مجال التميز

تميز دراسي و بحثي

البحوث المنشورة

البحث (1):

عنوان البحث:

Artificial Intelligence: A New Clinical Support Tool For Stress Echocardiography 

رابط إلى البحث:

Click here 

تاريخ النشر:

19/07/2018

موجز عن البحث:

Echocardiography remains the imaging modality of choice for the early detection and diagnosis of cardiovascular disease because it is portable, non-invasive, radiation-free, and allows real time imaging of the heart. Furthermore, echocardiography is relatively inexpensive when compared with other imaging modalities and so is accessible in the majority of healthcare settings around the world [1]. However, accurate diagnosis using echocardiography requires a high level of clinical skill and operator training to ensure good quality image acquisition, optimization and interpretation. Wide implementation of echocardiography guidelines have helped standardize these processes and ensured reproducible echocardiographic parameters. However interpretation remains dependent on operator experience and a limited set of echocardiography parameters [2]. Computational tools that allow complex, standardized analysis and quantification of images have emerged, which provide more comprehensive characterization of cardiac structure and function [3,4]. However, it is the combination of these approaches with artificial intelligence tools, such as deep learning, which can form the foundations of a new era of consistent and accurate echocardiography image interpretation. 

البحث (2): 

عنوان البحث:

Trial Of Exercise To Prevent Hypertension In Young Adults (TEPHRA) A Randomized Controlled Trial: Study Protocol 

رابط إلى البحث:

Click here 

تاريخ النشر:

22/10/2018

موجز عن البحث:

Background: Hypertension prevalence in young adults has increased and is associated with increased incidence of cerebrovascular and cardiovascular events in middle age. However, there is significant debate regards how to effectively manage young adult hypertension with recommendation to target lifestyle intervention. Surprisingly, no trials have investigated whether lifestyle advice developed for blood pressure control in older adults is effective in these younger populations.

Methods/Design: TEPHRA is an open label, parallel arm, randomised controlled trial in young adults with high normal and elevated blood pressure. The study will compare a supervised physical activity intervention consisting of 16 weeks structured exercise, physical activity self-monitoring and motivational coaching with a control group receiving usual care/minimal intervention. Two hundred young adults aged 18–35 years, including a subgroup of preterm born participants will be recruited through open recruitment and direct invitation. Participants will be randomised in a ratio of 1:1 to either the exercise intervention group or control group. Primary outcome will be ambulatory blood pressure monitoring at 16 weeks with measure of sustained effect at 12 months. Study measures include multimodal cardiovascular assessments; peripheral vascular measures, blood sampling, microvascular assessment, echocardiography, objective physical activity monitoring and a subgroup will complete multi-organ magnetic resonance imaging.

Discussion: The results of this trial will deliver a novel, randomised control trial that reports the effect of physical activity intervention on blood pressure integrated with detailed cardiovascular phenotyping in young adults. The results will support the development of future research and expand the evidence-based management of blood pressure in young adult populations.

Trial Registration: Clinicaltrials.gov registration number NCT02723552, registered on 30 March, 2016. 

البحث (3): 

عنوان البحث:

Artificial Intelligence And Echocardiography 

رابط إلى البحث:

Click here 

تاريخ النشر:

  29/10/2018

موجز عن البحث:

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a subfield of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.

البحث (4):

عنوان البحث:

Left Atrial Function In Heart Failure With Mid-Range Ejection Fraction Differs From That Of Heart Failure With Preserved Ejection Fraction: A 2d Speckle-Tracking Echocardiographic Study 

رابط إلى البحث:

Click here

تاريخ النشر:

04/12/2018

موجز عن البحث:

Aims: Heart failure (HF) with mid-range ejection fraction (HFmrEF) shares similar diagnostic criteria to HF with preserved ejection fraction (HFpEF). Whether left atrial (LA) function differs between HFmrEF and HFpEF is unknown. We, therefore, used 2D-speckle-tracking echocardiography (2D-STE) to assess LA phasic function in patients with HFpEF and HFmrEF.

Methods and results: Consecutive outpatients diagnosed with HF according to current European recommendations were prospectively enrolled. There were 110 HFpEF and 61 HFmrEF patients with sinus rhythm, and 37 controls matched by age. LA phasic function was analysed using 2D-STE. Peak-atrial longitudinal strain (PALS), peak-atrial contraction strain (PACS), and PALS−PACS were measured reflecting LA reservoir, pump, and conduit function, respectively. Among HF groups, most of left ventricular (LV) diastolic function measures, and LA volume were similar. Both HF groups had abnormal LA phasic function compared with controls. HFmrEF patients had worse LA phasic function than HFpEF patients even among patients with LA enlargement. Among patients with normal LA size, LA reservoir, and pump function remained worse in HFmrEF. Differences in LA phasic function between HF groups remained significant after adjustment for confounders. Global PALS and PACS were inversely correlated with brain natriuretic peptide, LA volume, E/A, E/eʹ, pulmonary artery systolic pressure, and diastolic dysfunction grade in both HF groups.

Conclusion: LA phasic function was worse in HFmrEF patients compared with those with HFpEF regardless of LA size, and independent of potential confounders. These differences could be attributed to intrinsic LA myocardial dysfunction perhaps in relation to altered LV function.

المؤتمرات العلمية:

المؤتمر (1):

عنوان المؤتمر:

The British Society Of Echocardiography Annual Meeting 2017

تاريخ الإنعقاد:

11/11/2017

مكان الإنعقاد:

Edinburgh, UK

طبيعة المشاركة:

Oral and poster presentation

عنوان المشاركة:

Left Ventricular Twist Mechanics In Hypertensive Patients With Preserved Left Ventricular Ejection Fraction And Its Relation To Left Atrial Phasic Function

ملخص المشاركة:

Aims: To evaluate the relation between left ventricular (LV) twist mechanics and left atrial (LA) phasic function in patients with systemic hypertension using speckle tracking echocardiography (STE). We hypothesised that the impairment of LA function is directly related to the reduction in LV untwisting rate (UTR) in hypertensive patients with preserved LV ejection fraction (EF).

Methodology: In this prospective study, 74 hypertensive patients (54.17 ± 16.37 years) with preserved EF (65.7 ± 7.29 %) were enrolled, and compared with 20 normotensive controls. Basal and apical parasternal short axis views were used to assess LV twist mechanics, and apical four and two chamber views were used to evaluate LA phasic function. By using EchoPac GE STE software, LV twist, UTR, and time to peak UTR were measured and LA longitudinal strain was obtained.

Result: Hypertensive patients with preserved LV EF showed reduced early diastolic UTR (P=0.0001), prolonged time to peak UTR (P<0.0001), impaired LA reservoir (P<0.0001) and conduit (P<0.0001) function when compared with controls. The reduction of LV UTR was positively correlated with the impairment of LA reservoir (r= 0.54, P<0.0001) and conduit (r=0.65, P<0.0001) function.

Conclusion: In hypertensive patients with preserved LV EF (>50%), the impairment of LA reservoir and conduit function was correlated positively with the reduction in LV UTR, and inversely with time to peak UTR. These correlations may contribute toward the impairment of LV relaxation in hypertensive patients. LV twisting and LA strain indices by STE enable early detection of diastolic abnormalities even in the presence of normal findings in conventional 2D echocardiography.

المؤتمر (2):

عنوان المؤتمر:

The British Cardiovascular Society Conference

تاريخ الإنعقاد:

03/06/2019

مكان الإنعقاد:

Manchester, UK

طبيعة المشاركة:

Paper presentation

عنوان المشاركة:

Introduction To Artificial Intelligence In Echocardiography: Current Concepts    

ملخص المشاركة:

Echocardiography plays a crucial role in the diagnosis and management of cardiovascular disease. However, interpretation remains largely reliant on the subjective expertise of the operator. As a result inter-operator variability and experience can lead to
incorrect diagnoses. Artificial intelligence (AI) technologies provide new possibilities for echocardiography to generate accurate, consistent and automated interpretation of echocardiograms, thus potentially reducing the risk of human error. In this review, we discuss a sub field of AI relevant to image interpretation, called machine learning, and its potential to enhance the diagnostic performance of echocardiography. We discuss recent applications of these methods and future directions for AI-assisted interpretation of echocardiograms. The research suggests it is feasible to apply machine learning models to provide rapid, highly accurate and consistent assessment of echocardiograms, comparable to clinicians. These algorithms are capable of accurately quantifying a wide range of features, such as the severity of valvular heart disease or the ischaemic burden in patients with coronary artery disease. However, the applications and their use are still in their infancy within the field of echocardiography. Research to refine methods and validate their use for automation, quantification and diagnosis are in progress. Widespread adoption of robust AI tools in clinical echocardiography practice should follow and have the potential to deliver significant benefits for patient outcome.

الرابط:

Click here