A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices.

Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and characterization of cardiovascular diseases, since it can identify abnormalities in structure and function of the myocardium non-invasively and without the need for ionizing radiation. However, in clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a completely automated pipeline for generating patient-specific 3D biventricular heart models from cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR images, segments them using a deep learning-based method to extract the heart contours, and aligns the contours in 3D space correcting possible misalignments due to breathing or subject motion first using the intensity and contours information from the cine data and next with the help of a statistical shape model. Finally, the sparse 3D representation of the contours is used to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our computational pipeline are used for simulations of electrical activation patterns, showing agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented here for patient-specific MR imaging-based 3D biventricular representations contribute to the efficient realization of precision medicine, enabling the enhanced interpretability of clinical data, the digital twin vision through patient-specific image-based modelling and simulation, and augmented reality applications. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

The gene regulatory program of Acrobeloides nanus reveals conservation of phylum-specific expression.

The evolution of development has been studied through the lens of gene regulation by examining either closely related species or extremely distant animals of different phyla. In nematodes, detailed cell- and stage-specific expression analyses are focused on the model Caenorhabditis elegans, in part leading to the view that the developmental expression of gene cascades in this species is archetypic for the phylum. Here, we compared two species of an intermediate evolutionary distance: the nematodes C. elegans (clade V) and Acrobeloides nanus (clade IV). To examine A. nanus molecularly, we sequenced its genome and identified the expression profiles of all genes throughout embryogenesis. In comparison with C. elegans, A. nanus exhibits a much slower embryonic development and has a capacity for regulative compensation of missing early cells. We detected conserved stages between these species at the transcriptome level, as well as a prominent middevelopmental transition, at which point the two species converge in terms of their gene expression. Interestingly, we found that genes originating at the dawn of the Ecdysozoa supergroup show the least expression divergence between these two species. This led us to detect a correlation between the time of expression of a gene and its phylogenetic age: evolutionarily ancient and young genes are enriched for expression in early and late embryogenesis, respectively, whereas Ecdysozoa-specific genes are enriched for expression during the middevelopmental transition. Our results characterize the developmental constraints operating on each individual embryo in terms of developmental stages and genetic evolutionary history.

Machine learning in the electrocardiogram.

The electrocardiogram is the most widely used diagnostic tool that records the electrical activity of the heart and, therefore, its use for identifying markers for early diagnosis and detection is of paramount importance. In the last years, the huge increase of electronic health records containing a systematised collection of different type of digitalised medical data, together with new tools to analyse this large amount of data in an efficient way have re-emerged the field of machine learning in healthcare innovation. This review describes the most recent machine learning-based systems applied to the electrocardiogram as well as pros and cons in the use of these techniques. Machine learning, including deep learning, have shown to be powerful tools for aiding clinicians in patient screening and risk stratification tasks. However, they do not provide the physiological basis of classification outcomes. Computational modelling and simulation can help in the interpretation and understanding of key physiologically meaningful ECG biomarkers extracted from machine learning techniques.

Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.

Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of 0.457 ±0.317 and 0.302 ±0.273 for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https://github.com/lileitech/MI_inverse_inference.

European guidelines for the diagnosis, treatment and follow-up of breast lesions with uncertain malignant potential (B3 lesions) developed jointly by EUSOMA, EUSOBI, ESP (BWG) and ESSO.

INTRODUCTION: Breast lesions of uncertain malignant potential (B3) include atypical ductal and lobular hyperplasias, lobular carcinoma in situ, flat epithelial atypia, papillary lesions, radial scars and fibroepithelial lesions as well as other rare miscellaneous lesions. They are challenging to categorise histologically, requiring specialist training and multidisciplinary input. They may coexist with in situ or invasive breast cancer (BC) and increase the risk of subsequent BC development. Management should focus on adequate classification and management whilst avoiding overtreatment. The aim of these guidelines is to provide updated information regarding the diagnosis and management of B3 lesions, according to updated literature review evidence. METHODS: These guidelines provide practical recommendations which can be applied in clinical practice which include recommendation grade and level of evidence. All sections were written according to an updated literature review and discussed at a consensus meeting. Critical appraisal by the expert writing committee adhered to the 23 items in the international Appraisal of Guidelines, Research and Evaluation (AGREE) tool. RESULTS: Recommendations for further management after core-needle biopsy (CNB) or vacuum-assisted biopsy (VAB) diagnosis of a B3 lesion reported in this guideline, vary depending on the presence of atypia, size of lesion, sampling size, and patient preferences. After CNB or VAB, the option of vacuum-assisted excision or surgical excision should be evaluated by a multidisciplinary team and shared decision-making with the patient is crucial for personalizing further treatment. De-escalation of surgical intervention for B3 breast lesions is ongoing, and the inclusion of vacuum-assisted excision (VAE) will decrease the need for surgical intervention in further approaches. Communication with patients may be different according to histological diagnosis, presence or absence of atypia, or risk of upgrade due to discordant imaging. Written information resources to help patients understand these issues alongside with verbal communication is recommended. Lifestyle interventions have a significant impact on BC incidence so lifestyle interventions need to be suggested to women at increased BC risk as a result of a diagnosis of a B3 lesion. CONCLUSIONS: These guidelines provide a state-of-the-art overview of the diagnosis, management and prognosis of B3 lesions in modern multidisciplinary breast practice.

Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic resonance imaging using realistic Purkinje networks for in silico clinical trials.

Cardiac in silico clinical trials can virtually assess the safety and efficacy of therapies using human-based modelling and simulation. These technologies can provide mechanistic explanations for clinically observed pathological behaviour. Designing virtual cohorts for in silico trials requires exploiting clinical data to capture the physiological variability in the human population. The clinical characterisation of ventricular activation and the Purkinje network is challenging, especially non-invasively. Our study aims to present a novel digital twinning pipeline that can efficiently generate and integrate Purkinje networks into human multiscale biventricular models based on subject-specific clinical 12-lead electrocardiogram and magnetic resonance recordings. Essential novel features of the pipeline are the human-based Purkinje network generation method, personalisation considering ECG R wave progression as well as QRS morphology, and translation from reduced-order Eikonal models to equivalent biophysically-detailed monodomain ones. We demonstrate ECG simulations in line with clinical data with clinical image-based multiscale models with Purkinje in four control subjects and two hypertrophic cardiomyopathy patients (simulated and clinical QRS complexes with Pearson's correlation coefficients > 0.7). Our methods also considered possible differences in the density of Purkinje myocardial junctions in the Eikonal-based inference as regional conduction velocities. These differences translated into regional coupling effects between Purkinje and myocardial models in the monodomain formulation. In summary, we demonstrate a digital twin pipeline enabling simulations yielding clinically consistent ECGs with clinical CMR image-based biventricular multiscale models, including personalised Purkinje in healthy and cardiac disease conditions.

The genome of Romanomermis culicivorax: revealing fundamental changes in the core developmental genetic toolkit in Nematoda.

BACKGROUND: The genetics of development in the nematode Caenorhabditis elegans has been described in exquisite detail. The phylum Nematoda has two classes: Chromadorea (which includes C. elegans) and the Enoplea. While the development of many chromadorean species resembles closely that of C. elegans, enoplean nematodes show markedly different patterns of early cell division and cell fate assignment. Embryogenesis of the enoplean Romanomermis culicivorax has been studied in detail, but the genetic circuitry underpinning development in this species has not been explored. RESULTS: We generated a draft genome for R. culicivorax and compared its gene content with that of C. elegans, a second enoplean, the vertebrate parasite Trichinella spiralis, and a representative arthropod, Tribolium castaneum. This comparison revealed that R. culicivorax has retained components of the conserved ecdysozoan developmental gene toolkit lost in C. elegans. T. spiralis has independently lost even more of this toolkit than has C. elegans. However, the C. elegans toolkit is not simply depauperate, as many novel genes essential for embryogenesis in C. elegans are not found in, or have only extremely divergent homologues in R. culicivorax and T. spiralis. Our data imply fundamental differences in the genetic programmes not only for early cell specification but also others such as vulva formation and sex determination. CONCLUSIONS: Despite the apparent morphological conservatism, major differences in the molecular logic of development have evolved within the phylum Nematoda. R. culicivorax serves as a tractable system to contrast C. elegans and understand how divergent genomic and thus regulatory backgrounds nevertheless generate a conserved phenotype. The R. culicivorax draft genome will promote use of this species as a research model.

In-silico drug trials for precision medicine in atrial fibrillation: From ionic mechanisms to electrocardiogram-based predictions in structurally-healthy human atria.

Atrial fibrillation (AF) inducibility, sustainability and response to pharmacological treatment of individual patients are expected to be determined by their ionic current properties, especially in structurally-healthy atria. Mechanisms underlying AF and optimal cardioversion are however still unclear. In this study, in-silico drug trials were conducted using a population of human structurally-healthy atria models to 1) identify key ionic current properties determining AF inducibility, maintenance and pharmacological cardioversion, and 2) compare the prognostic value for predicting individual AF cardioversion of ionic current properties and electrocardiogram (ECG) metrics. In the population of structurally-healthy atria, 477 AF episodes were induced in ionic current profiles with both steep action potential duration (APD) restitution (eliciting APD alternans), and high excitability (enabling propagation at fast rates that transformed alternans into discordant). High excitability also favored 211 sustained AF episodes, so its decrease, through prolonged refractoriness, explained pharmacological cardioversion. In-silico trials over 200 AF episodes, 100 ionic profiles and 10 antiarrhythmic compounds were consistent with previous clinical trials, and identified optimal treatments for individual electrophysiological properties of the atria. Algorithms trained on 211 simulated AF episodes exhibited >70% accuracy in predictions of cardioversion for individual treatments using either ionic current profiles or ECG metrics. In structurally-healthy atria, AF inducibility and sustainability are enabled by discordant alternans, under high excitability and steep restitution conditions. Successful pharmacological cardioversion is predicted with 70% accuracy from either ionic or ECG properties, and it is optimal for treatments maximizing refractoriness (thus reducing excitability) for the given ionic current profile of the atria.

Inference of ventricular activation properties from non-invasive electrocardiography.

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. Both electrocardiography and imaging are used for the clinical diagnosis of cardiac disease. The integration of multi-modal datasets through advanced computational methods could enable the development of the cardiac 'digital twin', a comprehensive virtual tool that mechanistically reveals a patient's heart condition from clinical data and simulates treatment outcomes. The adoption of cardiac digital twins requires the non-invasive efficient personalisation of the electrophysiological properties in cardiac models. This study develops new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography, cardiac magnetic resonance (CMR) imaging and modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical CMR imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac ventricular myocardial-mass volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties in sinus rhythm from non-invasive epicardial activation time maps and ECG recordings, achieving higher accuracy for the endocardial speed and sheet (transmural) speed than for the fibre or sheet-normal directed speeds.