Medical & Biological Engineering & Computing最新文献

There is no gold standard for objectively measuring pain; wearable devices cannot claim to measure pain itself, but may offer correlational insights through physiological signals. This scoping review synthesizes current evidence on pain-related assessment methods using wearable sensors across pediatric and adult populations. This review followed the PRISMA-ScR guidelines. A systematic literature search was conducted across PubMed, Cochrane Library, Scopus, Web of Science, CINAHL, and Ovid MEDLINE for studies published up to December 2024. A total of 24 studies met the inclusion criteria. The most used wearable devices included commercially available smartwatches, wristbands, and multisensor platforms. Physiological indicators associated with pain responses included heart rate, heart rate variability, electrocardiography, electrodermal activity, electromyography, surface electromyography, photoplethysmography, skin temperature, and electroencephalography, reflecting autonomic, muscular, and neural system activity. Wearable sensors represent a promising, non-invasive tool for capturing physiological pain-related responses, particularly in contexts where verbal self-report is not feasible. While these devices may support more responsive and continuous pain monitoring, they cannot replace self-report measures and should not be interpreted as providing objective pain measurements.

Surgical instrument segmentation plays an important role in robotic autonomous surgical navigation systems as it can accurately locate surgical instruments and estimate their posture, which helps surgeons understand the position and orientation of the instruments. However, there are still some problems affecting segmentation accuracy, like insufficient attention to the edges and center of surgical instruments, insufficient usage of low-level feature details, etc. To address these issues, a lightweight network for surgical instrument segmentation in gastrointestinal (GI) endoscopy (GESur_Net) is proposed. The pixel data aggregation (PDA) mechanism is proposed to analyze the pixel value distribution in the feature map to obtain the importance of each feature channel. The skip connection attention (SK_A) block is proposed to enhance the attention on critical regions of the surgical instruments. The global guidance attention (GGA) block is proposed to fuse high-level semantic information with low-level detailed features, enabling the acquisition of both fine-grained resolution and global semantic information. In addition, we constructed a new dataset, the Gastrointestinal Endoscopic Instrument (GEI) dataset, hoping to provide valuable resources for future research. Extensive experiments conducted on our presented GEI dataset and the Kvasir-instrument dataset demonstrate that the proposed GESur_Net increases the segmentation accuracy and outperforms state-of-the-art segmentation models.

The uniqueness of the electroencephalogram (EEG), a distinct biometric marker inherent to each individual, yields significant advantages for user authentication and identification in brain-computer interface (BCI) systems. However, EEG features can easily change according to the user's state, which may affect the performance of biometric recognition systems based on EEG. Notably, in EEG data collection for such systems, fatigue levels can fluctuate over time-a factor that has yet to be thoroughly investigated concerning its impact on individual recognition performance. This study explores the implications of fatigue on EEG-based personal recognition systems. We derived six sub-datasets from two simulated driving datasets, each labeled with varying levels of fatigue. From each sub-dataset, we extracted six features for identity recognition within and across different fatigue levels. Single-session and cross-session studies revealed that the disparity of EEG fatigue levels between the training and testing sets increased, and system recognition accuracy experienced a decline. Specifically, recognition accuracy typically fell by over 30 $\%$ after 90 min of simulated driving. Furthermore, identity recognition results are better when the training set includes EEG in more fatigued states compared to the test set, rather than the other way around. Crucially, the method based on functional connectivity features shows the best recognition accuracy under different fatigue levels. This research emphasizes the potential benefits of considering fatigue variations in EEG-based personal recognition systems.

Over the past few years, there has been an increase of clinical interest aimed at looking for correlations between morphology, extracted through statistical shape models (SSMs), and hemodynamics, extracted through computational fluid dynamics (CFD) simulations, in cardiovascular diseases. This study explores correlations between aortic morphology and hemodynamics in the thoracic aorta (TA). Existing research often simplifies geometries by excluding supra-aortic vessels due to software limitations in non-rigid registration. To overcome this, a novel algorithm was used to include these vessels in TA analysis. Principal component analysis reduced dimensionality, followed by automatic CFD simulations and correlation analysis between geometric and hemodynamic parameters. The first ( $M_0$ ) and second ( $M_1$ ) SSM modes explained 46.9 $\%$ and 22.4 $\%$ of dataset variance, respectively. Significant correlations were identified between $M_0$ and ascending TA aneurysm volume (Pr = 0.69), and $M_1$ and TA tortuosity (Pr = 0.60). Ten TA shapes were generated by varying standard deviations of $M_0$ and $M_1$ from -2 to +2, and CFD simulations revealed links between wall shear stress (WSS) indicators and TA morphology. This study presents a novel pipeline to analyze geometric and hemodynamic correlations using realistic TA geometries generated via SSM.

Three-dimensional (3D) ultrasound imaging offers a larger field of view and enables volumetric measurements. Among the versatile methods, free-hand 3D ultrasound imaging utilizing deep learning networks for spatial coordinate prediction exhibits advantages in terms of simplified device configuration and user-friendliness. However, this imaging method is restricted to predicting the relative spatial transformation between two consecutive 2D ultrasound images, resulting in substantial cumulative errors. When imaging large organs, cumulative errors can severely distort the 3D images. In this study, we proposed a labeling strategy based on the ultrasound image coordinate system, enhancing the network prediction accuracy. Meanwhile, pre-planning the scanning trajectory and using it to guide the network prediction significantly reduced cumulative error. Spinal 3D ultrasound imaging was performed on both healthy volunteers and scoliosis patients. Comparison of reconstruction results across different methods demonstrated that the proposed method improved the prediction accuracy by approximately 40% and reduced the cumulative error by nearly 80%. This method shows promise for application in various deep learning networks and different tissues and is expected to facilitate the broader clinical adoption of 3D ultrasound imaging.

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.

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.