Measurement Of Fraction Research Articles

Data taking strategy for ψ(3770) and ϒ(4S) branching fraction measurements at e+e- colliders

Abstract The $\psi(3770)$ and $\Upsilon(4S)$ states predominantly decay into open-flavor meson pairs, while the decays of $\psi(3770) \to \mbox{\text{non}-}D\bar{D}$ and $\Upsilon(4S) \to \mbox{\text{non}-}B\bar{B}$ are rare but crucial for elucidating the inner structure and decay dynamics of heavy quarkonium states. To achieve precise branching fraction measurements for the $\psi(3770) \to \mbox{\text{non}-}D\bar{D}$ and $\Upsilon(4S) \to \mbox{\text{non}-}B\bar{B}$ decays at the high luminosity $e^+e^-$ annihilation experiments, we employed Monte Carlo simulations and Fisher information to evaluate various data taking scenarios, ultimately determining the optimal scheme. The consistent results of both methodologies indicate that the optimal energy points for studies of $\psi(3770) \to \mbox{\text{non}-}D\bar{D}$ decays are $3.769~{\rm GeV}$ and $3.781~{\rm GeV}$, while those for $\Upsilon(4S) \to \mbox{\text{non}-}B\bar{B}$ decays are $10.574~{\rm GeV}$ and $10.585~{\rm GeV}$. In addition, we provide an analysis of the relationship between the integrated luminosity and the precision in branching fraction measurements derived from these data samples, with the branching fraction spanning several orders of magnitude. In addition, we studied the dependence of the precision in branching
fraction measurements on the integrated luminosity, with the branching fractions spanning several
orders of magnitude.

Biosensors Characterisation: Formal methods from the Perspective of Proteome Fractions

Abstract Many studies characterise transcription factors and other regulatory elements to control gene expression in recombinant systems. However, most lack a formal approach to analyse the inherent and context-specific variations of these regulatory components. This study addresses this gap by establishing a formal framework from which convenient methods are inferred to characterise regulatory circuits. We modelled the bacterial cell as a collection of proteome fractions. Deriving the time-dependent proteome fraction, we obtained a general theorem that describes its change as a function of its expression fraction, a specific portion of the total biosynthesis flux of the cell. Formal deduction reveals that when the proteome fraction reaches a maximum, it becomes equivalent to its expression fraction. This equation enables the reliable measurement of the expression fraction through direct protein quantification. In addition, the experimental data demonstrate a linear correlation between protein production rate and specific growth rate over a significant time period. This suggests a constant expression fraction within this window. For an IPTG biosensor, in five cellular contexts, expression fractions determined by the maximum method and the slope method produced strikingly similar dose-response parameters when independently fit to a Hill function. Furthermore, by analysing two more biosensors, for mercury and cumate detection, we demonstrate that the slope method can be applied effectively to various systems. Therefore, the concepts presented here provide convenient methods for obtaining dose-response parameters, clearly defining the time interval of their validity and offering a framework for interpreting typical biosensor outputs in terms of bacterial physiology.

Comparing fully automated 3DE quantification and cardiac magnetic resonance imaging for left ventricular volume and function evaluation in patients with dilated cardiomyopathy

Abstract Background Integration of artificial intelligence enhances precision, yielding dependable evaluations of left ventricular volumes and ejection fraction despite image quality variations. Dilated cardiomyopathy patients have low ejection fraction with dilated left ventricular volumes, which affect the diagnostic accuracy of automated 3D echocardiography. Establishing the diagnostic accuracy of fully automated 3DE quantification software, simplify and accelerate the measurement of left chamber volumes and ejection fraction. Objective In this manuscript, we present a cross-sectional study to assess and compare the diagnostic accuracy of automated 3D transthoracic echocardiography (3D TTE) to the standard cardiac magnetic resonance (CMR) imaging in patients with dilated cardiomyopathy. Methods In this cross-sectional study, 30 dilated cardiomyopathy patients with CMR imaging and comprehensive 3D TTE were included. 3D TTE and CMR imaging were performed within 24 hours. All 3D volume analyses were performed with fully automated quantification software using 3D images of 2,3, and 4-chamber views at the end of systole and diastole. Results Excellent Inter- and Intra-observer correlation coefficient was reported for automated 3D TTE software for all indexes. Automated 3D TTE software underestimated the mean value of LVESVI and LVESDI compared to the CMR. On the contrary, LVEF, LV Mass, LV Mass Index, and SV were overestimated. Contour correction was needed in 8 cases. In cases needing contour editing, no statistically significant difference was demonstrated except for LVEF and SV. Semiautomated 3D TTE software showed similar findings as automated, except that SV was underestimated. Correlation analysis demonstrated significant agreement between Automated 3D TTE software and CMR measurements for all indexes. Fully automated measurements exhibited excellent agreement for LVESVI (r=0.918) and LVEDVI (r=0.911), good agreement for SV (r=0.811), and acceptable agreement for LVEF (r=0.744). However, despite being significantly correlated, it did not reveal acceptable agreement for LV Mass (r= 0.498) and LV mass index (r=0.421). Semiautomated 3D TTE software revealed improved correlation for all indexes, except for LVEDVI and SV. Nevertheless, no significant difference was observed between the CMR agreement correlations with automated and semiautomated 3D TTE software measurements. Conclusion Automated 3D TTE software emerges as a rapid and viable imaging approach for evaluating the left ventricle's structure and function. Nevertheless, its volume assessments demonstrate a stronger correlation with CMR imaging compared to its functional assessments. Agreement between 3D TTE and CMR Inter- and Intra-observer correlation

Validation of a fully automated AI system for accurate left ventricular ejection fraction measurement in echocardiography

Abstract Background Two-dimensional echocardiography (Echo) is a feasible method for assessing left ventricular (LV) ejection fraction (EF) in clinical routine. However, LVEF assessment is dependant on the user’s expertise and is time consuming. Purpose We aimed to compare the accuracy of an artificial intelligence (AI) system in evaluating Echo exams and calculating biplane LVEF against human interpretation and cardiac computed tomography (CT), which was regarded as the gold standard. Methods A diverse cohort of patients (n=190) with symptomatic heart valve disease underwent both Echo and cardiac spiral CT on the same day. Biplane LVEF was manually traced online on apical 4- and 2-chamber views by experienced cardiologists (Human). After completion of the exam,a novel vendor-neutral AI system performed both automated evaluation of Echo exams, appropriate view selection, and calculations of biplane LVEF in one workflow (Figure 1A). LVEF results from CT were supervised by an experienced radiologist (>10 years) (Figure 1B). Pearson’s correlation coefficient (R), regression analysis, and mean absolute error (MAE) were used to assess the agreement of LVEF values between methods. Results The final cohort consisted of 182 patients with complete LVEF assessment by the AI (feasibility: 96%). Although LVEF from AI and Human showed good agreement (R=0.79 and MAE= 5.9), AI exhibited higher agreement with CT (R=0.88, MAE=5.1) than Human LVEF calculations with CT (R=0.79, MAE=5.9), as shown in Figures 1C and 1D. Conclusion The findings highlight the potential of a novel AI system for assessing biplane LVEF from Echo exams, comparing its performance to human operator-dependent methods and CT as the gold standard. By potentially enhancing accuracy in LVEF measurements, this AI system could represent a viable advancement in the field of Echo, aligning closely with the precision of CT assessments.

Comprehensive Echocardiographic Protocol for Pigs with Emphasis on Diastolic Function: Advantages over MRI Assessment.

Swine are increasingly utilized in cardiovascular research due to their anatomical and physiological similarities to humans, particularly for studying diastolic dysfunction. While MRI offers excellent structural imaging, echocardiography provides superior real-time assessment of diastolic parameters. To address the lack of standardized methods and reduce variability across studies, we present a comprehensive guide for performing echocardiography in Yorkshire pigs, detailing anatomical considerations, equipment requirements, and technical approaches. We describe systematic approaches for obtaining and optimizing right parasternal long and short-axis views, apical four-chamber, and subcostal imaging windows, with specific attention to anatomical variations from human cardiac orientation and standard clinical transducer positioning. These tomographic views enable comprehensive assessment of systolic and diastolic function, including ventricular volumes, wall thicknesses, chamber dimensions, ejection fraction, and Doppler measurements of blood flow and tissue velocities. This standardized methodology for echocardiographic images acquisition enhances data reliability in cardiovascular pig models, improving the interpretation of preclinical study results and strengthening translational research outcomes. The protocol also provides consistency for veterinary applications, making echocardiography a preferred modality for longitudinal studies in this valuable translational model.

Comparison of forest canopy gap fraction measurements from drone-based video frames, below-canopy hemispherical photography, and airborne laser scanning

ABSTRACT The amount of gaps in forest canopy is related to the radiation interception for photosynthesis and visibility through the canopy. The dependence of forest canopy gap fraction determination on view zenith angle was calculated from polar-transformed sparse ( ≈ 4 m − 2 ) airborne laser scanning (ALS) point clouds for a Scots pine (Pinus sylvestris L.) stand growing on Kiriku Bog, Estonia. Visibility of ground targets was estimated from video image frames taken during drone (UAV) overpass at low altitude (40 m). Below-canopy digital hemispherical images (DHP) were taken in zenith direction as reference measurements. Angular grids of 3 ∘ and 5 ∘ were used to match the three data sources so as to decrease uncertainties in measurement geometries. The linear relationship between DHP and UAV data had R 2 = 0.67, with most of the deviations occurring at gap boundaries. Relationships over individual targets between DHP and polar-transformed ALS data had 0.3 < R 2 ≤ 0.8 . However, the simulation overestimated gap fraction at smaller zenith angles because of uncertainties in constructing lidar pulse footprints from point data. We conclude that regional coverage by means of sparse ALS point clouds shows potential for the assessment of forest canopy gaps at off-nadir angles.

Observation of time-dependent CP violation and measurement of the branching fraction of B0→J/ψπ0 decays

We present a measurement of the branching fraction and time-dependent charge-parity (CP) decay-rate asymmetries in B0→J/ψπ0 decays. The data sample was collected with the Belle II detector at the SuperKEKB asymmetric e+e− collider in 2019–2022 and contains (387±6)×106 BB¯ meson pairs from ϒ(4S) decays. We reconstruct 392±24 signal decays and fit the CP parameters from the distribution of the proper-decay-time difference of the two B mesons. We measure the branching fraction to be (B0→J/ψπ0)=(2.00±0.12±0.09)×10−5 and the direct and mixing-induced CP asymmetries to be CCP=0.13±0.12±0.03 and SCP=−0.88±0.17±0.03, respectively, where the first uncertainties are statistical and the second are systematic. We observe mixing-induced CP violation with a significance of 5.0 standard deviations for the first time in this mode. Published by the American Physical Society 2025

Prediction of Radiation Therapy Induced Cardiovascular Toxicity from Pretreatment CT Images in Patients with Thoracic Malignancy via an Optimal Biomarker Approach.

Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes. The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT. Manual segmentations of 10 cardiovascular objects of interest were performed on pre-treatment non-contrast-enhanced CT simulation images in 125 patients with thoracic malignancy (41 who developed cardiotoxicity and 84 who did not after RT). 1078 features describing morphology, image intensity, and texture for each of these objects were extracted and the top 5 features among them that were most uncorrelated and showed the best ability to discriminate between cardiotoxicity/ no cardiotoxicity were determined. The best combination among all possible combinations among these 5 features that yielded the highest accuracy of prediction on a training data set was selected, an SVM classifier was then trained on this combination, and tested for prediction accuracy on an independent data set. Prediction accuracy was quantified for the optimal features derived from each object. The best feature combination based on 5 CT-based features derived from the left ventricle had the highest testing prediction accuracy of 0.88 among all objects. Prediction accuracies over all objects ranged from 0.76-0.88. Heart, Left Atrium, Aortic Arch, Thoracic Aorta, and Descending Thoracic Aorta showed the next best accuracy of 0.84. Most optimal features were texture properties based on the co-occurrence matrix. It is feasible to predict future cardiotoxicity following RT with high accuracy in individual patients with thoracic malignancy from available pre-treatment CT images via machine learning techniques.

Measurements of decay branching fractions of the Higgs boson to hadronic final states at the CEPC

Abstract The Circular Electron Positron Collider (CEPC) is a large-scale particle accelerator designed to collide electrons and positrons at high energies. One of the primary goals of the CEPC is to achieve high-precision measurements of the properties of the Higgs boson, facilitated by the large number of Higgs bosons that can be produced with significantly low contamination. The measurements of Higgs boson branching fractions into bb/cc/gg and tautau/WW*/ZZ*, where the W or Z bosons decay hadronically, are presented in the context of the CEPC experiment, assuming a scenario with 5600 fb-1 of collision data at a center-of-mass energy of 240 GeV. In this study the Higgs bosons are produced in association with a Z boson, with the Z boson decaying into a pair of muons (μ+μ-), which have high efficiency and high resolution. In order to separate all decay channels simultaneously with high accuracy, the Particle Flow Network (PFN), a graph-based machine learning model, is considered. The precise classification provided by the PFN is employed in measuring the branching fractions using the migration matrix method, which accurately corrects for detector effects in each decay channel. The statistical uncertainty of the measured branching ratio is estimated to be 0.55% in H → bb final state, and approximately 1.5%-16% in H → cc/gg/tautau/WW*/ZZ* final states. In addition, the main sources of systematic uncertainties to the measurement of the branching fractions are discussed. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

Characterizing the plasma protein binding profiles of chemistry diversified antisense oligonucleotides in human and mouse plasma using an ultrafiltration method.

Plasma protein binding plays a significant role in influencing the pharmacokinetic and pharmacodynamic properties of drugs. This study focuses on examining two pairs of sequence-matched ASOs: phosphorodiamidate morpholino oligomers (PMOs) and 2'-O-methoxyethyl/phosphorothioate (MOE/PS)-modified ASOs, to assess their plasma protein binding profiles. The binding of both PMO and MOE/PS-modified ASOs was investigated using an ultrafiltration method combined with hybridization electrochemiluminescence, allowing for the measurement of the unbound fraction (fu) in both mouse and human plasma. To further characterize the interaction between ASOs and plasma proteins, individual binding measurements were taken for five major proteins in human plasma: human serum albumin, α1-acid glycoprotein, human γ-globulin, low-density lipoprotein, and high-density lipoprotein. The results showed a notable difference in plasma protein binding between the two types of ASOs, with MOE/PS-modified ASOs exhibiting significantly higher binding compared to PMOs. The fu, plasma values revealed no significant species difference between mouse and human plasma. Additionally, a saturation point for fu, plasma was observed in MOE/PS-modified ASOs at concentrations above 1μM, whereas PMOs did not show saturation even at concentrations up to 10μM. Notably, human γ-globulins were found to have a predominant binding affinity for both MOE/PS and PMO ASOs at physiological concentrations, surpassing human serum albumin, the most abundant plasma protein. The results suggest that the chemistries of the ASOs, particularly their modifications, are key determinants of their binding profiles. The study also highlights the important, though previously overlooked, role of human γ-globulins in the plasma protein binding of ASOs. This could have implications for understanding ASO distribution and tissue disposition, which may inform the development and optimization of ASO-based therapies.

Measurement of the branching fraction of D+→ τ+ντ

By analyzing e+e− collision data with an integrated luminosity of 7.9 fb−1 collected with the BESIII detector at the center-of-mass energy of 3.773 GeV, the branching fraction of D+→ τ+ντ is determined as B = (9.9 ± 1.1stat± 0.5syst) × 10−4. Using the most precise result B(D+→ μ+νμ) = (3.981 ± 0.079stat± 0.040syst) × 10−4 [1], we determine Rτ/μ = Γ(D+ → τ+ντ)/Γ(D+ → μ+νμ) = 2.49 ± 0.31, achieving a factor of two improvement in precision compared to the previous BESIII result. This measurement is in agreement with the standard model prediction of lepton flavor universality within one standard deviation.

The Importance of Gas Starvation in Driving Satellite Quenching in Galaxy Groups at z ~ 0.8

Abstract We present results from a Keck/DEIMOS survey to study satellite quenching in group environments at z ~ 0.8 within the Extended Groth Strip (EGS). We target 11 groups in the EGS with extended X-ray emission. We obtain high-quality spectroscopic redshifts for group member candidates, extending to depths over 1 order of magnitude fainter than existing DEEP2/DEEP3 spectroscopy. This depth enables the first spectroscopic measurement of the satellite quiescent fraction down to stellar masses of ~109.5 M ⊙ at this redshift. By combining an infall-based environmental quenching model, constrained by the observed quiescent fractions, with infall histories of simulated groups from the IllustrisTNG100-1-Dark simulation, we estimate environmental quenching timescales (τ quench) for the observed group population. At high stellar masses (M⋆ = 1010.5 M ⊙) we find that τ quench = 2.4 + 0.2 − 0.2 Gyr, which is consistent with previous estimates at this epoch. At lower stellar masses (M⋆ = 109.5 M ⊙), we find that τ quench = 3.1 + 0.5 − 0.4 Gyr, which is shorter than prior estimates from photometry-based investigations. These timescales are consistent with satellite quenching via starvation, provided the hot gas envelope of infalling satellites is not stripped away. We find that the evolution in the quenching timescale between 0 &lt;z &lt;1 aligns with the evolution in the dynamical time of the host halo and the total cold gas depletion time. This suggests that the doubling of the quenching timescale in groups since z ~ 1 could be related to the dynamical evolution of groups or a decrease in quenching efficiency via starvation with decreasing redshift.

Direct high-precision radon quantification for interpreting high-frequency greenhouse gas measurements

Abstract. We present a protocol to improve confidence in reported radon activity concentrations, facilitating direct site-to-site comparisons and integration with co-located greenhouse gas (GHG) measurements within a network of three independently managed observatories in the UK. Translating spot measurements of atmospheric GHG amount fractions into regional flux estimates (“top-down” analysis) is usually performed with atmospheric transport models (ATMs), which calculate the sensitivity of regional emissions to changes in observed GHGs at a finite number of locations. However, the uncertainty of regional emissions is closely linked to ATM uncertainties. Radon, emitted naturally from the land surface, can be used as a tracer of atmospheric transport and mixing to independently evaluate the performance of such models. To accomplish this, the radon measurements need to have a comparable precision to the GHGs at the modelled temporal resolution. Australian Nuclear Science and Technology Organisation (ANSTO) dual-flow-loop two-filter radon detectors provide output every 30 min. The measurement accuracy at this temporal resolution depends on the characterization and removal of instrumental background, the calibration procedure, and response time correction. Consequently, unless these steps are standardized, measurement precision may differ between sites. Here we describe standardized approaches regarding (1) instrument maintenance, (2) quality control of the raw data stream, (3) determination and removal of the instrumental background, (4) calibration methods, and (5) response time correction (by deconvolution). Furthermore, we assign uncertainties for each reported 30 min radon estimate (assuming these steps have been followed) and validate the final result through comparison of diurnal and sub-diurnal radon characteristics with co-located GHG measurements. While derived for a network of UK observatories, the proposed standardized protocol could be equally applied to two-filter dual-flow-loop radon observations across larger networks, such as the Integrated Carbon Observation System (ICOS) or the Global Atmosphere Watch (GAW) baseline network.

On cumulative past information generating function

. This article studies the cumulative past information-generating function (CPIG) and relative cumulative past information-generating function (RCPIG). We study its properties. We establish its relation with generalized cumulative past entropy (GCPE). We define the multivariate version of the CPIG function. We discuss its connection with system reliability for the k-out-of-n system. We define the CPIG stochastic order and its relation with dispersive order. We provide the results for the CPIG measure of convoluted random variables in terms of the measures of its components. We find some inequalities relating to Shannon entropy, CPIG, and GCPE. Some characterization and estimation results regarding CPIG are also discussed. We study CPIG based on SRS and RSS schemes. We define divergence measures between two random variables: Jensen-cumulative past information-generating function (JCPIG), Jensen fractional cumulative past entropy measure, cumulative past Taneja entropy, and Jensen cumulative past Taneja entropy information measure.

Constraining the history of water and climate on Mars through light element stable isotope analysis of volatiles in returned martian samples

Much has been learned about Mars through data returned from space missions and analyses of martian meteorites. There are, however, many questions still outstanding which cannot currently be answered-including the issue of whether there is, or was, life on Mars. The return of a cache of samples-including of the atmosphere-from separate locations in Jezero Crater and with differing petrogeneses will provide the international community with the opportunity to explore part of the evolutionary history of Mars in great detail. Specifically, measurements of the isotopic compositions of the light elements H, C, N, O, Cl, and S can be used to follow how volatile species cycle through the different martian volatile reservoirs (atmosphere, lithosphere, cryosphere, and hydrosphere). Measurement of isotopic fractionation enables inference of the environmental conditions (e.g., temperature, water/rock ratio) under which fractionation occurred. Knowing the contextual relationship of the materials to their geological settings, coupled with precise compositional measurements will enable a more thorough understanding of martian volatile history and allow a picture to be constructed of water and climate on Mars as represented at Jezero Crater.

Accuracy of Proton Density Fat Fraction Measurement Using Chemical Shift-encoded MRI with Fast Imaging Techniques

To investigate the accuracy of proton density fat fraction (PDFF) measurement using chemical shift-encoded MRI (CSE-MRI) with fast imaging techniques in a phantom. A 1.5T imaging system (Prodiva; Philips Healthcare) and PDFF phantom (Fat Fraction Phantom Model 300; Calimetrix) were used in this study. The acquisitions without fast imaging techniques (conventional acquisition), with parallel imaging in phase-encode direction (SENSE acquisition), with compressed sensing (CS-SENSE acquisition), and with parallel imaging in both phase-encode and slice-encode direction (Dual-SENSE acquisition) were performed. The following acceleration factors in SENSE and CS-SENSE acquisition were used: 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, and 8.0. For Dual-SENSE acquisition, the same acceleration factors (1.5, 2.0, 3.0, 4.0, 5.0) were set in each of the two directions. The relationships between reference PDFF values and PDFF measurements obtained using each acquisition were assessed using linear regression analysis and Bland-Altman analysis. According to the linear regression analysis, the slopes and intercepts of regression lines were from 0.87 to 1.02 and from 0.06% to 3.55%, respectively. According to Bland-Altman analysis, there were fixed bias between reference PDFF values and PDFF measurements obtained using SENSE acquisition with reduction factor 8.0 and Dual-SENSE acquisition with reduction factor 5.0. For CS-SENSE acquisition with reduction factor from 7.0 to 8.0, SENSE acquisition with reduction factor from 3.0 to 8.0, and Dual-SENSE acquisition with reduction factor from 2.0 to 5.0, some vials had ±1.5% or more errors between the reference PDFF values and PDFF measurements in the range of 0% to 50% PDFF. In CS-SENSE acquisition, the accuracy of PDFF measurement was maintained within 1.5% up to a reduction factor 6.0. The accuracy of PDFF measurement was maintained within 1.5% up to a reduction factor 2.0 in SENSE acquisition and a reduction factor 1.5 in Dual-SENSE acquisition.

An automatic and real-time echocardiography quality scoring system based on deep learning to improve reproducible assessment of left ventricular ejection fraction.

Echocardiography can conveniently, rapidly, and economically evaluate the structure and function of the heart, and has important value in the diagnosis and evaluation of cardiovascular diseases (CVDs). However, echocardiography still exhibits significant variability in image acquisition and diagnosis, with a heavy dependency on the operator's experience. Image quality affects disease diagnosis in the later stage, and even image quality assessment still has variability in human evaluation. This study aimed to develop an automated and real-time quality assessment system using deep learning (DL) techniques while decreasing the measurement error of left ventricular ejection fraction (LVEF). This study involved over 5,000 echocardiography datasets from 2,461 participants across 10 medical centers in China to build the model. A 5-point quality scoring system was used to assess the integrity, clarity, and alignment of anatomical structures in each echocardiogram view. Additionally, an innovative DL model was developed to autonomously detect these essential cardiac anatomical structures in real-time, subsequently providing quality score estimations and LVEF. A total of 175 participants from two distinct external medical centers were enrolled for model validation. This dataset was employed to assess the consistency and repeatability of quality score and ejection fraction (EF) measurements, and the assessments made by human experts were compared with those of our model. The developed model demonstrated exceptional performance, achieving Intersection over Union (IoU) scores exceeding 0.8 for left ventricular (LV) segmentation, a mean average precision when IoU >0.5 (mAP50) of 0.91 for cardiac anatomical structures detection, and a 0.96±0.05 accuracy in view classification. The quality scores assessed by the model closely matched those of human experts, indicating strong agreement. The weighted average precision and weighted average recall scores fell within the range of 0.5 to 0.6. Notably, there was no statistically significant difference in LVEF assessments between human experts and our model (P=0.09), as demonstrated by an intraclass correlation coefficient (ICC) analysis of 0.821, reflecting high-level consistency. When assessing echocardiograms with high-quality scores, the model demonstrated a significantly closer alignment and a higher correlation coefficient with human experts (R=0.90±0.04). This study demonstrates that artificial intelligence-assisted echocardiography scoring system aligns well with manual quality scoring. Through the supervision of real-time echocardiogram quality, the artificial intelligence model can assist doctors in providing more reproducible and consistent assessments of cardiac function.

Fractional cumulative past inaccuracy measure, its dynamic version and applications in survival analysis

Fractional cumulative past inaccuracy measure, its dynamic version and applications in survival analysis

Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis.

T1 mapping on cardiac magnetic resonance (CMR) imaging is useful for diagnosis and prognostication in patients with light-chain cardiac amyloidosis (AL-CA). We conducted this study to evaluate the performance of T1 mapping parameters, derived from artificial intelligence (AI)-automated segmentation, for detection of cardiac amyloidosis (CA) in patients with left ventricular hypertrophy (LVH) and their prognostic values in patients with AL-CA. A total of 300 consecutive patients who underwent CMR for differential diagnosis of LVH were analyzed. CA was confirmed in 50 patients (39 with AL-CA and 11 with transthyretin amyloidosis), hypertrophic cardiomyopathy in 198, hypertensive heart disease in 47, and Fabry disease in 5. A semi-automated deep learning algorithm (Myomics-Q) was used for the analysis of the CMR images. The optimal cutoff extracellular volume fraction (ECV) for the differentiation of CA from other etiologies was 33.6% (diagnostic accuracy 85.6%). The automated ECV measurement showed a significant prognostic value for a composite of cardiovascular death and heart failure hospitalization in patients with AL-CA (revised Mayo stage III or IV) (adjusted hazard ratio 4.247 for ECV ≥40%, 95% confidence interval 1.215-14.851, p-value = 0.024). Incorporation of automated ECV measurement into the revised Mayo staging system resulted in better risk stratification (integrated discrimination index 27.9%, p = 0.013; categorical net reclassification index 13.8%, p = 0.007). T1 mapping on CMR imaging, derived from AI-automated segmentation, not only allows for improved diagnosis of CA from other etiologies of LVH, but also provides significant prognostic value in patients with AL-CA.

Changes of myocardial extracellular volume fraction measurements in acute versus chronic stage in a large animal infarct model

Changes of myocardial extracellular volume fraction measurements in acute versus chronic stage in a large animal infarct model