Antinuclear Antibody (ANA) testing is pivotal to help diagnose patients with a suspected autoimmune disease. The Indirect Immunofluorescence (IIF) microscopy performed with human epithelial type 2 (HEp-2) cells as the substrate is the reference ...

• IIF on Hep-2 cells is crucial for diagnosing autoimmune diseases.
• It involves one segmentation task and two classification tasks.
• A Multi-Task Learning approach could help but hasn’t been explored till now.
• We propose a Multi-...

The global burden of infectious diseases significantly affects mortality rates, with their varying symptoms making it challenging to assess and determine the severity of infections. Different countries face unique challenges related to these ...

• AI-driven infectious disease diagnosis via medical imagery and MCDM framework.
• Enhances decision-making for isolation, quarantine, or hospitalization.
• Advances in AI and pattern recognition for global disease management.

Accurate identification of drug-target interactions (DTIs) plays a crucial role in drug discovery. Compared with traditional experimental methods that are labor-intensive and time-consuming, computational methods for drug-target interactions ...

• To obtain features of proteins /drugs and DPPs , a double multi-view heterogeneous graph neural network is constructed.
• Multiple associative information between drugs and proteins are captured from multivariate heterogeneous network by ...

Multi-site MRI imaging poses a significant challenge due to the potential variations in images across different scanners at different sites. This variability can introduce ambiguity in further image analysis. Consequently, the image analysis ...

• We develop a deep learning model that learns features from multi-site datasets.
• Proposed model uses signal and FC matrices for enhanced ASD classification.
• The model consists of two sub-modules: MHACSM and RMCN.
• MHACSM extracts ...

Nowadays, the most adopted technique to address infertility problems is in vitro fertilisation (IVF). However, its success rate is limited, and the associated procedures, known as assisted reproduction technology (ART), suffer from a lack of ...

• AI can improve the outcomes of reproductive technologies, especially IVF.
• Many AI applications in IVF focus on embryo selection to optimize transfer.
• AI solutions for oocyte quality assessment can cut costs and boost IVF success ...

Mental fatigue is defined as a decline in the ability and efficiency of mental activities. A lot of research suggests that the transition from alertness to fatigue is accompanied by alterations in correlation patterns among various brain regions. ...

• Mental fatigue status features vary with channels, thus requiring model adaptation.
• Spectral and temporal connections reflect the EEG properties within individuals.
• Graph convolutional network with dual transformation learns ...

Physics-based computer models based on numerical solutions of the governing equations generally cannot make rapid predictions, which in turn limits their applications in the clinic. To address this issue, we developed a physics-informed neural ...

• Physics-informed neural network (PINN) model for rapid prediction of cardiovascular hemodynamics.
• PINN model achieved a maximum error of less than 5%.
• Inverse PINN modeling approach for rapid estimation of cardiac contractility (in ...

Clinical diagnoses are typically made by following a series of steps recommended by guidelines that are authored by colleges of experts. Accordingly, guidelines play a crucial role in rationalizing clinical decisions. However, they ...

• We adapt the reinforcement learning framework to diagnosis decision support.
• Our approach progressively constructs optimal sequences of actions to reach a diagnosis, which we refer to as diagnostic decision pathways.
• We perform an ...

The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising outcomes in discerning phenotypic characteristics and enhancing ...

• Deep learning and multimodal data to predict neurological deficits.
• Self-supervised contrastive learning to fuse heterogeneous multimodal features.
• Supervised contrastive learning to capture shared information among similar ...

Electrocardiogram (ECG) delineation is essential to the identification of abnormal cardiac status, especially when ECG signals are remotely monitored with wearable devices. The complexity and diversity of cardiac conditions generate numerous ...

• An abnormal recognition-assisted network links ECG delineation and disease diagnosis.
• Our onset-offset aware loss improves waveform boundary detection, reducing misdiagnosis.
• Our method shows favorable results on both wearable and ...

Mental health disorders pose an increasing public health challenge worsened by the COVID-19 pandemic. The pandemic highlighted gaps in preparedness, emphasizing the need for early identification of at-risk groups and ...

• The COVID-19 pandemic and lockdown worsened mental health, revealing a lack of preparedness to address this growing crisis.
• Interpretable machine learning predicts depression, anxiety, and stress, highlighting factors like poor health ...

Structural and functional brain networks are generated from two scan sequences of magnetic resonance imaging data, which can provide different perspectives for describing pathological changes caused by brain diseases. Recent studies found that ...

• We design a general end-to-end neural network framework for fusing structural and functional brain networks to identify brain diseases.
• We consider multi-granularity properties during the process of multi-modal brain network fusion for ...

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder, and it remains incurable. Currently there is no definitive biomarker for detecting PD, measuring its severity, or monitoring of treatments. Recently, oculomotor ...

• A self-supervised geometrical representation that reconstruct SPD matrices.
• A geometrical ocular fixation descriptor able to classify Parkinson patterns.
• A potential digital Parkinson Biomarker designed without expert-label ...

Dynamic functional connections (dFCs), can reveal neural activities, which provides an insightful way of mining the temporal patterns within the human brain and further detecting brain disorders. However, most existing studies focus on the dFCs ...

• Introducing a novel method to explore temporal patterns in dFCs.
• Combining Fourier transform with a non-stationary kernel to mine higher-order temporal patterns.
• Mining long-range relationships and complex temporal patterns in ...

In recent years, we have witnessed both artificial intelligence obtaining remarkable results in clinical decision support systems (CDSSs) and explainable artificial intelligence (XAI) improving the interpretability of these models. In turn, this ...

• Limited availability of data limits small hospitals in generating high- quality counterfactual explanations.
• Integrating federated learning mitigates this limitation and maintains data privacy.
• Benefit of using federated learning ...

To build datasets containing useful information from drug databases and recommend a list of drugs to physicians and patients with high accuracy by considering a wide range of features of people, diseases, and chemicals.

A ...

• Sentiment analysis was employed by NLP approaches in pre-processing.
• Neural network-based methods and RS algorithms were employed for modelling the system.
• We used knowledge from drug information and combined the model’s outcome ...

The integration of morphological attributes extracted from histopathological images and genomic data holds significant importance in advancing tumor diagnosis, prognosis, and grading. Histopathological images are acquired through microscopic ...

• Developed a multi-modal, multi-task framework for glioma diagnosis.
• Integrated Swin-Transformer v2 with contrastive learning to enhance image features.
• Implemented a novel gene selection method to reduce data redundancy.
• ...

Medical Concept Normalization (MCN) is a crucial process for deep information extraction and natural language processing tasks, which plays a vital role in biomedical research. Although MCN in English has achieved significant research ...

• Neural network architectural model we proposed significantly outperforms other previous methods.
• The framework of multi-task learning combine dynamic and static vectors.
• Explore the impact of knowledge-enhanced on the experiments.

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a ...

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• A trustworthy AI system has been generated to predict pubertal Insulin Resistance.
• Adequate integration of multi-omic data may enhance the system’s accuracy.
• Post-hoc ML explanations enable to identify potential insulin resistance ...

Dose prediction is a crucial step in automated radiotherapy planning for liver cancer. Several deep learning-based approaches for dose prediction have been proposed to enhance the design efficiency and quality of radiotherapy plan. However, these ...

• A multi-input feature extraction module is designed to extract multi-scale feature.
• An interactive attention module is proposed to highlight key features.
• An autoencoder network is constructed for generating a perceptual loss.