IEEE Journal of Biomedical and Health Informatics最新文献

Cell culture assays play a vital role in various fields of biology. Conventional assay techniques like immunohistochemistry, immunofluorescence, and flow cytometry offer valuable insights into cell phenotype and behavior. However, each of these techniques requires labeling or staining, and this is a major drawback, specifically in applications that require compact and integrated analytical devices. To address this shortcoming, CMOS capacitance sensors capable of conducting label-free cell culture assays have been proposed. In this paper, we present a computational framework for further augmenting the capabilities of these capacitance sensors. In our framework, identification and classification of mitosis and migration are achieved by leveraging observations from measured capacitance time series data. Specifically, we engineered two time series features that enable discriminating cell behaviors at the single-cell level. Our feature representation achieves an area under curve (AUC) of 0.719 in the receiver operating characteristic (ROC) curve. Additionally, we show that our feature representation technique is applicable across arbitrary experiments, as validated by a leave-one-run-out test yielding an F-1 score of 0.803 and a G-Mean of 0.647.

Low-dose digital radiography (DR) and computed tomography (CT) become increasingly popular due to reduced radiation dose. However, they often result in degraded images with lower signal-to-noise ratios, creating an urgent need for effective denoising techniques. The recent advancement of the single-image-based denoising approach provides a promising solution without requirement of pairwise training data, which are scarce in medical imaging. These methods typically rely on sampling image pairs from a noisy image for inter-supervised denoising. Although enjoying simplicity, the generated image pairs are at the same noise level and only include partial information about the input images. This study argues that generating image pairs at different noise levels while fully using the information of the input image is preferable since it could provide richer multi-perspective clues to guide the denoising process. To this end, we present a novel Multi-Level Noise Sampling (MNS) method for low-dose tomography denoising. Specifically, MNS method generates multi-level noisy sub-images by partitioning the highdimensional input space into multiple low-dimensional subspaces with a simple yet effective strategy. The superiority of the MNS method in single-image-based denoising over the competing methods has been investigated and verified theoretically. Moreover, to bridge the gap between selfsupervised and supervised denoising networks, we introduce an optimization function that leverages prior knowledge of multi-level noisy sub-images to guide the training process. Through extensive quantitative and qualitative experiments conducted on large-scale clinical low-dose CT and DR datasets, we validate the effectiveness and superiority of our MNS approach over other state-of-the-art supervised and self-supervised methods.

Incurable Alzheimer's disease (AD) plagues many elderly people and families. It is important to accurately diagnose and predict it at an early stage. However, the existing methods have shortcomings, such as inability to learn local and global information and the inability to extract effective features. In this paper, we propose a lightweight classification network Local and Global Graph ConvNeXt. This model has a hybrid architecture of convolutional neural network and Transformers. We build the Global NeXt Block and the Local NeXt Block to extract the local and global features of the structural magnetic resonance imaging (sMRI). These two blocks are optimized by adding global multilayer perceptron and locally grouped attention, respectively. Then, the features are fed into the pixel graph neural network to aggregate the valid pixel features using mask attention. In addition, we decoupled the loss by category to optimize the calculation of the loss. This method was tested on slices of the processed sMRI datasets from ADNI and achieved excellent performance. Our model achieves 95.81% accuracy with fewer parameters and floating point operations per second (FLOPS) than other classical efficient models in the diagnosis of AD.

Objective: Freezing of gait (FOG) in Parkinson's disease has a complex neurological mechanism. Compared with other modalities, electroencephalogram (EEG) can reflect FOG-related brain activity of both motor and non-motor symptoms. However, EEG-based FOG prediction methods often extract time, spatial, frequency, time-frequency, or phase information separately, which fragments the coupling among these heterogeneous features and cannot completely characterize the brain dynamics when FOG occurs.

Methods: In this study, dynamic spatiotemporal coherent modes of EEG were studied and used for FOG detection and prediction. A dynamic mode decomposition (DMD) method was first applied to extract the spatiotemporal coherent modes. Dynamic changes of the spatiotemporal modes, in both amplitude and phase of motor-related frequency bands, were evaluated with analytic common spatial patterns (ACSP) to extract the essential differences among normal, freezing, and transitional gaits.

Results: The proposed method was verified in practical clinical data. Results showed that, in the detection task, the DMD-ACSP achieved an accuracy of 86.4 ± 3.6% and a sensitivity of 83.5 ± 4.3%. In the prediction task, 86.5 ± 3.2% accuracy and 86.7 ± 7.8% sensitivity were achieved.

Conclusion: Comparative studies showed that the DMD-ACSP method significantly improves FOG detection and prediction performance. Moreover, the DMD-ACSP reveals the spatial patterns of dynamic brain functional connectivity, which best discriminate the different gaits.

Significance: The spatiotemporal coherent modes may provide a useful indication for personalized intervention and transcranial magnetic stimulation neuromodulation in medical practices.

The sellar region tumor is a brain tumor that only exists in the brain sellar, which affects the central nervous system. The early diagnosis of the sellar region tumor subtypes helps clinicians better understand the best treatment and recovery of pa-tients. Magnetic resonance imaging (MRI) has proven to be an effective tool for the early detection of sellar region tumors. However, the existing sellar region tumor diagnosis still remains challenging due to the small amount of dataset and data imbalance. To overcome these challenges, we propose a novel self-supervised multi-scale multi-modal graph pool Transformer (MMGPT) network that can enhance the multi-modal fusion of small and imbalanced MRI data of sellar region tumors. MMGPT can strengthen feature interaction between multi-modal images, which makes our model more robust. A contrastive learning equipped auto-encoder (CAE) via self-supervised learning (SSL) is adopted to learn more detailed information between different samples. The proposed CAE transfers the pre-trained knowledge to the downstream tasks. Finally, a hybrid loss is equipped to relieve the performance degradation caused by data imbalance. The experimental results show that the proposed method outperforms state-of-the-art methods and obtains higher accuracy and AUC in the classification of sellar region tumors.

Cloud computing and Internet of Things (IoT) technologies are gradually becoming the technological changemakers in cancer diagnosis. Blood cancer is an aggressive disease affecting the blood, bone marrow, and lymphatic system, and its early detection is crucial for subsequent treatment. Flow cytometry has been widely studied as a commonly used method for detecting blood cancer. However, the high computation and resource consumption severely limit its practical application, especifically in regions with limited medical and computational resources. In this study, with the help of cloud computing and IoT technologies, we develop a novel blood cancer dynamic monitoring diagnostic model named BloodPatrol based on an intelligent feature weight fusion mechanism. The proposed model is capable of capturing the dual-view importance relationship between cell samples and features, greatly improving prediction accuracy and significantly surpassing previous models. Besides, benefiting from the powerful processing ability of cloud computing, BloodPatrol can run on a distributed network to efficiently process large-scale cell data, which provides immediate and scalable blood cancer diagnostic services. We have also created a cloud diagnostic platform to facilitate access to our work, the latest access link and updates are available at: https://github.com/kkkayle/BloodPatrol.

Obstructive sleep apnea (OSA) in children is a prevalent and serious respiratory condition linked to cardiovascular morbidity. Polysomnography, the standard diagnostic approach, faces challenges in accessibility and complexity, leading to underdiagnosis. To simplify OSA diagnosis, deep learning (DL) algorithms have been developed using cardiac signals, but they often lack interpretability. Our study introduces a novel interpretable DL approach (SleepECG-Net) for directly estimating OSA severity in at-risk children. A combination of convolutional and recurrent neural networks (CNN-RNN) was trained on overnight electrocardiogram (ECG) signals. Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable Artificial Intelligence (XAI) algorithm, was applied to explain model decisions and extract ECG patterns relevant to pediatric OSA. Accordingly, ECG signals from the semi-public Childhood Adenotonsillectomy Trial (CHAT, n = 1610) and Cleveland Family Study (CFS, n = 64), and the private University of Chicago (UofC, n = 981) databases were used. OSA diagnostic performance reached 4-class Cohen's Kappa of 0.410, 0.335, and 0.249 in CHAT, UofC, and CFS, respectively. The proposal demonstrated improved performance with increased severity along with heightened cardiovascular risk. XAI findings highlighted the detection of established ECG features linked to OSA, such as bradycardia-tachycardia events and delayed ECG patterns during apnea/hypopnea occurrences, focusing on clusters of events. Furthermore, Grad-CAM heatmaps identified potential ECG patterns indicating cardiovascular risk, such as P, T, and U waves, QT intervals, and QRS complex variations. Hence, SleepECG-Net approach may improve pediatric OSA diagnosis by also offering cardiac risk factor information, thereby increasing clinician confidence in automated systems, and promoting their effective adoption in clinical practice.

Integrating diverse biomedical knowledge information is essential to enhance the accuracy and efficiency of medical diagnoses, facilitate personalized treatment plans, and ultimately improve patient outcomes. However, Biomedical Information Integration (BII) faces significant challenges due to variations in terminology and the complex structure of entity descriptions across different datasets. A critical step in BII is biomedical entity alignment, which involves accurately identifying and matching equivalent entities across diverse datasets to ensure seamless data integration. In recent years, Large Language Model (LLMs), such as Bidirectional Encoder Representations from Transformers (BERTs), have emerged as valuable tools for discerning heterogeneous biomedical data due to their deep contextual embeddings and bidirectionality. However, different LLMs capture various nuances and complexity levels within the biomedical data, and none of them can ensure their effectiveness in all heterogeneous entity matching tasks. To address this issue, we propose a novel Two-Stage LLM construction (TSLLM) framework to adaptively select and combine LLMs for Biomedical Information Integration (BII). First, a Multi-Objective Genetic Programming (MOGP) algorithm is proposed for generating versatile high-level LLMs, and then, a Single-Objective Genetic Algorithm (SOGA) employs a confidence-based strategy is presented to combine the built LLMs, which can further improve the discriminative power of distinguishing heterogeneous entities. The experiment utilizes OAEI's entity matching datasets, i.e., Benchmark and Conference, along with LargeBio, Disease and Phenotype datasets to test the performance of TSLLM. The experimental findings validate the efficiency of TSLLM in adaptively differentiating heterogeneous biomedical entities, which significantly outperforms the leading entity matching techniques.

This study explores the application of functional data analysis (FDA) to hand roll velocity during radial suturing on the SutureCoach bench simulator for evaluating open suturing performance. By treating temporal sensor data as mathematical functions, FDA provides a holistic view of the dynamic changes in hand roll, offering comprehensive assessments that are easily interpretable and clinically relevant. Cluster analysis was performed on hand roll profiles from 96 subjects, categorized into advanced surgeons, trainee surgeons, and novices. Functional k-means, using dynamic time-warping to align curves, were used to partition the data into two preset numbers of clusters (3 and 6). Both clustering models (3-cluster and 6-cluster) effectively clustered performance into groups with distinct characteristics and levels of skill (evident from visual inspection of cluster centroids). The relationship between cluster membership and suturing skills was corroborated using proxy measures of skill: expert global rating scale ratings, clinical status and expertise, and simulator-derived metrics. The findings of this study offer valuable insight into essential components of suturing skill and can improve the autonomy and efficiency of simulation-based suturing training. The clinical relevance of our results is immediately pertinent to the field of surgical skill assessment, where FDA-based methods could potentially be employed for objective feedback and training.

In motor imagery (MI) tasks for brain computer interfaces (BCIs), the spatial covariance matrix (SCM) of electroencephalogram (EEG) signals plays a critical role in accurate classification. Given that SCMs are symmetric positive definite (SPD), Riemannian geometry is widely utilized to extract classification features. However, calculating distances between SCMs is computationally intensive due to operations like eigenvalue decomposition, and classical optimization techniques, such as gradient descent, cannot be directly applied to Riemannian manifolds, making the computation of the Riemannian mean more complex and reliant on iterative methods or approximations. In this paper, we propose a novel multiclass classification framework that integrates Riemannian geometry and neural networks to mitigate these challenges. The framework comprises two modules: a Riemannian module with multiple branches and a classification module. During training, a fusion loss function is introduced to update the branch corresponding to the true label, while other branches are updated using different loss functions along with the classification module. Comprehensive experiments on four sets of MI EEG data demonstrate the efficiency and effectiveness of the proposed model.