bluuebunny/crossref_metadata_2025_split huggingface dataset:
Temporal and geogrpahic trends in female representation within European cardiovascular imaging among 13,436 individuals associated with EACVI
Authors: Berrill M , Santos C , Baltabaeva A 
Published: October 2024

Abstract

Introduction
Women are under-represented in cardiology trial enrolment, specialty training, faculty, scientific authorship and board membership in cardiology journals and societies. An EACVI Task Force on Women in Cardiovascular Imaging aims to promote women’s representation and leadership, but data is lacking in this area.

Purpose
To characterise the representation of women in cardiovascular imaging within Europe and examine temporal and geographic trends.

Methods
We scraped publicly available name data from over 100 online sources including EACVI taskforces, committees, research project members, consensus and position paper authors, board membership and past presidents alongside members of affiliated cardiovascular imaging national societies and working groups. We also included names from EACVI or national accreditation records for cardiac imaging. We recorded data on country location and year of affiliation. We defined gender in a binary manner as either male or female. The previously validated artificial intelligence (AI) system Namsor assigned gender based on name morphology and geolocation data. We pre-set a confidence of gender-name matching of 95%.

Results
The AI was able to assign gender at 95% confidence to 12,643/13,436 (94.1%) names. The overall number of names assigned female gender was 5903/12643 (46.7%), rejecting the null hypothesis that gender was equally distributed (i.e. 50% female) [p&lt;0.0001]. Among the 9712 individuals with year of affiliation we observed female representation to increase over time: 1989-2000 4/18 (22.2%), 2001-2004 107/287 (37.8%) 2005-2009 77/170 (45.3%), 2010-2014 109/203 (53.7%), 2015-2019 1063/2022 (51.3%) and 2020-2024 3352/3064 (52.2%) (Cochran-Armitage trend test 24.196 [p&lt;0.0001]) (Figure 1). Female representation was lower in countries outside of Europe, compared to within Europe; 740/2654 (27.9%) vs. 5163/9989 (51.7%) [p&lt;0.0001]. The lowest rate of female representation was in Saudia Arabia (15.1%), the highest in Lithuania (83.3%). Figure 2 displays a choropleth of gender representation (%) between European countries with n&gt;20.

Conclusion(s)
There are significant limitations in this form of analysis, including that we have arbritarily defined gender in a binary format, thereby excluding individuals who do not identify with such a social construct. It is also likely that the excluded names based on the 95% confidence of the AI software disproportionately represents individuals from ethnic minorities. However, our data provides a basic understanding of the representation of women in European cardiovascular imaging over time and between countries. We observed a trend of increasing female participation but with substantial variation between countries both inside and outside of Europe. This data may be useful to refine and direct efforts to improve female representation in areas in which it is needed most.
Detection and risk stratification of cardiac amyloidosis patients by integration of imaging and non-imaging data using a machine learning approach
Authors: Spielvogel C P , Haberl D , Kluge K , Mascherbauer K , Hennenberg J , Yu J , Ning J , Traub-Weidinger T , Calabretta R , Mascherbauer J , Kammerlander A , Hengstenberg C , Hacker M , Nitsche C 
Published: October 2024

Abstract

Background
With the advent of amyloid-targeting therapies, early and reliable diagnosis as well as precise risk estimation of cardiac amyloidosis (CA) have become of substantial importance. While the current diagnostic approach relies on difficult-to-standardize, visual interpretation of 99mTc-scintigraphy, risk assessment is largely based on a combination of blood and imaging parameters.

Purpose
Aim of this study is to assess the possibility of machine learning-based integration of scintigraphy, echocardiography, and non-imaging parameters such as blood parameters and comorbidities for the detection and risk stratification of patients with CA.

Methods
This study included all patients who underwent 99mTc-scintigraphy at a university affiliated tertiary care centre between 2010 and 2023. Two machine learning models were developed, and the relative predictive importance of their parameters were assessed (Figure 1): First, a model to detect patients with CA-suggestive uptake (Perugini grade 2/3) on 99mTc-scintigraphy scans; Second, a model to assess the risk of patients with CA-suggestive uptake for future heart failure hospitalization (HFH). A total of 58 features were extracted from electronic health records including blood, echocardiographic, demographic parameters and comorbidities. Scintigraphy imaging features were extracted from the raw imaging data using a deep learning approach. For the time to HFH prediction, a random survival forest machine learning model was employed.

Results
Overall, 12 380 consecutively enrolled patients were included, 279 (2.3%) of which were affected by CA-suggestive uptake. The machine learning model showed higher accuracy for the detection of CA (AUC 0.96 [95% CI 0.95-0.97], sensitivity 0.78 [95% CI 0.77-0.80] and specificity 0.99 [95% CI 0.99-0.99]. In the outcome analysis, the machine model showed good accuracy in predicting future HFH (C-index 0.71 [95% CI 0.68-0.75]). Parameter importance for the time to event analysis revealed right ventricular diameter, previous diagnosis of chronic heart failure and creatine kinase as the three most important predictors (Figure 2).

Conclusions
Detection of patients with cardiac amyloidosis and their risk estimation for heart failure can be aided using machine learning-based integration of imaging and non-imaging data. Our findings may guide novel approaches for assessing disease progression and treatment response.
Deep learning enables the fully automated quantification of high-resolution cardiac CT images: human in loop for model development
Authors: Mohammadi Kazaj P , Baj G , Schutze J , Stark A , Valenzuela W , Giannopoulos A A , Ralf Buechel R , Graeni C , Shiri I 
Published: October 2024

Abstract

Introduction
Cardiac computed tomography (CCT) images provide vast spatial and temporal information. Segmentation of the structure of interest is necessary to provide quantitative information. However, manual segmentation of cardiac substructures is highly time-consuming.

Objective
This study aims to develop a robust deep-learning segmentation model for cardiac substructure segmentation in 3D CCT images.

Method
We initially developed a 3D U-Net architecture, incorporating extensive augmentation, for the segmentation of the Left Ventricle Myocardium (LVM), the blood cavities of the Left and Right Ventricles (LV, RV), and the Left and Right Atria (LA, RA). This model utilized CT images from 20 patients who had available manual segmentation. Subsequently, we applied this initial model to datasets from two centers, including different generations of scanners. We then visually inspected the segmentation, selected the highest-quality segmentations, and modify it if necessary, and excluded the low-quality data. We continued fine-tuning the model with the new dataset until we reached a count of 456 patients. As there were multiple images from one patient during this model's development, we generated datasets to develop a new model using the entire new dataset to avoid any data leakage in the reported metrics. The model was developed using data from Center 1 (5-fold cross-validation) and then evaluated on an external test set from Center 2 (126 patients) and also 4D images from Center 1. Various metrics were calculated to assess the model's performance.

Results
In the internal test set, utilizing 5-fold CV, the model demonstrated high segmentation accuracy, achieving an overall Dice coefficient of 0.98, with individual scores of 0.96 for LVM, 0.98 for LV, 0.98 for RV, 0.99 for RA, and 0.98 for LA. We assessed the automatic quantification capability of the segmentation model from 256 patients, resulting in an R2 value greater than 0.92 for both LV end-diastolic and systolic volumes. In contrast, the model maintained robust performance on the external dataset, achieving an overall Dice coefficient of 0.96, with individual scores of 0.95 for LVM, 0.98 for LV, 0.97 for RV, 0.98 for RA, and 0.94 for LA.

Conclusion
We provided a large cohort of ground truth segmentations for different cardiac CT substructures using a human-in-the-loop strategy. Subsequently, we developed a deep learning model for highly accurate automated segmentation of these substructures. This model allows for fast and fully automatic segmentation and quantification of 3D and 4D cardiac CT images, from which various quantitative metrics can be extracted from different cardiac substructures.
Identifying at-risk patients for atrial high-rate episodes by machine learning
Authors: Chen Y , Gerra L G , Gue Y G , Mcdowell G M , Boriani G B , Zheng Y L Z , Lip G Y H L 
Published: October 2024

Abstract

Background
Current evidence suggests that atrial high rate episodes (AHRE)/subclinical atrial fibrillation (AF) are associated with increasing risk of clinical AF, and subsequently, ischaemic stroke and other adverse cardiovascular events.

Purpose
To construct a high-performance model by machine learning (ML) for the prediction of AHRE detected by a cardiac implantable electronic device (CIED) capable of atrial sensing in a European cohort.

Methods
We utilized data from a prospective cohort of patients with CIED. AHRE was defined as an episode lasting at least 5 minutes with atrial rate ≥ 175 beats per minute. We divided the cohort into training and internal validation cohorts in 7:3 and used 5-fold cross-validation in the training cohort to assess model performance and prevent model overfitting. Pre-selected features were imported into eight common ML algorithms to predict AHRE, including light gradient boosting machine (LightGBM), random forest (RF), logistic regression, support vector machine, multiple perceptron, eXtreme gradient boosting machine (XGBoost), Gaussian naïve Bayes, and K nearest neighbours. The importance ranking of the features was interpreted by calculating the SHapley Additive exPlanationation (SHAP) value for each feature within the optimal model.

Results
A total of 500 consecutive patients with CIEDs (mean age 70.2±13.3; 39.2% female) were included. Of these, 164 patients (32.8%) were found to have AHRE. Among all ML models, RF was the best performing model, which obtained the highest AUC (0.738), recall (0.673) and F1-score (0.584) in the internal validation cohort. The results of SHAP showed that antiplatelet, statin, age, white blood cell count, and cardiomyopathy were the first five important features, indicating that the RF model had good interpretability and reliability.

Conclusion
Our study is the first to demonstrate in a European cohort the effectiveness and feasibility of ML algorithms in predicting AHRE in patients detected by CIEDs. The best RF models require further external validation to assess generalizability to other cohorts.Figure 1Figure 2
Improving cardiovascular risk stratification with ECG-predicted risk factors in primary prevention
Authors: Knorr M S , Brederecke J , Bremer J P , Neyazi M , Blankenberg S , Doerr M , Vollmer M , Schnabel R B 
Published: October 2024

Abstract

Background
Cardiovascular risk- and screening scores guide clinical practice. Built upon simple risk factors, they could benefit from information on actual, subclinical cardiac changes. The ECG provides proxies for various cardiac states, which can be detected using machine learning. We systematically assess the added value of ECG-based cardiovascular risk factor estimates for six outcomes in two population-based cohorts: Study of Health in Pomerania (SHIP-Start) and UK Biobank (UKB).

Methods
We employed convolutional neural networks to analyze ECGs from the Hamburg City Health Study (HCHS) for the assessment of multiple risk factors, including age, weight, diabetes, atrial fibrillation (AF) and heart failure. Cox models were fitted on UKB and SHIP to predict 5 and 15 year time-to-event. Endpoints were death, cardiovascular death, AF, stroke, heart failure and myocardial infarction. Covariates were sex, age, body mass index, diabetes, hypertension and smoking (Cox-RF), ECG-predicted risk factors (Cox-AI), and a combination (Cox-RF/AI). Stratified Monte Carlo cross-validation was used as validation and groups of C-indices were compared using Mann-Whitney U tests.

Results
The study included 9103 HCHS participants (mean age 62.0±8.4 years, 51% women), 3546 SHIP participants (age 49.7±16.4, 51% women), and 32828 UKB participants (age 64±7.6 years, 52% women). Cox-AI performed comparable or better than Cox-RF for AF (SHIP: 0.88 vs. 0.84, UKB: 0.71 vs. 0.72) and heart failure (SHIP: 0.8 vs. 0.79, UKB: 0.76 vs. 0.75), but was less strong for the other outcomes. The combination of information on cardiovascular risk factors and ECG information (Cox-RF/AI) significantly improved the predictive ability compared to risk factors alone (Cox-RF) for AF (SHIP: 0.89 vs. 0.84, UKB: 0.76 vs. 0.72), cardiovascular death (SHIP: 0.90 vs. 0.89, UKB: 0.75 vs. 0.74) and heart failure (SHIP: 0.81 vs. 0.79, UKB: 0.80 vs. 0.75).

Discussion
ECG-based risk factors complement risk stratification by incorporating nuanced information on cardiac function and structure, improving risk prediction and thus possibly clinical decision making and patient outcomes.
Rhythm classifier: artificial intelligence for rhythm classification in insertable cardiac monitor
Authors: Majumder S , Sarkar S , Landman S , King K 
Published: October 2024

Abstract

Background
Though detections improved significantly, separating atrial tachycardia/atrial flutter (AT/AFL) from atrial fibrillations (AF) in insertable cardiac monitors (ICM) remains a challenge.

Objective
Using artificial intelligence (AI) algorithms to classify AF and AT/AFL in ICM detected episodes, which may reduce episode review burden.

Methods
Both raw EGM signal and the QRS diminished version of the raw signal were used to extract 14 AF and AT/AFL features from each episode. Extracted features were then represented as an ensemble of features and used to train and test the AI model. Modified ResNet18 network was used as the AI model. All episodes from a random selection of 80% of patients were used as training dataset, another 10% were used as validation, and 10% were used as test dataset, to evaluate model performance. AI model probability threshold was chosen based on validation set results. Episode detection sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristics (ROC) curve in the independent test dataset are reported.

Results
AF and AT/AFL episodes detected in 918 LINQ ICM patients (5,253 AF and 4,705 AT/AFL – 745 pts and 8,950 false detection episodes) were included. Training dataset consisted of 15,183 episodes (735 pts), validation set consisted of 1,901 episodes (91 pts), and test set consisted of 1,824 episodes (92 pts). At chosen threshold in validation set, AF and AT/AFL detection had sensitivity of 90% and 73.3%, respectively, and specificity of 93.2% and 95%, respectively, with AUC of 0.966. In test data, AF and AT/AFL obtained relative sensitivity of 84.7% and 85.7%, respectively, with specificity of 88% and 86.5%, respectively (Fig. A). With ROC-AUC of 0.956 (Fig. B), the relative diagnostic yield of AF and AT/AFL on test data was 95.6%.

Conclusion
The trained AI model provides a relative sensitivity of about 85% for AF vs. AT/AFL classification and reduces inappropriate ICM detections by 79%. Adding more episodes may improve the performance.
Preventing unnecessary echocardiograms in de novo patients referred to the cardiology outpatient clinic using electrocardiogram-based deep learning
Authors: Arends B , Vessies M , Van Amsterdam W , Teske A , Van Der Harst P , Van Osch D , Van Es R 
Published: October 2024

Abstract

Introduction
The growing demand on European echocardiography labs, driven by the rise in structural heart disease (SHD), is further complicated by a shortage of skilled sonographers, necessitating resource optimisation strategies. Electrocardiogram (ECG)-based deep learning models may assist in streamlining the echocardiography workflow.

Purpose
This study sought to develop a deep learning model capable of identifying patients at very low risk of SHD in a population of de novo patients referred to the cardiology outpatient clinic.

Methods
We identified all adult patients who underwent a 12-lead ECG and echocardiogram at a Dutch academic teaching hospital between 2011 and August 2023. Echocardiogram outcomes were extracted from reports, including left ventricular systolic dysfunction (LVSD), left ventricular dilatation (LVD), aortic stenosis (AS), aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR), from which a composite outcome label was derived. A cohort of patients newly referred to our cardiology outpatient clinic with a primary complaint of chest pain, dyspnea, palpitations, dizziness, a murmur, or an abnormal ECG was held out as a test set, while the remaining dataset was split patient-wise into training (90%) and validation (10%) sets. A convolutional neural network was trained using only the ECG tracing as input and the composite label as outcome.

Results
We included 92,210 ECG-echo pairs from 44,785 patients (19,717 female) with 25,543 (27.7%) abnormal ECG-echo pairs. Among 2724 newly referred patients in the test set, 210/1389 males (15.1%) and 201/1335 females (15.1%) were diagnosed with SHD. In the test set, the model for predicting composite outcome demonstrated an area-under-the-curve, negative predictive value, and specificity of 0.74 (95% confidence interval [CI] 0.71-0.79), 0.94 (95% CI 0.92-0.95), and 0.47 (95% CI 0.45-0.49), respectively.

Conclusion
This study demonstrates the feasibility of employing deep learning models based solely on electrocardiogram (ECG) data to identify patients at very low risk of structural heart disease (SHD). Further validation and prospective studies are warranted to confirm the generalizability and clinical utility of our findings in diverse patient populations and healthcare settings.
Barriers and facilitators for implementation of AI algorithms for ECG analysis and triage in patients with chest pain
Authors: Arends B , Mccormick J , Heus P , Van Der Harst P , Van Es R 
Published: October 2024

Abstract

Introduction
Numerous artificial intelligence (AI) algorithms have been developed to assist in clinical decision-making, with a focus on areas like electrocardiogram (ECG) analysis due to the signal complexity and the varying levels of expertise among healthcare professionals. However, ensuring acceptance of these algorithms by end-users is paramount for successful implementation.

Purpose
This study aimed to identify barriers and facilitators related to implementation of an AI algorithm for ECG analysis and triage in patients with chest pain.

Methods
A three-round modified electronic Delphi study was conducted among Dutch potential end-users of an ECG-AI algorithm, including physicians, nurses and ambulance professionals. During the first round, participants brainstormed on barriers and facilitators, which were subsequently mapped to the Consolidated Framework for Implementation Research (CFIR). In the following two rounds, participants reviewed the identified barriers and facilitators for relevance and ranked them according to their importance.

Results
Twenty-five out of the initial forty respondents completed all three rounds, resulting in a dropout rate of 38%. Four barriers and twelve facilitators were identified. The most important facilitator was "Faster recognition of subtle ECG abnormalities", while "Poor model performance" emerged as the most important barrier. The most frequently mentioned corresponding CFIR domains were the innovation and inner setting domains.

Conclusion
The identification of barriers and facilitators provides valuable insights into the acceptance and utilisation of ECG-AI algorithms by end-users. By considering the CFIR domains, including innovation and inner setting, this study offers a comprehensive understanding of the contextual factors that influence the successful implementation of AI algorithms in healthcare settings. Addressing these barriers and facilitators will be crucial to facilitating future implementation processes.Barriers and facilitators
External validation of a machine learning based classification algorithm for ambulatory heart rhythm diagnostics using smartphone-photoplethysmography - the SMARTBEATS algorithm study
Authors: Fernstad J , Svennberg E , Aberg P , Kemp Gudmundsdottir K , Engdahl J 
Published: October 2024

Abstract

Introduction
Smartphone-photoplethysmography (PPG) can be used for heart rhythm monitoring. According to current guidelines, ECG verification is required for a new diagnosis of atrial fibrillation (AF). Accurate machine learning (ML) based automatic algorithms have the potential to facilitate clinical applications generating large numbers of PPG recordings, by offloading the need for manual over-read.

Purpose
The aim of this study was to evaluate the diagnostic performance of an automatic ML-based algorithm for heart rhythm diagnostics using smartphone-PPG recorded by patients with AF in an unsupervised ambulatory setting.

Methods
Patients undergoing direct current cardioversion (DCCV) at a university hospital for treatment of AF or atrial flutter (AFL) were asked to perform one-minute heart rhythm recordings post-cardioversion at least twice daily for 30 days. All participants were provided with an iPhone 7 smartphone running the CORAI Heart Monitor PPG application simultaneously with a single-lead ECG recording (KardiaMobile).
The automatic algorithm uses support vector machines (SVM) to classify heart rhythm from smartphone-PPG recordings. The SVM classifier evaluated here was trained on ambulatory PPG recordings made by patients in a separate cardioversion cohort, ensuring complete independence between training and validation sets. PPG recordings in the validation cohort were analyzed by the automatic algorithm and diagnostic performance was calculated by comparing heart rhythm classification output from the SVM classifier to the heart rhythm diagnosis from the simultaneous ECG recordings. ECG recordings were interpreted independently by two cardiologists and were set as gold standard.

Results
The SVM classifier was trained on data from 153 patients with 11,749 PPG recordings, of which 5,873 (50.0%) were labelled as AF and 5,876 (50.0%) as sinus rhythm (SR), as assessed by manual reading. In the validation cohort, 280 patients, with a median age of 69.0 years (31% women), registered 18,005 simultaneous PPG and ECG recordings. Recordings interpreted as AFL on ECG (2.0%), as having insufficient quality on ECG (4.9%) or PPG (2.8%), and low certainty algorithmic classifications (1.2%) were excluded, leaving 16,057 PPG recordings. Included ECG recordings consisted of 71.2% interpreted as (SR) and 28.8% as AF. Algorithm classification of the PPG recordings in the validation cohort diagnosed AF (sensitivity) in 99.7% and SR (specificity) in 99.7% of the recordings, with an overall accuracy of 99.7%. F1-score was 99.4% and area under the ROC curve (AUC) was 0.999.

Conclusion
A machine learning based algorithm for automatic heart rhythm diagnostics showed excellent diagnostic performance for smartphone-PPG recordings in an unsupervised ambulatory setting, minimizing the need for ECG verification in cardioversion populations.