Publications
124 results found
Ali N, Saqi K, Arnold AD, et al., 2023, Left bundle branch pacing with and without anodal capture: impact on ventricular activation pattern and acute haemodynamics., Europace, Vol: 25
AIMS: Left bundle branch pacing (LBBP) can deliver physiological left ventricular activation, but typically at the cost of delayed right ventricular (RV) activation. Right ventricular activation can be advanced through anodal capture, but there is uncertainty regarding the mechanism by which this is achieved, and it is not known whether this produces haemodynamic benefit. METHODS AND RESULTS: We recruited patients with LBBP leads in whom anodal capture eliminated the terminal R-wave in lead V1. Ventricular activation pattern, timing, and high-precision acute haemodynamic response were studied during LBBP with and without anodal capture. We recruited 21 patients with a mean age of 67 years, of whom 14 were males. We measured electrocardiogram timings and haemodynamics in all patients, and in 16, we also performed non-invasive mapping. Ventricular epicardial propagation maps demonstrated that RV septal myocardial capture, rather than right bundle capture, was the mechanism for earlier RV activation. With anodal capture, QRS duration and total ventricular activation times were shorter (116 ± 12 vs. 129 ± 14 ms, P < 0.01 and 83 ± 18 vs. 90 ± 15 ms, P = 0.01). This required higher outputs (3.6 ± 1.9 vs. 0.6 ± 0.2 V, P < 0.01) but without additional haemodynamic benefit (mean difference -0.2 ± 3.8 mmHg compared with pacing without anodal capture, P = 0.2). CONCLUSION: Left bundle branch pacing with anodal capture advances RV activation by stimulating the RV septal myocardium. However, this requires higher outputs and does not improve acute haemodynamics. Aiming for anodal capture may therefore not be necessary.
Ali N, Arnold AD, Miyazawa AA, et al., 2023, Septal scar as a barrier to left bundle branch area pacing, PACE-PACING AND CLINICAL ELECTROPHYSIOLOGY, Vol: 46, Pages: 1077-1084, ISSN: 0147-8389
Seligman H, Patel SB, Alloula A, et al., 2023, Development of artificial intelligence tools for invasive Doppler-based coronary microvascular assessment., Eur Heart J Digit Health, Vol: 4, Pages: 291-301
AIMS: Coronary flow reserve (CFR) assessment has proven clinical utility, but Doppler-based methods are sensitive to noise and operator bias, limiting their clinical applicability. The objective of the study is to expand the adoption of invasive Doppler CFR, through the development of artificial intelligence (AI) algorithms to automatically quantify coronary Doppler quality and track flow velocity. METHODS AND RESULTS: A neural network was trained on images extracted from coronary Doppler flow recordings to score signal quality and derive values for coronary flow velocity and CFR. The outputs were independently validated against expert consensus. Artificial intelligence successfully quantified Doppler signal quality, with high agreement with expert consensus (Spearman's rho: 0.94), and within individual experts. Artificial intelligence automatically tracked flow velocity with superior numerical agreement against experts, when compared with the current console algorithm [AI flow vs. expert flow bias -1.68 cm/s, 95% confidence interval (CI) -2.13 to -1.23 cm/s, P < 0.001 with limits of agreement (LOA) -4.03 to 0.68 cm/s; console flow vs. expert flow bias -2.63 cm/s, 95% CI -3.74 to -1.52, P < 0.001, 95% LOA -8.45 to -3.19 cm/s]. Artificial intelligence yielded more precise CFR values [median absolute difference (MAD) against expert CFR: 4.0% for AI and 7.4% for console]. Artificial intelligence tracked lower-quality Doppler signals with lower variability (MAD against expert CFR 8.3% for AI and 16.7% for console). CONCLUSION: An AI-based system, trained by experts and independently validated, could assign a quality score to Doppler traces and derive coronary flow velocity and CFR. By making Doppler CFR more automated, precise, and operator-independent, AI could expand the clinical applicability of coronary microvascular assessment.
Kanagaratnam P, Francis DP, Chamie D, et al., 2023, A randomised controlled trial to investigate the use of acute coronary syndrome therapy in patients hospitalised with COVID-19: the C19-ACS trial, Journal of Thrombosis and Haemostasis, Vol: 21, Pages: 2213-2222, ISSN: 1538-7836
BACKGROUND: Patients hospitalised with COVID-19 suffer thrombotic complications. Risk factors for poor outcomes are shared with coronary artery disease. OBJECTIVES: To investigate efficacy of an acute coronary syndrome regimen in patients hospitalised with COVID-19 and coronary disease risk factors. PATIENTS/METHODS: A randomised controlled open-label trial across acute hospitals (UK and Brazil) added aspirin, clopidogrel, low-dose rivaroxaban, atorvastatin, and omeprazole to standard care for 28-days. Primary efficacy and safety outcomes were 30-day mortality and bleeding. The key secondary outcome was a daily clinical status (at home, in hospital, on intensive therapy unit admission, death). RESULTS: 320 patients from 9 centres were randomised. The trial terminated early due to low recruitment. At 30 days there was no significant difference in mortality (intervention: 11.5% vs control: 15%, unadjusted OR 0.73, 95%CI 0.38 to 1.41, p=0.355). Significant bleeds were infrequent and not significantly different between the arms (intervention: 1.9% vs control 1.9%, p>0.999). Using a Bayesian Markov longitudinal ordinal model, it was 93% probable that intervention arm participants were more likely to transition to a better clinical state each day (OR 1.46, 95% CrI 0.88 to 2.37, Pr(Beta>0)=93%; adjusted OR 1.50, 95% CrI 0.91 to 2.45, Pr(Beta>0)=95%) and median time to discharge home was two days shorter (95% CrI -4 to 0, 2% probability that it was worse). CONCLUSIONS: Acute coronary syndrome treatment regimen was associated with a reduction in the length of hospital stay without an excess in major bleeding. A larger trial is needed to evaluate mortality.
Zaman S, Padayachee Y, Shah M, et al., 2023, Smartphone-based remote monitoring in heart failure with reduced ejection fraction: retrospective cohort study of secondary care use and costs, JMIR Cardio, Vol: 7, ISSN: 2561-1011
BACKGROUND: Despite effective therapies, the economic burden of heart failure with reduced ejection fraction (HFrEF) is driven by frequent hospitalizations. Treatment optimization and admission avoidance rely on frequent symptom reviews and monitoring of vital signs. Remote monitoring (RM) aims to prevent admissions by facilitating early intervention, but the impact of noninvasive, smartphone-based RM of vital signs on secondary health care use and costs in the months after a new diagnosis of HFrEF is unknown. OBJECTIVE: The purpose of this study is to conduct a secondary care health use and health-economic evaluation for patients with HFrEF using smartphone-based noninvasive RM and compare it with matched controls receiving usual care without RM. METHODS: We conducted a retrospective study of 2 cohorts of newly diagnosed HFrEF patients, matched 1:1 for demographics, socioeconomic status, comorbidities, and HFrEF severity. They are (1) the RM group, with patients using the RM platform for >3 months and (2) the control group, with patients referred before RM was available who received usual heart failure care without RM. Emergency department (ED) attendance, hospital admissions, outpatient use, and the associated costs of this secondary care activity were extracted from the Discover data set for a 3-month period after diagnosis. Platform costs were added for the RM group. Secondary health care use and costs were analyzed using Kaplan-Meier event analysis and Cox proportional hazards modeling. RESULTS: A total of 146 patients (mean age 63 years; 42/146, 29% female) were included (73 in each group). The groups were well-matched for all baseline characteristics except hypertension (P=.03). RM was associated with a lower hazard of ED attendance (hazard ratio [HR] 0.43; P=.02) and unplanned admissions (HR 0.26; P=.02). There were no differences in elective admissions (HR 1.03, P=.96) or outpatient use (HR 1.40; P=.18) between the 2 groups. These differences were sustai
Auton A, Zaman S, Padayachee Y, et al., 2023, Smartphone-based remote monitoring for chronic heart failure: mixed methods analysis of user experience from patient and nurse perspectives, JMIR Nursing, Vol: 6, ISSN: 2562-7600
BACKGROUND: Community-based management by heart failure specialist nurses (HFSNs) is key to improving self-care in heart failure with reduced ejection fraction. Remote monitoring (RM) can aid nurse-led management, but in the literature, user feedback evaluation is skewed in favor of the patient rather than nursing user experience. Furthermore, the ways in which different groups use the same RM platform at the same time are rarely directly compared in the literature. We present a balanced semantic analysis of user feedback from patient and nurse perspectives of Luscii, a smartphone-based RM strategy combining self-measurement of vital signs, instant messaging, and e-learning. OBJECTIVE: This study aims to (1) evaluate how patients and nurses use this type of RM (usage type), (2) evaluate patients' and nurses' user feedback on this type of RM (user experience), and (3) directly compare the usage type and user experience of patients and nurses using the same type of RM platform at the same time. METHODS: We performed a retrospective usage type and user experience evaluation of the RM platform from the perspective of both patients with heart failure with reduced ejection fraction and the HFSNs using the platform to manage them. We conducted semantic analysis of written patient feedback provided via the platform and a focus group of 6 HFSNs. Additionally, as an indirect measure of tablet adherence, self-measured vital signs (blood pressure, heart rate, and body mass) were extracted from the RM platform at onboarding and 3 months later. Paired 2-tailed t tests were used to evaluate differences between mean scores across the 2 timepoints. RESULTS: A total of 79 patients (mean age 62 years; 35%, 28/79 female) were included. Semantic analysis of usage type revealed extensive, bidirectional information exchange between patients and HFSNs using the platform. Semantic analysis of user experience demonstrates a range of positive and negative perspectives. Positive impacts includ
Zaman S, Vimalesvaran K, Howard JP, et al., 2023, Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI, Journal of Medical Artificial Intelligence, Vol: 6, ISSN: 2617-2496
BACKGROUND: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images. This allows the automated removal of slices that do not contain the left ventricular (LV) myocardium. METHODS: Anonymised LV short-axis slices from 300 random scans (3,552 individual images) were extracted. Each image's anatomical position relative to the LV was labelled using two different strategies performed for 5 hours each: (I) 'one-image-at-a-time': each image labelled according to its position: 'too basal', 'LV', or 'too apical' individually by one of three experts; and (II) 'multiple-image-ranking': three independent experts ordered slices according to their relative position from 'most-basal' to 'most apical' in batches of eight until each image had been viewed at least 3 times. Two convolutional neural networks were trained for a three-way classification task (each model using data from one labelling strategy). The models' performance was evaluated by accuracy, F1-score, and area under the receiver operating characteristics curve (ROC AUC). RESULTS: After excluding images with artefact, 3,323 images were labelled by both strategies. The model trained using labels from the 'multiple-image-ranking strategy' performed better than the model using the 'one-image-at-a-time' labelling strategy (accuracy 86% vs. 72%, P=0.02; F1-score 0.86 vs. 0.75; ROC AUC 0.95 vs. 0.86). For expert clinicians performing this task manually the intra-observer variability was low (Cohen's κ=0.90), but the inter-observer variability was higher (Cohen's &kap
Ali N, Arnold AD, Miyazawa AA, et al., 2023, Comparison of methods for delivering cardiac resynchronization therapy: an acute electrical and haemodynamic within-patient comparison of left bundle branch area, His bundle, and biventricular pacing, EP Europace, Vol: 25, Pages: 1060-1067, ISSN: 1099-5129
AimsLeft bundle branch area pacing (LBBAP) is a promising method for delivering cardiac resynchronization therapy (CRT), but its relative physiological effectiveness compared with His bundle pacing (HBP) is unknown. We conducted a within-patient comparison of HBP, LBBAP, and biventricular pacing (BVP).Methods and resultsPatients referred for CRT were recruited. We assessed electrical response using non-invasive mapping, and acute haemodynamic response using a high-precision haemodynamic protocol. Nineteen patients were recruited: 14 male, mean LVEF of 30%. Twelve had time for BVP measurements. All three modalities reduced total ventricular activation time (TVAT), (ΔTVATHBP -43 ± 14 ms and ΔTVATLBBAP −35 ± 20 ms vs. ΔTVATBVP −19 ± 30 ms, P = 0.03 and P = 0.1, respectively). HBP produced a significantly greater reduction in TVAT compared with LBBAP in all 19 patients (−46 ± 15 ms, −36 ± 17 ms, P = 0.03). His bundle pacing and LBBAP reduced left ventricular activation time (LVAT) more than BVP (ΔLVATHBP −43 ± 16 ms, P < 0.01 vs. BVP, ΔLVATLBBAP −45 ± 17 ms, P < 0.01 vs. BVP, ΔLVATBVP −13 ± 36 ms), with no difference between HBP and LBBAP (P = 0.65). Acute systolic blood pressure was increased by all three modalities. In the 12 with BVP, greater improvement was seen with HBP and LBBAP (6.4 ± 3.8 mmHg BVP, 8.1 ± 3.8 mmHg HBP, P = 0.02 vs. BVP and 8.4 ± 8.2 mmHg for LBBAP, P = 0.3 vs. BVP), with no difference between HBP and LBBAP (P = 0.8).ConclusionHBP delivered better ventricular resynchronization than LBBAP because right ventricular activation was slower during LBBAP. But LBBAP was not inferior to HBP with respect to LV electrical resynchronization and acute haemodynamic response.
Lane ES, Jevsikov J, Shun-shin MJ, et al., 2023, Automated multi-beat tissue Doppler echocardiography analysis using deep neural networks, MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, ISSN: 0140-0118
On Y, Vimalesvaran K, Galazis C, et al., 2023, Automatic Aortic Valve Pathology Detection from 3-Chamber Cine MRI with Spatio-Temporal Attention Maps, Pages: 648-657, ISSN: 0302-9743
The assessment of aortic valve pathology using magnetic resonance imaging (MRI) typically relies on blood velocity estimates acquired using phase contrast (PC) MRI. However, abnormalities in blood flow through the aortic valve often manifest by the dephasing of blood signal in gated balanced steady-state free precession (bSSFP) scans (Cine MRI). We propose a 3D classification neural network (NN) to automatically identify aortic valve pathology (aortic regurgitation, aortic stenosis, mixed valve disease) from Cine MR images. We train and test our approach on a retrospective clinical dataset from three UK hospitals, using single-slice 3-chamber cine MRI from N = 576 patients. Our classification model accurately predicts the presence of aortic valve pathology (AVD) with an accuracy of 0.85 ± 0.03 and can also correctly discriminate the type of AVD pathology (accuracy: 0.75 ± 0.03 ). Gradient-weighted class activation mapping (Grad-CAM) confirms that the blood pool voxels close to the aortic root contribute the most to the classification. Our approach can be used to improve the diagnosis of AVD and optimise clinical CMR protocols for accurate and efficient AVD detection.
Howard J, Chow K, Chacko L, et al., 2022, Automated inline myocardial segmentation of joint T1 and T2 mapping using deep learning, Radiology: Artificial Intelligence, Vol: 1, Pages: 1-1, ISSN: 2638-6100
Purpose:To develop an artificial intelligence (AI) solution for automated segmentation and analysis of joint cardiac MRI T1 and T2 short-axis mapping.Materials and Methods:In this retrospective study, a joint T1 and T2 mapping sequence was used to acquire 4240 maps from 807 patients across 2 hospitals (March-November 2020). 509 maps from 94 consecutive patients were assigned to a holdout testing set. A convolutional neural network was trained to segment the endocardial and epicardial contours using an edge probability estimation approach. Training labels were segmented by an expert cardiologist. Predicted contours were processed to yield mapping values for each of the 16 AHA segments. Network segmentation performance and segment-wise measurements on the testing set were compared with two experts on the holdout testing set. The AI model was fully integrated using Gadgetron inline AI to run on MRI scanners.Results:A total of 3899 maps (92%) were deemed artifact-free and suitable for human segmentation. AI segmentation closely matched that of each expert (mean Dice coefficient 0.82 ± [SD] 0.07, 0.86 ± 0.06), comparing favorably with interexpert agreement (0.84 ± 0.06). AI-derived segment-wise values for native T1, postcontrast T1 and T2 mapping correlated with experts (R2 0.96, 0.98, 0.87, respectively versus expert 1; 0.97, 0.99, 0.97 versus expert 2) and fell within the range of interexpert reproducibility (R2 = 0.97, 0.99, 0.90). The AI has since been deployed at two hospitals, enabling automated inline analysis.Conclusion:Automated inline analysis of joint T1 and T2 mapping allows accurate segment-wise tissue characterization, with performance equivalent to human experts.
Padayachee Y, Shah M, Auton A, et al., 2022, Smartphone-based remote monitoring (RM) in chronic heart failure reduces emergency hospital attendances, unplanned admissions and secondary care costs: a retrospective cohort study, ESC Congress, Publisher: Oxford University Press, Pages: 2816-2816, ISSN: 0195-668X
Auton A, Padayachee Y, Samways J, et al., 2022, Smartphone-based remote monitoring in chronic heart failure: patient & clinician user experience, impact on patient engagement and quality of life, European Heart Journal, Vol: 43, Pages: 2808-2808, ISSN: 0195-668X
BackgroundHeart failure with reduced ejection fraction (HFrEF) lowers patients' quality of life (QoL) [1]. Digital interventions such as ESC's “Heart Failure Matters” website aim to encourage patient-engagement & self-management [2], which remain major challenges in HFrEF care. Although remote monitoring (RM) has been tested in HFrEF with inconclusive impact on prognosis [3], its impact on patients' experience and engagement is unclear [4]. Furthermore, the perspective of clinicians using RM technologies remains unknown. We present users' experience of Luscii, a novel smartphone-based RM platform enabling HFrEF patients to submit clinical measurements, symptoms, complete educational modules, & communicate with HF specialist nurses (HFSNs).Purpose(I) To evaluate the usage-type & user experience of patients and HFSNs.(II) To assess the impact of using the RM platform on self-reported QoLMethodsA two-part retrospective analysis of HFrEF patients from our regional service using the RM platform: Part A: Thematic analysis of patient feedback provided via the platform and a focus group of six HFSNs. Part B: Scores for a locally-devised HF questionnaire (HFQ), depression (PHQ-9) & anxiety (GAD-7) questionnaires were extracted from the RM platform at two timepoints: at on-boarding and 3 months after. Paired non-parametric tests were used to evaluate difference between median scores across the two time points.Results83 patients (mean age 62 years; 27% female) used the RM platform between April and November 2021. 2 dropped out & 2 died before 3 months. Part A: Patients and HFSNs exchanged information on many topics via the platform, including patient educational modules (Figure 1). Thematic analysis revealed positive and negative impacts with many overlapping subthemes between the two user groups (Figure 2). Part B: At 3 months there was no difference in HFQ score (19 vs. 18, p=0.57, maximum possible score = 50). PHQ-9 (3 vs. 3, p=0.48, maximum
Vimalesvaran K, Uslu F, Zaman S, et al., 2022, Machine learning can accurately detect abnormal aortic valves in CMR, Publisher: OXFORD UNIV PRESS, Pages: 236-236, ISSN: 0195-668X
Rosmini S, Seraphim A, Knott K, et al., 2022, Non-invasive characterization of pleural and pericardial effusions using T1 mapping by magnetic resonance imaging, EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 23, Pages: 1117-1126, ISSN: 2047-2404
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- Citations: 6
Chilcott J, Mcbeath K, Cole G, et al., 2022, Development of a new cardiac amyloid multidisciplinary team in a tertiary London centre, Publisher: WILEY, Pages: 104-105, ISSN: 1388-9842
Kelshiker M, Seligman H, Howard JAMES, et al., 2022, Coronary flow reserve and cardiovascular outcomes: a systematic review and meta-analysis, European Heart Journal, Vol: 43, Pages: 1582-1593, ISSN: 0195-668X
Aims: This meta-analysis aims to quantify the association of reduced coronary flow with all3 cause mortality and major adverse cardiovascular events (MACE) across a broad range of patient groups and pathologies. Methods and Results: We systematically identified all studies between 1st January 2000 and1st August 2020, where coronary flow was measured and clinical outcomes were reported. The endpoints were all-cause mortality and MACE. Estimates of effect were calculated from published hazard ratios using a random-effects model. 79 studies, including 59,740 subjects were included. Abnormal coronary flow reserve (CFR) was associated with a higher incidence of all-cause mortality (HR 3.78, 95% CI 2.39-5.97) and a higher incidence of MACE (HR 3.42, 95% CI 2.92-3.99). Each 0.1-unit reduction in CFR was associated with a proportional increase in mortality (per 0.1 CFR unit HR 1.16, 95% CI 1.04-1.29) and MACE (per 0.1 CFR unit HR 1.08, 95% CI 1.04-1.11)). In patients with isolated coronary microvascular dysfunction, an abnormal CFR was associated with a higher incidence of mortality (HR 5.44, 95% CI 3.78-7.83) and MACE (HR 3.56, 95% CI 2.14-5.90). Abnormal CFR was also associated with a higher incidence of MACE in patients with acute coronary syndromes (HR 3.76, 95% CI 2.35-6.00), heart failure (HR 6.38, 95% CI 1.95-20.90), heart transplant (HR 3.32, 95% CI 2.34-4.71) and diabetes mellitus (HR 7.47, 95% CI 3.37-16.55). Conclusions: Reduced coronary flow is strongly associated with increased risk of all-cause mortality and MACE across a wide range of pathological processes. This finding supports recent recommendations that coronary flow should be measured more routinely in clinical practice to target aggressive vascular risk modification for individuals at higher risk
Seraphim A, Dowsing B, Rathod KS, et al., 2022, Quantitative Myocardial Perfusion Predicts Outcomes in Patients With Prior Surgical Revascularization, JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, Vol: 79, Pages: 1141-1151, ISSN: 0735-1097
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Kelshiker M, Seligman H, Howard JP, et al., 2022, CORONARY FLOW RESERVE AND CARDIOVASCULAR OUTCOMES: A SYSTEMATIC REVIEW AND META-ANALYSIS, 71st Annual Scientific Session and Expo of the American-College-of-Cardiology (ACC), Publisher: ELSEVIER SCIENCE INC, Pages: 989-989, ISSN: 0735-1097
Ahmad Y, Kane C, Arnold AD, et al., 2022, Randomized blinded placebo-controlled trials of renal sympathetic denervation for hypertension: a meta-analysis, Cardiovascular Revascularization Medicine, Vol: 34, Pages: 112-118, ISSN: 1553-8389
BackgroundThe efficacy of renal denervation has been controversial, but the procedure has now undergone several placebo-controlled trials. New placebo-controlled trial data has recently emerged, with longer follow-up of one trial and the full report of another trial (which constitutes 27% of the total placebo-controlled trial data). We therefore sought to evaluate the effect of renal denervation on ambulatory and office blood pressures in patients with hypertension.MethodsWe systematically identified all blinded placebo-controlled randomized trials of catheter-based renal denervation for hypertension. The primary efficacy outcome was ambulatory systolic blood pressure change relative to placebo. A random-effects meta-analysis was performed.Results6 studies randomizing 1232 patients were eligible. 713 patients were randomized to renal denervation and 519 to placebo. Renal denervation significantly reduced ambulatory systolic blood pressure (−3.52 mmHg; 95% CI −4.94 to −2.09; p < 0.0001), ambulatory diastolic blood pressure (−1.93 mmHg; 95% CI −3.04 to −0.83, p = 0.0006), office systolic blood pressure size (−5.10 mmHg; 95% CI −7.31 to −2.90, p < 0.0001) and office diastolic pressure (effect size −3.11 mmHg; 95% CI −4.43 to −1.78, p < 0.0001). Adverse events were rare and not more common with denervation.ConclusionsThe totality of blinded, randomized placebo-controlled data shows that renal denervation is safe and provides genuine reduction in blood pressure for at least 6 months post-procedure. If this effect continues in the long term, renal denervation might provide a life-long 10% relative risk reduction in major adverse cardiac events and 7.5% relative risk reduction in all-cause mortality.
Zaman S, Petri C, Vimalesvaran K, et al., 2022, Automatic diagnosis labeling of cardiovascular MRI by using semisupervised natural language processing of text reports, Radiology: Artificial Intelligence, Vol: 4, ISSN: 2638-6100
A semisupervised natural language processing (NLP) algorithm, based on bidirectional transformers, accurately categorized diagnoses from cardiac MRI text of radiology reports for the labeling of MR images; the model had a higher accuracy than traditional NLP models and performed faster labeling than clinicians.
Li Z, Petri C, Howard J, et al., 2022, PAT-CNN: automatic segmentation and quantification of pericardial adipose tissue from t2-weighted cardiac magnetic resonance images, Statistical Atlases and Computational Modeling of the Heart (STACOM), Publisher: Springer Nature Switzerland, Pages: 359-368, ISSN: 0302-9743
Background: Increased pericardial adipose tissue (PAT) is associated with many types of cardiovascular disease (CVD). Although cardiac magnetic resonance images (CMRI) are often acquired in patients with CVD, there are currently no tools to automatically identify and quantify PAT from CMRI. The aim of this study was to create a neural network to segment PAT from T2-weighted CMRI and explore the correlations between PAT volumes (PATV) and CVD outcomes and mortality.Methods: We trained and tested a deep learning model, PAT-CNN, to segment PAT on T2-weighted cardiac MR images. Using the segmentations from PAT-CNN, we automatically calculated PATV on images from 391 patients. We analysed correlations between PATV and CVD diagnosis and 1-year mortality post-imaging.Results: PAT-CNN was able to accurately segment PAT with Dice score/ Hausdorff distances of 0.74 ± 0.03/27.1 ± 10.9 mm, similar to the values obtained when comparing the segmentations of two independent human observers (0.76 ± 0.06/21.2 ± 10.3 mm). Regression models showed that, independently of sex and body-mass index, PATV is significantly positively correlated with a diagnosis of CVD and with 1-year all cause mortality (p-value < 0.01).Conclusions: PAT-CNN can segment PAT from T2-weighted CMR images automatically and accurately. Increased PATV as measured automatically from CMRI is significantly associated with the presence of CVD and can independently predict 1-year mortality.
Vimalesvaran K, Uslu F, Zaman S, et al., 2022, Detecting Aortic Valve Pathology from the 3-Chamber Cine Cardiac MRI View, Editors: Wang, Dou, Fletcher, Speidel, Li, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 571-580, ISBN: 978-3-031-16430-9
Thornton GD, Shetye A, Knight DS, et al., 2021, Myocardial Perfusion Imaging After Severe COVID-19 Infection Demonstrates Regional Ischemia Rather Than Global Blood Flow Reduction, FRONTIERS IN CARDIOVASCULAR MEDICINE, Vol: 8, ISSN: 2297-055X
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Kellman P, Xue H, Chow K, et al., 2021, Bright-blood and dark-blood phase sensitive inversion recovery late gadolinium enhancement and T1 and T2 maps in a single free-breathing scan: an all-in-one approach, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 23, ISSN: 1097-6647
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- Citations: 4
Howard J, Zaman S, Francis D, et al., 2021, INTELLIGENT LOCALISERS: AN INTEGRATED TIME-SAVING DEEP LEARNING SOLUTION FOR THE PLANNING OF CINE IMAGING AND IDENTIFICATION OF UNEXPECTED FINDINGS FROM A SINGLE TRANSAXIAL STACK, Annual Meeting of the British-Society-of-Cardiovascular-Magnetic-Resonance (BSCMR), Publisher: BMJ PUBLISHING GROUP, Pages: A5-A6, ISSN: 1355-6037
Bachtiger P, Park S, Letchford E, et al., 2021, Triage-HF plus: 12-month study of remote monitoring pathway for triage of heart failure risk initiated during the Covid-19 pandemic, Publisher: OXFORD UNIV PRESS, Pages: 3082-3082, ISSN: 0195-668X
Stowell C, Howard J, Cole G, et al., 2021, Automated left ventricular dimension assessment using artificial intelligence, Publisher: OXFORD UNIV PRESS, Pages: 1-1, ISSN: 0195-668X
Stowell C, Howard J, Demetrescu C, et al., 2021, Fully automated global longitudinal strain assessment using artificial intelligence developed and validated by a UK-wide echocardiography expert collaborative, Publisher: OXFORD UNIV PRESS, Pages: 2-2, ISSN: 0195-668X
Naderi H, Robinson S, Swaans MJ, et al., 2021, Adapting the role of handheld echocardiography during the COVID-19 pandemic: A practical guide, PERFUSION-UK, Vol: 36, Pages: 547-558, ISSN: 0267-6591
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