Bioengineering

Predicting implantation outcomes in vitro fertilization (IVF) is challenging because clinically relevant information becomes available at different points during treatment. In this study, we developed a stage-aware, leakage-conscious, and explainable machine-learning framework to predict implantation using a public IVF dataset. {beta}-HCG was excluded to avoid potential downstream target leakage. The data were divided into training, validation, and independent test sets. Several machine-learning models, including Logistic Regression, Random Forest (RF), Extra Trees, XGBoost, and Hist Gradient Boosting, were optimized using stratified cross-validation. A fixed RF model was then evaluated at three stages of the IVF process, using baseline couple characteristics, gamete and fertilization variables, and embryological variables, respectively. Model performance improved as additional treatment-related information became available, with the largest improvement observed after the inclusion of embryological variables. The final RF model showed good predictive performance on the independent test set. The number of embryos was consistently identified as the most important predictor across RF, permutation, and SHAP analyses. Overall, the findings suggest that IVF implantation prediction becomes more accurate as later-stage treatment information is incorporated, particularly information related to embryological characteristics. The proposed approach also provides interpretable predictions while reducing the risk of information leakage. Further evaluation using independent clinical datasets is needed before the framework can be considered for clinical use.

Obstetric anal sphincter injury (OASI) requires immediate postpartum recognition for primary repair. Diagnosis relies primarily on clinical examination, as endoanal ultrasound (EAUS) is rarely a bedside option. Therefore, objective diagnostic assessment is most needed during the first postpartum hours. However, it is challenging to obtain the anatomically verified data needed to develop diagnostic technologies for this clinical window. We evaluated the translation of a machine-learning-assisted impedance spectroscopy model into its intended immediate postpartum setting. The model lifecycle comprised three sequential phases: V1 development using anatomically verified data from a prospective multicenter study (N=152); V2 real-world adaptation, comprising post-market data collection, retraining (N=144) to adapt to acute physiological tissue changes, and subsequent separate evaluation (N=125); and prospective external validation of V2 in an unseen cohort (N=70). During this separate evaluation, V2 achieved 89.6% accuracy. In external validation, all examinations occurred within 4 hours of birth (94.3% within the first hour). Against subsequent EAUS, V2 achieved 86.2% sensitivity, 92.7% specificity, and 90.0% overall accuracy. The V2 model was prospectively externally validated in its intended immediate-postpartum setting, supporting ML-assisted impedance spectroscopy as an objective adjunct, and not a replacement, to clinical OASI assessment. Further validation is required to confirm generalizability.

Introduction Autologous and allogeneic chondrocyte cell sheet transplantation has shown safety and potential efficacy for cartilage defects associated with knee osteoarthritis. Although polydactyly-derived chondrocyte (PDC) cell sheets have been used clinically for allogeneic transplantation, a scalable and reliable alternative cell source is needed. This study aimed to establish a proof-of-concept protocol for generating chondrocyte cell sheets from clinical-grade human induced pluripotent stem cells (iPS cells). Methods Cartilaginous tissue was generated from human iPS cells, and isolated chondrocytes were used to fabricate iPS cell-derived chondrocyte sheets. In Phase 1, sheets were produced in high-serum medium under normoxic, hypoxic, or hypoxic/high-seeding-density conditions and compared with PDC sheets using a three-dimensional in vitro model and xenogeneic orthotopic transplantation in an immunodeficient rat model. In Phase 2, sheets were produced in low-serum medium under hypoxic/high-seeding-density conditions and evaluated for cartilage-related gene expression, secreted factors, cell surface marker expression, and in vivo cartilage repair. Results In Phase 1, hypoxic/high-seeding-density culture enhanced in vitro chondrogenic properties. However, after transplantation, iPS cell-derived chondrocyte sheets formed fibrotic repair tissue, whereas PDC sheets generated hyaline-like cartilage. In Phase 2, low-serum culture increased expression of cartilage-related genes, including collagen type II alpha 1 (COL2A1) and SRY-box transcription factor 9 (SOX9), enhanced secretion of transforming growth factor beta 1 and melanoma inhibitory activity, and upregulated CD9, CD26, CD56, CD201, CD227, and GD2. Low-serum iPS cell-derived chondrocyte sheets regenerated hyaline cartilage at 4 weeks, and the repair tissue was maintained at 12 weeks without bone formation. Conclusions A robust fabrication protocol enabled human iPS cell-derived chondrocyte sheets to regenerate hyaline-like cartilage in vivo. These findings support their potential as a scalable cell source for clinical cartilage repair.

Published artificial intelligence for keratoconus is unproven in fellow eyes that topography and tomography call normal, the eyes where surgical decisions are made, although it reports near-perfect accuracy for early disease. In a registered systematic review of 573 studies (PROSPERO CRD420261441197), none of 2,035 claim-by-stratum units from 212 adjudicated studies established discrimination in these eyes. Most units (90.0%) answered a different question, never constructed the target population or certified its normality; external validation did not test label generation, and five systematic reviews did not assess it. In a public cohort, a frozen score yielded an area under the receiver operating characteristic curve of 0.996 against a same-eye index label and 0.549 against a contralateral proxy; its inputs reconstructed the label-generating index (r = 0.991), so accuracy there measured agreement with the index. Anchor-gap evaluation measures accuracy in these eyes against the contralateral clinical diagnosis.

Conventional clinical assessment of upper limb kinematics relies on goniometry or visual estimation, methods that provide limited information on multi-segment 3D kinematics and can have poor inter-rater reliability. Inertial measurement units (IMUs) represent a viable alternative; however, extracting anatomically meaningful joint angles from skin-mounted sensors remains challenging owing to sensor-to-segment misalignment, soft-tissue artefact, and suboptimal orientation estimation under dynamic conditions. This study developed and validated a multi-segment IMU-based framework for upper limb kinematic assessment, addressing three stages of the measurement pipeline: (i) sensor fusion gain optimisation as a function of task mean segment angular velocity; (ii) accuracy of an anatomical landmark registration-based alignment method; and (iii) comparative evaluation of landmark-based alignment against manual and neutral-pose alignment approaches. Twenty participants with typical shoulder function performed clinical and functional upper limb tasks, recorded simultaneously with an optoelectronic motion capture system and six IMUs tracking the thorax, clavicle, scapula, humerus, forearm, and hand. Orientation estimation used a gradient-descent complementary filter with velocity-dependent gain modulation. Sensor-to-segment alignment was achieved via manual, neutral-pose, and landmark registration methods. Mean sensor orientation errors ranged from 1.5 to 3 degrees across segments/tasks and increased with angular velocity. Landmark registration produced substantially lower joint angle errors than manual and neutral-pose alignment across all joints (root mean square error range: 3.7-7.8 degrees; manual: 8.3-24.2 degrees; neutral: 15.5-35.5 degrees). These results indicate that IMU-derived kinematic accuracy is context-dependent, varying with joint and movement type, suggesting that accuracy should be established for the specific movements and joints relevant to each clinical application.

Cardiotocography (CTG) is the standard of care for intrapartum fetal monitoring, yet its poor specificity drives unnecessary intervention without reducing hypoxic-ischemic encephalopathy (HIE). Most artificial intelligence (AI) models are trained exclusively on the final hour before delivery, a window only identifiable retrospectively. We hypothesized that this framework prevents models from learning the early evolving signs of HIE. We analyzed 174,186 deliveries, with gestational age GA[≥]35 weeks, from Kaiser Permanente Northern California. Forty CTG features were extracted from 20-minute epochs to train Random Forest classifiers targeting severe acidosis (N=2,636) and clinically validated HIE (N=304). The training window was progressively extended from the final hour to the full duration of labor, and time from labor onset (TLO) was added as a feature. Models were evaluated continuously throughout labor at a fixed 15% false positive rate. Our results showed that the early identification of fetuses at risk of HIE improved as the training window was extended, plateauing at 18 hours. Adding TLO gave the highest early-warning performance: 40.3% of HIE cases identified at least 3 hours and 27.3% at least 6 hours before delivery, absolute gains of 8.4% and 10.7% over the last-hour classifier. Finally, 55.7% of HIE cases were identified at least 40 minutes before delivery. In conclusion, classifiers trained only on the last hour may identify HIE patterns that occur near delivery; time-aware models trained across labor map the temporal evolution of these patterns and provide actionable early warnings. Thus, AI systems for intrapartum monitoring must account for the temporal evolution of labor to shift from end-of-labor diagnosis to early-warning decision support.

Predictive biomechanical models of the tricuspid valve require accurate representation of the chordae tendineae, yet subject-specific chordal geometry is difficult to reconstruct from non-invasive imaging. Here, we adopt a framework for generating functionally equivalent synthetic chordae without prior knowledge of in vivo chordal attachments. To this end, we first adapt an anatomy-informed hyperelastic shape-matching method that establishes correspondence between end-diastolic and end-systolic leaflet configurations using chordal-mimicking forces, a rigid contact template, and a Gaussian-smoothed locally corrective pressure field. We then generate synthetic chordal insertion sites using zone-based rejection sampling and calibrate the unloaded length of each chord by combining reaction forces with the chordal stress-stretch relationship. The framework was evaluated using Texas TriValve 1.1, a high-fidelity finite element model of a human tricuspid valve validated against beating-heart echocardiography. We found that shape matching reproduced the target end-systolic geometry with a mean inter-surface distance of 0.29 {+/-} 0.35 mm. Moreover, synthetic chordal insertions faithfully reproduce end-systolic leaflet deformations. We subsequently examined synthetic chordal configurations containing 202, 225, and 450 insertions, informed by measurements from eight explanted human tricuspid valves. Here, increasing insertion number reduced mean inter-surface distance from 0.63 {+/-} 0.52 mm to 0.49 {+/-} 0.44 mm and reduced contact area errors from 3.52% to 0.59%. Across all configurations, mean maximum principal stretch errors in leaflet belly regions remained below 2.4%, while areal strain errors ranged from 0.34% to 10.01%. These results demonstrate that anatomically informed shape matching coupled with stress-based chordal calibration can reproduce tricuspid valve closure without explicit subject-specific chordal geometry. This framework provides a foundation for generating synthetic subvalvular anatomy for future imaging-derived, predictive tricuspid valve models.

Purpose: Electrical stimulation of the occipital cortex evokes visual percepts known as phosphenes. To date, most human studies have examined phosphenes elicited by large surface or depth electrodes. These differ considerably from the small penetrating intracortical microelectrodes capable of stimulating highly localized cortical populations that are the basis of current next-generation devices. Here, we provide a detailed characterization of phosphenes evoked by such intracortical microelectrodes. Methods: As part of the CORTIVIS clinical trial, a Utah Electrode Array with 100 penetrating electrodes was implanted in the early visual cortex of four blind volunteers. Participants completed drawing and description tasks in response to single-electrode stimulation. Two participants also performed forced-choice tasks to determine whether phosphenes elicited by different electrodes could be reliably discriminated. Results: Single-electrode stimulation reliably evoked phosphenes in all participants. Most percepts appeared in the contralateral visual field, although two participants reported near-midline ipsilateral percepts. Phosphenes ranged from simple dots to elongated, textured, circular, or otherwise idiosyncratic forms. One participant reported distinguishable phosphene colors, which depended on electrode and amplitude structure and varied across sessions. The two participants tested on forced-choice discrimination could successfully identify electrode-specific phosphenes across multiple sessions. Conclusions: Intracortical microstimulation of the human visual cortex produces phosphenes with diverse, electrode-specific appearances that are recognizable across repeated trials. These findings indicate that penetrating microelectrodes often evoke more structured percepts than simple points or blobs, providing foundational evidence that electrode-specific encoding strategies will be necessary in future visual prosthetic devices.

Background: Pre-operative triage of adnexal masses with serum CA-125, HE4, and ultrasound (O-RADS) has limited accuracy, contributing to unnecessary surgery. We develop and validate a first of its kind, biomarker panel-free plasma proteomic classifier that distinguishes malignant from benign adnexal masses from a single blood draw, operating directly on discovery-mode mass spectrometry. Methods: In this multicenter prospective observational study, plasma from women with adnexal masses and from healthy controls at a large urban academic center (discovery and four validation cohorts) and an external academic center from another Urban-suburban area (external validation) was analyzed by data-independent-acquisition mass spectrometry (Orbitrap Astral), quantifying >1,000 proteins per sample. Cancerous (malignant, borderline adnexal mass, or metastasis to ovary) versus benign adnexal masses were classified with ADAPT-MS (Adaptive Diagnostic Architecture for Personalized Testing by Mass Spectrometry), which retrains an ElasticNet model for each sample based on the proteins measured. The primary outcome was discrimination (area under the receiver operating characteristic curve [AUC]). Pre-operative O-RADS scores and serum CA-125 were compared and combined with proteomics on matched subsets. Findings: Using a discovery cohort (n=1062), an ADAPT-MS classifier, validated across five independent cohorts (n=623), achieved a summary AUC of 0.835 (cohort range 0.779-0.904). Among patients with pre-operative O-RADS (n=231), proteomics (AUC 0.852) was comparable to O-RADS alone (0.863) and combining them raised discrimination to 0.922. Proteomics outperformed serum CA-125 (0.915 versus 0.777; n=125) and proteome-derived ROMA-like and two-marker surrogates. Biologically, the classification-relevant proteins are enriched for host response proteins rather than tumor cell associated protein changes, comprising ECM remodeling, complement and innate immune response pathways. This explains good detection of tumor with heterogenous pathology and also metastasis to ovary. Interpretation: A single plasma proteomic measurement interpreted using adaptive machine learning discriminates malignant from benign adnexal masses with good internal and external validation, and is complementary to ultrasound, warranting prospective evaluation as a panel-free diagnostic adjunct to imaging.

A persistent challenge in cartilage tissue engineering is identifying cell sources capable of regenerating cartilage with native-like properties. Variability in chondrogenic potential among human- and animal-derived cells complicates graft development and disease modeling. Here we benchmarked ten chondrogenic cell sources under standardized culture conditions using hyaluronan-alginate biomaterials and implementing a functionality index combining histological and mechanical metrics referenced to native tissue. Mesenchymal stem cells and fetal chondroprogenitors demonstrated superior regenerative capacity, achieving 103% and 95% of native cartilage functionality, respectively. In contrast, adult articular chondrocytes from healthy and osteoarthritic donors produced fibrocartilaginous tissue with functionality scores below 50%. These findings provide a quantitative framework to guide cell source selection and optimize cartilage graft standardization for clinical applications and physiologically relevant disease models, addressing regulatory demands for product characterization.

Current treatment of orthopedic ligament injury relies principally on non-regenerative materials and surgical repair rather than strategies that promote intrinsic tissue regeneration. This study evaluated whether biologic augmentation with XWRAP, a human amniotic membrane allograft, combined with a cryopreserved amniotic-fluid-derived biologic containing micronized human amnion membrane, promotes regeneration of ligament architecture in a rabbit medial collateral ligament (MCL) gap-defect model. Standardized 4-mm mid-substance MCL gap defects were created in New Zealand White rabbits (n=3) and either treated with the amniotic membrane/amniotic-fluid biologic combination or left untreated, with intact contralateral or adjacent MCL serving as native controls; ligaments were evaluated by transmission electron microscopy at 2 and 6 weeks post-procedure, with collagen fibril diameter quantified from calibrated high-magnification micrographs. Treated defects developed significantly larger mean collagen fibril diameters than untreated defects at both time points (75 vs. 50 nm at 2 weeks and 88 vs. 62 nm at 6 weeks; p<0.0001 for each comparison), reaching approximately 90% of native, uninjured ligament fibril diameter (98 nm) by 6 weeks. Treated defects also developed a broader, bimodal fibril-diameter distribution resembling native ligament architecture, whereas untreated defects remained characterized by a narrow, unimodal population of small-diameter fibrils typical of scar-associated repair. These findings indicate that biologic augmentation with XWRAP combined with an amniotic-fluid-derived biologic promotes a regenerative, rather than purely reparative, healing response following ligament injury, progressively restoring collagen fibril architecture toward that of native tissue in this preclinical model.

Healing of the gastrointestinal (GI) tract following surgical interventions is governed by coupled mechanobiological processes and may lead to severe complications such as perforation or stricture formation. A first comprehensive mechanobiological modeling framework of GI healing is herein proposed. The framework addresses two clinically relevant applications of bioprinting: endoscopic treatment of GI tissue defects and bioprinting-assisted surgical anastomosis. Tissue remodeling is based upon a hyperelastic material model with fiber dispersion coupling the spatiotemporal reaction diffusion dynamics of fibroblasts and cytokines with collagen deposition, reorientation, and plastic deformation. An active stress formulation drives fibroblasts and myofibroblast contraction while a multiplicative decomposition approach is adopted for collagen remodeling as a permanent change. The governing equations are discretised in time with an implicit Euler scheme, finite element in space and solved by a staggered algorithm implemented in the open-source platform FEniCS. Numerical simulations show that the initial collagen fibers content and orientation strongly influence fibroblast infiltration and scar contracture formation. The study quantifies the advantage of having a collagen bioprinted architecture aligned with tissue fibers, to reduce structural remodeling distortion. The model also predicts a strong correlation between collagen deposition and luminal burst pressure during the recovery of mechanical integrity in GI anastomosis.

Abstract Purpose To apply a mathematical model integrating vascular permeability, lesion transmural pressure, choroidal excess pressure, and wall stability to incomplete fluid response and massive submacular hemorrhage in neovascular age-related macular degeneration. Methods Permeability was calculated from aqueous vascular endothelial growth factor (VEGF). Windkessel gave usual lesion pressure from choriocapillaris pressure and high inflow from an arterial-level puncture pressure used as a working value. Choroidal excess pressure was calculated from choroidal thickness and a pachy index as increased outflow resistance. Exudative change used a revised Starling equation scaled to early central-retinal-thickness reduction, with 40-m threshold. A relative rupture-hazard index was calculated from Laplace wall stress and wall stability. VEGF, pressures, and that reduction were from the literature. Pressure was isolated from permeability and choroidal excess. Results In the model, raising lesion pressure from choroidal-excess lesion pressure to high inflow increased untreated exudative change from 150 to 760 m. Residual under saturation was 64 m as a lower-end reference (Monte Carlo 69 - 286 m; median, 170) and 90 m with high-hazard choroidal excess; both above 40 m. Reducing choroidal thickness in the thickened choroidal-excess state added 13 - 14 m, smaller than that residual. Lowering lesion pressure from high inflow to usual lesion pressure (21 to 10 mmHg) reduced the index to 0.64 of the high-hazard reference, and to 0.34 with wall stabilization. Conclusion Lesion transmural pressure due to high inflow left residual exudation even after permeability suppression with anti-VEGF. Fluid resolution on optical coherence tomography may not parallel a reduction in the rupture-hazard index, depending on wall stability.

Objective: Current guidelines recommend different adjuvant treatment strategies for women with endometrioid early stage endometrial cancer exhibiting a p53 abnormal pattern. We aimed to evaluate recurrence patterns and explore the clinical implications for adjuvant therapy in this population. Materials and methods: This multicenter retrospective study included 86 patients with endometrioid, p53 abnormal, FIGO stage I, II endometrial cancer treated between 2013 and 2023 at six hospitals in the Republic of Korea. Clinicopathologic data were collected through medical record review. Progression free survival (PFS) was estimated using the Kaplan Meier method, and prognostic factors were analyzed using Cox proportional hazards regression models. Results: The 12, 24, and 36 month PFS rates were 91%, 78%, and 76%, respectively. Among the 16 patients who experienced recurrence, 12 (75%) had distant metastases. Adjuvant chemotherapy showed a trend toward improved PFS compared with adjuvant radiation therapy with or without chemotherapy (p=0.10). In multivariable analysis, the p53 overexpression pattern was independently associated with worse PFS compared with the p53 null pattern (HR 8.59, 95% CI 1.61 to 45.79, p=0.01). Conclusion: Given the poor PFS, the predominance of distant recurrence and the observed trend toward improved PFS with chemotherapy, adjuvant chemotherapy may be considered in women with endometrioid early stage endometrial cancer exhibiting a p53 abnormal pattern. Further studies are warranted to clarify the prognostic impact of p53 abnormality subtypes.

Background: Artificial intelligence (AI) is increasingly used in healthcare, but data on its real-world use and perceptions among obstetricians and gynecologists remain limited. Objectives: To assess familiarity, use, perceptions, and expectations regarding AI among physicians practicing obstetrics and gynecology in France. Study Design: We conducted a cross-sectional survey between June 2 and July 30, 2025, mainly distributed through professional organizations or associations and social media. Participation was voluntary and self-selected. AI use was defined as any use of an AI tool in routine clinical practice, regardless of type or frequency. Comparisons were made between AI users and non-users and between junior and senior physicians. Results: Among 469 respondents, representing approximately 5% of the target population, 199 of 466 (42.7%) reported AI use in practice; three respondents were excluded from this analysis because of discordant questionnaire responses. AI use was associated with male gender and professional status, with residents more represented among users. Junior physicians reported greater AI use than senior physicians and expressed greater concern regarding medical confidentiality. Current applications primarily involved administrative tasks, medical writing, literature retrieval, and translation; anticipated future uses centered on clinical decision support and medical data interpretation. Perceptions of several AI applications differed between groups. Conclusions: AI was reported to be used by a substantial proportion of respondents, primarily for supportive and informational tasks. These findings support further evaluation of training, validation, governance, and safe implementation of AI in obstetrics and gynecology.

Abstract Background Advanced in vitro models of the human heart have become promising tools for studying cardiac diseases and drug discovery. Three-dimensional (3D) engineered heart tissues (EHTs) provide more adult cardiac tissue-like structural organization compared to traditional two-dimensional (2D) in vitro models with straightforward contractile readouts. However, the assessment of the architecture of EHTs, especially the quantification of cellular and tissue structural components, has remained poorly studied. Here we demonstrate the use of light-sheet microscopy for structural imaging of intact EHTs and provide quantification of the EHT cellular and subcellular structures. Methods We developed an in-house inverted selective plane illumination microscopy (iSPIM) platform and combined it with iterative indirect immunofluorescence (IT-IF) staining for better signal acquisition. We developed an image-processing and analysis pipeline dedicated to volumetric EHT analysis. Results With this integrated platform, we achieved volumetric imaging of intact EHTs and quantification of 3D tissue architecture, revealing aligned cardiomyocytes within the imaged volume and increasing cardiac nuclei content towards the core of the tissue. The developed methods allowed the detection of heterogeneities between tissue sections, demonstrating the sensitivity required for future disease modeling or maturation studies. Conclusions The developed integrated platform opens new possibilities for volumetric analysis and characterization of 3D tissue structure using iSPIM, providing new insights into EHT architecture and the use of EHTs as disease modeling tools.

Background and Objective: High-speed and high-resolution retinal imaging using adaptive optics line confocal ophthalmoscopy (AOLCO) resolves individual red blood cells (erythrocytes) flowing through the smallest retinal capillaries, but quantifying their velocity first requires removing linear and nonlinear distortions caused by eye motion that may result in a displacement of tens to hundreds of pixels between frames. We present a computational pipeline that stabilizes AOLCO capillary video and measures erythrocyte velocity in true physical units, delivered as an interactive 3D Slicer extension and a batch command-line tool sharing one analysis core. Methods: Our video analysis pipeline has four stages: preprocessing, registration, postprocessing, and velocity measurement. During preprocessing, illumination is corrected, and closed-eye frames are identified. During registration, each frame is aligned to a reference frame using affine and, when needed, B-Spline registration, initialized by our robust and fast (GPU-based) translation-registration algorithm to accommodate large eye movements (e.g., microsaccades). During postprocessing, adjacent registered frames are differenced to suppress stationary structures, and a 2D projection of the frame-difference video reveals the capillary network. During velocity measurement, an operator manually identifies capillary segments on the 2D projection image. Spatiotemporal images are then generated along these segments from the frame-difference video, and blood-flow velocity curves are obtained from these images using the Radon transform. Results: We validate the velocimetry of the pipeline against synthetic ground-truth flow data, and the experiments show that the velocimetry recovers the speed within an error of 4% The GPU-based translation registration algorithm aligns the large eye motions (~90 um per frame on average) and runs about 3.6 x (100 Hz) to 38 x (400 Hz) faster than the baseline method. Across the full cohort (2344 videos, 57 subjects, 30 Hz to 400 Hz), our registration approach improves structural similarity (SSIM) of the video frames on every acquisition from 0.73 to 0.86. The recovered blood flow velocities are physiologically plausible, of the same order as published AO measurements. Conclusions: Our computational pipeline enables AOLCO velocimetry on the capillary-level.

Existing prediction models for obstetric anal sphincter injuries (OASIS) often rely on traditional regression, lack external validation, and, when fitted on multicenter data, fail to account for hospital-level clustering. We aimed to develop and temporally validate mixed-effects machine learning models for OASIS and compare them with traditional regression models. A secondary aim was to evaluate whether macrosomia ([&ge;]4000 g) could substitute for continuous birth weight. We conducted a retrospective nationwide cohort study of singleton, term ([&ge;]37 weeks), cephalic vaginal deliveries in the Dutch Perinatal Registry. Data from 2016 to 2019 were used for model development with three-fold cross-validation; data from 2020 were reserved for temporal validation. Candidate predictors included 18 demographic, obstetric, and neonatal factors. We developed six models: mixed-effects gradient boosting (MEGB), mixed-effects LASSO (ME-LASSO), and backward stepwise logistic regression, each with either birth weight or macrosomia. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC), calibration by calibration plots, and prediction accuracy by the Brier score. Among 614,255 deliveries, OASIS occurred in 2.6%. In temporal validation including macrosomia, MEGB achieved the highest discrimination (AUC 0.727; 95% CI 0.719-0.735), significantly outperforming ME-LASSO (AUC 0.719; 95% CI 0.710-0.727; P<.0001) and stepwise logistic regression (AUC 0.714; 95% CI 0.706-0.722; P<.0001). Including birth weight marginally improved performance, with macrosomia serving as an effective proxy. Consistently important predictors were nulliparity, prolonged second stage, mediolateral episiotomy, advanced maternal age, operative vaginal delivery, macrosomia, and previous cesarean delivery. Uncalibrated MEGB showed poorer calibration, but isotonic recalibration substantially improved it. After calibration, all models showed excellent agreement between predicted and observed risks. MEGB provided improved discrimination and comparable calibration relative to ME-LASSO and stepwise logistic regression. Replacing continuous birth weight with a binary macrosomia indicator resulted in minimal performance loss, suggesting it may suffice for clinical risk prediction across all three modeling approaches.

Marker-based optical motion capture remains the gold standard for kinematic analysis across biomechanical research. However, when tracking dynamic movements, severe occlusions often create missing data and noisy signals that require time-consuming manual cleaning. Existing automated methods usually rely on predefined skeleton models or need massive training datasets for machine learning solutions. This study presents the AMP: an automated marker based motion capture processing pipeline. The AMP is a fully analytical approach that uses hierarchical algorithms including 1) rigid-body constraint, 2) Kalman Filter state estimation and 3) kinematic pattern match to label markers and fill data gaps without manual human intervention. First, the algorithm identifies and removes mislabeling markers before any data scrubbing. During the data cleaning process, our hierarchical algorithm uses a translation-based step to track segments even when there are insufficient donor markers to form a full rigid body. To reconstruct missing marker trajectories, the pipeline initially uses Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation for short marker gaps. However, the PCHIP creates massive spatial errors during long occlusions, the system automatically switches to rigid-body tracking for gaps over 40 ms. Results showed the pipeline drastically reduced processing time compared to manual human expert methods by 73.5%. In addition, the AMP kept spatial errors within 10 mm of the manual ground truth. By using this combined hierarchical approach to match marker processing accuracy, the pipeline provides a fast, general-purpose tool that fully automates data cleaning and saves hours of manual effort.

Objective: The aim of the current study was to evaluate the feasibility of a multi-channel distributed wearable ultrasound (D-WUS) system capable of simultaneously probing dynamic muscle activity across multiple anatomical sites to assess neuromuscular coordination during physical activity. Methods: Twenty NCAA Division I women athletes (8 with prior knee injury) performed countermovement jumps (CMJs) on dual force plates while wearing a four-channel D-WUS system targeting the vastus lateralis (VL) and vastus medialis oblique (VMO) in both limbs. Real-time M-mode ultrasound data were processed using a composite factorization approach integrating nonnegative matrix factorization and principal component analysis (CF-PCA) to extract dominant muscle-contraction patterns. Correlation and timing metrics between VL and VMO were compared across injury status. Results: CMJ performance metrics (jump height, RSImod) did not differ between groups (p > 0.05). However, D-WUS revealed significant differences in muscle coordination in the right limb: VL-VMO correlation (p = 0.029) and timing symmetry (p = 0.019) were reduced in participants with prior knee injury; left-limb differences were not significant. Right-limb timing symmetry was negatively correlated with SMFA score (rho = -0.48, p = 0.033). Functional survey scores (SMFA) also differed significantly (p = 0.001), while VISA-P scores did not. Conclusion: These preliminary findings provide feasibility-level evidence that distributed wearable ultrasound can detect limb-specific neuromuscular coordination differences not reflected in force-plate biomechanics, motivating larger prospective studies to establish reliability and clinical utility. Significance: With further validation, this approach may offer a scalable, objective means of monitoring muscle contraction during rehabilitation.