Medical & Biological Engineering & Computing最新文献

Sperm head morphology has been identified as a characteristic that can be used to predict a male's semen quality. Here, harnessing the close relationship considering sperm head shape to quality and morphology, we propose a joint learning model for sperm head segmentation and morphological category prediction. In the model, the sperm category prediction and the ellipticity, calculated by using the segmented sperm head profile, are used to synthesize the morphology to which the sperm belongs. In traditional clinical testing, fertility experts analyze sperm morphology by 2D images of sperm samples, which cannot represent the whole character of their quality and morphological category. To overcome the problem that single-angle 2D images cannot accurately identify sperm morphology, we use a deep-learning-based tracking and detection system to dynamically acquire sperm images with multiple frames and angles and then use the multi-frame and multi-angle time-series images of sperm to determine sperm morphology based on the multi-task model proposed in this study. Performing better than 3D sperm reconstruction and traditional computer-assisted sperm assessment systems, this approach enables end-to-end analysis of viable spermatozoa, requiring minimal computing power and utilizing equipment already available in most embryology laboratories.

Fetal standard plane detection is essential in prenatal care, enabling accurate assessment of fetal development and early identification of potential anomalies. Despite significant advancements in machine learning (ML) in this domain, its integration into clinical workflows remains limited-primarily due to the lack of standardized, end-to-end operational frameworks. To address this gap, we introduce FetalMLOps, the first comprehensive MLOps framework specifically designed for fetal ultrasound imaging. Our approach adopts a ten-step MLOps methodology that covers the entire ML lifecycle, with each phase meticulously adapted to clinical needs. From defining the clinical objective to curating and annotating fetal US datasets, every step ensures alignment with real-world medical practice. ETL (extract, transform, load) processes are developed to standardize, anonymize, and harmonize inputs, enhancing data quality. Model development prioritizes architectures that balance accuracy and efficiency, using clinically relevant evaluation metrics to guide selection. The best-performing model is deployed via a RESTful API, following MLOps best practices for continuous integration, delivery, and performance monitoring. Crucially, the framework embeds principles of explainability and environmental sustainability, promoting ethical, transparent, and responsible AI. By operationalizing ML models within a clinically meaningful pipeline, FetalMLOps bridges the gap between algorithmic innovation and real-world application, setting a precedent for trustworthy and scalable AI adoption in prenatal care.

Patients with lower limb impairments often face sit-to-stand-to-sit motion challenges. The patients utilize a greater trunk flexion angle at seat-off time to mitigate knee moment. Alternative methods of STSTS motion strategies are required to study and understand the various patterns to guide physical rehabilitation programs in clinical practice. Four different STSTS strategies-Natural, Full Flexion, Pelvis-spine alignment, and Frame-Assisted-were experimented with twenty healthy subjects in a 3D motion capture lab, and inverse kinematics and dynamics methods were used for motion analysis in Visual 3D. At seat-off time in full flexion, the maximum trunk flexion angle is 58.77(± 17.92) degrees, duration is 1.63 s, 27% of the cycle, which reduces knee moment by -0.466(± 0.2) N.m/kg, increased hip moment by 0.67(± 0.312) N.m/kg, and ankle moment by 0.225(± 0.09) N.m/kg for the compensation. The compensatory movement also occurred while sitting down. Frame-assisted STSTS motion reduced knee moments without increases in hip and ankle moments at the maximum of trunk flexion angle while standing and sitting, and its motion patterns are similar to pelvis-spine alignment and natural strategies. These findings provide valuable insights for physiotherapists to predict the current stage of the patient for clinical assessment and guide in the design and development of medical devices.

Although biomedical engineering (BME) is a profession with ethical responsibilities comparable to those in medicine, it has, until now, lacked a counterpart to the Hippocratic Oath. While professional societies have established codes of ethics for biomedical engineers, these documents lack the symbolic and ceremonial significance of an oath or pledge. By contrast, the recitation of the Hippocratic Oath, or its modern version, the "Physician's Pledge," serves as a powerful rite of passage for medical students, fostering a strong sense of ethical duty at the start of their professional journey. However, the content of the Hippocratic Oath includes elements specific to clinical practice and is not directly applicable to biomedical engineering. To fill this gap, we have created a "Biomedical Engineer's Pledge," comprising a preamble, ten promises, and a concluding statement, to inspire ethical awareness and establish a meaningful graduation tradition.

Persistent atrial fibrillation is the most common sustained cardiac arrhythmia, frequently linked with increased mortality and morbidity. Electrical cardioversion (ECV) remains the gold standard for sinus rhythm (SR) restoration, even though presenting potential adverse effects and a high relapsing rate. Predicting ECV outcome from the 12-lead ECG could reduce healthcare costs while preventing complications in patients unlikely to maintain SR. To this end, atrial activity (AA) organization has been traditionally evaluated through the amplitude and dominant frequency of the fibrillatory waves at lead II. However, physiological systems are known to exhibit complex dynamics across multiple time-scales, making multiscale (MSE) entropy measures a more suitable tool, as they can incorporate relevant information that may have been previously overlooked. Here, the predictive power of different MSE-based indices for the ECV outcome in 58 patients is evaluated. AA was estimated using a QT segment cancellation algorithm. Patients were classified based on SR maintenance after a 30-day follow-up. Results show that traditionally used indices report the highest predictive rate over the limb leads (79%). However, they are outperformed by Refined MSE over precordial leads (87%). Moreover, when considering statistical modeling techniques such as support vector machines, the prediction accuracy is increased (98%). In conclusion, MSE-based indices computed from precordial leads can robustly predict ECV outcome with higher accuracy than traditional approaches.

Minimally invasive neurosurgery presents specific challenges due to the limited operative space and complex cranial anatomy, requiring highly precise and safe surgical guidance. Augmented Reality (AR) technology offers the potential to improve surgical accuracy and safety by overlaying critical digital information onto real-world surgical environments. In this study, we present a study that aims to compare four AR visualization methods-2D flat display, smart tablet, head-mounted display (HMD), and 3D autostereoscopic display-in guiding minimally invasive neurosurgical procedures, specifically focusing on ventriculocentesis. The effectiveness of the AR methods was evaluated through comprehensive user studies involving 32 participants (including 11 experienced surgeons), with assessment focused on critical performance metrics including accuracy, completion time, usability, and cognitive workload during simulated surgical procedures. Results demonstrated that 3D visualization methods significantly outperformed traditional 2D approaches in terms of puncture accuracy and angular precision. Specifically, surgeons showed a statistically significant improvement in localization accuracy, with mean error reduced from 2.69 mm to 1.67 mm, and angular deviation from 5.62° to 1.54°. In comparing the two 3D visualization systems, the HMD exhibited superior task completion efficiency, while the 3D autostereoscopic display demonstrated higher usability scores and lower perceived workload ratings. Notably, the 3D systems effectively reduced the performance disparity between novice and experienced practitioners, suggesting their potential to accelerate the learning curve for less experienced users. We conclude that AR holds significant potential to enhance performance and decision-making in minimally invasive neurosurgical guidance.

Glioblastoma (GBM) carries poor prognosis; epidermal-growth-factor-receptor (EGFR) mutations further shorten survival. We propose a fully automated MRI-based decision-support system (DSS) that segments GBM and classifies EGFR status, reducing reliance on invasive biopsy. The segmentation module (UNet SI) fuses multiresolution, entropy-ranked shearlet features with CNN features, preserving fine detail through identity long-skip connections, to yield a Lightweight 1.9 M-parameter network. Tumour masks are fed to an Inception ResNet-v2 classifier via a 512-D bottleneck. The pipeline was five-fold cross-validated on 98 contrast-enhanced T1-weighted scans (Memorial Hospital; Ethics 24.12.2021/008) and externally validated on BraTS 2019. On the Memorial cohort UNet SI achieved Dice 0.873, Jaccard 0.853, SSIM 0.992, HD95 24.19 mm. EGFR classification reached Accuracy 0.960, Precision 1.000, Recall 0.871, AUC 0.94, surpassing published state-of-the-art results. Inference time is ≤ 0.18 s per slice on a 4 GB GPU. By combining shearlet-enhanced segmentation with streamlined classification, the DSS delivers superior EGFR prediction and is suitable for integration into routine clinical workflows.

Quantifying cardiac functional parameters is crucial for assessing the toxicity of environmental chemicals on the cardiovascular system. Current methodologies for evaluating zebrafish cardiac function largely rely on tedious manual annotations and inaccurate semi-automatic or automatic measurements, hindering accurate and comprehensive functional evaluation. In this paper, we propose a framework for automatically quantifying cardiac functional parameters from zebrafish heartbeat videos by exploring universal segmentation models. We benchmarked 20 state-of-the-art deep segmentation models for automated segmentation of zebrafish ventricles and pericardia. The best-performing model, Mask2Former, was selected to segment ventricles and pericardia from the heartbeat videos. Seven cardiac functional parameters for zebrafish embryos, including heart rate, stroke volume, cardiac output, maximum ventricular area, ejection fraction, diastole to systole ratio, and pericardial arc length, were then computed based on the quantification of ventricular changes and pericardial morphologies. Experiments on 178 zebrafish heartbeat videos reveal that the trained Mask2Former exhibited remarkably superior performance, attaining an IoU of 93.46 $\%$ and Dice of 96.58 $\%$ for ventricular segmentation, and an IoU of 83.31 $\%$ and Dice of 90.89 $\%$ for pericardial segmentation. Compared to manual measurements, the automatically quantified cardiac functional parameters consistently show high accuracy, with relative errors below 10.0 $\%$ . Our framework presents a novel, rapid, and reliable tool for evaluating the toxicity of environmental chemicals on the cardiovascular system.

In clinical practice, the upper limb function of hemiplegic post-stroke patients is commonly evaluated using clinical tests and questionnaires. Performing a reliable investigation of compensatory strategies adopted for the upper limb movement may shed light on the basis of motor control and the mechanism of functional recovery. To quantitatively evaluate the compensatory strategies in post-stroke hemiplegic patients, we conducted an observational study in which 36 hemiplegic patients were enrolled and were stratified according to the Fugl-Meyer score. We assessed compensatory strategies in upper limb movements, specifically reaching (RCH) and hand-to-mouth (HTM) movements, using the Kinect V2 device. 11 severe, 8 severe-moderate, 9 moderate-mild, and 8 mild patients and 17 controls participated in the study. Our results showed that severe, severe-moderate, and moderate-mild patients can be discriminated from healthy participants in almost all parameters. In particular, patients showed a reduced ROM of the shoulder in RCH, higher shoulder and elbow vertical displacement, and lower wrist vertical displacement in HTM. Interestingly, compensatory parameters also discriminate mild patients from healthy controls, such as head frontal and vertical displacements. Our protocol works effectively and the instrumental assessment of compensatory strategies in post-stroke patients allows to discriminate different levels of impairments even with low-cost devices.