Machine Learning Models for Pancreatic Cancer Risk Prediction Using Electronic Health Record Data-A Systematic Review and Assessment.

INTRODUCTION: Accurate risk prediction can facilitate screening and early detection of pancreatic cancer (PC). We conducted a systematic review to critically evaluate effectiveness of machine learning (ML) and artificial intelligence (AI) techniques applied to electronic health records (EHR) for PC risk prediction. METHODS: Ovid MEDLINE(R), Ovid EMBASE, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science were searched for articles that utilized ML/AI techniques to predict PC, published between January 1, 2012, and February 1, 2024. Study selection and data extraction were conducted by 2 independent reviewers. Critical appraisal and data extraction were performed using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies checklist. Risk of bias and applicability were examined using prediction model risk of bias assessment tool. RESULTS: Thirty studies including 169,149 PC cases were identified. Logistic regression was the most frequent modeling method. Twenty studies utilized a curated set of known PC risk predictors or those identified by clinical experts. ML model discrimination performance (C-index) ranged from 0.57 to 1.0. Missing data were underreported, and most studies did not implement explainable-AI techniques or report exclusion time intervals. DISCUSSION: AI/ML models for PC risk prediction using known risk factors perform reasonably well and may have near-term applications in identifying cohorts for targeted PC screening if validated in real-world data sets. The combined use of structured and unstructured EHR data using emerging AI models while incorporating explainable-AI techniques has the potential to identify novel PC risk factors, and this approach merits further study.

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging.

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives.

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems.

Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device-the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson's disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.

Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity.

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

Ionization Gas Sensor using Suspended Carbon Nanotube Beams.

An ionization sensor based on suspended carbon nanotubes (CNTs) was presented. A suspended CNT beam was fabricated by a low-temperature surface micromachining process using SU8 photoresist as the sacrificial layer. Application of a bias to the CNT beam generated very high non-linear electric fields near the tips of individual CNTs sufficient to ionize target gas molecules and initiate a breakdown current. The sensing mechanism of the CNT ionization sensor was discussed. The sensor response was tested in air, nitrogen, argon, and helium ambients. Each gas demonstrated a unique breakdown signature. Further, the sensor was tested with gaseous mixtures. The sensor exhibited good long-term stability and had comparable performance to other reported CNT-based ionization sensors in literature, which use high-temperature vapor deposition methods to grow CNTs. The sensor notably allowed for lower ionization voltages due to its reduced ionization gap size.

Low-Hysteresis and Fast Response Time Humidity Sensors Using Suspended Functionalized Carbon Nanotubes.

A humidity sensor using suspended carbon nanotubes (CNTs) was fabricated using a low-temperature surface micromachining process. The CNTs were functionalized with carboxylic acid groups that facilitated the interaction of water vapor with the CNTs. The humidity sensor showed a response time of 12 s and a recovery time of 47 s, along with superior hysteresis and stable performance. The hysteresis curve area of the suspended structure is 3.6, a 3.2-fold reduction in comparison to the non-suspended structure. A comparative study between suspended and non-suspended devices highlights the advantages of using a suspended architecture.

Regional assessment of in vivo myocardial stiffness using 3D magnetic resonance elastography in a porcine model of myocardial infarction.

PURPOSE: The stiffness of a myocardial infarct affects the left ventricular pump function and remodeling. Magnetic resonance elastography (MRE) is a noninvasive imaging technique for measuring soft-tissue stiffness in vivo. The purpose of this study was to investigate the feasibility of assessing in vivo regional myocardial stiffness with high-frequency 3D cardiac MRE in a porcine model of myocardial infarction, and compare the results with ex vivo uniaxial tensile testing. METHODS: Myocardial infarct was induced in a porcine model by embolizing the left circumflex artery. Fourteen days postinfarction, MRE imaging was performed in diastole using an echocardiogram-gated spin-echo echo-planar-imaging sequence with 140-Hz vibrations and 3D MRE processing. The MRE stiffness and tensile modulus from uniaxial testing were compared between the remote and infarcted myocardium. RESULTS: Myocardial infarcts showed increased in vivo MRE stiffness compared with remote myocardium (4.6 ± 0.7 kPa versus 3.0 ± 0.6 kPa, P = 0.02) within the same pig. Ex vivo uniaxial mechanical testing confirmed the in vivo MRE results, showing that myocardial infarcts were stiffer than remote myocardium (650 ± 80 kPa versus 110 ± 20 kPa, P = 0.01). CONCLUSIONS: These results demonstrate the feasibility of assessing in vivo regional myocardial stiffness with high-frequency 3D cardiac MRE. Magn Reson Med 79:361-369, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Cardiac MR elastography using reduced-FOV, single-shot, spin-echo EPI.

PURPOSE: To implement a reduced field of view (rFOV) technique for cardiac MR elastography (MRE) and to demonstrate the improvement in image quality of both magnitude images and post-processed MRE stiffness maps compared to the conventional full field of view (full-FOV) acquisition. METHODS: With Institutional Review Board approval, 17 healthy volunteers underwent both full-FOV and rFOV cardiac MRE scans using 140-Hz vibrations. Two cardiac radiologists blindly compared the magnitude images and stiffness maps and graded the images based on several image quality attributes using a 5-point ordinal scale. Fisher's combined probability test was performed to assess the overall evaluation. The octahedral shear strain-based signal-to-noise ratio (OSS-SNR) and median stiffness over the left ventricular myocardium were also compared. RESULTS: One volunteer was excluded because of an inconsistent imaging resolution during the exam. In the remaining 16 volunteers (9 males, 7 females), the rFOV scans outperformed the full-FOV scans in terms of subjective image quality and ghosting artifacts in the magnitude images and stiffness maps, as well as the overall preference. The quantitative measurements showed that rFOV had significantly higher OSS-SNR (median: 1.4 [95% confidence interval (CI): 1.2-1.5] vs. 2.1 [95% CI: 1.8-2.4]), P < 0.05) compared to full-FOV. Although no significant change was found in the median myocardial stiffness between the 2 scans, we observed a decrease in the stiffness variation within the myocardium from 2.1 kPa (95% CI: [1.9, 2.3]) to 1.9 kPa (95% CI: [1.7, 2.0]) for full-FOV and rFOV, respectively (P < 0.05) in a subgroup of 7 subjects with ghosting present in the myocardium. CONCLUSION: This pilot volunteer study demonstrated that rFOV cardiac MRE has the capability to reduce ghosting and to improve image quality in both MRE magnitude images and stiffness maps. Magn Reson Med 80:231-238, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Suspended Carbon Nanotubes for Humidity Sensing.

A room temperature microfabrication technique using SU8, an epoxy-based highly functional photoresist as a sacrificial layer, is developed to obtain suspended aligned carbon nanotube beams. The humidity-sensing characteristics of aligned suspended single-walled carbon nanotube films are studied. A comparative study between suspended and non-suspended architectures is done by recording the resistance change in the nanotubes under humidity. For the tests, the humidity was varied from 15% to 98% RH. A comparative study between suspended and non-suspended devices shows that the response and recovery times of the suspended devices was found to be almost 3 times shorter than the non-suspended devices. The suspended devices also showed minimal hysteresis even after 10 humidity cycles, and also exhibit enhanced sensitivity. Repeatability tests were performed by subjecting the sensors to continuous humidification cycles. All tests reported here have been performed using pristine non-functionalized nanotubes.

Quantitative 3D magnetic resonance elastography: Comparison with dynamic mechanical analysis.

PURPOSE: Magnetic resonance elastography (MRE) is a rapidly growing noninvasive imaging technique for measuring tissue mechanical properties in vivo. Previous studies have compared two-dimensional MRE measurements with material properties from dynamic mechanical analysis (DMA) devices that were limited in frequency range. Advanced DMA technology now allows broad frequency range testing, and three-dimensional (3D) MRE is increasingly common. The purpose of this study was to compare 3D MRE stiffness measurements with those of DMA over a wide range of frequencies and shear stiffnesses. METHODS: 3D MRE and DMA were performed on eight different polyvinyl chloride samples over 20-205 Hz with stiffness between 3 and 23 kPa. Driving frequencies were chosen to create 1.1, 2.2, 3.3, 4.4, 5.5, and 6.6 effective wavelengths across the diameter of the cylindrical phantoms. Wave images were analyzed using direct inversion and local frequency estimation algorithm with the curl operator and compared with DMA measurements at each corresponding frequency. Samples with sufficient spatial resolution and with an octahedral shear strain signal-to-noise ratio > 3 were compared. RESULTS: Consistency between the two techniques was measured with the intraclass correlation coefficient (ICC) and was excellent with an overall ICC of 0.99. CONCLUSIONS: 3D MRE and DMA showed excellent consistency over a wide range of frequencies and stiffnesses. Magn Reson Med 77:1184-1192, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

In vivo, high-frequency three-dimensional cardiac MR elastography: Feasibility in normal volunteers.

PURPOSE: Noninvasive stiffness imaging techniques (elastography) can image myocardial tissue biomechanics in vivo. For cardiac MR elastography (MRE) techniques, the optimal vibration frequency for in vivo experiments is unknown. Furthermore, the accuracy of cardiac MRE has never been evaluated in a geometrically accurate phantom. Therefore, the purpose of this study was to determine the necessary driving frequency to obtain accurate three-dimensional (3D) cardiac MRE stiffness estimates in a geometrically accurate diastolic cardiac phantom and to determine the optimal vibration frequency that can be introduced in healthy volunteers. METHODS: The 3D cardiac MRE was performed on eight healthy volunteers using 80 Hz, 100 Hz, 140 Hz, 180 Hz, and 220 Hz vibration frequencies. These frequencies were tested in a geometrically accurate diastolic heart phantom and compared with dynamic mechanical analysis (DMA). RESULTS: The 3D Cardiac MRE was shown to be feasible in volunteers at frequencies as high as 180 Hz. MRE and DMA agreed within 5% at frequencies greater than 180 Hz in the cardiac phantom. However, octahedral shear strain signal to noise ratios and myocardial coverage was shown to be highest at a frequency of 140 Hz across all subjects. CONCLUSION: This study motivates future evaluation of high-frequency 3D MRE in patient populations. Magn Reson Med 77:351-360, 2017. © 2016 Wiley Periodicals, Inc.

Cardiac MR elastography for quantitative assessment of elevated myocardial stiffness in cardiac amyloidosis.

PURPOSE: To evaluate if cardiac magnetic resonance elastography (MRE) can measure increased stiffness in patients with cardiac amyloidosis. Myocardial tissue stiffness plays an important role in cardiac function. A noninvasive quantitative imaging technique capable of measuring myocardial stiffness could aid in disease diagnosis, therapy monitoring, and disease prognostic strategies. We recently developed a high-frequency cardiac MRE technique capable of making noninvasive stiffness measurements. MATERIALS AND METHODS: In all, 16 volunteers and 22 patients with cardiac amyloidosis were enrolled in this study after Institutional Review Board approval and obtaining formal written consent. All subjects were imaged head-first in the supine position in a 1.5T closed-bore MR imager. 3D MRE was performed using 5 mm isotropic resolution oblique short-axis slices and a vibration frequency of 140 Hz to obtain global quantitative in vivo left ventricular stiffness measurements. The median stiffness was compared between the two cohorts. An octahedral shear strain signal-to-noise ratio (OSS-SNR) threshold of 1.17 was used to exclude exams with insufficient motion amplitude. RESULTS: Five volunteers and six patients had to be excluded from the study because they fell below the 1.17 OSS-SNR threshold. The myocardial stiffness of cardiac amyloid patients (median: 11.4 kPa, min: 9.2, max: 15.7) was significantly higher (P = 0.0008) than normal controls (median: 8.2 kPa, min: 7.2, max: 11.8). CONCLUSION: This study demonstrates the feasibility of 3D high-frequency cardiac MRE as a contrast-agent-free diagnostic imaging technique for cardiac amyloidosis. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1361-1367.

Understanding Women's Awareness and Access to Preconception Health Care in a Rural Population: A Cross Sectional Study.

Despite evidence of the benefits of preconception health care (PCHC), little is known about awareness and access to PCHC for rural, reproductive-aged women. This study aimed to assess the prevalence of PCHC conversations between rural reproductive-age women and health care providers, PCHC interventions received in the past year, and ascertain predictors of PCHC conversations and interventions. Women (n = 868; 18-45 years) completed a questionnaire including reproductive history, health care services utilization, and interest in PCHC. The prevalence of health care providers' PCHC conversations was 53.9 %, and the mean number of interventions reported was 2.6 ± 2.7 (±SD). Significant predictors of PCHC conversation based on adjusted odds ratios from logistic regression were race (Native American 76 % greater than White), health care provider type (non-physician 63 % greater than physician), visits to a health care provider (3+ times 32 % greater than 1-2 times), and pregnancy planning (considering in next 1-5 years 51 % greater than no plans). Significant predictors of PCHC interventions received in the past 12 months based on adjusted risk ratios from negative binomial regression were race (Native American 22 % greater than White), PCHC conversation with a health care provider (yes 52 % lower than no), reporting PCHC as beneficial (yes 32 % greater than don't know), and visits to a health care provider in the past year (3+ times 90 % greater than 1-2 times). Increasing conversations about PCHC between health care providers and their reproductive-aged patients can improve awareness and increase their likelihood of receiving all of the recommended interventions.

Ectopic expression of miR160 results in auxin hypersensitivity, cytokinin hyposensitivity, and inhibition of symbiotic nodule development in soybean.

Symbiotic root nodules in leguminous plants result from interaction between the plant and nitrogen-fixing rhizobia bacteria. There are two major types of legume nodules, determinate and indeterminate. Determinate nodules do not have a persistent meristem, while indeterminate nodules have a persistent meristem. Auxin is thought to play a role in the development of both these types of nodules. However, inhibition of rootward auxin transport at the site of nodule initiation is crucial for the development of indeterminate nodules but not determinate nodules. Using the synthetic auxin-responsive DR5 promoter in soybean (Glycine max), we show that there is relatively low auxin activity during determinate nodule initiation and that it is restricted to the nodule periphery subsequently during development. To examine if and what role auxin plays in determinate nodule development, we generated soybean composite plants with altered sensitivity to auxin. We overexpressed microRNA393 to silence the auxin receptor gene family, and these roots were hyposensitive to auxin. These roots nodulated normally, suggesting that only minimal/reduced auxin signaling is required for determinate nodule development. We overexpressed microRNA160 to silence a set of repressor auxin response factor transcription factors, and these roots were hypersensitive to auxin. These roots were not impaired in epidermal responses to rhizobia but had significantly reduced nodule primordium formation, suggesting that auxin hypersensitivity inhibits nodule development. These roots were also hyposensitive to cytokinin and had attenuated expression of key nodulation-associated transcription factors known to be regulated by cytokinin. We propose a regulatory feedback loop involving auxin and cytokinin during nodulation.

Image-Decomposition-Enhanced Deep Learning for Detection of Rotor Cores in Cardiac Fibrillation.

OBJECTIVE: Rotors, regions of spiral wave reentry in cardiac tissues, are considered as the drivers of atrial fibrillation (AF), the most common arrhythmia. Whereas physics-based approaches have been widely deployed to detect the rotors, in-depth knowledge in cardiac physiology and electrogram interpretation skills are typically needed. The recent leap forward in smart sensing, data acquisition, and Artificial Intelligence (AI) has offered an unprecedented opportunity to transform diagnosis and treatment in cardiac ailment, including AF. This study aims to develop an image-decomposition-enhanced deep learning framework for automatic identification of rotor cores on both simulation and optical mapping data. METHODS: We adopt the Ensemble Empirical Mode Decomposition algorithm (EEMD) to decompose the original image, and the most representative component is then fed into a You-Only-Look-Once (YOLO) object-detection architecture for rotor detection. Simulation data from a bi-domain simulation model and optical mapping acquired from isolated rabbit hearts are used for training and validation. RESULTS: This integrated EEMD-YOLO model achieves high accuracy on both simulation and optical mapping data (precision: 97.2%, 96.8%, recall: 93.8%, 92.2%, and F1 score: 95.5%, 94.4%, respectively). CONCLUSION: The proposed EEMD-YOLO yields comparable accuracy in rotor detection with the gold standard in literature.

Machine Learning-Based Prediction Models for Clostridioides difficile Infection: A Systematic Review.

INTRODUCTION: Despite research efforts, predicting Clostridioides difficile incidence and its outcomes remains challenging. The aim of this systematic review was to evaluate the performance of machine learning (ML) models in predicting C. difficile infection (CDI) incidence and complications using clinical data from electronic health records. METHODS: We conducted a comprehensive search of databases (OVID, Embase, MEDLINE ALL, Web of Science, and Scopus) from inception up to September 2023. Studies employing ML techniques for predicting CDI or its complications were included. The primary outcome was the type and performance of ML models assessed using the area under the receiver operating characteristic curve. RESULTS: Twelve retrospective studies that evaluated CDI incidence and/or outcomes were included. The most commonly used ML models were random forest and gradient boosting. The area under the receiver operating characteristic curve ranged from 0.60 to 0.81 for predicting CDI incidence, 0.59 to 0.80 for recurrence, and 0.64 to 0.88 for predicting complications. Advanced ML models demonstrated similar performance to traditional logistic regression. However, there was notable heterogeneity in defining CDI and the different outcomes, including incidence, recurrence, and complications, and a lack of external validation in most studies. DISCUSSION: ML models show promise in predicting CDI incidence and outcomes. However, the observed heterogeneity in CDI definitions and the lack of real-world validation highlight challenges in clinical implementation. Future research should focus on external validation and the use of standardized definitions across studies.

Gene therapy for heart failure: A novel treatment for the age old disease.

Across the globe, cardiovascular disease (CVD) is the leading cause of mortality. According to reports, around 6.2 million people in the United states have heart failure. Current standards of care for heart failure can delay but not prevent progression of disease. Gene therapy is one of the novel treatment modalities that promises to fill this limitation in the current standard of care for Heart Failure. In this paper we performed an extensive search of the literature on various advances made in gene therapy for heart failure till date. We review the delivery methods, targets, current applications, trials, limitations and feasibility of gene therapy for heart failure. Various methods have been employed till date for administering gene therapies including but not limited to arterial and venous infusion, direct myocardial injection and pericardial injection. Various strategies such as AC6 expression, S100A1 protein upregulation, VEGF-B and SDF-1 gene therapy have shown promise in recent preclinical trials. Furthermore, few studies even show that stimulation of cardiomyocyte proliferation such as through cyclin A2 overexpression is a realistic avenue. However, a considerable number of obstacles need to be overcome for gene therapy to be part of standard treatment of care such as definitive choice of gene, gene delivery systems and a suitable method for preclinical trials and clinical trials on patients. Considering the challenges and taking into account the recent advances in gene therapy research, there are encouraging signs to indicate gene therapy for heart failure to be a promising treatment modality for the future. However, the time and feasibility of this option remains in a situation of balance.

Sex Differences in Aging-related Myocardial Stiffening Quantitatively Measured with MR Elastography.

Purpose To investigate the feasibility of using quantitative MR elastography (MRE) to characterize the influence of aging and sex on left ventricular (LV) shear stiffness. Materials and Methods In this prospective study, LV myocardial shear stiffness was measured in 109 healthy volunteers (age range: 18-84 years; mean age, 40 years ± 18 [SD]; 57 women, 52 men) enrolled between November 2018 and September 2019, using a 5-minute MRE acquisition added to a clinical MRI protocol. Linear regression models were used to estimate the association of cardiac MRI and MRE characteristics with age and sex; models were also fit to assess potential age-sex interaction. Results Myocardial shear stiffness significantly increased with age in female (age slope = 0.03 kPa/year ± 0.01, P = .009) but not male (age slope = 0.008 kPa/year ± 0.009, P = .38) volunteers. LV ejection fraction (LVEF) increased significantly with age in female volunteers (0.23% ± 0.08 per year, P = .005). LV end-systolic volume (LVESV) decreased with age in female volunteers (-0.20 mL/m2 ± 0.07, P = .003). MRI parameters, including T1, strain, and LV mass, did not demonstrate this interaction (P > .05). Myocardial shear stiffness was not significantly correlated with LVEF, LV stroke volume, body mass index, or any MRI strain metrics (P > .05) but showed significant correlations with LV end-diastolic volume/body surface area (BSA) (slope = -3 kPa/mL/m2 ± 1, P = .004, r2 = 0.08) and LVESV/BSA (-1.6 kPa/mL/m2 ± 0.5, P = .003, r2 = 0.08). Conclusion This study demonstrates that female, but not male, individuals experience disproportionate LV stiffening with natural aging, and these changes can be noninvasively measured with MRE. Keywords: Cardiac, Elastography, Biological Effects, Experimental Investigations, Sexual Dimorphisms, MR Elastography, Myocardial Shear Stiffness, Quantitative Stiffness Imaging, Aging Heart, Myocardial Biomechanics, Cardiac MRE Supplemental material is available for this article. Published under a CC BY 4.0 license.