Medical & Biological Engineering & Computing最新文献

Numerical simulations and clinical measurements of nasal resistance are in quantitative disagreement. The order of magnitude of this mismatch, that sometimes exceeds 100%, is such that known sources of uncertainty cannot explain it. The goal of the present work is to examine a source of bias introduced by the design of medical devices, which has not been considered until now as a possible explanation. We study the effect of the location of the probe on the rhinomanometer that is meant to measure the ambient pressure. Rhinomanometry is carried out on a 3D silicone model of a patient-specific anatomy; a clinical device and dedicated sensors are employed side-by-side for mutual validation. The same anatomy is also employed for numerical simulations, with approaches spanning a wide range of fidelity levels. We find that the intrinsic uncertainty of the numerical simulations is of minor importance. To the contrary, the position of the pressure tap intended to acquire the external pressure in the clinical device is crucial, and can cause a mismatch comparable to that generally observed between in-silico and in-vivo rhinomanometry data. A source of systematic bias may therefore exist in rhinomanometers, designed under the assumption that measurements of the nasal resistance are unaffected by the flow development within the instruments.

The validation of multimodal deep learning models for medical image translation is limited by the lack of high-quality, paired datasets. We propose a novel framework that leverages computational phantoms to generate realistic CT and MRI images, enabling reliable ground-truth datasets for robust validation of artificial intelligence (AI) methods that generate synthetic CT (sCT) from MRI, specifically for radiotherapy applications. Two CycleGANs (cycle-consistent generative adversarial networks) were trained to transfer the imaging style of real patients onto CT and MRI phantoms, producing synthetic data with realistic textures and continuous intensity distributions. These data were evaluated through paired assessments with original phantoms, unpaired comparisons with patient scans, and dosimetric analysis using patient-specific radiotherapy treatment plans. Additional external validation was performed on public CT datasets to assess the generalizability to unseen data. The resulting, paired CT/MRI phantoms were used to validate a GAN-based model for sCT generation from abdominal MRI in particle therapy, available in the literature. Results showed strong anatomical consistency with original phantoms, high histogram correlation with patient images (HistCC = 0.998 ± 0.001 for MRI, HistCC = 0.97 ± 0.04 for CT), and dosimetric accuracy comparable to real data. The novelty of this work lies in using generated phantoms as validation data for deep learning-based cross-modality synthesis techniques.

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.

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.

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.

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.

The 3D spinal model plays a crucial role in the assessment and treatment decision of adolescent idiopathic scoliosis. The complex 3D shape of the spine cannot be fully captured by a single radiograph. A 3D spine reconstruction framework is developed in this study. First, a dual-training strategy for Generative Adversarial Networks (GANs) is proposed, which generates high-quality 3D spinal structures. Second, an adaptive scale-agnostic attention mechanism is integrated to establish cross-layer feature correlations and dynamically allocate weights. This mechanism ensures the preservation of the crucial information across all scales throughout the feature extraction process. The proposed method has been validated on 49 cases of scoliosis. Experiments show that surface overlap and volume Dice coefficient are 0.92 and 0.94, respectively. Compared with the state-of-the-art methods, the proposed method reduces the average surface distance by 0.16 mm. The results demonstrate its effectiveness in reconstructing the 3D spine from a single radiograph.