Medical & Biological Engineering & Computing最新文献

The common black box nature of machine learning models is an obstacle to their application in health care context. Their widespread application is limited by a significant "lack of trust." So, the main goal of this work is the development of an evaluation approach that can assess, simultaneously, trust and performance. Trust assessment is based on (i) model robustness (stability assessment), (ii) confidence (95% CI of geometric mean), and (iii) interpretability (comparison of respective features ranking with clinical evidence). Performance is assessed through geometric mean. For validation, in patients' stratification in cardiovascular risk assessment, a Portuguese dataset (N=1544) was applied. Five different models were compared: (i) GRACE score, the most common risk assessment tool in Portugal for patients with acute coronary syndrome; (ii) logistic regression; (iii) Naïve Bayes; (iv) decision trees; and (v) rule-based approach, previously developed by this team. The obtained results confirm that the simultaneous assessment of trust and performance can be successfully implemented. The rule-based approach seems to have potential for clinical application. It provides a high level of trust in the respective operation while outperformed the GRACE model's performance, enhancing the required physicians' acceptance. This may increase the possibility to effectively aid the clinical decision.

Aneurysms are bulges of an artery, which require clinical management solutions. Due to the inherent advantages, endovascular coil filling is emerging as the treatment of choice for intracranial aneurysms (IAs). However, after successful treatment of coil embolization, there is a serious risk of recurrence. It is well known that optimal packing density will enhance treatment outcomes. The main objective of endovascular coil embolization is to achieve flow stasis by enabling significant reduction in intra-aneurysmal flow and facilitate thrombus formation. The present study numerically investigates the effect of framing coil orientation on intra-aneurysmal hemodynamics. For the purpose of analysis, actual shape of the embolic coil is used, instead of simplified ideal coil shape. Typically used details of the framing coil are resolved for the analysis. However, region above the framing coil is assumed to be filled with a porous medium. Present simulations have shown that orientation of the framing coil loop (FCL) greatly influences the intra-aneurysmal hemodynamics. The FCLs which were placed parallel to the outlets of basilar tip aneurysm (Coil A) were found to reduce intra-aneurysmal flow velocity that facilitates thrombus formation. Involving the coil for the region is modeled using a porous medium model with a packing density of 20 $\%$ . The simulations indicate that the framing coil loop (FCL) has a significant influence on the overall outcome.

Blindness is preventable by early detection of ocular abnormalities. Computer-aided diagnosis for ocular abnormalities is built by analyzing retinal imaging modalities, for instance, Color Fundus Photography (CFP). This research aims to propose a multi-label detection of 28 ocular abnormalities consisting of frequent and rare abnormalities from a single CFP by using transformer-based semantic dictionary learning. Rare labels are usually ignored because of a lack of features. We tackle this condition by adding the co-occurrence dependency factor to the model from the linguistic features of the labels. The model learns the relation between spatial features and linguistic features represented as a semantic dictionary. The proposed method treats the semantic dictionary as one of the main important parts of the model. It acts as the query while the spatial features are the key and value. The experiments are conducted on the RFMiD dataset. The results show that the proposed method achieves the top 30% in Evaluation Set on the RFMiD dataset challenge. It also shows that treating the semantic dictionary as one of the strong factors in model detection increases the performance when compared with the method that treats the semantic dictionary as a weak factor.

The brain-computer interfaces (BCIs) facilitate the users to exploit information encoded in neural signals, specifically electroencephalogram (EEG), to control devices and for neural rehabilitation. Mental imagery (MI)-driven BCI predicts the user's pre-meditated mental objectives, which could be deployed as command signals. This paper presents a novel learning-based framework for classifying MI tasks using EEG-based BCI. In particular, our work focuses on the variation in inter-session data and the extraction of multi-spectral user-tailored features for robust performance. Thus, the goal is to create a calibration-free subject-adaptive learning framework for various mental imagery tasks not restricted to motor imagery alone. In this regard, critical spectral bands and the best temporal window are first selected from the EEG training trials of the subject based on the Riemannian user learning distance metric (Dscore) that checks for distinct and stable patterns. The filtered covariance matrices of the EEG trials in each spectral band are then transformed towards a reference covariance matrix using the Riemannian transfer learning, enabling the different sessions to be comparable. The evaluation of our proposed Selective Time-window and Multi-scale Filter-Bank with Adaptive Riemannian (STFB-AR) features on four public datasets, including disabled subjects, showed around 15% and 8% improvement in mean accuracy over baseline and fixed filter-bank models, respectively.

The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunction. Accurately predicting the demand for IMV is vital for clinical decision-making. However, current techniques are invasive and challenging to implement in pre-hospital and emergency rescue settings. To address this issue, a real-time prediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achieving an AUC value of 0.935 (95% CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpassing the performance of traditional risk adjustment algorithms (OSI(oxygenation saturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the model's decision-making. These findings highlight the substantial potential of a machine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand. Such a model can provide technical support for predicting the need for IMV in pre-hospital and disaster scenarios.

Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase.

Radiotherapy is recognized as the primary treatment for nasopharyngeal carcinoma (NPC). Rapid and accurate dose prediction is crucial for enhancing the quality and efficiency of radiotherapy planning. However, the current dose prediction model based on 2D architecture cannot effectively learn the spatial information among slices. Although some studies have explored the incorporation of interslice features through 3D architecture, the resolution properties of medical image anisotropy significantly limit the predictive performance. To address the issues, we propose a novel deformable dose prediction network based on hybrid 2D and 3D convolution for NPC radiotherapy. Specifically, the proposed model innovatively incorporates a 2.5D architecture based on hybrid 2D and 3D convolution, and effectively utilizes the directional information within anisotropic resolutions to achieve cross-scale feature extraction. Additionally, deformable convolution is introduced into the model to enhance the receptive field and effectively handle multi-scale spatial transformations. To improve channel correlation and reduce redundant features, we design a Residual Deformable Squeeze-and-Excitation Module. We conduct extensive experiments on an internal dataset, and the results show that the proposed model outperforms other existing methods in most dosimetric criteria. The proposed model has superior dose prediction performance in NPC radiotherapy, and has important clinical significance for assisting physicists to optimize the treatment plan and improve standardization of radiotherapy planning. The source code is available at https://github.com/CDUTJ102/2.5D-Deformable-UNet .

Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically in classifying and detecting various diseases. However, when confronted with limited data, these networks face a critical vulnerability, often succumbing to overfitting by excessively memorizing the limited information available. This work addresses the challenge mentioned above by improving the supervised contrastive learning method leveraging both image-level labels and domain-specific augmentations to enhance model robustness. This approach integrates self-supervised pre-training with a two-stage supervised contrastive learning strategy. In the first stage, we employ a modified supervised contrastive loss that not only focuses on reducing false negatives but also introduces an elimination effect to address false positives. In the second stage, a relaxing mechanism is introduced that refines positive and negative pairs based on similarity, ensuring that only relevant image representations are aligned. We evaluate our method on the BreakHis dataset, which consists of breast cancer histopathology images, and demonstrate an increase in classification accuracy by 1.45% in the image level, compared to the state-of-the-art method. This improvement corresponds to 93.63% absolute accuracy, highlighting the effectiveness of our approach in leveraging properties of data to learn more appropriate representation space. The code implementation of this study is accessible on GitHub https://github.com/matinamehdizadeh/Breast-Cancer-Detection .

Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179 mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550 mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.

In lumbar spinal fusion, the titanium cage tends to cause stress shielding due to their high elastic modulus, which can lead to degenerative lesions in adjacent spinal segments. Furthermore, polyetheretherketone (PEEK) cages have certain material characteristics that do not promote bone ingrowth and fusion stability. In this study, a new cage was designed, and its biomechanical performance in percutaneous endoscopic posterior lumbar interbody fusion (PE-PLIF) was analyzed using the finite element (FE) method. A complete model of the L4-L5 lumbar spine was established, and static and harmonic vibration FE analysis models were developed based on it. The biomechanical properties of titanium, PEEK, and combined cage in PE-PLIF fusion were compared. The strain capacity of the combined fusion increased by 9.5% when compared to the titanium fusion. The surgical model under the combined fusion reduces the L5 endplate stress by 12% in the forward flexion condition and the fusion stress by 17% in the vibration condition compared to the model supported by the titanium fusion, and the differences in screw stress and mobility among the three models are not significant in multiple conditions. Consequently, the combined cage demonstrates a certain reduction in the stress-shielding effect when compared to the titanium cage; it has better fusion effect and provides theoretical support and guidance for the design of the clinical fusion cage.