Deep learning of left atrial structure and function provides link to atrial fibrillation risk.

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.

Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping.

Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems. In genome-wide association analyses, we identified 27, 18, 11 and 10 independent genetic loci associated with liver, pancreas, myocardial and renal cortex T1 time, respectively. There was a modest genetic correlation between the examined organs. Several loci overlapped across the examined organs implicating genes involved in a myriad of biologic pathways including metal ion transport (SLC39A8, HFE and TMPRSS6), glucose metabolism (PCK2), blood group antigens (ABO and FUT2), immune function (BANK1 and PPP3CA), inflammation (NFKB1) and mitosis (CENPE). Finally, we found that an increasing number of organs with T1 time falling in the top quintile was associated with increased mortality in the population. Individuals with a high burden of fibrosis in ≥3 organs had a 3-fold increase in mortality compared to those with a low burden of fibrosis across all examined organs in multivariable-adjusted analysis (hazard ratio = 3.31, 95% confidence interval 1.77-6.19; P = 1.78 × 10-4). By leveraging machine learning to quantify T1 time across multiple organs at scale, we uncovered new organ-specific and shared biologic pathways underlying fibrosis that may provide therapeutic targets.

3-Dimensional Evaluation of Two PNAM Techniques (Modified Grayson & AlignerNAM) on Facial Soft Tissue Morphology: A Randomised Clinical Trial.

OBJECTIVE: Evaluate facial changes after Presurgical Naso-Alveolar Molding (PNAM) in unilateral cleft lip and palate (UCLP) patients treated with Modified Grayson Technique and AlignerNAM (with DynaCleft nasal elevator) using a 3D facial scan. DESIGN: Randomised clinical trial. SETTING: Institutional study. Participants: 20 UCLP patients allocated to two groups (10 patients each). INTERVENTIONS: Group A patients underwent PNAM with Modified Grayson Technique and Group B patients underwent AlignerNAM (with DynaCleft nasal elevator). Their 3D facial scans were obtained by using an iOSbased application (Bellus3D FaceApp) mounted on a novel frame. These .stl files were analysed using 3D software (GOM INSPECT) at three-time intervals; before intervention (T0), after intervention (T1) and one month after lip repair surgery (T2). MAIN OUTCOME MEASURE(S): Changes in facial and nasolabial morphology. RESULTS: Both techniques brought significant improvement in the columellar length, nasal tip projection, columella angle, nasal tip angle and a significant reduction in cleft width. At T1, a statistically significant difference in angular and linear measurements was present in both groups. At T2, no statistically significant difference in linear parameters was observed between the two groups except for the outer lateral height of the non-cleft side, basal lateral height of the non-cleft side, and philtrum width. Similar pattern was observed in angular measurements with no statistically significant difference between the two groups except in nasolabial angle, anterior nasal base triangle III, and anterior nasal root triangle. CONCLUSIONS: Aligner NAM and Modified Grayson technique are equally effective PNAM methods with similar clinical results in nasolabial morphology after lip repair surgery.

Comparative assessment of facemask therapy with and without skeletal anchorage in growing Class III patients with unilateral cleft lip and palate (UCLP): A single-center, prospective randomized clinical trial.

OBJECTIVE: The purpose of this study was to evaluate the maxillary protraction effect of facemask therapy with and without skeletal anchorage in growing Class III patients with unilateral cleft lip and palate (UCLP). MATERIALS AND METHODS: Thirty patients (aged 9-13 years) with UCLP having a GOSLON score 3 were selected for this prospective clinical study. The patients were allocated into two groups using computer generated random number table. Group I (facemask therapy along with two I shaped miniplates, FM + MP) and Group II (facemask mask along with tooth-anchored appliance, FM). Skeletal and dental parameters were evaluated on pre- and post-treatment lateral cephalograms and pharyngeal airway on cone-beam computed tomography systems (CBCT) for assessment of the treatment changes. RESULTS: Both methods proved to be effective with statistically significant improvements in skeletal and dental parameters (p < .05). Skeletal parameters (e.g., SNA, convexity-point A, ANB) with the FM + MP group showed greater change compared to those with FM group (SNA, 2.56°; convexity-point A, 1.22°; ANB, 0.35°). Significant proclination of maxillary incisors was observed in the FM group as compared to FM + MP group (U1 to NA, 5.4°; 3.37 mm). A statistically significant increase in pharyngeal airway volume was noted in both groups (p < .05). CONCLUSION: While both therapies are effective in protracting the maxilla in growing patients with UCLP, the FM + MP allows for a greater skeletal correction, minimizing the dental side effects seen with FM therapy alone. Thus, FM + MP appears to be a promising adjunct in reducing the severity of Class III skeletal correction needed in patients with cleft lip and palate (CLP).

Natural Language Processing for Adjudication of Heart Failure in a Multicenter Clinical Trial: A Secondary Analysis of a Randomized Clinical Trial.

Importance: The gold standard for outcome adjudication in clinical trials is medical record review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication of medical records by natural language processing (NLP) may offer a more resource-efficient alternative but this approach has not been validated in a multicenter setting. Objective: To externally validate the Community Care Cohort Project (C3PO) NLP model for heart failure (HF) hospitalization adjudication, which was previously developed and tested within one health care system, compared to gold-standard CEC adjudication in a multicenter clinical trial. Design, Setting, and Participants: This was a retrospective analysis of the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial, which compared 2 influenza vaccines in 5260 participants with cardiovascular disease at 157 sites in the US and Canada between September 2016 and January 2019. Analysis was performed from November 2022 to October 2023. Exposures: Individual sites submitted medical records for each hospitalization. The central INVESTED CEC and the C3PO NLP model independently adjudicated whether the cause of hospitalization was HF using the prepared hospitalization dossier. The C3PO NLP model was fine-tuned (C3PO + INVESTED) and a de novo NLP model was trained using half the INVESTED hospitalizations. Main Outcomes and Measures: Concordance between the C3PO NLP model HF adjudication and the gold-standard INVESTED CEC adjudication was measured by raw agreement, κ, sensitivity, and specificity. The fine-tuned and de novo INVESTED NLP models were evaluated in an internal validation cohort not used for training. Results: Among 4060 hospitalizations in 1973 patients (mean [SD] age, 66.4 [13.2] years; 514 [27.4%] female and 1432 [72.6%] male]), 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was good agreement between the C3PO NLP and CEC HF adjudications (raw agreement, 87% [95% CI, 86-88]; κ, 0.69 [95% CI, 0.66-0.72]). C3PO NLP model sensitivity was 94% (95% CI, 92-95) and specificity was 84% (95% CI, 83-85). The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% (95% CI, 92-94) and κ of 0.82 (95% CI, 0.77-0.86) and 0.83 (95% CI, 0.79-0.87), respectively, vs the CEC. CEC reviewer interrater reproducibility was 94% (95% CI, 93-95; κ, 0.85 [95% CI, 0.80-0.89]). Conclusions and Relevance: The C3PO NLP model developed within 1 health care system identified HF events with good agreement relative to the gold-standard CEC in an external multicenter clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. Further study is needed to determine whether NLP will improve the efficiency of future multicenter clinical trials by identifying clinical events at scale.

Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise.

AIMS: To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET). METHODS AND RESULTS: V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]. CONCLUSION: Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification.

Researchers here present data describing a method of estimating exercise capacity from the resting electrocardiogram. Electrocardiogram estimation of exercise capacity was accurate and was found to predict the onset of the wide range of cardiovascular diseases including heart attacks, heart failure, arrhythmia, and death.This approach offers the ability to estimate exercise capacity without dedicated exercise testing and may enable efficient risk stratification of cardiac patients at scale.

Deep Learning-Enabled Assessment of Left Heart Structure and Function Predicts Cardiovascular Outcomes.

BACKGROUND: Deep learning interpretation of echocardiographic images may facilitate automated assessment of cardiac structure and function. OBJECTIVES: We developed a deep learning model to interpret echocardiograms and examined the association of deep learning-derived echocardiographic measures with incident outcomes. METHODS: We trained and validated a 3-dimensional convolutional neural network model for echocardiographic view classification and quantification of left atrial dimension, left ventricular wall thickness, chamber diameter, and ejection fraction. The training sample comprised 64,028 echocardiograms (n = 27,135) from a retrospective multi-institutional ambulatory cardiology electronic health record sample. Validation was performed in a separate longitudinal primary care sample and an external health care system data set. Cox models evaluated the association of model-derived left heart measures with incident outcomes. RESULTS: Deep learning discriminated echocardiographic views (area under the receiver operating curve >0.97 for parasternal long axis, apical 4-chamber, and apical 2-chamber views vs human expert annotation) and quantified standard left heart measures (R2 range = 0.53 to 0.91 vs study report values). Model performance was similar in 2 external validation samples. Model-derived left heart measures predicted incident heart failure, atrial fibrillation, myocardial infarction, and death. A 1-SD lower model-left ventricular ejection fraction was associated with 43% greater risk of heart failure (HR: 1.43; 95% CI: 1.23-1.66) and 17% greater risk of death (HR: 1.17; 95% CI: 1.06-1.30). Similar results were observed for other model-derived left heart measures. CONCLUSIONS: Deep learning echocardiographic interpretation accurately quantified standard measures of left heart structure and function, which in turn were associated with future clinical outcomes. Deep learning may enable automated echocardiogram interpretation and disease prediction at scale.

Is caries prevalence site-specific in cleft lip and palate patients? A systematic review and meta-analysis.

Background: Previous systematic reviews indicate that there is an increased prevalence of caries in cleft patients in comparison to their healthy control group. To date, the prevalence of caries between unilateral cleft lip and palate (UCLP) and bilateral cleft lip and palate (BCLP) has not been quantitatively evaluated. This review aims to include published studies that examined caries prevalence in patients with UCLP and BCLP to find out whether a quantitative difference exists in caries experience among them. Materials and Methods: Medline/PubMed, Scopus, and EBSCOhost databases were searched from inception to November 2021. The protocol was registered with PROSPERO registration no. CRD2021292425. Prevalence-based studies that evaluated caries experience using the decayed-missing-filled teeth (DMFT) index in the permanent dentition or dmft in case of primary dentition in patients with UCLP or BCLP were included in the analysis with the outcome given in mean and standard deviation. Meta-analysis was performed using a random effect model through a forest plot. An adapted version of the Newcastle-Ottawa Scale for cross-sectional studies was modified to assess the quality of included studies. Results: Three studies were included in the review. The difference in caries prevalence was statistically significant in the permanent and primary dentition which were evaluated using DMFT and dmft scores with P = 0.01 and P = 0.03, respectively. Forest plot values were obtained for permanent dentition (DMFT) and primary dentition (dmft), 0.57 (95% confidence interval [CI]: 1.03-0.11) and 0.36 (95% CI: 0.69-0.03), respectively. The result of the meta-analysis indicates that patients with BCLP have higher caries prevalence. Conclusion: The outcome of the study indicates a higher occurrence of caries in patients with BCLP than UCLP in both permanent and primary dentition.

Natural Language Processing for Adjudication of Heart Failure Hospitalizations in a Multi-Center Clinical Trial.

Background: The gold standard for outcome adjudication in clinical trials is chart review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication by natural language processing (NLP) may offer a more resource-efficient alternative. We previously showed that the Community Care Cohort Project (C3PO) NLP model adjudicates heart failure (HF) hospitalizations accurately within one healthcare system. Methods: This study externally validated the C3PO NLP model against CEC adjudication in the INVESTED trial. INVESTED compared influenza vaccination formulations in 5260 patients with cardiovascular disease at 157 North American sites. A central CEC adjudicated the cause of hospitalizations from medical records. We applied the C3PO NLP model to medical records from 4060 INVESTED hospitalizations and evaluated agreement between the NLP and final consensus CEC HF adjudications. We then fine-tuned the C3PO NLP model (C3PO+INVESTED) and trained a de novo model using half the INVESTED hospitalizations, and evaluated these models in the other half. NLP performance was benchmarked to CEC reviewer inter-rater reproducibility. Results: 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was high agreement between the C3PO NLP and CEC HF adjudications (agreement 87%, kappa statistic 0.69). C3PO NLP model sensitivity was 94% and specificity was 84%. The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% and kappa of 0.82 and 0.83, respectively. CEC reviewer inter-rater reproducibility was 94% (kappa 0.85). Conclusion: Our NLP model developed within a single healthcare system accurately identified HF events relative to the gold-standard CEC in an external multi-center clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. NLP may improve the efficiency of future multi-center clinical trials by accurately identifying clinical events at scale.

Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis.

INTRODUCTION: The classification and management of pulmonary hypertension (PH) is challenging due to clinical heterogeneity of patients. We sought to identify distinct multimorbid phenogroups of patients with PH that are at particularly high-risk for adverse events. METHODS: A hospital-based cohort of patients referred for right heart catheterization between 2005-2016 with PH were included. Key exclusion criteria were shock, cardiac arrest, cardiac transplant, or valvular surgery. K-prototypes was used to cluster patients into phenogroups based on 12 clinical covariates. RESULTS: Among 5208 patients with mean age 64±12 years, 39% women, we identified 5 distinct multimorbid PH phenogroups with similar hemodynamic measures yet differing clinical outcomes: (1) "young men with obesity", (2) "women with hypertension", (3) "men with overweight", (4) "men with cardiometabolic and cardiovascular disease", and (5) "men with structural heart disease and atrial fibrillation." Over a median follow-up of 6.3 years, we observed 2182 deaths and 2002 major cardiovascular events (MACE). In age- and sex-adjusted analyses, phenogroups 4 and 5 had higher risk of MACE (HR 1.68, 95% CI 1.41-2.00 and HR 1.52, 95% CI 1.24-1.87, respectively, compared to the lowest risk phenogroup 1). Phenogroup 4 had the highest risk of mortality (HR 1.26, 95% CI 1.04-1.52, relative to phenogroup 1). CONCLUSIONS: Cluster-based analyses identify patients with PH and specific comorbid cardiometabolic and cardiovascular disease burden that are at highest risk for adverse clinical outcomes. Interestingly, cardiopulmonary hemodynamics were similar across phenogroups, highlighting the importance of multimorbidity on clinical trajectory. Further studies are needed to better understand comorbid heterogeneity among patients with PH.

Influence of social media and corrected smile photographs in patients with malocclusion.

INTRODUCTION: The increased use of social networking sites, such as Instagram (Meta Platforms, Menlo Park, Calif), has been silently affecting facial satisfaction among patients. However, the potential of Instagram to motivate participants for orthodontic treatment when used with an adjunct, a photograph editing software, is yet to be assessed. METHODS: From the initial 300 participants, 256 were included and randomly divided into an experimental group (participants were asked to provide their frontal smiling photograph) and a control group. The photographs received were corrected using photograph editing software and were shown along with other ideal smile photographs in an Instagram account to the experimental group, whereas the control group participants had access to only the ideal smile photographs. After browsing, the participants were given a modified version of the Malocclusion-Related Quality of Life Questionnaire. RESULTS: Questions assessing the general perception about one's smile, comparison with peers, desire to undergo orthodontic treatment, and the role of socioeconomic status showed a statistically significant difference (P <0.05) as most of the control group participants were unsatisfied with their teeth, had less desire to undergo orthodontic treatment and did not feel family's financial income to be a hurdle, contrary to the experimental group participants. A statistically significant difference (P <0.05) was also seen in assessing external acceptance, speech difficulties, and the influence of Instagram on orthodontic treatment, whereas the influence of photograph editing software did not show the same. CONCLUSIONS: The study concluded that the experimental group participants were motivated to undergo orthodontic treatment after viewing their corrected photograph.

An assessment of masticatory efficiency and occlusal load distribution in adolescent patients undergoing orthodontic treatment with functional jaw orthopedics: A prospective cohort study.

INTRODUCTION: Functional jaw orthopaedics, produces a radical change in the occlusal scheme and the masticatory apparatus, particularly in patients with Class II malocclusion. It remains to be seen how the changes brought about by a functional appliance alter the masticatory ability of a growing child, who needs the necessary nutrition to properly grow the craniofacial region. MATERIALS AND METHODS: Pretreatment and Post-treatment values of masticatory efficiency and the distribution of the occlusal load at centric occlusion were evaluated and compared for 20 patients with Class II division 1 malocclusion undergoing functional jaw orthopaedics. RESULTS: Significant increase in the masticatory efficiency was seen during and after treatment (p < 0.5) There was an increase in the anterior distribution of occlusal load associated with a concomitant decrease in the posterior region at centric occlusion. CONCLUSION: Improvement in the masticatory efficiency was observed after treatment of a retrognathic mandible with functional jaw orthopaedics in the adolescent participants with Class II malocclusion. This highlights the importance of treatment with functional jaw orthopaedics, which apart from providing esthetic and functional improvement also improves the ability of a growing child to extract proper nutrition from his/her diet.

Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.

BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well understood. We hypothesized that there might be a genetic basis for an AI algorithm for predicting the 5-year risk of new-onset AF using 12-lead ECGs (ECG-AI)-based risk estimates. METHODS: We applied a validated ECG-AI model for predicting incident AF to ECGs from 39 986 UK Biobank participants without AF. We then performed a genome-wide association study (GWAS) of the predicted AF risk and compared it with an AF GWAS and a GWAS of risk estimates from a clinical variable model. RESULTS: In the ECG-AI GWAS, we identified 3 signals (P<5×10-8) at established AF susceptibility loci marked by the sarcomeric gene TTN and sodium channel genes SCN5A and SCN10A. We also identified 2 novel loci near the genes VGLL2 and EXT1. In contrast, the clinical variable model prediction GWAS indicated a different genetic profile. In genetic correlation analysis, the prediction from the ECG-AI model was estimated to have a higher correlation with AF than that from the clinical variable model. CONCLUSIONS: Predicted AF risk from an ECG-AI model is influenced by genetic variation implicating sarcomeric, ion channel and body height pathways. ECG-AI models may identify individuals at risk for disease via specific biological pathways.

A Three-Dimensional Scale for the Qualitative and Quantitative Assessments of Secondary Alveolar Bone Grafting (SABG) in Unilateral Cleft Lip and Palate Patients Using Cone-Beam Computed Tomography (CBCT).

Background The objective of our study was to derive an objective assessment scale for three-dimensional (3D) qualitative and quantitative evaluation of secondary alveolar bone grafting (SABG) using cone-bone computed tomography (CBCT) in patients with unilateral cleft lip and palate (UCLP). Methods CBCT scans for pre- and 3-month post-SABG were reviewed for bone volume, height, width, and density of the bony bridge formed in the cleft defect in 20 patients with UCLP. Basic descriptive and principal component analysis was used to extract the various sub-components of the scale. Spearman's correlation was used to check the validity of the scale, and intra-class coefficient (ICC) and Cronbach's α were calculated to establish the reliability and retest applicability of the scale. Results Each CBCT scan was assessed in five areas: cementoenamel junction (CEJ), root apex, root midpoint, 3 and 6 mm below CEJ, and tabulated in percentiles of 20, 25, 40, 50, 60, and 75 for all the parameters (bone volume, density, and width). These scores were validated when correlated to the scale given by Kamperos et al. Cronbach's α for the domains demonstrated acceptable to excellent internal consistency. The ICC showed good test-retest reliability having a range of scores from 0.89 to 0.94. Conclusion The proposed scale for the 3D assessment of SABG in patients with UCLP provides gradation for the objective assessment of the bony bridge. This gradation enables the qualitative and quantitative assessments of the bony bridge, thus allowing each clinician to judge SABG more conclusively.

The Genetic Determinants of Aortic Distention.

BACKGROUND: As the largest conduit vessel, the aorta is responsible for the conversion of phasic systolic inflow from ventricular ejection into more continuous peripheral blood delivery. Systolic distention and diastolic recoil conserve energy and are enabled by the specialized composition of the aortic extracellular matrix. Aortic distensibility decreases with age and vascular disease. OBJECTIVES: In this study, we sought to discover epidemiologic correlates and genetic determinants of aortic distensibility and strain. METHODS: We trained a deep learning model to quantify thoracic aortic area throughout the cardiac cycle from cardiac magnetic resonance images and calculated aortic distensibility and strain in 42,342 UK Biobank participants. RESULTS: Descending aortic distensibility was inversely associated with future incidence of cardiovascular diseases, such as stroke (HR: 0.59 per SD; P = 0.00031). The heritabilities of aortic distensibility and strain were 22% to 25% and 30% to 33%, respectively. Common variant analyses identified 12 and 26 loci for ascending and 11 and 21 loci for descending aortic distensibility and strain, respectively. Of the newly identified loci, 22 were not significantly associated with thoracic aortic diameter. Nearby genes were involved in elastogenesis and atherosclerosis. Aortic strain and distensibility polygenic scores had modest effect sizes for predicting cardiovascular outcomes (delaying or accelerating disease onset by 2%-18% per SD change in scores) and remained statistically significant predictors after accounting for aortic diameter polygenic scores. CONCLUSIONS: Genetic determinants of aortic function influence risk for stroke and coronary artery disease and may lead to novel targets for medical intervention.

Cross-modal autoencoder framework learns holistic representations of cardiovascular state.

A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.

Genetics of myocardial interstitial fibrosis in the human heart and association with disease.

Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor ß1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.

Artificial intelligence-enabled classification of hypertrophic heart diseases using electrocardiograms.

Background: Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care. Objective: To evaluate if artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH. Methods: We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression ("LVH-Net"). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I ("LVH-Net Lead I") or lead II ("LVH-Net Lead II") from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH. Results: The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93-0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90-0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well. Conclusion: An artificial intelligence-enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules.