Computer Methods and Programs in Biomedicine

BackgroundOptical Coherence Tomography Angiography (OCTA) provides high-resolution visualization of retinal microvasculature, with vascular density (VD) serving as a one of the key quantitative metrics. However, VD measurements are highly sensitive to image binarization step, and no standardized approach exists. MethodsWe analyzed 51 OCTA scans (human and porcine) using 29 binarization algorithms, including global and local thresholding techniques from ImageJ and DoxaPy, as well as Random Walker segmentation. VD was calculated for each binarization algorithm and compared against Optovue-generated values (ground truth). Results were evaluated using hierarchical clustering and agreement between them was determined by Bland-Altman analysis. ResultsWolf algorithm was found to exhibit least deviation from mean Optovue VD values for human SCP layer (46.5 {+/-} 1.2% vs. 48.3 {+/-} 1.4%; p = 3.62 x 10-5); however, there is not significant difference between VDs from Optovue and Wolf algorithms from porcine SCP layer (46.2 {+/-} 1.8 % vs 46.3 {+/-} 1.4% ; p=0.74). For DCP layer, Phansalkar algorithm exhibited least VD variability (50.7 {+/-} 2.0% vs. 51.9 {+/-} 1.7%; p=2.53 x 10-4) in the human cohort. Whereas Percentile algorithm exhibited least, non-significant variations in the porcine DCP layer VD (50.0 {+/-} 1.4 % vs. 50.3 {+/-} 1.4%; p=0.75). DiscussionEach binarization technique evaluated in this study impacts OCTA-derived VD measurements differently. Local adaptive algorithms collectively outperform global methods, particularly for SCP analysis. Standardization of image processing pipelines and layer-specific optimization are essential to improve reproducibility and clinical consistency.

Central venous pressure (CVP), a key component of hemodynamic monitoring, is widely used to guide fluid resuscitation in critically ill patients. It is typically measured using central venous line catheterization, which is the gold standard, but this method is invasive, time-consuming, and associated with complications. This study aims to investigate whether machine learning (ML)-based analysis of features extracted from a non-invasive, standard-of-care waveform--the photoplethysmography (PPG) signal--can identify patients with elevated CVP. We trained Light Gradient-Boosting Machine (LightGBM) model using a large perioperative dataset (MLORD), containing 17,327 surgical patients from 2019 to 2022 at UCLA. For this study, we selected 1665 patients with both PPG and CVP waveforms available. A total of 843 PPG features per cardiac cycle (CC) were extracted from the PPG waveforms using a signal processing-based feature extraction tool, along with the simultaneous maximum value calculated from the corresponding CCs in the CVP waveform. Additionally, for each patient, the average and standard deviation of each PPG feature, as well as the mean of the maximum CVP values, were calculated across all cardiac cycles, resulting in 843 averaged PPG features, 843 PPG feature standard deviations, and one averaged maximum CVP value per patient. The average maximum CVP value was used as the ground truth to classify patients as either normal (5 [≤] CVP [≤] 15 mmHg) or elevated (CVP > 15 mmHg). Of the 1,665 patients, 1,182 were normal and 483 were elevated. The dataset was split into 90% for training (1,063 normal and 435 elevated) and 10% for testing (119 normal and 48 elevated). From the 1686 PPG features (843 averaged and 843 standard deviation), 246 were selected for model development using the Recursive Feature Elimination with Cross-Validation (RFECV) approach. To further enhance performance, hyperparameters were tuned through 5-fold cross-validation on the training set. Finally, the best-performing configuration was retrained on the full training data, and its performance was evaluated on the held-out test set. To provide a robust estimate and confidence interval, a bootstrapping procedure with 100 iterations was performed on the test set. The LightGBM classifier achieved a mean area under the receiver operating characteristic curve (AUC) of 0.79 (95% CI: 0.71-0.84) and mean accuracy of 0.71 (95% CI: 0.65-0.77), demonstrating good discriminatory power in distinguishing between patients with normal and elevated CVP. This study highlights the ability of PPG-derived features to discriminate between patients with normal and elevated CVP using ML. These early findings lay the groundwork for future research aimed at developing non-invasive approaches to CVP assessment.

The present study presents a systematic approach for generating data-driven synthetic cerebral aneurysm geometries and evaluating their hemodynamics through computational fluid dynamics. Seven patient-specific aneurysm geometries from the right internal carotid artery were reconstructed from time-of-flight magnetic resonance angiography images and standardized through orientation alignment, followed by non-rigid registration onto a common spherical point cloud as a template. Principal component analysis (PCA) was then applied to the aligned point-cloud data to quantify morphological variability and parameterize shape deformation. The first four principal components captured over 90% of the total variance; however, higher-order components were required to capture the detailed geometrical features of the original geometries. Computational fluid dynamic simulations were performed on the PCA-based synthetic geometries under pulsatile flow conditions to investigate the influence of shape variations on intra-aneurysmal flow patterns, time-averaged wall shear stress (TAWSS), and oscillatory shear index (OSI). The first principal component score (PCS1), which was associated with changes in aneurysm height and dome width, had the strongest effects on TAWSS and OSI levels. Lower PCS1 values, which corresponded to taller and more oblique domes, produced slower adjacent flow and elevated OSI, whereas higher PCS1 values increased TAWSS. The second principal component score primarily modulated lateral geometric asymmetry and further influenced OSI distribution for the lower PCS1 values. Collectively, these findings indicate that PCA-based shape parameterization provides a practical approach for generating synthetic aneurysm datasets and systematically assessing how specific morphological features govern hemodynamic behavior. The proposed approach is expected to contribute to the future development of surrogate modeling and data-driven hemodynamic prediction.

Computational growth and remodeling (G&R) models have been extentively used to investigate abdominal aortic aneurysm (AAA) progression and to support clinical decision-making. However, the development of robust predictive models is often limited by the scarcity of large-scale longitudinal imaging datasets. In this study, we propose a physics-based G&R framework to simulate AAA shape evolution and generate a virtual cohort of aneurysms, thereby addressing data limitations and enabling integration with data-driven machine learning approaches for growth prediction. The proposed arterial G&R model incorporates key mechanisms influencing aneurysm progression, including elastin degradation and stress-mediated collagen production. A modified elastin degradation formulation was introduced to generate realistic aneurysm geometries exhibiting clinically relevant features such as asymmetry and tortuosity. By systematically varying parameters governing elastin damage and collagen production, 200 distinct G&R simulations were performed to produce a diverse set of AAA geometries. The dataset was further expanded using kriging-based spatial interpolation to construct a large in silico cohort. The synthetic dataset, combined with longitudinal imaging data from 25 patients, was used to train and validate four machine learning models: Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). A two-step training strategy was adopted to predict maximum aneurysm diameter and growth rate based on prior geometric characteristics. The LSTM model achieved the highest performance for maximum diameter prediction (R{superscript 2} = 0.92), while the RNN demonstrated strong overall performance (R{superscript 2} = 0.90 for maximum diameter and 0.89 for growth rate). The DBN and GRU models also showed competitive predictive capability. Overall, this study demonstrates that integrating physics-based G&R simulations with machine learning enables accurate prediction of AAA growth and maximum diameter. The proposed framework provides a scalable strategy for augmenting limited clinical datasets and offers a promising tool to support personalized risk assessment and treatment planning.

BackgroundEndovascular aortic aneurysm repair (EVAR) is effective in preventing rupture of abdominal aortic aneurysm (AAA), but endoleak remains a serious postoperative complication. Accurate prediction of endoleak risk is a significant clinical challenge. PurposeThis study aimed to evaluate the value of a Point Cloud Neural Network (PCNN) in predicting endoleaks after EVAR by integrating multimodal features. Materials and MethodsWe collected follow-up data from 381 AAA patients. Radiomic characteristics of the procedural intraluminal thrombus and morphological parameters were extracted following medical image segmentation and 3D reconstruction. Hemodynamic parameters, including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT), were obtained through a semi-automated computational fluid dynamics (CFD) workflow. Six traditional machine learning models and four PCNN architectures were developed with progressively added feature sets: 1) medical history and morphology (H+M); 2) H+M+R; 3) H+M+CFD; and 4) all features combined (H+M+R+CFD). ResultsTraditional ML models showed limited performance (AUC range: 0.55-0.77). In contrast, PCNN models demonstrated substantially improved predictive capability. The baseline PCNN (H+M) achieved an AUC of 0.81. The RA-PCNN model incorporating radiomic features showed a 6.58% improvement (AUC=0.86). The CFD-PCNN model with hemodynamic parameters exhibited a 13.0% increase (AUC=0.91), with superior F1-score (0.78) and recall (0.88). The multimodal RA-CFD-PCNN model performed best, achieving an AUC of 0.93, accuracy of 0.90, and F1-score of 0.83. ConclusionThis study establishes a PCNN-based framework for endoleak prediction that significantly outperforms traditional machine learning methods, providing an effective approach for assessing endoleaks in AAA patients. Summary statementThis study developed a PCNN-based framework integrating clinical, morphologic al, radiomic, and hemodynamic features from 381 AAA patients to predict endoleaks after EVAR. Results demonstrated superior performance over traditional ML, with hemodynamic parameters providing a major performance boost, highlighting the value of physiological and biomechanical feature integration for vascular disease prediction. Key ResultsThe multimodal PCNN model integrating all features achieved an AUC of 0.93, significantly outperforming traditional machine learning models (AUCs 0.55-0.77). Incorporating hemodynamic parameters provided the greatest performance increase, with the CFD-PCNN models AUC increasing by 13.0% to 0.91 compared to the baseline PCNN (AUC=0.81). The model combining radiomics and hemodynamics (RA-CFD-PCNN) achieved the highest F1-score of 0.83 and AUC of 0.93, demonstrating robust predictive accuracy.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSAimsC_ST_ABSWe aim to develop a patient-specific computational model to predict the risk of Ventricular Tachycardia (VT) in patients with Biventricular Cardiac Resynchronization Therapy (BiV-CRT) device. Patients are indeed at risk of developing arrhythmias due to BiV-CRT pacing, a known potential complication that puts the cardiologist on guard against its prevention. Materials and MethodsWe consider three non-ischemic fibrotic patients. Patient-specific left ventricle geometries and fibrosis regions are extracted from Cine-MRI and LGE-MRI. The electrophysiology model, based on the monodomain equation and on the Ten Tusscher-Panfilov (TTP06) ionic current model, is personalized using pre-operative Electro-Anatomical Mappings System data. The TTP06 parameters are adapted to reflect the altered electrical properties of the fibrotic tissue. To test inducibility, we use an [S]1-[S]2 stimulation protocol: [S]1 simulates the clinical BiV-CRT pacing with patient-specific VV-delay, followed by a [S]2 ectopic impulse. This procedure is repeated for ten ectopic sites. The arrhytmogenic risk is quantified by the number of ectopic sites that successfully generates a reentry loop. ConclusionThe models prediction of VT risk is consistent with the long-term clinical follow-up for all the patients. Arrhythmic patients show a higher number of ectopic sites from which a reentry loop is generated compared to the non-arrhythmic patient. This study provides a first, preliminary attempt towards the use of computational tools in assessing the vulnerability of the arrhythmic substrate during BiV-CRT pacing in non-ischemic patients. In future, such tools could serve as a powerful non-invasive diagnostic metric to inform clinicians about possible therapies to associate to BiV-CRT.

Deep vein thrombosis is a disease that occurs when a blood clot is formed in a vein and occludes the vessel lumen, blocking the blood flow, causing pain and even disability and possibly leading to complications such as postthrombotic syndrome and pulmonary embolism. Treatments for DVT include mechanical thrombectomy: introducing a device into the vasculature to remove thrombus. Currently used devices either macerate the thrombus to aid removal or pierce the thrombus to reach its distal side. This can pose risk of fragmentation or distal embolization, or in case of fibrous, cohesive thrombi can be hard to achieve due to their resistance to deformation. The following study proposes an alternative approach of bypasing the thrombus via the space between the thrombus and the vessel wall in order to avoid thrombus penetration. The design implements a strategy of simultaneously gripping the clot and expanding the vessel lumen in order to create space between the thrombus and the vessel wall while advancing along the clots length in incremental steps. The prototype has been evaluated in a custom-made experimental setup using phantom vessels and thrombi analogs. The proof-of-concept experiments have shown that the device can successfully bypass and in some cases even remove thrombi. The study shows promising results for this new kind of device and can be a foundation for future research into applying similar removal strategies in thrombectomy.

BackgroundSegmentation is a routine, yet time-consuming and subjective step in the analysis of positron emission tomography (PET) images. Automatic methods to do it have been suggested, but recent method development has focused on supervised approaches. The previously published unsupervised segmentation methods for PET images are outdated for the arising dynamic human total-body PET images now enabled by the evolving scanner technology. MethodsIn this study, we introduce an unsupervised general purpose automatic segmentation method for modern PET images consisting of tens of millions of voxels. We provide its implementation in an easy-to-use format and demonstrate its performance on two datasets of real human total-body images scanned using different radiotracers. Results and conclusionsOur results show that the suggested method can identify functionally distinct areas within the anatomical organs. Combined with anatomical segments obtained from other imaging modalities, this enables great potential to improve clinically meaningful segmentation and reduce time-consuming manual work.

I.AO_SCPLOWBSTRACTC_SCPLOWCoronary Artery Disease (CAD) is a leading cause of cardiovascular-related mortality and affects 20.5 million people in the United States and approximately 315 million people worldwide in 2022. The asymptomatic and progressive nature of CAD presents challenges for early diagnosis and timely intervention. Traditional diagnostic methods such angiography and stress tests are known to be resource-intensive and prone to human error. This calls for a need for automated and time-effective detection methods. In this paper, this paper introduces a novel approach to the diagnosis of CAD based on a Convolutional Neural Network (CNN) with a temporal attention mechanism. The model will be developed on an architecture that will automatically extract and emphasize critical features from sequential medical imaging data from coronary angiograms, allowing subtle signs of CAD to be easily spotted, which could not have been detected by convention. The temporal attention mechanism strengthens the ability of a model to focus on relevant temporal patterns, thus improving sensitivity and robustness in detecting CAD for various stages of the disease. Experimental validation on a large and diverse dataset demonstrates the efficacy of the proposed method, with significant improvements in both detection accuracy and processing time compared to traditional CNN architectures. The results of this study propose a scalable solution system for the diagnosis of CAD. This proposed system can be integrated into clinical workflows to assist healthcare professionals. Ultimately, this research contributes to the field of AI-driven healthcare solutions and has the potential to reduce the global burden of CAD through early automated detection.

This study investigates the efficacy of transformer-based deep learning architectures--specifically, Vision Transformer (ViT), Class Attention in Image Transformers (CaiT), and Data-Efficient Image Transformers (DeiT)--for the binary classification of colorectal polyps using the Minimalist Histopathology Image Analysis Dataset (MHIST). The dataset comprises 3,152 hematoxylin and eosin (H&E)-stained Formalin Fixed Paraffin-Embedded (FFPE) images annotated as either Hyperplastic Polyps (HP) or Sessile Serrated Adenomas (SSA). A rigorous evaluation was conducted using a 5-fold stratified cross-validation methodology, and performance was quantified using metrics including accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results revealed that transformer architectures, particularly CaiT (accuracy of 90.18%, AUC-ROC of 95.52%), outperformed traditional convolutional neural networks (CNNs). The superior performance of CaiT is attributed to its specialized class-attention mechanisms, effectively capturing nuanced morphological differences essential for accurate histopathological classification. These findings underscore the potential of transformer-based models to enhance diagnostic precision, reduce variability in pathological assessment, and facilitate earlier and more reliable colorectal cancer screening.

Abdominal aortic aneurysms (AAA) affect more than 1% of adults over 50 and carry significant mortality risk. Current surveillance relies on intermittent imaging (ultrasound or MRI) at 6-24 month intervals, which may miss rapid growth acceleration between visits. We investigate the feasibility of continuous aneurysm diameter tracking using photoplethysmography (PPG) signals. Using a one-dimensional hemodynamic model that simulates pulse wave propagation from the heart to the digital artery, we demonstrate that while single-observation diameter estimation is fundamentally limited by noise and confounding variables, aggregating thousands of observations over one or more days may achieve sub-millimeter precision. Specifically, the lower bound error analysis shows diameter uncertainty decreases to 0.7 mm with 1,600 measurements under baseline noise conditions. We validate this approach through 12- month tracking simulations of eight virtual patients with constant and accelerating growth rates, achieving root-mean-square tracking errors of [~]0.3 mm. Furthermore, we demonstrate that patient-specific model calibration from clinical measurements, despite yielding imperfect parameter estimates, still enables accurate diameter tracking (median RMSE = 0.49 mm across 50 virtual patients). These results suggest that wearable PPG monitoring could complement traditional imaging for aneurysm surveillance, potentially enabling earlier detection of growth acceleration and more timely clinical intervention. Data and Code AvailabilityAll data produced in the present study and code for generating said data are available upon reasonable request to the authors. Institutional Review Board (IRB)This research does not require IRB approval since it is not "human subjects research" as it does not include activities that involve interaction with individuals or access to identifiable private information.

BackgroundSampling techniques have poor accuracy for classifying biliary strictures as benign or malignant. Previously, a cholangioscopy artificial intelligence (AI) outperfromed sampling techniques based solely on analysis of previously recorded cholangioscopy footage. The aim of this trial was to compare the performance of a real-time cholangioscopy AI to both sampling techniques and human observers for the task of biliary stricture classification. MethodsA cholangioscopy AI computer connected directly to a cholangioscope console. The computer analyzed the cholangioscopy video stream during procedures for suspected biliary strictures. The primary outcome of the study was comparison of the performance of cholangioscopy AI to sampling techniques - brush cytology and transpapillary forceps biopsy - for biliary stricture classification. Secondary outcomes included comparison of the AI classification performance to that of human observers (separated into junior-level and experienced-level cohorts) who reviewed the cholangioscopy footage. ResultsA total of 41 patients were enrolled in the trial and had biliary strictures analyzed by cholangioscopy AI. For the classification of strictures, the AI had greater classification accuracy than standard sampling techniques (87.8% versus 67.4%; p = 0.043). Additionally, the cholangioscopy AI was significantly more accurate for biliary stricture classification than both junior-level (87.8% versus 61.5%; p = 0.001) and experienced endoscopists (87.8% versus 63.15%; p = 0.011). ConclusionsThis trial demonstrates that sampling techniques and human assessment of biliary strictures are flawed and there may be a benefit to the use of a cholangioscopy AI system to aid in biliary stricture classification.

BackgroundQuantitative myocardial blood flow (MBF) and myocardial flow reserve (MFR) provide incremental diagnostic and prognostic value in cardiac PET, but their widespread use is limited by the technical demands of dynamic imaging protocols. We evaluated the feasibility of using artificial intelligence (AI) to predict MBF and MFR from static and gated PET images, without the need for dynamic acquisition. MethodsA machine learning (XGBoost) model was trained on 82Rb PET multi-center dataset using static perfusion imaging, injected dose, hemodynamic measures, clinical data and CT-derived features (including body composition) from the hybrid CT attenuation scan. Model performance was evaluated externally in an independent cohort. ResultsIn total, 10,566 (derivation-cohort) and 7,842 (external-cohort) patients were included in this multi-center study. On the external-cohort, AI approach achieved an Area under the curve (AUC) of 0.92 (0.92-0.93) for abnormal stress MBF and 0.91 (0.90-0.92) for abnormal MFR; Intra-class correlation (ICC) 0.80 (0.78-0.82) and 0.78 (0.76-0.79), respectively. AI MFR closely mirrored the prognostic performance of measured MFR, showing nearly identical Kaplan-Meier risk stratification (both p<0.0001) and maintaining strong, and independently significant associations with all-cause mortality (HR 3.4 [2.8-4.2] vs. 4.6 [3.6-5.8]; both p<0.001), and demonstrated similar added value to perfusion for mortality prediction. ConclusionAI-predicted virtual stress MBF and MFR assessment using static and gated PET data is feasible and generalizable across cohorts. By removing the dependency on dynamic acquisitions, this approach has the potential to broaden the clinical adoption of flow quantification. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/26345376v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@ec2522org.highwire.dtl.DTLVardef@17a04aforg.highwire.dtl.DTLVardef@1c99db7org.highwire.dtl.DTLVardef@1918c8f_HPS_FORMAT_FIGEXP M_FIG STRUCTURED GRAPHICAL ABSTRACT PET: Positron Emission Tomography, CT: Computed Tomography, MFR: Myocardial Flow Reserve C_FIG Key Question: Can machine learning models trained on dynamic PET datasets accurately predict regional stress myocardial blood flow (MBF) and myocardial flow reserve (MFR) from static image features, physiological parameters, and CT-based anatomical measures? Key Finding: Artificial intelligence can accurately estimate MBF and MFR from non-dynamic PET data, with strong agreement to reference standards. Take-home Message: By eliminating reliance on dynamic PET acquisitions, machine-learning has the potential to broaden clinical adoption of quantitative flow assessment.

Background and ObjectiveThe dysbiosis of human gut microbiome has been increasingly seen to have a relation in the development of autoimmune diseases, with specific microbial signatures having causative association with specific conditions. Inflammatory bowel disease (IBD) is one such autoimmune ailment. This paper proposes a predictive tool that can identify the IBD status of an individual based on the composition of the gut microbiome using machine learning and AI agents driven techniques. The technology can strengthen the suspicion of a potential IBD diagnosis a patient may have based on their gut microbiome profile. MethodsThe tool processes patient gut metagenome using integrated Kneaddata and MetaPhlAn to generate taxonomic profiles. These are fed into an XGBoost classifier to predict IBD or healthy status. Dysbiotic taxa are identified via Z-score and fold change. CrewAI delivers personalized probiotic recommendations based on diagnosis and dysbiosis. ResultsThe tuned XGBoost model achieved 86.6% accuracy. On validation using single ulcerative colitis sample, the tool correctly predicted IBD status but misclassified it as Crohns disease(possibly due to overlapping microbial signatures), identifying Faecalibacterium and Flavonifractor as dysbiotic taxa.The probiotic recommended was Faecalibacterium prausnitzii, backed with reasoning basedon scientific literature. ConclusionsDespite limited validation sample size, the high accuracy, correct IBD detection, dysbiosis analysis and elaborate probiotic recommendation suggest promising potential; further validation needed

Handheld ultrasound devices have revolutionized point-of-care diagnostics, but their effectiveness remains limited by operator dependency and the need for specialized training. This paper presents an intelligent guidance and diagnostic assistance system for the handheld wireless ultrasound device, enabling automated carotid artery and thyroid examinations through handheld operation. Drawing inspiration from the Actor-Critic framework, we implement a simulation-based reinforcement learning approach for real-time probe navigation toward standard anatomical views. The system integrates YOLOv8n-based detection networks for carotid plaque and thyroid nodule identification, achieving real-time inference at 30 frames per second. Furthermore, we propose a hybrid measurement approach combining UNet segmentation with the Snake algorithm for precise biometric quantification, including carotid intima-media thickness (IMT), lumen diameter, and lesion dimensions. Experimental validation on clinical datasets demonstrates that the proposed system achieves 91.2% accuracy in standard plane acquisition, 87.5% mean average precision (mAP) for plaque detection, and 89.3% mAP for nodule identification. Measurement results show excellent agreement with expert sonographers, with IMT measurements exhibiting a mean absolute difference of 0.08 mm. These findings demonstrate the feasibility of intelligent handheld ultrasound examination, significantly reducing operator dependency while maintaining diagnostic accuracy comparable to experienced clinicians.

Burn injuries are a significant concern in developing countries due to limited infrastructure, and treating them remains a major challenge. The manual assessment of burn severity is subjective and depends, to a large extent, on individual expertise. Artificial intelligence can automate this task with greater accuracy and improved predictions, which can assist healthcare professionals in making more informed decisions while triaging burn injuries. This study established a model pipeline for detecting burn injuries in images using multiple deep learning models, including U-Net, DenseNet, ResNet, VGG, EfficientNet, and transfer learning with the Segment Anything Model2 (SAM2). The problem statement was divided into two stages: 1) removing the background and 2) burn skin segmentation. ResNet50, used as an encoder with a U-Net decoder, performs better for the background removal task, achieving an accuracy of 0.9757 and an intersection over union (Jaccard index) of 0.9480. DenseNet169, used as an encoder with a U-Net decoder, performs well in burn skin segmentation, achieving an accuracy of 0.9662 and an intersection over Union of 0.8504. The dataset collected during the project is available for download to facilitate further research and advancements (Link to dataset: https://geninfo.iitr.ac.in/projects). TBSA was estimated from predicted burn masks using scale-based calibration

BackgroundMenopausal obesity is a type of obesity in women during menopause where the decline of ovarian function and the decrease of estrogen levels lead to an imbalance between energy intake and consumption in the body, resulting in fat accumulation and weight gain. Moxibustion, as a green therapy of non-interventional external treatment that prevents and treats diseases through thermal stimulation of relevant acupoints, has been widely used in clinical practice because of its simplicity, convenience, effectiveness, low price and high compliance. PurposeTo clarify the pathogenesis of menopausal obesity and the biological mechanism of moxibustion treatment for menopausal obesity. MethodsWe selected 9 plasma samples from menopausal obese patients before and after moxibustion treatment, as well as 9 plasma samples from the healthy control group. After sample mixing and replication, DIA quantitative proteomics analysis was used to screen out differentially expressed proteins, and bioinformatics analysis was conducted. ResultsThe plasma proteomic analysis revealed a significant increase in the protein expression levels of APOC2 and PZP in menopausal obesity patients. These differential proteins primarily participate in biological regulation, cell metabolism, and reproductive development processes. Their biological processes and molecular functions are mainly associated with enzyme inhibitor activity, calcium-dependent protein binding, lipid localization, and plasma lipoprotein particle assembly. The pathogenesis of menopause obesity is linked to the accumulation of visceral fat resulting from changes in sex hormone levels and reduced energy consumption following the decline of female ovarian function. Following moxibustion treatment, there was a notable down-regulation in the plasma levels of sialoglycoprotein receptor 2 (ASGR2), membranin A1 (ANXA1), and human heterogeneous nuclear ribonucleoprotein C (HNRNPC) among menopausal obesity patients. Their biological processes and molecular functions were primarily concentrated on intracellular hagy, nucleic acid binding, tissue regeneration, and neutrophil clearance. ConclusionThe mechanism underlying moxibustions effectiveness in treating menopausal obesity may involve down-regulating HNRNPC expression, activating the PI3K/Akt/mTOR autophagy signaling pathway, regulating hormone levels to delay ovarian aging thereby improving lipid metabolism.

Current clinical risk stratification for thoracic aortic aneurysms (TAA) relies primarily on maximum diameter, which is a poor predictor of rupture. Recent fluid-structure interaction studies have identified a dimensionless "flutter instability parameter" (N{omega} ) that accurately classifies abnormal aortic growth. However, this parameter currently serves as a static diagnostic snapshot. In this work, we propose a proof-of-concept computational framework that links flutter instability to microstructural tissue damage via a coupled system of ordinary differential equations (ODEs). We model a feedback loop where flutter-induced energy dissipation drives elastin degradation and collagen remodeling, which in turn reduces wall stiffness and amplifies the instability. To address the challenge of unobservable tissue properties, we implement a Bayesian inference engine to infer model parameters. We demonstrate feasibility on a synthetic patient cohort calibrated to published clinical growth rates and diameters. Our results show that this approach can infer hidden damage parameters and capture the qualitative bifurcation between stabilizing remodeling and runaway aneurysm expansion. While validation on real patient data remains essential, this work establishes the mathematical foundation for transforming a static physiomarker into a personalized prognostic trajectory.

BackgroundBody composition strongly influences clinical outcomes in older adults, yet body mass index (BMI) lacks discriminatory power, and standard tools such as bioelectrical impedance analysis (BIA), dual-energy X-ray absorptiometry are not routinely accessible. Deep learning enables scalable, opportunistic assessment of body composition from chest radiographs (CXRs), one of the most widely available imaging modalities. Methods and FindingsUsing the Inception-V3 architecture, we developed a deep-learning model using 107,568 paired CXR and BIA records (2016-2018). The model was temporally validated on a separate dataset of 77,655 records (2014-2015). Our model predicted skeletal muscle mass (SMM) and fat mass (FM) with high accuracy (SMM: Pearson r = 0.967, MAE 1.40 kg; FM: r = 0.924, MAE 1.61 kg). In a cohort of 5,932 older adults (aged [&ge;]65years), a 1-SD increase in CXR-predicted skeletal muscle index (SMI) was associated with a significant reduction in 10-year all-cause mortality (Hazard Ratio [HR] 0.65 [95% CI 0.58-0.73] for men; 0.80 [0.67-0.97] for women). In an external validation of 925 geriatric clinic patients, predicted SMI also showed comparable associations with geriatric parameters, including lower odds of sarcopenia (per 1 SD increase: 0.29 [0.22-0.38] for men; 0.25 [0.18-0.34] for women) and frailty (0.62 [0.48-0.78] for men; 1.00 [0.81-1.23] for women). These associations were more robust than those of BMI. Key limitations include the retrospective, single-center design and the use of a relatively healthy screening population. ConclusionA deep learning model applied to routine CXRs enables accurate estimation of skeletal muscle and fat mass, demonstrating prognostic and functional relevance comparable to BIA measurements. This approach may serve as a practical, low-cost tool for risk stratification and long-term care planning, particularly in older adults.

The aim of this study was to investigate the effects of cognitive dual-tasking on low-frequency oscillations during quiet standing in older adults. Thirty-two older adults (age 71{+/-}8 yrs) were categorized into high- and low-risk fall groups. Both groups performed three trials each of the following tasks: 1) quiet standing with eyes open - on a force plate; 2) quiet standing with eyes open and verbal memory encoding task - on a force plate; and 3) quiet sitting with eyes open and verbal memory encoding task - not on a force plate. We found that: A) older adults at high fall risk exhibit greater postural sway when compared with older adults at low fall risk, B) most of the absolute and normalized wavelet power from 0-4 Hz is concentrated within the 0-1 Hz frequency band across all directions, and C) the absolute change in wavelet power in the 0-1 Hz band from single to dual-task is associated with increased total COP sway displacement irrespective of fall risk group. Based on these findings, it is concluded that nonlinear postural sway measures provide valuable insights into age-associated changes in fall risk and dual-task performance. Focusing on low-frequency oscillations, particularly in the 0-1 Hz band, could enable the earlier identification of individuals at high risk of falls and a better understanding of how the dual-tasking paradigm challenges the aging population.