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