Use of artificial intelligence to automatically predict the optimal patient-specific inversion time for late gadolinium enhancement imaging. Tool development and clinical validation

Introduction With the worldwide diffusion of cardiac magnetic resonance (CMR), demand on image quality has grown. CMR late gadolinium enhancement (LGE) imaging provides critical diagnostic and prognostic information, and guides management. The identification of optimal Inversion Time (TI), a time-sensitive parameter closely linked to contrast kinetics, is pivotal for correct myocardium nulling. However, determining the optimal TI can be challenging in some diseases and for less experienced operators. Purpose To develop and test an artificial intelligence tool to automatically predict the personalised optimal TI in LGE imaging. Methods The tool, named THAITI, consists of a Random Forest regression model. It considers, as input parameters, patient-specific TI determinants (age, gender, weight, height, kidney function, heart rate) and CMR scan-specific TI determinants (B0, contrast type and dose, time elapsed from contrast injection). THAITI was trained on 219 patients (3585 images) with mixed conditions who underwent CMR (1.5T; Gadobutrol; averaged, MOCO, free-breathing true-FISP IR [1]) for clinical reasons. The dataset was split with a 90–10 policy: 90% of data for training, and 10% for testing. THAITI’s hyperparameters were optimised by embedding k-fold cross validation into an evolutionary computation algorithm, and the best performing model was finally evaluated on the test set. A graphical user interface was also developed. Clinical validation was performed on 55 consecutive patients, randomised to experimental (THAITI-set TI) vs control (operator-set TI) group. Image quality was assessed blindly by 2 independent experienced operators by a 4-points Likert scale, and by means of the contrast/enhancement ratio (CER) (i.e., signal intensity of enhanced/remote myocardium ratio). Results In the testing set, the TI predicted by THAITI differed from the ground truth by ≥ 5ms in 16% of cases. At clinical validation, myocardial nulling quality did not differ between the experimental vs the control group either by CER or visual assessment, with an overall "optimal" or "good" nulling in 96% vs 93%, respectively. Conclusions Using main determinants of contrast kinetics, THAITI efficiently predicted the optimal TI for CMR-LGE imaging. The tool works as a stand-alone on laptops/mobile devices, not requiring adjunctive scanner technology and thus has great potential for diffusion, including in small or recently opened CMR services, and in low-resource settings. Additional development is ongoing to increase generalisability (multi-vendor, multi-sequence, multi-contrast) and to test its potential to further improve CMR-LGE image quality and reduce the need for repeated imaging for inexperienced operators. Figure 1. Top: THAITI interface. Bottom: examples of experimental group CMR-LGE imaging. Table 1. Control vs experimental group. Data expressed as absolute number (%), mean ± SD, median [IQR]. ⧧ T-test; * Chi-square

Sex Dimorphism in the Myocardial Response to Aortic Stenosis

OBJECTIVES: The goal of this study was to explore sex differences in myocardial remodeling in aortic stenosis (AS) by using echocardiography, cardiac magnetic resonance (CMR), and biomarkers. BACKGROUND: AS is a disease of both valve and left ventricle (LV). Sex differences in LV remodeling are reported in AS and may play a role in disease phenotyping. METHODS: This study was a prospective assessment of patients awaiting surgical valve replacement for severe AS using echocardiography, the 6-min walking test, biomarkers (high-sensitivity troponin T and N-terminal pro-brain natriuretic peptide), and CMR with late gadolinium enhancement and extracellular volume fraction, which dichotomizes the myocardium into matrix and cell volumes. LV remodeling was categorized into normal geometry, concentric remodeling, concentric hypertrophy, and eccentric hypertrophy. RESULTS: In 168 patients (age 70 ± 10 years, 55% male, indexed aortic valve area 0.40 ± 0.13 cm2/m2, mean gradient 47 ± 4 mm Hg), no sex or age differences in AS severity or functional capacity (6-min walking test) were found. CMR captured sex dimorphism in LV remodeling not apparent by using 2-dimensional echocardiography. Normal geometry (82% female) and concentric remodeling (60% female) dominated in women; concentric hypertrophy (71% male) and eccentric hypertrophy (76% male) dominated in men. Men also had more evidence of LV decompensation (pleural effusions), lower left ventricular ejection fraction (67 ± 16% vs. 74 ± 13%; p < 0.001), and higher levels of N-terminal pro-brain natriuretic peptide (p = 0.04) and high-sensitivity troponin T (p = 0.01). Myocardial fibrosis was higher in men, with higher focal fibrosis (late gadolinium enhancement 16.5 ± 11.2 g vs. 10.5 ± 8.9 g; p < 0.001) and extracellular expansion (matrix volume 28.5 ± 8.8 ml/m2 vs. 21.4 ± 6.3 ml/m2; p < 0.001). CONCLUSIONS: CMR revealed sex differences in associations between AS and myocardial remodeling not evident from echocardiography. Given equal valve severity, the myocardial response to AS seems more maladaptive in men than previously reported. (Regression of Myocardial Fibrosis After Aortic Valve Replacement [RELIEF-AS]; NCT02174471.)

Dark blood ischemic LGE segmentation using a deep learning approach

The extent of ischemic scar detected by Cardiac Magnetic Resonance (CMR) with late gadolinium enhancement (LGE) is linked with long-term prognosis, but scar quantification is time-consuming. Deep Learning (DL) approaches appear promising in CMR segmentation. Purpose: To train and apply a deep learning approach to dark blood (DB) CMR-LGE for ischemic scar segmentation, comparing results to 4-Standard Deviation (4-SD) semi-automated method. Methods: We trained and validated a dual neural network infrastructure on a dataset of DB-LGE short-axis stacks, acquired at 1.5T from 33 patients with ischemic scar. The DL architectures were an evolution of the U-Net Convolutional Neural Network (CNN), using data augmentation to increase generalization. The CNNs worked together to identify and segment 1) the myocardium and 2) areas of LGE. The first CNN simultaneously cropped the region of interest (RoI) according to the bounding box of the heart and calculated the area of myocardium. The cropped RoI was then processed by the second CNN, which identified the overall LGE area. The extent of scar was calculated as the ratio of the two areas. For comparison, endo- and epi-cardial borders were manually contoured and scars segmented by a 4-SD technique with a validated software. Results: The two U-Net networks were implemented with two free and open-source software library for machine learning. We performed 5-fold cross-validation over a dataset of 108 and 385 labelled CMR images of the myocardium and scar, respectively. We obtained high performance (&gt; ∼0.85) as measured by the Intersection over Union metric (IoU) on the training sets, in the case of scar segmentation. With regards to heart recognition, the performance was lower (&gt; ∼0.7), although improved (∼ 0.75) by detecting the cardiac area instead of heart boundaries. On the validation set, performances oscillated between 0.8 and 0.85 for scar tissue recognition, and dropped to ∼0.7 for myocardium segmentation. We believe that underrepresented samples and noise might be affecting the overall performances, so that additional data might be beneficial. Figure1: examples of heart segmentation (upper left panel: training; upper right panel: validation) and of scar segmentation (lower left panel: training; lower right panel: validation). Conclusion: Our CNNs show promising results in automatically segmenting LV and quantify ischemic scars on DB-LGE-CMR images. The performances of our method can further improve by expanding the data set used for the training. If implemented in a clinical routine, this process can speed up the CMR analysis process and aid in the clinical decision-making

Sex dimorphism in the myocardial response to aortic stenosis

Objectives: The goal of this study was to explore sex differences in myocardial remodeling in aortic stenosis (AS) by using echocardiography, cardiac magnetic resonance (CMR), and biomarkers. Background: AS is a disease of both valve and left ventricle (LV). Sex differences in LV remodeling are reported in AS and may play a role in disease phenotyping. Methods: This study was a prospective assessment of patients awaiting surgical valve replacement for severe AS using echocardiography, the 6-min walking test, biomarkers (high-sensitivity troponin T and N-terminal pro-brain natriuretic peptide), and CMR with late gadolinium enhancement and extracellular volume fraction, which dichotomizes the myocardium into matrix and cell volumes. LV remodeling was categorized into normal geometry, concentric remodeling, concentric hypertrophy, and eccentric hypertrophy

Vitamin D and COVID-19 severity and related mortality: a prospective study in Italy

Background: Vitamin D deficiency has been suggested to favor a poorer outcome of Coronavirus disease-19 (COVID-19). We aimed to assess if 25-hydroxyvitamin-D (25OHD) levels are associated with interleukin 6 (IL-6) levels and with disease severity and mortality in COVID-19. Methods: We prospectively studied 103 in-patients admitted to a Northern-Italian hospital (age 66.1 ± 14.1 years, 70 males) for severely-symptomatic COVID-19. Fifty-two subjects with SARS-CoV-2 infection but mild COVID-19 symptoms (mildly-symptomatic COVID-19 patients) and 206 subjects without SARS-CoV-2 infection were controls. We measured 25OHD and IL-6 levels at admission and focused on respiratory outcome during hospitalization. Results: Severely-symptomatic COVID-19 patients had lower 25OHD levels (18.2 ± 11.4 ng/mL) than mildly-symptomatic COVID-19 patients and non-SARS-CoV-2-infected controls (30.3 ± 8.5 ng/mL and 25.4 ± 9.4 ng/mL, respectively, p &lt; 0.0001 for both comparisons). 25OHD and IL-6 levels were respectively lower and higher in severely-symptomatic COVID-19 patients admitted to intensive care Unit [(ICU), 14.4 ± 8.6 ng/mL and 43.0 (19.0–56.0) pg/mL, respectively], than in those not requiring ICU admission [22.4 ± 1.4 ng/mL, p = 0.0001 and 16.0 (8.0–32.0) pg/mL, p = 0.0002, respectively]. Similar differences were found when comparing COVID-19 patients who died in hospital [13.2 ± 6.4 ng/mL and 45.0 (28.0–99.0) pg/mL] with survivors [19.3 ± 12.0 ng/mL, p = 0.035 and 21.0 (10.5–45.9) pg/mL, p = 0.018, respectively). 25OHD levels inversely correlated with: i) IL-6 levels (ρ − 0.284, p = 0.004); ii) the subsequent need of the ICU admission [relative risk, RR 0.99, 95% confidence interval (95%CI) 0.98–1.00, p = 0.011] regardless of age, gender, presence of at least 1 comorbidity among obesity, diabetes, arterial hypertension, creatinine, IL-6 and lactate dehydrogenase levels, neutrophil cells, lymphocytes and platelets count; iii) mortality (RR 0.97, 95%CI, 0.95–0.99, p = 0.011) regardless of age, gender, presence of diabetes, IL-6 and C-reactive protein and lactate dehydrogenase levels, neutrophil cells, lymphocytes and platelets count. Conclusion: In our COVID-19 patients, low 25OHD levels were inversely correlated with high IL-6 levels and were independent predictors of COVID-19 severity and mortality

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) mod-els based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo-and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (&gt; 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets.Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker ( &lt;1 min versus 7 +/- 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.(c) 2022 Elsevier B.V. All rights reserved

Clinical recommendations for high altitude exposure of individuals with pre-existing cardiovascular conditions : A joint statement by the European Society of Cardiology, the Council on Hypertension of the European Society of Cardiology, the European Society of Hypertension, the International Society of Mountain Medicine, the Italian Society of Hypertension and the Italian Society of Mountain Medicine

The travelling options currently available allow an increasingly large number of individuals, including sedentary people, the elderly and diseased patients, to reach high altitude (HA) locations, defined as locations higher than 2500 m above sea level (asl),1S i.e. the altitude above which many of the physiological responses that represent challenges for the human body start developing. Physiological acclimatization mechanisms impose an increased workload on the cardiovascular system, but the actual risk of adverse cardiovascular events associated with HA exposure is still a debated issue. The aim of this article is to review the available evidence on the effects of HA in cardiovascular patients and to address their risk of developing clinically relevant events. This was done through multiple Medline searches on the PubMed database, with the main aim of promoting a generally safe access to mountains. Searched terms included a combination of either ‘high altitude’ or ‘hypobaric hypoxia’ plus each of the following: ‘physiology’, ‘maladaption’, ‘cardiovascular response’, ‘systemic hypertension’, ‘pulmonary hypertension’, ‘ischaemic heart disease’, ‘cardiac revascularisation’, ‘heart failure’, ‘congenital heart disease’, ‘arrhythmias’, ‘implantable cardiac devices’, ‘stroke’, ‘cerebral haemorrhage’, ‘exercise’, ‘sleep apnea’. Compared with a previous review article on this topic,2S we now include the most recent data on hypoxia-induced changes in left ventricular (LV) systolic and diastolic function, lung function and ventilation control, blood coagulation, and on the effects of pharmacological interventions. We also offer an update on the clinical and pathophysiological findings related to the exposure to altitude of patients with pre-existing cardiovascular conditions (ischaemic heart disease, heart failure, and arterial and pulmonary hypertension).</p

May Measurement Month 2018: a pragmatic global screening campaign to raise awareness of blood pressure by the International Society of Hypertension

Aims Raised blood pressure (BP) is the biggest contributor to mortality and disease burden worldwide and fewer than half of those with hypertension are aware of it. May Measurement Month (MMM) is a global campaign set up in 2017, to raise awareness of high BP and as a pragmatic solution to a lack of formal screening worldwide. The 2018 campaign was expanded, aiming to include more participants and countries. Methods and results Eighty-nine countries participated in MMM 2018. Volunteers (≥18 years) were recruited through opportunistic sampling at a variety of screening sites. Each participant had three BP measurements and completed a questionnaire on demographic, lifestyle, and environmental factors. Hypertension was defined as a systolic BP ≥140 mmHg or diastolic BP ≥90 mmHg, or taking antihypertensive medication. In total, 74.9% of screenees provided three BP readings. Multiple imputation using chained equations was used to impute missing readings. 1 504 963 individuals (mean age 45.3 years; 52.4% female) were screened. After multiple imputation, 502 079 (33.4%) individuals had hypertension, of whom 59.5% were aware of their diagnosis and 55.3% were taking antihypertensive medication. Of those on medication, 60.0% were controlled and of all hypertensives, 33.2% were controlled. We detected 224 285 individuals with untreated hypertension and 111 214 individuals with inadequately treated (systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg) hypertension. Conclusion May Measurement Month expanded significantly compared with 2017, including more participants in more countries. The campaign identified over 335 000 adults with untreated or inadequately treated hypertension. In the absence of systematic screening programmes, MMM was effective at raising awareness at least among these individuals at risk

Metodo per determinare un tempo di inversione ottimale per una sequenza “Inversion Recovery” di risonanza magnetica utilizzabile per l’acquisizione di immagini tardive dopo somministrazione di un mezzo di contrasto paramagnetico

Il tempo di inversione (TI) è la misurazione del lasso di tempo tra impulsi di radiofrequenza e statici (di campionamento), necessaria per ottenere il segnale di risonanza magnetica (RM) e rilevare via immagini il rilassamento di un tessuto da esaminare. Ad oggi il TI viene stimato dall'operatore di RM, in base all'esperienza personale o spesso per tentativi e errori, o sottoponendo il paziente a diversi tempi di inversione per confrontarne le immagini acquisite, la cui qualità non è comunque garantita. Tempi e costi di somministrazione del test, qualità e leggibilità delle immagini possono però ora essere migliorati attraverso un modello di machine learning, capace di tararsi sui dati significativi del paziente e sui parametri specifici dell'esame da eseguire. Il modello è stato addestrato su una molteplicità di valutazioni campione in esami del miocardio ed è potenzialmente applicabile ad altri esami di RM