video-based heart rate measure within the 3-MST was selected for further investigation. Heart rate measurements in any of the combinations of the six Fitzpatrick skin tones and 8 smartphones resulted in a concordance of pc≥0.81. Performance did not correlate with device cost, with all phones selling under US $200 performing better than pc>0.92. CONCLUSIONS: These findings demonstrate the importance of validating mobile health measures in the real world across a diverse cohort and spectrum of hardware. The 3-MST protocol, termed as heart snapshot, measured VO2max with similar accuracy to supervised in-clinic tests such as the Tecumseh (pc=0.94) protocol, while also generalizing to remote and unsupervised measurements. Heart snapshot measurements demonstrated fidelity across demographic variation in age and sex, across diverse skin pigmentation, and between various iOS and Android phone configurations. This software is freely available for all validation data and analysis code.

Modeling the effect of exposure notification and non-pharmaceutical interventions on COVID-19 transmission in Washington state.

Contact tracing is increasingly used to combat COVID-19, and digital implementations are now being deployed, many based on Apple and Google's Exposure Notification System. These systems utilize non-traditional smartphone-based technology, presenting challenges in understanding possible outcomes. In this work, we create individual-based models of three Washington state counties to explore how digital exposure notifications combined with other non-pharmaceutical interventions influence COVID-19 disease spread under various adoption, compliance, and mobility scenarios. In a model with 15% participation, we found that exposure notification could reduce infections and deaths by approximately 8% and 6% and could effectively complement traditional contact tracing. We believe this can provide health authorities in Washington state and beyond with guidance on how exposure notification can complement traditional interventions to suppress the spread of COVID-19.

Bending the Curve in Cardiovascular Disease Mortality: Bethesda + 40 and Beyond.

More than 40 years after the 1978 Bethesda Conference on the Declining Mortality from Coronary Heart Disease provided the scientific community with a blueprint for systematic analysis to understand declining rates of coronary heart disease, there are indications the decline has ended or even reversed despite advances in our knowledge about the condition and treatment. Recent data show a more complex situation, with mortality rates for overall cardiovascular disease, including coronary heart disease and stroke, decelerating, whereas those for heart failure are increasing. To mark the 40th anniversary of the Bethesda Conference, the National Heart, Lung, and Blood Institute and the American Heart Association cosponsored the "Bending the Curve in Cardiovascular Disease Mortality: Bethesda + 40" symposium. The objective was to examine the immediate and long-term outcomes of the 1978 conference and understand the current environment. Symposium themes included trends and future projections in cardiovascular disease (in the United States and internationally), the evolving obesity and diabetes epidemics, and harnessing emerging and innovative opportunities to preserve and promote cardiovascular health and prevent cardiovascular disease. In addition, participant-led discussion explored the challenges and barriers in promoting cardiovascular health across the lifespan and established a potential framework for observational research and interventions that would begin in early childhood (or ideally in utero). This report summarizes the relevant research, policy, and practice opportunities discussed at the symposium.

The American Heart Association 2030 Impact Goal: A Presidential Advisory From the American Heart Association.

Each decade, the American Heart Association (AHA) develops an Impact Goal to guide its overall strategic direction and investments in its research, quality improvement, advocacy, and public health programs. Guided by the AHA's new Mission Statement, to be a relentless force for a world of longer, healthier lives, the 2030 Impact Goal is anchored in an understanding that to achieve cardiovascular health for all, the AHA must include a broader vision of health and well-being and emphasize health equity. In the next decade, by 2030, the AHA will strive to equitably increase healthy life expectancy beyond current projections, with global and local collaborators, from 66 years of age to at least 68 years of age across the United States and from 64 years of age to at least 67 years of age worldwide. The AHA commits to developing additional targets for equity and well-being to accompany this overarching Impact Goal. To attain the 2030 Impact Goal, we recommend a thoughtful evaluation of interventions available to the public, patients, providers, healthcare delivery systems, communities, policy makers, and legislators. This presidential advisory summarizes the task force's main considerations in determining the 2030 Impact Goal and the metrics to monitor progress. It describes the aspiration that these goals will be achieved by working with a diverse community of volunteers, patients, scientists, healthcare professionals, and partner organizations needed to ensure success.

Ferumoxytol-enhanced cardiovascular magnetic resonance detection of early stage acute myocarditis.

BACKGROUND: The diagnostic utility of cardiovascular magnetic resonance (CMR) is limited during the early stages of myocarditis. This study examined whether ferumoxytol-enhanced CMR (FE-CMR) could detect an earlier stage of acute myocarditis compared to gadolinium-enhanced CMR. METHODS: Lewis rats were induced to develop autoimmune myocarditis. CMR (3 T, GE Signa) was performed at the early- (day 14, n = 7) and the peak-phase (day 21, n = 8) of myocardial inflammation. FE-CMR was evaluated as % myocardial dephasing signal loss on gradient echo images at 6 and 24 h (6 h- & 24 h-FE-CMR) following the administration of ferumoxytol (300µmolFe/kg). Pre- and post-contrast T2* mapping was also performed. Early (EGE) and late (LGE) gadolinium enhancement was obtained after the administration of gadolinium-DTPA (0.5 mmol/kg) on day 14 and 21. Healthy rats were used as control (n = 6). RESULTS: Left ventricular ejection fraction (LVEF) was preserved at day 14 with inflammatory cells but no fibrosis seen on histology. EGE and LGE at day 14 both showed limited myocardial enhancement (EGE: 11.7 ± 15.5%; LGE: 8.7 ± 8.7%; both p = ns vs. controls). In contrast, 6 h-FE-CMR detected extensive myocardial signal loss (33.2 ± 15.0%, p = 0.02 vs. EGE and p < 0.01 vs. LGE). At day 21, LVEF became significantly decreased (47.4 ± 16.4% vs control: 66.2 ± 6.1%, p < 0.01) with now extensive myocardial involvement detected on EGE, LGE, and 6 h-FE-CMR (41.6 ± 18.2% of LV). T2* mapping also detected myocardial uptake of ferumoxytol both at day 14 (6 h R2* = 299 ± 112 s- 1vs control: 125 ± 26 s- 1, p < 0.01) and day 21 (564 ± 562 s- 1, p < 0.01 vs control). Notably, the myocardium at peak-phase myocarditis also showed significantly higher pre-contrast T2* (27 ± 5 ms vs control: 16 ± 1 ms, p < 0.001), and the extent of myocardial necrosis had a strong positive correlation with T2* (r = 0.86, p < 0.001). CONCLUSIONS: FE-CMR acquired at 6 h enhance detection of early stages of myocarditis before development of necrosis or fibrosis, which could potentially enable appropriate therapeutic intervention.

Deep learning predicts hip fracture using confounding patient and healthcare variables.

Hip fractures are a leading cause of death and disability among older adults. Hip fractures are also the most commonly missed diagnosis on pelvic radiographs, and delayed diagnosis leads to higher cost and worse outcomes. Computer-aided diagnosis (CAD) algorithms have shown promise for helping radiologists detect fractures, but the image features underpinning their predictions are notoriously difficult to understand. In this study, we trained deep-learning models on 17,587 radiographs to classify fracture, 5 patient traits, and 14 hospital process variables. All 20 variables could be individually predicted from a radiograph, with the best performances on scanner model (AUC = 1.00), scanner brand (AUC = 0.98), and whether the order was marked "priority" (AUC = 0.79). Fracture was predicted moderately well from the image (AUC = 0.78) and better when combining image features with patient data (AUC = 0.86, DeLong paired AUC comparison, p = 2e-9) or patient data plus hospital process features (AUC = 0.91, p = 1e-21). Fracture prediction on a test set that balanced fracture risk across patient variables was significantly lower than a random test set (AUC = 0.67, DeLong unpaired AUC comparison, p = 0.003); and on a test set with fracture risk balanced across patient and hospital process variables, the model performed randomly (AUC = 0.52, 95% CI 0.46-0.58), indicating that these variables were the main source of the model's fracture predictions. A single model that directly combines image features, patient, and hospital process data outperforms a Naive Bayes ensemble of an image-only model prediction, patient, and hospital process data. If CAD algorithms are inexplicably leveraging patient and process variables in their predictions, it is unclear how radiologists should interpret their predictions in the context of other known patient data. Further research is needed to illuminate deep-learning decision processes so that computers and clinicians can effectively cooperate.

Physical activity, sleep and cardiovascular health data for 50,000 individuals from the MyHeart Counts Study.

Studies have established the importance of physical activity and fitness for long-term cardiovascular health, yet limited data exist on the association between objective, real-world large-scale physical activity patterns, fitness, sleep, and cardiovascular health primarily due to difficulties in collecting such datasets. We present data from the MyHeart Counts Cardiovascular Health Study, wherein participants contributed data via an iPhone application built using Apple's ResearchKit framework and consented to make this data available freely for further research applications. In this smartphone-based study of cardiovascular health, participants recorded daily physical activity, completed health questionnaires, and performed a 6-minute walk fitness test. Data from English-speaking participants aged 18 years or older with a US-registered iPhone who agreed to share their data broadly and who enrolled between the study's launch and the time of the data freeze for this data release (March 10 2015-October 28 2015) are now available for further research. It is anticipated that releasing this large-scale collection of real-world physical activity, fitness, sleep, and cardiovascular health data will enable the research community to work collaboratively towards improving our understanding of the relationship between cardiovascular indicators, lifestyle, and overall health, as well as inform mobile health research best practices.

In Vivo Translation of the CIRPI System: Revealing Molecular Pathology of Rabbit Aortic Atherosclerotic Plaques.

Thin-cap fibroatheroma (TCFA) are the unstable lesions in coronary artery disease that are prone to rupture, resulting in substantial morbidity and mortality worldwide. However, their small size and complex morphologic and biologic features make early detection and risk assessment difficult. We tested our newly developed catheter-based Circumferential-Intravascular-Radioluminescence-Photoacoustic-Imaging (CIRPI) system in vivo to enable detection and characterization of vulnerable plaque structure and biology in rabbit abdominal aorta. Methods: The CIRPI system includes a novel optical probe combining circumferential radioluminescence imaging and photoacoustic tomography (PAT). The probe's CaF2:Eu-based scintillating imaging window captures radioluminescence images (360° view) of plaques by detecting ß-particles during 18F-FDG decay. A tunable laser-based PAT characterizes tissue constituents of plaque at 7 different wavelengths-540 and 560 nm (calcification), 920 nm (cholesteryl ester), 1040 nm (phospholipids), 1180 nm (elastin/collagen), 1210 nm (cholesterol), and 1235 nm (triglyceride). A single B-scan is concatenated from 330 A-lines captured during a 360° rotation. The abdominal aorta was imaged in vivo in both atherosclerotic rabbits (Watanabe Heritable Hyper Lipidemic [WHHL], 13-mo-old male, n = 5) and controls (New Zealand White, n = 2). Rabbits were fasted for 6 h before 5.55 × 107 Bq (1.5 mCi) of 18F-FDG were injected 1 h before the imaging procedure. Rabbits were anesthetized, and the right or left common carotid artery was surgically exposed. An 8 French catheter sheath was inserted into the common carotid artery, and a 0.035-cm (0.014-in) guidewire was advanced to the iliac artery, guided by x-ray fluoroscopy. A bare metal stent was implanted in the dorsal abdominal aorta as a landmark, followed by the 7 French imaging catheters that were advanced up to the proximal stent edge. Our CIRPI and clinical optical coherence tomography (OCT) were performed using pullback and nonocclusive flushing techniques. After imaging with the CIRPI system, the descending aorta was flushed with contrast agent, and OCT images were obtained with a pullback speed of 20 mm/s, providing images at 100 frames/s. Results were verified with histochemical analysis. Results: Our CIRPI system successfully detected the locations and characterized both stable and vulnerable aortic plaques in vivo among all WHHL rabbits. Calcification was detected from the stable plaque (540 and 560 nm), whereas TCFA exhibited phospholipids/cholesterol (1040 nm, 1210 nm). These findings were further verified with the clinical OCT system showing an area of low attenuation filled with lipids within TCFA. PAT images illustrated broken elastic fiber/collagen that could be verified with the histochemical analysis. All WHHL rabbits exhibited sparse to severe macrophages. Only 4 rabbits showed both moderate-to-severe level of calcifications and cholesterol clefts. However, all rabbits exhibited broken elastic fibers and collagen deposition. Control rabbits showed normal wall thickness with no presence of plaque tissue compositions. These findings were verified with OCT and histochemical analysis. Conclusion: Our novel multimodality hybrid system has been successfully translated to in vivo evaluation of atherosclerotic plaque structure and biology in a preclinical rabbit model. This system proposed a paradigm shift that unites molecular and pathologic imaging technologies. Therefore, the system may enhance the clinical evaluation of TCFA, as well as expand our understanding of coronary artery disease.

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

MOTIVATION: Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. RESULTS: We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. AVAILABILITY AND IMPLEMENTATION: Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.

The effect of digital physical activity interventions on daily step count: a randomised controlled crossover substudy of the MyHeart Counts Cardiovascular Health Study.

BACKGROUND: Smartphone apps might enable interventions to increase physical activity, but few randomised trials testing this hypothesis have been done. The MyHeart Counts Cardiovascular Health Study is a longitudinal smartphone-based study with the aim of elucidating the determinants of cardiovascular health. We aimed to investigate the effect of four different physical activity coaching interventions on daily step count in a substudy of the MyHeart Counts Study. METHODS: In this randomised, controlled crossover trial, we recruited adults (aged ≥18 years) in the USA with access to an iPhone smartphone (Apple, Cupertino, CA, USA; version 5S or newer) who had downloaded the MyHeart Counts app (version 2.0). After completion of a 1 week baseline period of interaction with the MyHeart Counts app, participants were randomly assigned to receive one of 24 permutations (four combinations of four 7 day interventions) in a crossover design using a random number generator built into the app. Interventions consisted of either daily prompts to complete 10 000 steps, hourly prompts to stand following 1 h of sitting, instructions to read the guidelines from the American Heart Association website, or e-coaching based upon the individual's personal activity patterns from the baseline week of data collection. Participants completed the trial in a free-living setting. Due to the nature of the interventions, participants could not be masked from the intervention. Investigators were not masked to intervention allocation. The primary outcome was change in mean daily step count from baseline for each of the four interventions, assessed in the modified intention-to-treat analysis set, which included all participants who had completed 7 days of baseline monitoring and at least 1 day of one of the four interventions. This trial is registered with ClinicalTrials.gov, NCT03090321. FINDINGS: Between Dec 12, 2016, and June 6, 2018, 2783 participants consented to enrol in the coaching study, of whom 1075 completed 7 days of baseline monitoring and at least 1 day of one of the four interventions and thus were included in the modified intention-to-treat analysis set. 493 individuals completed the full set of assigned interventions. All four interventions significantly increased mean daily step count from baseline (mean daily step count 2914 [SE 74]): mean step count increased by 319 steps (75) for participants in the American Heart Association website prompt group (p<0·0001), 267 steps (74) for participants in the hourly stand prompt group (p=0·0003), 254 steps (74) for participants in the cluster-specific prompts group (p=0·0006), and by 226 steps (75) for participants in the 10 000 daily step prompt group (p=0·0026 vs baseline). INTERPRETATION: Four smartphone-based physical activity coaching interventions significantly increased daily physical activity. These findings suggests that digital interventions delivered via a mobile app have the ability to increase short-term physical activity levels in a free-living cohort. FUNDING: Stanford Data Science Initiative.

Sexual Dimorphism of Coronary Artery Disease in a Low- and Intermediate-Risk Asymptomatic Population: Association with Coronary Vessel Wall Thickness at MRI in Women.

PURPOSE: To demonstrate the association between coronary vessel wall thickness (VWT) measured at MRI and coronary artery disease (CAD) risk in asymptomatic groups at low and intermediate risk on the basis of Framingham score. MATERIALS AND METHODS: A total of 131 asymptomatic adults were prospectively enrolled. All participants underwent CT angiography for scoring CAD, and coronary VWT was measured at 3.0-T MRI. Nonlinear single and multivariable regression analyses with consideration for interaction with sex were performed to investigate the association of traditional atherosclerotic risk factors and VWT with CT angiography-based CAD scores. RESULTS: The analysis included 62 women and 62 men with low or intermediate Framingham score of less than 20%. Age (mean age, 45.0 years ± 14.5 [standard deviation]) and body mass index were not different between the groups. Age, sex, and VWT were individually significantly associated with all CT angiography-based CAD scores (P < .05). Additionally, sex was a significant effect modifier of the associations with all CAD scores. In men, age was the only statistically significant independent risk factor of CAD; in women, VWT was the only statistically significant independent surrogate associated with increased CAD scores (P < .05). CONCLUSION: In asymptomatic women, VWT MRI was the primary independent surrogate of CAD, whereas age was the strongest risk factor in men. This study suggests that VWT may be used as a CAD surrogate in women at low or intermediate risk of CAD. Further longitudinal studies are required to determine the potential implication and use of this MRI technique for the preventative management of CAD in women.© RSNA, 2019.

Finger-Wearable Blood Pressure Monitor.

Convenient and painless blood pressure measurement can enable increased user adoption of regular monitoring and early intervention for hypertension, which is a significant cause of mortality worldwide. This paper introduces a fingerwearable blood pressure measurement device to enable frequent daytime and nocturnal monitoring. The blood pressure measurement is achieved using a two-dimensional capacitive tactile sensor array that is located next to a digital artery. A pumpdriven pneumatic bladder presses the tactile array and the finger towards each other to obtain a pressure sweep versus time. The digital artery pressure waveform data collected during this sweep are used to estimate arterial blood pressure. A clinical study (N =97) was conducted to obtain training (N =49) and validation (N =19) data for blood pressure algorithm development and test (N =29) data to determine the estimation accuracy compared to brachial dual-observer auscultation. On the test set, the mean and standard deviation of the error in the systolic blood pressure estimate are 0.9 mmHg and 6.9 mmHg, respectively, while the corresponding quantities for diastolic blood pressure are -3.2 mmHg and 7.0 mmHg, respectively. These results compare favorably to blood pressure accuracy requirements specified by international standards.