MethodsX

Distal radius fractures (DRFs) can delay return to activities of daily living and social participation because of postoperative pain, temporary joint immobilization, and limited wrist and forearm range of motion. The Ghost System developed at Saitama Prefectural University, Japan, combines visual action observation with tendon vibration stimulation and has shown potential as an adjunct to conventional rehabilitation. This Study Protocol describes a modified Ghost system intended to improve clinical implementation by replacing the head-mounted virtual reality display with iPad-based action observation and by using a wristband-type vibrator. This single-center, single-arm, open-label feasibility trial will enroll 10 adults after palmar locking plate fixation for DRF. The intervention will be delivered twice weekly during outpatient rehabilitation follow-up sessions from the early postoperative period (postoperative days 2-10 after enrollment) through the approved early postoperative rehabilitation period (generally up to postoperative week 8), in parallel with standard rehabilitation practices. Primary feasibility and preliminary clinical outcomes include device fit and acceptability, pain assessed using a 100-mm Visual Analog Scale, and wrist/forearm range of motion. Secondary implementation and safety outcomes include Disabilities of the Arm, Shoulder and Hand (DASH), Patient-Rated Wrist Evaluation (PRWE), Hand20 Questionnaire (HANDS-20), EuroQol 5 Dimensions 5 Levels (EQ-5D-5L), body ownership and hand-illusion questionnaires, setup time, setup errors, adherence, adverse events, and device incidents. We hypothesize that the modified Ghost system will be feasible and acceptable for early postoperative outpatient rehabilitation and will be delivered without serious device-related adverse events. Clinical outcomes will be summarized descriptively to inform a future controlled study rather than to establish efficacy.

Minimally invasive endovascular procedures offer reduced surgical trauma, shorter recovery times, and improved outcomes, but rely on 2D fluoroscopic X-ray imaging, which provides limited depth perception and exposes patients and clinicians to ionizing radiation. Here we present an augmented reality (AR) system that fuses intravascular ultrasound (IVUS) and electromagnetic (EM) position tracking with preoperative computed tomography (CT) to produce an anatomically accurate, deformation-corrected navigational reference. A robotic device performs ECG-gated pullback of the IVUS probe, capturing 4D aortic motion across the cardiac cycle. We introduce a deep learning architecture for extracting vascular lumen boundaries and side-branch orifices from artifact-prone IVUS streams, and a semantically driven non-rigid CT-IVUS fusion pipeline robust to false positive landmarks. We evaluate the platform with trained surgeons in benchtop phantom studies and in-vivo ovine models, and demonstrate its application to fenestrated endovascular aneurysm repair (FEVAR). Compared to fluoroscopy alone, AR guidance significantly reduces cannulation time, radiation exposure, and cognitive workload, while improving procedural efficiency and safety. Our IVUS-EM and CT aortic datasets are released open source.

Background: Despite the common use of local heating and intradermal microdialysis perfusion of acetylcholine (ACh) to probe cutaneous endothelium and nitric oxide (NO)-dependent dilation, sex differences in microvascular responsiveness to these stimuli in healthy young adults remain incompletely understood. Methods: Cutaneous vasodilation was assessed in response to local heating to 39{degrees}C and 42{degrees}C and graded perfusion of ACh (10-10 to 10-1 mol/L) alone or concurrently with 15 mM NG-nitro-L-arginine methyl ester (L-NAME; NO synthase inhibitor) using laser-Doppler flowmetry coupled with intradermal microdialysis in 80 young adults (40 females). Results: Local heating to 42{degrees}C elicited greater endothelium- and NO-dependent dilation than heating to 39{degrees}C in both groups (p<0.001), but no sex differences were observed at either temperature (p=0.65). ACh-induced endothelium-dependent dilation also was not different between sexes (p=0.08), but the NO-dependent component was greater in females than in males (p=0.01). In young females, menstrual cycle day (range: day 2-33) was not associated with endothelium- or NO-dependent dilation in response to any stimulus (all p[&ge;]0.19), regardless of hormonal contraceptive use. Conclusions: Taken together, these findings suggest that sex differences in microvascular NO bioavailability in healthy young adults depend on the stimulus used to elicit cutaneous vasodilation and, in females, microvascular endothelium- and NO-dependent dilation are not meaningfully influenced by menstrual cycle phase.

Microcomputed tomography (micro-CT) is a useful tool that can be utilised for 3D structural characterisation and volumetric quantification of small biological specimens. Its potential application is particularly valuable within the field of cardiac development, where phenotypic profiling at the whole organ, cell, and molecular level is often most informative within the same sample. Consequently, this study sought to develop a multimodal imaging protocol to enable 3D phenotypic characterisation of embryonic avian hearts (iodine-based contrast X-ray imaging) prior to immunohistochemistry-based cell and molecular analysis. Micro-CT parameters were tested to establish an optimal protocol for 3D analysis of embryonic cardiac specimens across multiple developmental timepoints. Optimised parameters provided reliable and reproducible 3D analysis of cardiac macrostructures. Sodium thiosulphate treatment of X-ray imaged hearts effectively reversed the iodine-based contrast stain whilst maintaining antigen availability of nuclear, membranous, and cytoplasmic targets in traditional downstream imaging studies. Together, this study demonstrates a robust and highly efficient multimodal imaging strategy to comprehensively characterise cardiac morphology in avian embryos and may serve as a versatile foundation for a broad range of bioimaging applications within the wider scientific community.

Spinal cord injury (SCI) causes rapid and severe bone loss in the paralysed lower limbs, particularly at the distal femur and proximal tibia, where fragility fracture risk is high. In vitro nanoscale vibration at 1 kHz has been shown to promote osteogenic differentiation and inhibit osteoclastogenesis, suggesting potential as a targeted mechanical intervention. This study aimed to develop and evaluate a wearable device for delivering and monitoring localised nanovibration at the distal femur in individuals with SCI. The device delivered continuous sinusoidal nanoscale stimulation at 1 kHz via a bone-conduction transducer, with an opposing accelerometer used to monitor transmitted vibration in real time. Design and target-site selection were refined through two healthy-volunteer investigations comparing the distal femur, proximal tibia, and distal tibia. Bovine femur experiments characterised vibration transmission under controlled benchtop conditions. Preliminary repeated-use feasibility was assessed in one individual with motor-complete SCI. Healthy volunteer testing showed that although the ankle initially produced the highest transmitted amplitudes, these were highly variable, and positioning was inconsistent. Within the knee region, the distal femur provided the most practical and repeatable site for a wearable application. In bovine femur experiments, scanning laser vibrometry demonstrated measurable vibration on the condylar surface opposite the transducer, and depth-resolved measurements confirmed that nanoscale vibration remained detectable within bone. A gel interface layer reduced the transmitted amplitude. In the feasibility evaluation, 61 sessions were completed over 14 weeks, with logged accelerometry confirming repeated nanoscale vibration transmission. These findings establish feasibility and support further device optimisation and translational studies.

Urinary stone disease is a common urological condition with increasing incidence, particularly in developed countries. Laser lithotripsy (LL) has become a preferred minimally invasive treatment due to its high precision and low tissue damage. Recent studies suggest that cavitation plays a critical role in stone damage during LL, and three-dimensional passive cavitation mapping (3D-PCM) has emerged as a promising tool for detecting these events. However, clinical translation of 3D-PCM remains challenging due to limitations in imaging depth, field of view (FOV), and procedural compatibility. Here, we present a large-FOV dual-modality imaging system (3D-PCM and B-mode ultrasound) based on a large-aperture planar ultrasound array. Through array optimization and model-based reconstruction, our system achieves an expanded FOV of ~40*40mm^2 at a clinically relevant imaging depth of ~110mm, while maintaining high spatial resolution of ~0.6 mm laterally and ~0.4 mm axially. In vivo experiments in a porcine model demonstrate that the reconstructed cavitation distribution correlates well with stone damage. Our technology has the potential to provide real-time treatment feedback during LL without disrupting the standard workflow.

Cystatin-C is an important renal function biomarker, and conventional quantification requires centralized laboratory analyzers, which limits timely testing in primary care and resource-limited settings. To address this need, we developed and validated a simple, rapid, and quantitative smartphone-based (SP) lateral flow immunoassay (LFIA) for measuring serum Cystatin-C. The SP-LFIA platform consists of a colorimetric LFIA strip and a custom SP reader with uniform LED illumination and macro lens for image capture. Quantitative image analysis of the colorimetric signal is performed by a dedicated application using a pre-defined third order polynomial calibration model. Following systematic optimization, the assay demonstrated a wide quantitative range of 0.32-8.00 mg/L, with a limit of detection of 0.15 mg/L. Analytical validation conducted according to CLSI guidelines showed excellent precision, with intra- and inter-assay coefficients of variation below 10%, and no significant interference from bilirubin, triglycerides, hemoglobin, or rheumatoid factor. Accelerated stability testing confirmed robust strip performance after storage at 50 {degrees}C for 28 days. Method comparison using 100 clinical serum samples showed high agreement with a commercial PETIA reference method (R{superscript 2} = 0.993) and minimal bias. These results indicate that the developed smartphone-based LFIA provides a reliable, cost-effective, and practical tool for point-of-care Cystatin-C monitoring.

Abstract Background: Non-specific chronic low back pain (NSCLBP) is a major cause of disability worldwide and is associated with low-grade systemic inflammation. This study investigated the effects of aerobic exercise on inflammatory biomarkers, pain intensity, and quality of life among individuals with NSCLBP. Methods: In this parallel-group randomized controlled trial, 41 participants with NSCLBP were allocated to either an aerobic exercise plus health education group (n=21) or a health education-only control group (n=20). Participants in the intervention group completed supervised aerobic cycling three times weekly for 12 weeks. Outcome assessors and laboratory personnel were blinded to group allocation. Outcomes were measured at baseline, Week 8, and Week 12. Results: Interaction effects were observed for TNF- (p=0.046), IL-6 (p<0.001), hs-CRP (p<0.001), and pain intensity (p<0.001). Significant improvements were also observed across all WHOQOL-BREF quality-of-life domains (all p<0.05). After adjustment for baseline values and age, participants in the intervention group had significantly lower Week 12 IL-6 (p=0.013), hs-CRP (p<0.001), and pain intensity (p<0.001) than controls. No serious adverse events were reported. Conclusions: Aerobic exercise combined with health education produced greater improvements in inflammatory biomarkers, pain intensity, and quality of life than health education alone among individuals with NSCLBP. These findings support the integration of structured aerobic exercise into rehabilitation programmes for chronic low back pain. Keywords: Non-specific chronic low back pain; aerobic exercise; inflammation; IL-6; hs-CRP; pain intensity; quality of life; randomized controlled trial.

SignificanceDigital Holographic Microscopy (DHM) provides label-free quantitative phase images (QPIs) of living cells and has become a powerful tool for studying cellular morphology and dynamics. While most DHM studies have focused on cell-level analysis, the quantitative characterization of neuronal network organization and maturation from DHM images remains largely unexplored, highlighting the need for dedicated computational approaches. AimWe aimed to develop an automated framework combining deep-learning-based image analysis and graph theory to quantitatively characterize the organization, connectivity, and maturation of neuronal networks in primary rat cortical cultures imaged by DHM. ApproachTwo U-Net convolutional neural networks were trained on manually annotated DHM phase images to segment neuronal cell bodies and neurites. The resulting segmentation maps were used to infer putative morphological connections between neurons and generate graph representations of neuronal networks, referred to as graph fingerprints. A panel of 18 connectomics-inspired graph features was then computed to characterize local and global properties of network organization across four stages of culture maturation. ResultsThe mean area under the receiver operating characteristic curves was 0.98 for cell-body and 0.91 for neurite segmentation, indicating near-perfect identification. Graph-theoretical analysis revealed reproducible topological changes during network maturation in vitro, including increased density, reduced modularity, and progressive network integration. Correlation analysis showed that the 18 graph features grouped into two highly correlated families. A Random Forest classifier identified density and modularity as the most informative descriptors, achieving an accuracy of 87% in classifying maturation stages of neuronal cultures. ConclusionsOur results demonstrate that combining DHM, deep-learning-based segmentation, and graphtheoretical analysis enables quantitative characterization of neuronal network organization and maturation from label-free phase images. This framework provides a foundation for future studies of pharmacological experiments, neuronal network phenotyping, and human induced pluripotent stem cell (hiPSC)-derived neuronal cultures, where quantitative assessment of network organization remains a major challenge.

Members of the transforming growth factor-{beta} (TGF-{beta}) superfamily, including inhibins and activins, are structurally related glycoprotein dimers that regulate reproductive and endocrine signaling. Their high degree of molecular similarity presents challenges for label-free analytical discrimination. To evaluate the ability of Raman spectroscopy to distinguish closely related TGF-{beta} superfamily proteins based on intrinsic vibrational fingerprints. Raman spectra of recombinant human Inhibin -subunit, Inhibin B ({beta}B homodimer), and Activin A ({beta}A--{beta}A) were acquired using confocal Raman microscopy with 532 nm excitation. Spectra were baseline-corrected, area-normalized, and analysed using principal component analysis (PCA). Distinct spectral signatures were observed across the 500--1800 cm-1 region. Differences within the S--S stretching region (500--550 cm-1) were consistent with variations in disulfide-bond environments, with the Inhibin -subunit exhibiting the highest relative intensity in this region. Variations in the amide I band (1600--1700 cm-1) suggested differences in protein secondary structure, while aromatic amino acid vibrations provided additional discriminatory features. PCA revealed clear clustering and separation of all three protein classes based on their Raman fingerprints. Raman spectroscopy enables label-free differentiation of structurally related endocrine glycoproteins and demonstrates potential for the structural characterization and classification of inhibin and activin proteins within the TGF-{beta} superfamily.

BACKGROUND: Older adults with cancer often struggle to achieve recommended levels of physical activity and dietary intake. Ecological momentary assessment combined with accelerometry can provide insights into the temporal dynamics of psychological and behavioral processes at the individual level, such as motivation towards health behaviors. OBJECTIVE: This N-of-1 study aims to improve physical activity and nutritional behaviors among older patients with cancer using an mHealth behavioral intervention. METHODS: A single-subject ABA' design will be employed among older patients with cancer ([&ge;] 70 years). A 2-week baseline phase (A) will be followed by an 8-week intervention phase (B) and a two-week withdrawal phase (A'). Throughout all these phases, participants will complete a daily data collection protocol combining ecological momentary assessment questionnaires and an ActiGraph wGT3X-BT accelerometer worn on the waist to measure physical activity. Ecological momentary assessment questionnaires will be delivered via a digital application to collect information on nutritional behavior, fatigue, and motivational constructs based on the Theory of Planned Behavior. The intervention (B) will consist of an mHealth intervention based on behavior change techniques, delivered via weekly calls, personalized messages, and a digital application. Data will be analyzed using piecewise and segmented regression models. In addition, a semi-structured interview will be conducted to assess patient experience. These qualitative data will help identify contextual factors, such as treatment-related side effects or variations in health status, that may have influenced behavior change and participation in data collection. CONCLUSION: This N-of-1 study explores intra-individual behavioral dynamics using intensive longitudinal data rather than testing a strictly reversible intervention effect. The mHealth intervention is based on behavior change techniques and tailored to each patient, with adjustments made based on repeated daily assessments in a real-world setting using a wGT3X-BT accelerometer and ecological momentary assessment questionnaires. The results will contribute to the evidence base for mHealth interventions designed to promote sustained physical activity and dietary intake among older adults with cancer.

Mechanical forces have a major effect on cell behavior. Most cells in vitro are grown under static conditions on hard tissue culture plastic, conditions that do not accurately reflect living tissues. The ability of cells to sense and respond to mechanical forces is essential for key biological processes, including development, proliferation, and migration. Disruption of the ability to respond to mechanical forces are known to be a critical factor in many diseases, including cardiovascular disease, progeria, and cancer. Here, we present the design, fabrication, and biological testing of a custom-built cell-stretching device that applies controlled biaxial strain to cells cultured on a polydimethylsiloxane (PDMS) membrane. We then used this device to examine how cells respond to strain. In response to biaxial strain, MCF-7 cells activated the mechanosensitive immediate early gene (IEX-1), with its expression increasing significantly after 1 and 3 hours of stretching. Cells exposed to mechanical strain also remodeled their cytoskeleton in a direction-dependent manner. Under uniaxial strain, actin filaments reoriented perpendicular to the stretch direction, whereas biaxially stretched cells do not promote directional reorientation, but instead appear to reinforce actin at the cell periphery. Similarly, cells under uniaxial strain exhibited changes in nuclear orientation and shape that were not observed under biaxial strain. Nuclear area remained unchanged in either strain condition. These results highlight that the biaxial stretcher can be used to apply strain to cells, and that cells respond differently to biaxial strain compared to what has been reported for uniaxial strain.

The Human Reference Atlas (HRA) enables multiscale data exploration and visualization. We present "HRA: Powers of Ten," a virtual reality (VR) application for integrating, harmonizing, and visualizing data within the HRA Organ Gallery. It enables immersive navigation from a whole-body view of 81 organs to datasets across 5 organs, 5 assay types, and 4 spatial scales using a Multiscale Elevator System. The application, data, and code are available open-source.

A test technique for measuring high-frequency transient current components in deep brain tissue is presented. The technique applies a voltage pulse with a high value in dV/dt, generating a corresponding current pulse with high dI/dt that can elicit measurable transient current responses from the electrode/tissue interface and adjacent brain tissue; responses are analyzed in the frequency domain by Fast Fourier Transform at a 200 kHz sampling frequency. The method was motivated by prior evidence that ferritin and neuromelanin in catecholaminergic tissue may support high-frequency conduction properties that have not previously been characterized in vivo. The protocol was applied in 277 measurements across five Sprague Dawley rats at cortical and basal ganglia locations in different locations in the brain. Preliminary spectral results show differences between catecholaminergic regions and cortical tissue that support further development and validation of the method.

Background: We propose the biological drift framework for the interpretation of biological test results: a z-score-like framework based on optimized and personalized reference populations and a distance-to-optimum drift metric for longitudinal interpretation relative to an estimated individual optimum. We benchmarked biological drifts against Reference Change Values (RCVs), which are used to interpret serial laboratory results by defining the minimum change expected to exceed normal within-subject biological variation CVi. Objectives: To benchmark biological drifts against the classical biological-variation framework and assess their consistency with RCV thresholds across routine biomarkers. Methods: For 62 routine biomarkers, biological drift levels were compared with RCVs after transformation to test the consistency between the two frameworks. Results: Severe biological drifts mostly exceeded the 95% RCV threshold, indicating changes unlikely to be explained by short-term biological variation alone. In contrast, moderate drifts reached the 95% RCV threshold for approximately one in two biomarkers, suggesting that many moderate distance-to-optimum deviations may remain within expected variability, particularly for biomarkers with large within-subject variation CVi. Results are particularly interesting for the follow-up of people with diabetes and for the management of thyroid and hepatic disorders. Conclusions: Biological drifts derived from optimized personalized reference populations are broadly consistent with the RCV framework for identifying biologically meaningful deviations from the optimum and may therefore be relevant for the monitoring of certain biomarkers across several medical conditions in clinical practice.

Hypoxia-inducible factor prolyl hydroxylase (HIF-PH) inhibitors are widely used for the treatment of renal anemia; however, their effects on intraocular vascular endothelial growth factor (VEGF) expression remain unclear. In this study, we examined the effects of all five HIF-PH inhibitors --roxadustat, daprodustat, vadadustat, enarodustat, and molidustat--on Vegfa expression in the retina in mice. C57BL/6J mice were orally administered each inhibitor. Six hours after administration, the kidney, retina, and liver were collected, and transcription levels were quantified by real-time quantitative reverse transcription PCR. Renal Epo transcription was significantly increased by molidustat (P < 0.01), roxadustat (P < 0.01), and enarodustat (P < 0.05). Retinal Vegfa transcription was significantly increased by four inhibitors (P < 0.01), with molidustat showing no significant effect. In the liver, Vegfa transcription was increased by daprodustat (P < 0.05) and vadadustat (P < 0.01). Furthermore, renal Epo and retinal Vegfa transcription levels showed a moderate positive correlation with a marginal trend toward statistical significance (r = 0.37, P = 0.08). These findings indicate that HIF-PH inhibitors differentially regulate hypoxia-responsive genes across tissues and suggest that retinal VEGF upregulation should be considered when evaluating the safety of these agents.

Nephrons are the functional units of the kidney; within each nephron, the glomerulus is the initial site of selective filtration that allows removal of waste products while preserving proteins in the bloodstream. Each glomerulus consists of a network of capillaries surrounded by specialized epithelial cells, podocytes, which mediate selective filtration. Abnormalities in glomerular structure impair renal function, resulting in proteinuria and kidney disease. Although several microscopy-based approaches exist to characterize glomerular architecture and structural abnormalities, quantitative analysis is often limited by labor-intensive image segmentation. In this study we present a semi-automated approach for segmentation and analysis of glomerular architecture from three-dimensional confocal microscopy data. Using mTmG transgenic mice that express membrane-associated EGFP in podocytes and membrane-associated tdTomato across all other cell types, we reconstruct podocyte processes and glomerular capillaries from volumetric renal images. This semi-automated approach reduces manual segmentation effort and supports more efficient, standardized analysis of glomerular architecture in three-dimensional confocal microscopy datasets.

Background and Objective: Indications for right ventricular assist device (RVAD) insertion include right heart failure after implantation of a left ventricular assist device or early graft failure following heart transplantation. This study aimed to investigate how the upstream and downstream circulatory network interacts with the Impella RP(R) device. Methods: A numerical model of the Impella RP(R) was implemented within CARDIOSIM(C) software platform for this study. In the numerical configuration, the RVAD aspirated blood from either the right atrium (RA-PA connection) or the right ventricle (RV-PA connection) and delivered it to the pulmonary artery. Only RA-PA connection is the currently used setting for Impella RP(R) in clinical practice. Based on right ventricular (RV) decompression and total flow, our study may help define the need for a direct RV-unloading Impella RP(R). Results: The simulations showed that activating the RVAD in RA-PA mode, regardless of its rotational speed, the mean pulmonary artery pressure (PAP) percentage change was higher than the unsupported condition when the mean systemic venous pressure (SVP) and the pulmonary artery wedge pressure (PAWP) were both set to 20 mmHg. When RV-PA connection was applied, a similar trend was observed although the PAP percentage changes were about halved compared to the RA-PA connection. Conclusions: The Impella RP(R) has the potential to become a valid option for RV support based on current experimental and simulation data. Although already in use, further evaluation in the clinical setting will likely confirm its potential and lead to a more routinely application for RV support.

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the leading electrical causes of sudden cardiac death, but automated detection is limited by strong class imbalance, where lethal arrhythmias account for fewer than 22% of ECG segments. In this setting, standard classifiers can achieve high accuracy by predicting normal rhythm in most cases while missing many lethal events, a failure mode referred to as rare-class collapse. We evaluated six imbalance-handling approaches: naive logistic regression, inverse-frequency reweighting, label-distribution-aware margin loss (LDAM), cost-sensitive training, two-stage cascade classification, and anomaly detection on 15,614 ECG segments from three PhysioNet databases (VTaC, VFDB, CUDB), with an overall normal-to-lethal ratio of 3.6:1. All methods were assessed at a fixed operating point of 95% specificity using recall, area under the precision-recall curve (AUPRC), and missed-lethal-event rate (MLER). The naive model achieved 45.1% recall (MLER = 0.549), missing 564 of 1,027 lethal events despite 84.1% accuracy. The two-stage cascade performed best, with 65.2% recall, AUPRC of 0.821, and MLER of 0.348, reducing missed events by 37% and achieving the highest decision-curve net benefit. Per-source analysis showed near-complete VF detection (recall up to 0.975) but much lower VT detection (recall 0.183), suggesting a feature-space limitation due to spectral similarity between organized VT and rapid sinus rhythm. Overall, the results show that evaluation metrics strongly influence the visibility of rare-class failure, and that cascade-based methods outperform simpler reweighting approaches for detecting lethal arrhythmias.

Summary Background A comprehensive transthoracic echocardiogram involves the assessment of over 70 parameters, placing a substantial burden on sonographers and physicians for manual annotation with considerable inter-observer variability. Prior open-source segmentation models have largely addressed 2D B-mode ventricular function, leaving a gap in the spectral Doppler and atrial measurements required for valvular and diastolic assessment such as velocity-time integral (VTI) and atrial chamber size. Methods In this retrospective multi-cohort study, we developed EchoNet-Segmentation, comprehensive task-specific deep learning segmentation models for left and right atrial area and VTI Doppler measurements. Training used 186,712 sonographer-annotated images from 93,978 studies (56,855 patients) at Cedars-Sinai Medical Center (CSMC). Performance was evaluated on a held-out CSMC test set, a CSMC temporal split, an external Kaiser Permanente Northern California cohort, and the public MIMIC-Echo dataset. Findings On the CSMC held-out test set, our AI models showed strong agreement with sonographer measurements, with R2 of 0.817-0.882 and mean absolute error (MAE) of 1.13-3.80 cm for automated VTI measurements, and R2 of 0.675-0.747 and MAE of 2.48-2.52 cm2 for left and right atrial area segmentation. Performance was consistently confirmed on the CSMC temporal split (VTI: R2 0.606-0.866, atrial area: R2 0.694-0.705) and on the KPNC external cohort (VTI: R2 0.575-0.859, atrial area: R2 0.803-0.876), on the MIMIC-Echo dataset. Robustness was demonstrated on a different vendor's machines and across subgroups. EchoNet-Segmentation outperformed an open-source medical image foundation model with bounding-box, point prompt configurations on R2, MAE, and Dice score on both held-out test dataset and MIMIC apical four-chamber data. Interpretation EchoNet-Segmentation is the first open-source framework that delivers accurate, generalizable automated measurement across several key routine echocardiographic parameters, supporting end-to-end automation of clinically important echocardiographic assessments. Public release of model weights, code, and demonstration tools can facilitate reproducibility, research use and clinical deployment.