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20 pages, 5336 KiB

Open AccessArticle

Wearable 12-Lead ECG Acquisition Using a Novel Deep Learning Approach from Frank or EASI Leads with Clinical Validation

by Fan Fu, Dacheng Zhong, Jiamin Liu, Tianxiang Xu, Qin Shen, Wei Wang, Songsheng Zhu and Jianqing Li

Bioengineering 2024, 11(3), 293; https://doi.org/10.3390/bioengineering11030293 - 21 Mar 2024

Viewed by 720

Abstract

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (V_X [...] Read more.

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (V_X, V_Y, and V_Z) or EASI leads (V_ES, V_AS, and V_AI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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27 pages, 17814 KiB

Open AccessArticle

Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset

by Lingyu Liu, Lan Lin, Shen Sun and Shuicai Wu

Bioengineering 2024, 11(2), 124; https://doi.org/10.3390/bioengineering11020124 - 26 Jan 2024

Viewed by 1036

Abstract

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), [...] Read more.

Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELODIC, seamlessly integrated into the UKB imaging processing pipeline. The Lasso algorithm was employed for brain-age prediction, utilizing derived phenotypes obtained from brain imaging data. Subpopulations of accelerated brain aging (ABA) and resilient brain aging (RBA) were delineated based on the error between actual age and predicted brain age. The ABA subgroup comprised 1949 subjects (experimental group), while the RBA subgroup comprised 3203 subjects (control group). Semi-supervised heterogeneity through discriminant analysis (HYDRA) refined and characterized the ABA subgroups based on distinctive neuroimaging features. HYDRA systematically stratified ABA subjects into three subtypes: SubGroup 2 exhibited extensive gray-matter atrophy, distinctive white-matter patterns, and unique connectivity features, displaying lower cognitive performance; SubGroup 3 demonstrated minimal atrophy, superior cognitive performance, and higher physical activity; and SubGroup 1 occupied an intermediate position. This investigation underscores pronounced structural and functional heterogeneity in ABA, revealing three subtypes and paving the way for personalized neuroprotective treatments for age-related neurological, neuropsychiatric, and neurodegenerative diseases. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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16 pages, 7659 KiB

Open AccessArticle

RGGC-UNet: Accurate Deep Learning Framework for Signet Ring Cell Semantic Segmentation in Pathological Images

by Tengfei Zhao, Chong Fu, Wei Song and Chiu-Wing Sham

Bioengineering 2024, 11(1), 16; https://doi.org/10.3390/bioengineering11010016 - 23 Dec 2023

Viewed by 852

Abstract

Semantic segmentation of Signet Ring Cells (SRC) plays a pivotal role in the diagnosis of SRC carcinoma based on pathological images. Deep learning-based methods have demonstrated significant promise in computer-aided diagnosis over the past decade. However, many existing approaches rely heavily on stacking [...] Read more.

Semantic segmentation of Signet Ring Cells (SRC) plays a pivotal role in the diagnosis of SRC carcinoma based on pathological images. Deep learning-based methods have demonstrated significant promise in computer-aided diagnosis over the past decade. However, many existing approaches rely heavily on stacking layers, leading to repetitive computational tasks and unnecessarily large neural networks. Moreover, the lack of available ground truth data for SRCs hampers the advancement of segmentation techniques for these cells. In response, this paper introduces an efficient and accurate deep learning framework (RGGC-UNet), which is a UNet framework including our proposed residual ghost block with ghost coordinate attention, featuring an encoder-decoder structure tailored for the semantic segmentation of SRCs. We designed a novel encoder using the residual ghost block with proposed ghost coordinate attention. Benefiting from the utilization of ghost block and ghost coordinate attention in the encoder, the computational overhead of our model is effectively minimized. For practical application in pathological diagnosis, we have enriched the DigestPath 2019 dataset with fully annotated mask labels of SRCs. Experimental outcomes underscore that our proposed model significantly surpasses other leading-edge models in segmentation accuracy while ensuring computational efficiency. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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13 pages, 1086 KiB

Open AccessArticle

GNN-surv: Discrete-Time Survival Prediction Using Graph Neural Networks

by So Yeon Kim

Bioengineering 2023, 10(9), 1046; https://doi.org/10.3390/bioengineering10091046 - 06 Sep 2023

Cited by 1 | Viewed by 1373

Abstract

Survival prediction models play a key role in patient prognosis and personalized treatment. However, their accuracy can be improved by incorporating patient similarity networks, which uncover complex data patterns. Our study uses Graph Neural Networks (GNNs) to enhance discrete-time survival predictions (GNN-surv) by [...] Read more.

Survival prediction models play a key role in patient prognosis and personalized treatment. However, their accuracy can be improved by incorporating patient similarity networks, which uncover complex data patterns. Our study uses Graph Neural Networks (GNNs) to enhance discrete-time survival predictions (GNN-surv) by leveraging relationships in these networks. We build these networks using cancer patients’ genomic and clinical data and train various GNN models on them, integrating Logistic Hazard and PMF survival models. GNN-surv models exhibit superior performance in survival prediction across two urologic cancer datasets, outperforming traditional MLP models. They maintain robustness and effectiveness under varying graph construction hyperparameter

μ

values, with performance boosts of up to 14.6% and 7.9% in the time-dependent concordance index and reductions in the integrated brier score of 26.7% and 24.1% in the BLCA and KIRC datasets, respectively. Notably, these models also maintain their effectiveness across three different types of GNN models, suggesting potential adaptability to other cancer datasets. The superior performance of our GNN-surv models underscores their wide applicability in the fields of oncology and personalized medicine, providing clinicians with a more accurate tool for patient prognosis and personalized treatment planning. Future studies can further optimize these models by incorporating other survival models or additional data modalities. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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16 pages, 8834 KiB

Open AccessArticle

RGSB-UNet: Hybrid Deep Learning Framework for Tumour Segmentation in Digital Pathology Images

by Tengfei Zhao, Chong Fu, Ming Tie, Chiu-Wing Sham and Hongfeng Ma

Bioengineering 2023, 10(8), 957; https://doi.org/10.3390/bioengineering10080957 - 12 Aug 2023

Cited by 2 | Viewed by 1110

Abstract

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of [...] Read more.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumour with high incidence and mortality rates. Early screening for CRC can improve cure rates and reduce mortality. Recently, deep convolution neural network (CNN)-based pathological image diagnosis has been intensively studied to meet the challenge of time-consuming and labour-intense manual analysis of high-resolution whole slide images (WSIs). Despite the achievements made, deep CNN-based methods still suffer from some limitations, and the fundamental problem is that they cannot capture global features. To address this issue, we propose a hybrid deep learning framework (RGSB-UNet) for automatic tumour segmentation in WSIs. The framework adopts a UNet architecture that consists of the newly-designed residual ghost block with switchable normalization (RGS) and the bottleneck transformer (BoT) for downsampling to extract refined features, and the transposed convolution and 1 × 1 convolution with ReLU for upsampling to restore the feature map resolution to that of the original image. The proposed framework combines the advantages of the spatial-local correlation of CNNs and the long-distance feature dependencies of BoT, ensuring its capacity of extracting more refined features and robustness to varying batch sizes. Additionally, we consider a class-wise dice loss (CDL) function to train the segmentation network. The proposed network achieves state-of-the-art segmentation performance under small batch sizes. Experimental results on DigestPath2019 and GlaS datasets demonstrate that our proposed model produces superior evaluation scores and state-of-the-art segmentation results. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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23 pages, 10657 KiB

Open AccessArticle

Exploring the Possibility of Measuring Vertebrae Bone Structure Metrics Using MDCT Images: An Unpaired Image-to-Image Translation Method

by Dan Jin, Han Zheng and Huishu Yuan

Bioengineering 2023, 10(6), 716; https://doi.org/10.3390/bioengineering10060716 - 12 Jun 2023

Viewed by 926

Abstract

Bone structure metrics are vital for the evaluation of vertebral bone strength. However, the gold standard for measuring bone structure metrics, micro-Computed Tomography (micro-CT), cannot be used in vivo, which hinders the early diagnosis of fragility fractures. This paper used an unpaired image-to-image [...] Read more.

Bone structure metrics are vital for the evaluation of vertebral bone strength. However, the gold standard for measuring bone structure metrics, micro-Computed Tomography (micro-CT), cannot be used in vivo, which hinders the early diagnosis of fragility fractures. This paper used an unpaired image-to-image translation method to capture the mapping between clinical multidetector computed tomography (MDCT) and micro-CT images and then generated micro-CT-like images to measure bone structure metrics. MDCT and micro-CT images were scanned from 75 human lumbar spine specimens and formed training and testing sets. The generator in the model focused on learning both the structure and detailed pattern of bone trabeculae and generating micro-CT-like images, and the discriminator determined whether the generated images were micro-CT images or not. Based on similarity metrics (i.e., SSIM and FID) and bone structure metrics (i.e., bone volume fraction, trabecular separation and trabecular thickness), a set of comparisons were performed. The results show that the proposed method can perform better in terms of both similarity metrics and bone structure metrics and the improvement is statistically significant. In particular, we compared the proposed method with the paired image-to-image method and analyzed the pros and cons of the method used. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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14 pages, 2356 KiB

Open AccessArticle

Personalized Explanations for Early Diagnosis of Alzheimer’s Disease Using Explainable Graph Neural Networks with Population Graphs

by So Yeon Kim

Bioengineering 2023, 10(6), 701; https://doi.org/10.3390/bioengineering10060701 - 08 Jun 2023

Cited by 2 | Viewed by 1951

Abstract

Leveraging recent advances in graph neural networks, our study introduces an application of graph convolutional networks (GCNs) within a correlation-based population graph, aiming to enhance Alzheimer’s disease (AD) prognosis and illuminate the intricacies of AD progression. This methodological approach leverages the inherent structure [...] Read more.

Leveraging recent advances in graph neural networks, our study introduces an application of graph convolutional networks (GCNs) within a correlation-based population graph, aiming to enhance Alzheimer’s disease (AD) prognosis and illuminate the intricacies of AD progression. This methodological approach leverages the inherent structure and correlations in demographic and neuroimaging data to predict amyloid-beta (A

β

) positivity. To validate our approach, we conducted extensive performance comparisons with conventional machine learning models and a GCN model with randomly assigned edges. The results consistently highlighted the superior performance of the correlation-based GCN model across different sample groups in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, suggesting the importance of accurately reflecting the correlation structure in population graphs for effective pattern recognition and accurate prediction. Furthermore, our exploration of the model’s decision-making process using GNNExplainer identified unique sets of biomarkers indicative of A

β

positivity in different groups, shedding light on the heterogeneity of AD progression. This study underscores the potential of our proposed approach for more nuanced AD prognoses, potentially informing more personalized and precise therapeutic strategies. Future research can extend these findings by integrating diverse data sources, employing longitudinal data, and refining the interpretability of the model, which potentially has broad applicability to other complex diseases. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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17 pages, 5681 KiB

Open AccessArticle

Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks

by So-Hyeon Yoo, Guanghao Huang and Keum-Shik Hong

Bioengineering 2023, 10(6), 685; https://doi.org/10.3390/bioengineering10060685 - 04 Jun 2023

Viewed by 1216

Abstract

Activated channels of functional near-infrared spectroscopy are typically identified using the desired hemodynamic response function (dHRF) generated by a trial period. However, this approach is not possible for an unknown trial period. In this paper, an innovative method not using the dHRF is [...] Read more.

Activated channels of functional near-infrared spectroscopy are typically identified using the desired hemodynamic response function (dHRF) generated by a trial period. However, this approach is not possible for an unknown trial period. In this paper, an innovative method not using the dHRF is proposed, which extracts fluctuating signals during the resting state using maximal overlap discrete wavelet transform, identifies low-frequency wavelets corresponding to physiological noise, trains them using long-short term memory networks, and predicts/subtracts them during the task session. The motivation for prediction is to maintain the phase information of physiological noise at the start time of a task, which is possible because the signal is extended from the resting state to the task session. This technique decomposes the resting state data into nine wavelets and uses the fifth to ninth wavelets for learning and prediction. In the eighth wavelet, the prediction error difference between the with and without dHRF from the 15-s prediction window appeared to be the largest. Considering the difficulty in removing physiological noise when the activation period is near the physiological noise, the proposed method can be an alternative solution when the conventional method is not applicable. In passive brain-computer interfaces, estimating the brain signal starting time is necessary. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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14 pages, 2238 KiB

Open AccessArticle

Named Entity Recognition of Diabetes Online Health Community Data Using Multiple Machine Learning Models

by Qian Xu, Yue Zhou, Bolin Liao, Zirui Xin, Wenzhao Xie, Chao Hu and Aijing Luo

Bioengineering 2023, 10(6), 659; https://doi.org/10.3390/bioengineering10060659 - 29 May 2023

Cited by 1 | Viewed by 1343

Abstract

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online [...] Read more.

The rising prevalence of diabetes and the increasing awareness of self-health management have resulted in a surge in diabetes patients seeking health information and emotional support in online health communities. Consequently, there is a vast database of patient consultation information in these online health communities. However, due to the heterogeneity and incompleteness of the content, mining medical information and patient health data from these communities can be a challenge. To address this issue, we built the RoBERTa-BiLSTM-CRF (RBC) model for identifying entities in the online health community of diabetes. We selected 1889 question–answer texts from the most active online health community in China, Good Doctor Online, and used these public data to identify five types of entities. In addition, we conducted a comparative evaluation with three other commonly used models to validate the performance of our proposed model, including RoBERTa-CRF (RC), BilSTM-CRF (BC), and RoBERTa-Softmax (RS). The results showed that the RBC model achieved excellent performance on the test set, with an accuracy of 81.2% and an F1 score of 80.7%, outperforming the performance of traditional entity recognition models in named entity recognition in online medical communities for doctors and diabetes patients. The high performance of entity recognition in online health communities will provide a crucial knowledge source for constructing medical knowledge graphs. This integration would help alleviate the growing demand for medical consultations and the strain on healthcare resources, while assisting healthcare professionals in making informed decisions and providing personalized services to patients. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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17 pages, 2598 KiB

Open AccessArticle

GSN-HVNET: A Lightweight, Multi-Task Deep Learning Framework for Nuclei Segmentation and Classification

by Tengfei Zhao, Chong Fu, Yunjia Tian, Wei Song and Chiu-Wing Sham

Bioengineering 2023, 10(3), 393; https://doi.org/10.3390/bioengineering10030393 - 22 Mar 2023

Cited by 6 | Viewed by 1936

Abstract

Nuclei segmentation and classification are two basic and essential tasks in computer-aided diagnosis of digital pathology images, and those deep-learning-based methods have achieved significant success. Unfortunately, most of the existing studies accomplish the two tasks by splicing two related neural networks directly, resulting [...] Read more.

Nuclei segmentation and classification are two basic and essential tasks in computer-aided diagnosis of digital pathology images, and those deep-learning-based methods have achieved significant success. Unfortunately, most of the existing studies accomplish the two tasks by splicing two related neural networks directly, resulting in repetitive computation efforts and a redundant-and-large neural network. Thus, this paper proposes a lightweight deep learning framework (GSN-HVNET) with an encoder–decoder structure for simultaneous segmentation and classification of nuclei. The decoder consists of three branches outputting the semantic segmentation of nuclei, the horizontal and vertical (HV) distances of nuclei pixels to their mass centers, and the class of each nucleus, respectively. The instance segmentation results are obtained by combing the outputs of the first and second branches. To reduce the computational cost and improve the network stability under small batch sizes, we propose two newly designed blocks, Residual-Ghost-SN (RGS) and Dense-Ghost-SN (DGS). Furthermore, considering the practical usage in pathological diagnosis, we redefine the classification principle of the CoNSeP dataset. Experimental results demonstrate that the proposed model outperforms other state-of-the-art models in terms of segmentation and classification accuracy by a significant margin while maintaining high computational efficiency. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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15 pages, 1792 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network

by Gilsang Yoo, Hyeoncheol Kim and Sungdae Hong

Bioengineering 2023, 10(3), 361; https://doi.org/10.3390/bioengineering10030361 - 15 Mar 2023

Cited by 7 | Viewed by 2385

Abstract

In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of [...] Read more.

In recent years, the development of adaptive models to tailor instructional content to learners by measuring their cognitive load has become a topic of active research. Brain fog, also known as confusion, is a common cause of poor performance, and real-time detection of confusion is a challenging and important task for applications in online education and driver fatigue detection. In this study, we propose a deep learning method for cognitive load recognition based on electroencephalography (EEG) signals using a long short-term memory network (LSTM) with an attention mechanism. We obtained EEG signal data from a database of brainwave information and associated data on mental load. We evaluated the performance of the proposed LSTM technique in comparison with random forest, Adaptive Boosting (AdaBoost), support vector machine, eXtreme Gradient Boosting (XGBoost), and artificial neural network models. The experimental results demonstrated that the proposed approach had the highest accuracy of 87.1% compared to those of other algorithms, including random forest (64%), AdaBoost (64.31%), support vector machine (60.9%), XGBoost (67.3%), and artificial neural network models (71.4%). The results of this study support the development of a personalized adaptive learning system designed to measure and actively respond to learners’ cognitive load in real time using wireless portable EEG systems. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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Review

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51 pages, 795 KiB

Open AccessReview

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

by Xi Xu, Jianqiang Li, Zhichao Zhu, Linna Zhao, Huina Wang, Changwei Song, Yining Chen, Qing Zhao, Jijiang Yang and Yan Pei

Bioengineering 2024, 11(3), 219; https://doi.org/10.3390/bioengineering11030219 - 25 Feb 2024

Viewed by 2054

Abstract

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution [...] Read more.

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer’s disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making. Full article

(This article belongs to the Special Issue Biomedical Application of Big Data and Artificial Intelligence)

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