Evaluation of RETRO-Mapping for electrophysiological features including direction of plane activity during atrial fibrillation using multipolar catheters in humans.

INTRODUCTION: A quantifiable, automated standard of analyzing heart rhythm has long eluded cardiologists due, in part, to the limitations in technology and the ability to analyze large electrogram datasets. In this proof-of-concept study, we propose new measures to quantify plane activity in atrial fibrillation (AF) using our Representation of Electrical Tracking of Origin (RETRO)-Mapping software. METHODS: We recorded 30 s segments of electrograms at the lower posterior wall of the left atrium using a 20-pole double loop catheter (AFocusII). The data were analyzed with the custom RETRO-Mapping algorithm in MATLAB. Thirty second segments were analyzed for number of activation edges, conduction velocity (CV), cycle length (CL), activation edge direction, and wavefront direction. These features were compared across 34 613 plane edges in three types of AF: persistent AF treated with amiodarone (11 906 wavefronts), persistent AF without amiodarone (14 959 wavefronts), and paroxysmal AF (7748 wavefronts). Change in activation edge direction between subsequent frames and change in overall wavefront direction between subsequent wavefronts were analyzed. RESULTS: All activation edge directions were represented across the lower posterior wall. The median change in activation edge direction followed a linear pattern for all three types of AF with R2 = 0.932 for persistent AF treated without amiodarone, R2 = 0.942 for paroxysmal AF, and R2 = 0.958 for persistent AF treated with amiodarone. All medians and the standard deviation error bars remained below 45° (suggesting all activation edges were traveling within a 90° sector, a criterion for plane activity). The directions of approximately half of all wavefronts (56.1% for persistent without amiodarone, 51.8% for paroxysmal, 48.8% for persistent with amiodarone) were predictive of the directions of the subsequent wavefront. CONCLUSION: RETRO-Mapping can measure electrophysiological features of activation activity and this proof-of-concept study suggests that this can be extended to the detection of plane activity in three types of AF. Wavefront direction may have a role in future work for predicting plane activity. For this study, we focused more on the ability of the algorithm to detect plane activity and less the differences between the types of AF. Future work should be in validating these results with a larger data set and comparing with other types of activation such as rotational, collision, and focal. Ultimately, this work can be implemented in real-time for prediction of wavefronts during ablation procedures.

Impact of chronic hypoxia on proximal pulmonary artery wave propagation and mechanical properties in rats.

Arterial stiffness and wave reflection are important components of the ventricular afterload. Therefore, we aimed to assess the arterial wave characteristics and mechanical properties of the proximal pulmonary arteries (PAs) in the hypoxic pulmonary hypertensive rat model. After 21 days in normoxic or hypoxic chambers (24 animals/group), animals underwent transthoracic echocardiography and PA catheterization with a dual-tipped pressure and Doppler flow sensor wire. Wave intensity analysis was performed. Artery rings obtained from the pulmonary trunk, right and left PAs, and aorta were subjected to a tensile test to rupture. Collagen and elastin content were determined. In hypoxic rats, proximal PA wall thickness, collagen content, tensile strength per unit collagen, maximal elastic modulus, and wall viscosity increased, whereas the elastin-to-collagen ratio and arterial distensibility decreased. Arterial pulse wave velocity was also increased, and the increase was more prominent in vivo than ex vivo. Wave intensity was similar in hypoxic and normoxic animals with negligible wave reflection. In contrast, the aortic maximal elastic modulus remained unchanged, whereas wall viscosity decreased. In conclusion, there was no evidence of altered arterial wave propagation in proximal PAs of hypoxic rats while the extracellular matrix protein composition was altered and collagen tensile strength increased. This was accompanied by altered mechanical properties in vivo and ex vivo. NEW & NOTEWORTHY In rats exposed to chronic hypoxia, we have shown that pulse wave velocity in the proximal pulmonary arteries increased and pressure dependence of the pulse wave velocity was steeper in vivo than ex vivo leading to a more prominent increase in vivo.

Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography.

BACKGROUND: Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced measurements such as myocardial strain. METHODS AND RESULTS: We developed and evaluated algorithms which can automatically extract global and regional cardiac velocity, and identify end-diastolic and end-systolic frames. We acquired apical four-chamber 2D echocardiographic video recordings, each at least 10 heartbeats long, acquired twice at frame rates of 52 and 79 frames/s from 19 patients, yielding 38 recordings. Five experienced echocardiographers independently marked end-systolic and end-diastolic frames for the first 10 heartbeats of each recording. The automated algorithm also did this. Using the average of time points identified by five human operators as the reference gold standard, the individual operators had a root mean square difference from that gold standard of 46.5 ms. The algorithm had a root mean square difference from the human gold standard of 40.5 ms (P<.0001). Put another way, the algorithm-identified time point was an outlier in 122/564 heartbeats (21.6%), whereas the average human operator was an outlier in 254/564 heartbeats (45%). CONCLUSION: An automated algorithm can identify the end-systolic and end-diastolic frames with performance indistinguishable from that of human experts. This saves staff time, which could therefore be invested in assessing more beats, and reduces uncertainty about the reliability of the choice of frame.

Aortic reservoir pressure corresponds to cyclic changes in aortic volume: physiological validation in humans.

OBJECTIVE: Aortic reservoir pressure indices independently predict cardiovascular events and mortality. Despite this, there has never been a study in humans to determine whether the theoretical principles of the mathematically derived aortic reservoir pressure (RP(derived)) and excess pressure (XP(derived)) model have a real physiological basis. This study aimed to directly measure the aortic reservoir (AR(direct); by cyclic change in aortic volume) and determine its relationship with RP(derived), XP(derived), and aortic blood pressure (BP). APPROACH AND RESULTS: Ascending aortic BP and Doppler flow velocity were recorded via intra-arterial wire in 10 men (aged 62 ± 12 years) during coronary artery bypass surgery. Simultaneous ascending aortic transesophageal echocardiography was used to measure AR(direct). Published mathematical formulae were used to determine RP(derived) and XP(derived). AR(direct) was strongly and linearly related to RP(derived) during systole (r=0.988; P<0.001) and diastole (r=0.985; P<0.001). Peak cross-correlation (r=0.98) occurred at a phase lag of 0.004 s into the cardiac cycle, suggesting close temporal agreement between waveforms. The relationship between aortic BP and AR(direct) was qualitatively similar to the cyclic relationship between aortic BP and RP(derived), with peak cross-correlations occurring at identical phase lags (AR(direct) versus aortic BP, r=0.96 at 0.06 s; RP(derived) versus aortic BP, r=0.98 at 0.06 s). CONCLUSIONS: RP(derived) is highly correlated with changes in proximal aortic volume, consistent with its physiological interpretation as corresponding to the instantaneous volume of blood stored in the aorta. Thus, aortic reservoir pressure should be considered in the interpretation of the central BP waveform.

Uncovering hidden treasures: Mapping morphological changes in the differentiation of human mesenchymal stem cells to osteoblasts using deep learning.

Deep Learning (DL) is becoming an increasingly popular technology being employed in life sciences research due to its ability to perform complex and time-consuming tasks with significantly greater speed, accuracy, and reproducibility than human researchers - allowing them to dedicate their time to more complex tasks. One potential application of DL is to analyze cell images taken by microscopes. Quantitative analysis of cell microscopy images remain a challenge - with manual cell characterization requiring excessive amounts of time and effort. DL can address these issues, by quickly extracting such data and enabling rigorous, empirical analysis of images. Here, DL is used to quantitively analyze images of Mesenchymal Stem Cells (MSCs) differentiating into Osteoblasts (OBs), tracking morphological changes throughout this transition. The changes in morphology throughout the differentiation protocol provide evidence for a distinct path of morphological transformations that the cells undergo in their transition, with changes in perimeter being observable before changes in eceentricity. Subsequent differentiation experiments can be quantitatively compared with our dataset to concretely evaluate how different conditions affect differentiation and this paper can also be used as a guide for researchers on how to utilize DL workflows in their own labs.

Automated multi-beat tissue Doppler echocardiography analysis using deep neural networks.

Tissue Doppler imaging is an essential echocardiographic technique for the non-invasive assessment of myocardial blood velocity. Image acquisition and interpretation are performed by trained operators who visually localise landmarks representing Doppler peak velocities. Current clinical guidelines recommend averaging measurements over several heartbeats. However, this manual process is both time-consuming and disruptive to workflow. An automated system for accurate beat isolation and landmark identification would be highly desirable. A dataset of tissue Doppler images was annotated by three cardiologist experts, providing a gold standard and allowing for observer variability comparisons. Deep neural networks were trained for fully automated predictions on multiple heartbeats and tested on tissue Doppler strips of arbitrary length. Automated measurements of peak Doppler velocities show good Bland-Altman agreement (average standard deviation of 0.40 cm/s) with consensus expert values; less than the inter-observer variability (0.65 cm/s). Performance is akin to individual experts (standard deviation of 0.40 to 0.75 cm/s). Our approach allows for > 26 times as many heartbeats to be analysed, compared to a manual approach. The proposed automated models can accurately and reliably make measurements on tissue Doppler images spanning several heartbeats, with performance indistinguishable from that of human experts, but with significantly shorter processing time. HIGHLIGHTS: • Novel approach successfully identifies heartbeats from Tissue Doppler Images • Accurately measures peak velocities on several heartbeats • Framework is fast and can make predictions on arbitrary length images • Patient dataset and models made public for future benchmark studies.

Doppler assessment of aortic stenosis: a 25-operator study demonstrating why reading the peak velocity is superior to velocity time integral.

Aims: Measurements with superior reproducibility are useful clinically and research purposes. Previous reproducibility studies of Doppler assessment of aortic stenosis (AS) have compared only a pair of observers and have not explored the mechanism by which disagreement between operators occurs. Using custom-designed software which stored operators' traces, we investigated the reproducibility of peak and velocity time integral (VTI) measurements across a much larger group of operators and explored the mechanisms by which disagreement arose. Methods and results: Twenty-five observers reviewed continuous wave (CW) aortic valve (AV) and pulsed wave (PW) left ventricular outflow tract (LVOT) Doppler traces from 20 sequential cases of AS in random order. Each operator unknowingly measured each peak velocity and VTI twice. VTI tracings were stored for comparison. Measuring the peak is much more reproducible than VTI for both PW (coefficient of variation 10.1 vs. 18.0%; P < 0.001) and CW traces (coefficient of variation 4.0 vs. 10.2%; P < 0.001). VTI is inferior because the steep early and late parts of the envelope are difficult to trace reproducibly. Dimensionless index improves reproducibility because operators tended to consistently over-read or under-read on LVOT and AV traces from the same patient (coefficient of variation 9.3 vs. 17.1%; P < 0.001). Conclusion: It is far more reproducible to measure the peak of a Doppler trace than the VTI, a strategy that reduces measurement variance by approximately six-fold. Peak measurements are superior to VTI because tracing the steep slopes in the early and late part of the VTI envelope is difficult to achieve reproducibly.

Open-source, vendor-independent, automated multi-beat tissue Doppler echocardiography analysis.

Current guidelines for measuring cardiac function by tissue Doppler recommend using multiple beats, but this has a time cost for human operators. We present an open-source, vendor-independent, drag-and-drop software capable of automating the measurement process. A database of ~8000 tissue Doppler beats (48 patients) from the septal and lateral annuli were analyzed by three expert echocardiographers. We developed an intensity- and gradient-based automated algorithm to measure tissue Doppler velocities. We tested its performance against manual measurements from the expert human operators. Our algorithm showed strong agreement with expert human operators. Performance was indistinguishable from a human operator: for algorithm, mean difference and SDD from the mean of human operators' estimates 0.48 ± 1.12 cm/s (R2 = 0.82); for the humans individually this was 0.43 ± 1.11 cm/s (R2 = 0.84), -0.88 ± 1.12 cm/s (R2 = 0.84) and 0.41 ± 1.30 cm/s (R2 = 0.78). Agreement between operators and the automated algorithm was preserved when measuring at either the edge or middle of the trace. The algorithm was 10-fold quicker than manual measurements (p < 0.001). This open-source, vendor-independent, drag-and-drop software can make peak velocity measurements from pulsed wave tissue Doppler traces as accurately as human experts. This automation permits rapid, bias-resistant multi-beat analysis from spectral tissue Doppler images.

Frame rate required for speckle tracking echocardiography: A quantitative clinical study with open-source, vendor-independent software.

OBJECTIVES: To determine the optimal frame rate at which reliable heart walls velocities can be assessed by speckle tracking. BACKGROUND: Assessing left ventricular function with speckle tracking is useful in patient diagnosis but requires a temporal resolution that can follow myocardial motion. In this study we investigated the effect of different frame rates on the accuracy of speckle tracking results, highlighting the temporal resolution where reliable results can be obtained. MATERIAL AND METHODS: 27 patients were scanned at two different frame rates at their resting heart rate. From all acquired loops, lower temporal resolution image sequences were generated by dropping frames, decreasing the frame rate by up to 10-fold. RESULTS: Tissue velocities were estimated by automated speckle tracking. Above 40 frames/s the peak velocity was reliably measured. When frame rate was lower, the inter-frame interval containing the instant of highest velocity also contained lower velocities, and therefore the average velocity in that interval was an underestimate of the clinically desired instantaneous maximum velocity. CONCLUSIONS: The higher the frame rate, the more accurately maximum velocities are identified by speckle tracking, until the frame rate drops below 40 frames/s, beyond which there is little increase in peak velocity. We provide in an online supplement the vendor-independent software we used for automatic speckle-tracked velocity assessment to help others working in this field.

A long-term follow-up of patients with prolonged asystole of greater than 15s on head-up tilt testing.

BACKGROUND: Head-up tilt (HUT) is used for diagnosis of vasovagal syncope (VVS), and can provoke cardioinhibition. VVS is usually considered benign, however pacemaker insertion may be indicated in some patients. We sought to characterize the long-term outcomes of patients with prolonged asystole (>15s) on HUT. METHODS: We conducted a retrospective study on patients with asystole >15s on HUT identified from 5133 patients who were investigated between 1998 and 2012 at our institution. Patients were mailed questionnaires or telephoned to ascertain outcomes. Where contact was not possible, the patients' general practitioners were contacted to request up-to-date information. RESULTS: A total of 26 patients with a mean age of 45 ± 18 years and a mean duration of asystole on HUT of 26 ± 7s were successfully followed up from a total of 77 patients identified. The follow-up duration was 99 ± 39 months. Six patients had undergone pacemaker (PPM) implantation. Of the patients without PPM, 16 reported spontaneously improved symptoms. Ten patients sustained injury prior to HUT compared with one after HUT, when a clear diagnosis was made and management advice was given. There were no major injuries or deaths after HUT. The 6 patients with PPMs had a mean age of 60 ± 16 (67% male) at HUT. Four patients had no further syncope after PPM and two demonstrated improvement but still experienced recurrent syncope. CONCLUSIONS: Prolonged asystole (>15s) on tilt does not necessarily predict adverse outcomes with most patients improving spontaneously over the long-term. Pacemaker insertion in selected patients may reduce syncope recurrence but does not always abolish it.

Defining the real-world reproducibility of visual grading of left ventricular function and visual estimation of left ventricular ejection fraction: impact of image quality, experience and accreditation.

Left ventricular function can be evaluated by qualitative grading and by eyeball estimation of ejection fraction (EF). We sought to define the reproducibility of these techniques, and how they are affected by image quality, experience and accreditation. Twenty apical four-chamber echocardiographic cine loops (Online Resource 1-20) of varying image quality and left ventricular function were anonymized and presented to 35 operators. Operators were asked to provide (1) a one-phrase grading of global systolic function (2) an "eyeball" EF estimate and (3) an image quality rating on a 0-100 visual analogue scale. Each observer viewed every loop twice unknowingly, a total of 1400 viewings. When grading LV function into five categories, an operator's chance of agreement with another operator was 50% and with themself on blinded re-presentation was 68%. Blinded eyeball LVEF re-estimates by the same operator had standard deviation (SD) of difference of 7.6 EF units, with the SD across operators averaging 8.3 EF units. Image quality, defined as the average of all operators' assessments, correlated with EF estimate variability (r = -0.616, p < 0.01) and visual grading agreement (r = 0.58, p < 0.01). However, operators' own single quality assessments were not a useful forewarning of their estimate being an outlier, partly because individual quality assessments had poor within-operator reproducibility (SD of difference 17.8). Reproducibility of visual grading of LV function and LVEF estimation is dependent on image quality, but individuals cannot themselves identify when poor image quality is disrupting their LV function estimate. Clinicians should not assume that patients changing in grade or in visually estimated EF have had a genuine clinical change.

Physiology of oxygen uptake kinetics: Insights from incremental cardiopulmonary exercise testing in the Study of Health in Pomerania.

BACKGROUND: Cardiopulmonary exercise testing allows for assessment of cardiac and respiratory limitation, but is often affected by patient effort. Indices of oxygen kinetics, including the oxygen uptake efficiency slope (OUES), oxygen uptake-work-rate slope (VO2-WR slope) and the heart rate-oxygen uptake slope (HR-VO2 slope) are relatively effort independent but may be affected by patient characteristics. The objective of this study is to identify the impact of factors, such as age, gender, body size, respiratory function, smoking and beta-blockade on these parameters, as well as generate predictive equations. METHODS: 1708 volunteers from the population-based Study of Health in Pomerania underwent an incremental bicycle exercise protocol. Markers of oxygen kinetics were calculated. Participants with structural heart disease, echocardiographic or lung function pathology were excluded, leaving 577 males and 625 females. Age, height, weight, smoking, forced expiratory volume in 1 s (FEV1) and beta-blockers were analysed for their influencing power by gender. Quantile regression analysis determined the reference equations for each parameter. RESULTS: Age, gender, height, weight and FEV1 (but not percent predicted FEV1) are strongly related to OUES. Participants using beta-blockers and male smokers had significantly lower OUES values. VO2-WR slope was minimally affected by age, gender, weight and FEV1. Gender, height, weight and beta-blocker use, but not FEV1 and smoking status, were related to the HR-VO2 slope whilst age was only related in females. CONCLUSIONS: Markers of oxygen kinetics are differentially affected by patient characteristics. This study provides normal reference values for these variables thereby facilitating interpretation of oxygen uptake kinetics in health and disease.

Test-retest repeatability of cardiopulmonary exercise test variables in patients with cardiac or respiratory disease.

BACKGROUND: The test-retest reliability for multiple cardiopulmonary exercise test (CPX) variables has not been compared in a single study and the influence of different diseases on test-retest reliability has not been examined. We investigated different measures of test-retest reliability for multiple variables and compared them by category of cardiac or respiratory disease. METHODS: Patients with chronic obstructive airways disease (n = 24), heart failure (n = 43), or severe mitral valve disease (n = 26) were recruited into a prospective study. Each patient underwent two bicycle ergometer tests; the first, a familiarization test, with a 10 W/min ramp, and the second a personalized ramp based on the results of the familiarization test to elicit maximal effort within 8-10 min. Intraclass correlation coefficients (ICC) and coefficients of variation between the two tests were calculated. Influence of potential modifiers was assessed using repeated measures analysis of variance. RESULTS: Peak VO2 (ICC 0.95, 95% CI 0.94-0.97), oxygen uptake efficiency slope (ICC 0.93, 95% CI 0.90-0.95), O2 pulse (ICC 0.96, 95% CI 0.94-0.97), and the VE/VCO2 ratio at the nadir (ICC 0.92, 95% CI 0.89-0.95) all showed excellent test-retest reliability, with within-subject coefficients of variation <0.12. VO2 at the anaerobic threshold (ICC 0.84, 95% CI 0.78-0.89) and the VE/VCO2 slope (ICC 0.88, 95% CI 0.79-0.93) showed good test-retest reliability, although inferior to peak VO2. Age, gender, body mass index, disease aetiology, protocol change, and intertest interval did not affect the reliability of most variables. CONCLUSIONS: CPX showed high test-retest reliability; certain variables such as peak VO2 and oxygen uptake efficiency slope outperform others. These results identify which variables are most suitable for serial testing of patients with three common disease aetiologies owing to their superior reproducibility.

Automated speckle tracking algorithm to aid on-axis imaging in echocardiography.

Obtaining a "correct" view in echocardiography is a subjective process in which an operator attempts to obtain images conforming to consensus standard views. Real-time objective quantification of image alignment may assist less experienced operators, but no reliable index yet exists. We present a fully automated algorithm for detecting incorrect medial/lateral translation of an ultrasound probe by image analysis. The ability of the algorithm to distinguish optimal from sub-optimal four-chamber images was compared to that of specialists-the current "gold-standard." The orientation assessments produced by the automated algorithm correlated well with consensus visual assessments of the specialists ([Formula: see text]) and compared favourably with the correlation between individual specialists and the consensus, [Formula: see text]. Each individual specialist's assessments were within the consensus of other specialists, [Formula: see text] of the time, and the algorithm's assessments were within the consensus of specialists 85% of the time. The mean discrepancy in probe translation values between individual specialists and their consensus was [Formula: see text], and between the automated algorithm and specialists' consensus was [Formula: see text]. This technology could be incorporated into hardware to provide real-time guidance for image optimisation-a potentially valuable tool both for training and quality control.

Automated aortic Doppler flow tracing for reproducible research and clinical measurements.

In clinical practice, echocardiographers are often unkeen to make the significant time investment to make additional multiple measurements of Doppler velocity. Main hurdle to obtaining multiple measurements is the time required to manually trace a series of Doppler traces. To make it easier to analyze more beats, we present the description of an application system for automated aortic Doppler envelope quantification, compatible with a range of hardware platforms. It analyses long Doppler strips, spanning many heartbeats, and does not require electrocardiogram to separate individual beats. We tested its measurement of velocity-time-integral and peak-velocity against the reference standard defined as the average of three experts who each made three separate measurements. The automated measurements of velocity-time-integral showed strong correspondence (R(2) = 0.94) and good Bland-Altman agreement (SD = 1.39 cm) with the reference consensus expert values, and indeed performed as well as the individual experts ( R(2) = 0.90 to 0.96, SD = 1.05 to 1.53 cm). The same performance was observed for peak-velocities; ( R(2) = 0.98, SD = 3.07 cm/s) and ( R(2) = 0.93 to 0.98, SD = 2.96 to 5.18 cm/s). This automated technology allows > 10 times as many beats to be analyzed compared to the conventional manual approach. This would make clinical and research protocols more precise for the same operator effort.

Guidance for accurate and consistent tissue Doppler velocity measurement: comparison of echocardiographic methods using a simple vendor-independent method for local validation.

BACKGROUND: Variability has been described between different echo machines and different modalities when measuring tissue velocities. We assessed the consistency of tissue velocity measurements across different modalities and different manufacturers in an in vitro model and in patients. Furthermore, we present freely available software tools to repeat these evaluations. METHODS AND RESULTS: We constructed a simple setup to generate reproducible motion and used it to compare velocities measured using three echocardiographic modalities: M-mode, speckle tracking, and tissue Doppler, with a straightforward, non-ultrasound, optical gold standard. In the clinical phase, 25 patients underwent M-mode, speckle tracking, and tissue Doppler measurements of s', e', and a' velocities. In vitro, the M-mode and speckle tracking velocities agreed with optical assessment. Of the three possible tissue Doppler measurement conventions (outer, middle, and inner edge) only the middle agreed with optical assessment (discrepancy -0.20 (95% CI -0.44 to 0.03) cm/s, P = 0.11, outer +5.19 (4.65 to 5.73) cm/s, P < 0.0001, inner -6.26 (-6.87 to -5.65) cm/s, P < 0.0001). A similar pattern occurred across all four studied manufacturers. M-mode was therefore chosen as the in vivo gold standard. Clinical measurements of s' velocities by speckle tracking and the middle line of the tissue Doppler showed concordance with M-mode, while the outer line overestimated significantly (+1.27(0.96 to 1.59) cm/s, P < 0.0001) and the inner line underestimated (-1.82 (-2.11 to -1.52) cm/s, P < 0.0001). CONCLUSIONS: Echocardiographic velocity measurements can be more consistent than previously suspected. The statistically modal velocity, found at the centre of the spectral pulsed wave tissue Doppler envelope, most closely represents true tissue velocity. This article includes downloadable, vendor-independent software enabling calibration of echocardiographic machines using a simple, inexpensive in vitro setup.

A novel fully automated method for mitral regurgitant orifice area quantification.

BACKGROUND: Effective regurgitant orifice area (EROA) in mitral regurgitation (MR) is difficult to quantify. Clinically it is measured using the proximal isovelocity surface area (PISA) method, which is intrinsically not automatable, because it requires the operator to manually identify the mitral valve orifice. We introduce a new fully automated algorithm, ("AQURO"), which calculates EROA directly from echocardiographic colour M-mode data, without requiring operator input. METHODS: Multiple PISA measurements were compared to multiple AQURO measurements in twenty patients with MR. For PISA analysis, three mutually blinded observers measured EROA from the four stored video loops. For AQURO analysis, the software automatically processed the colour M-mode datasets and analysed the velocity field in the flow-convergence zone to extract EROA directly without any requirement for manual radius measurement. RESULTS: Reproducibility, measured by intraclass correlation (ICC), for PISA was 0.80, 0.83 and 0.83 (for 3 observers respectively). Reproducibility for AQURO was 0.97. Agreement between replicate measurements calculated using Bland-Altman standard deviation of difference (SDD) was 21,17 and 17mm(2)for the three respective observers viewing independent video loops using PISA. Agreement between replicate measurements for AQURO was 6, 5 and 7mm(2)for automated analysis of the three pairs of datasets. CONCLUSIONS: By eliminating the need to identify the orifice location, AQURO avoids an important source of measurement variability. Compared with PISA, it also reduces the analysis time allowing analysis and averaging of data from significantly more beats, improving the consistency of EROA quantification. AQURO, being fully automated, is a simple, effective enhancement for EROA quantification using standard echocardiographic equipment.

A new automated system to identify a consistent sampling position to make tissue Doppler and transmitral Doppler measurements of E, E' and E/E'.

BACKGROUND: Transmitral pulse wave (PW) Doppler and annular tissue Doppler velocity measurements provide valuable diagnostic and prognostic information. However, they depend on an echocardiographer manually selecting positions to make the measurements. This is time-consuming and open to variability, especially by less experienced operators. We present a new, automated method to select consistent Doppler velocity sites to measure blood flow and muscle function. METHODS: Our automated algorithm combines speckle tracking and colour flow mapping to locate the septal and lateral mitral valve annuli (to measure peak early diastolic velocity, E') and the mitral valve inflow (to measure peak inflow velocity, E). We also automate peak velocity measurements from resulting PW Doppler traces. The algorithm-selected locations and time taken to identify them were compared against a panel of echo specialists - the current "gold standard". RESULTS: The algorithm identified positions to measure Doppler velocities within 3.6 ± 2.2mm (mitral inflow), 3.2 ± 1.8mm (septal annulus) and 3.8 ± 1.5mm (lateral annulus) of the consensus of 3 specialists. This was less than the average 4mm fidelity with which the specialists could themselves identify the points. The automated algorithm could potentially reduce the time taken to make these measurements by 60 ± 15%. CONCLUSIONS: Our automated algorithm identified sampling positions for measurement of mitral flow, septal and lateral tissue velocities as reliably as specialists. It provides a rapid, easy method for new specialists and potentially non-specialists to make automated measurements of key cardiac physiological indices. This could help support decision-making, without introducing delay and extend availability of echocardiography to more patients.