Background and Motivation In carotid ultrasound imaging, robust plaque segmentation and measurement are key requirements for dependable diagnosis and cardiovascular disease (CVD) risk evaluation. The latest techniques based on solo single stage deep learning UNet (M1:SS-UNet) leads to inconsistency in low contrast scans. We hypothesize that variants of UNet such as transformers can be more powerful paradigms. Method Design and develop novel single-stage UNet-based transformer (M6:SSwAttSkip-XmerBot-UNet) for the far wall segmentation and intima-media thickness/plaque area measurements in Japanese diabetic cohort. We use augmented data and cross-validation protocol for performance evaluation and generalization. Performance evaluation includes 16 novel metrics, namely model GFLOPS, model size, jaccard index, performance to complexity, and training time. We benchmarked our novel transformer system against six models utilizing single and double stage attention-based configurations namely M1:SS-UNet, single-stage with attention-UNet (M2:SSwAttSkip-UNet), double-stage with attention-UNet (M3:DSwAttSkip-UNet), double-stage with attention in decoder UNet (M4:DSwAttDeco-UNet), single stage with transformer in the bottle neck layer (M5:SSTransBot-UNet) and single stage with swin-transformer in every layer(M7:SSSwin-UNet). Results The results showed that M6:SSwAttSkip-XmerBot-UNet achieved a perfect normalization score of 100%, while M7:SSSwin-UNet and M5:SSTransBot-UNet followed closely by 86.07%. These models consistently outperformed M1:SS-UNet, M2:SSwAttSkip-UNet, M3:DSwAttSkip-UNet and M4:DSwAttDeco-UNet across all the 16 metrics. The final ranking of the UNet models were: SSwAttSkip-XmerBot-UNet > SSSwin-UNet > SSTransBot-UNet > DSwAttDeco-UNet > SS-UNet > SSwAttSkip-UNet > DSwAttSkip-UNet. M6:SSwAttSkip-XmerBot-UNet demonstrates comparable effectiveness with greater efficiency, making it a strong alternative. Conclusions We conclude that transformer-based models like M6:SSwAttSkip-XmerBot-UNet provide highly accurate, reliable, and automated technique that segments and measures the risk of CVD.

Background and motivation: MicroRNAs (miRNAs) regulate gene expression and are critical to disease development and progression. Accurate miRNA sequence classification remains a key challenge due to short lengths, conserved motifs, and species variability. Although transformer models have shown promise, they often depend on generic tokenizers (e.g., k-mers, BPE) that disrupt biologically meaningful subsequences. Method: We propose GenAI 4.0, which implements a novel technique called BioBPE, a biologically-informed extension of Byte-Pair Encoding that prioritizes the preservation of conserved miRNA motifs during vocabulary construction. Unlike frequency-only schemes, BioBPE incorporates a domain-specific weighting function into the merge scoring process to align tokenization with the biological signal. To evaluate its efficacy, we conducted a comprehensive benchmark across six transformer models using five different tokenization. Experiments were performed on binary and multiclass classification datasets from MirGeneDB v3.0. Results: BioBPE-tokenized models achieved up to 8.6% improvement in classification accuracy and approximately 18-45% faster convergence to standard tokenizers. Statistical tests (paired t-tests, Wilcoxon) confirmed significance, and BioBPE achieved a mean motif preservation rate of approximately 92% across vocabulary sizes. Conclusions: Our findings demonstrate that tokenization is a central determinant of success in biological sequence classification tasks, and not just a preprocessing step. By explicitly aligning token boundaries with biological structures, BioBPE bridges the gap between generic NLP tokenizers and domain-specific sequence modeling.

BACKGROUND:This review examines enhancements to the U-Net's ability to represent complex spatial structures for segmentation through attention mechanisms. The study surveys applications across both medical and non-medical domains, focusing on cSAM, cCAM, CBAM, and their associated serial (sfSCAM) and parallel (pfSCAM) fusion architectures. Beyond accuracy, the analysis also considers backbone design, diversity of evaluation metrics, pruning strategies, and potential sources of bias. METHODOLOGY:Following PRISMA guidelines, we systematically searched major academic databases using U-Net- and attention-related keywords. The final corpus was constructed through stepwise exclusions (E1-E3) from an initial set of 1807 retrieved records. Spatial, channel, and hybrid attention modules were taxonomized; their placement within encoder, decoder, skip, and bottleneck blocks was identified; and application trends across domains such as liver, brain, skin, and retinal imaging were summarized. FINDINGS:Attention-augmented U-Net models consistently improved the segmentation of subtle, scale-dependent structures across modalities, including CT, MRI, ultrasound, dermoscopy, fundus, and satellite imaging. Spatial attention in skip connections and decoders reduced noise while preserving boundaries, whereas encoder-level channel attention improved semantic weighting; CBAM combined both effects. Parallel fusion (pfSCAM) balanced accuracy and efficiency, while serial fusion (sfSCAM) enabled stable feature refinement. Attention placement strongly influenced global-local fusion and noise suppression. Pruning reduced computational cost with minimal performance loss, although evaluation remained focused on Dice and cross-entropy, with limited use of IoU, Hausdorff distance, and F1-score. CONCLUSION:Spatial and channel attention, along with their serial and parallel fusion variants, function as structural design principles within the U-Net. Effective practice integrates careful module placement, pruning or quantization strategies, and explicit bias auditing.

Cardiovascular disease (CVD) remains one of the leading causes of global mortality and disability. Advances in computational modeling and artificial intelligence have enhanced CVD risk prediction by integrating multivariate clinical and biochemical features. Recent developments in deep learning, especially transformer-based models for tabular data, have demonstrated superior capabilities in capturing nonlinear and high-dimensional biomarker interactions. This study proposes a predictive framework that uses statistical and clinical biomarker data to assess CVD risk. Traditional machine learning models (logistic regression, Gaussian Naïve Bayes, linear discriminant analysis, AdaBoost, and XGBoost) were compared with deep learning models (gated recurrent unit [GRU] and long short-term memory [LSTM]) and transformer-based models—self-attention and intersample attention transformer (SAINT), feature tokenizer (FT), and tab transformer. Experiments were conducted to evaluate the impact of data augmentation, analyze learning behavior through loss and accuracy curves, and assess a fusion approach combining tab transformer with recurrent networks. Model performance was evaluated using accuracy and receiver operating characteristic analysis. Transformer-based models consistently outperformed conventional machine learning and deep learning methods. SAINT and FT achieved an area under the curve (AUC) of 0.8875 and 0.9489, respectively. The tab transformer demonstrated the highest performance with an AUC of 0.9728. The fusion of the tab transformer with GRU and LSTM further enhanced predictive precision, improving representation learning and generalization for CVD risk prediction. The proposed transformer-based framework offers a robust, scalable, and interpretable solution for accurate CVD risk assessment. Its superior predictive capability highlights the potential for integration into clinical decision-support systems for early diagnosis and patient management.

Background and Motivation: Classification of transcriptomic gene data is essential for Cardiovascular disease (CVD) risk, particularly in Hypertrophic Cardiomyopathy (HCM) and Acute Myocardial Infarction (AMI) patients. Existing approaches suffer from limited feature representation and weak biological context modeling. To address these gaps, we propose AtheroEdge™ 5.0, which incorporates three novel Transformers (Xmers): Neuro-Topology (NT), Self-Supervised Contrastive Learning with Alignment and Random Feature Masking (SCARF), and Temporal Diffusion Gene (TDG). Method: Twelve Artificial Intelligence models: three novel Xmers (Models A), three Legacy Xmers (Models B), three Deep Learning (Models C), and three machine learning models (Models D) were designed. Feature engineering included Differential Expression Analysis (DEA) for gene selection and normalization of two different cardiac datasets: HCM and AMI. (iii) Performance was evaluated using K10 cross-validation. The AtheroEdge™ 5.0 was scientifically validated using (a) unseen datasets, (b) K-effect, (c) Generalization-effect, and (d) Local Interpretable Model-agnostic Explanations (LIME)-based Models. Software verification was conducted using Coronary Artery Disease data. Reliability and stability tests were conducted. We hypothesized that: (a) Models A outperform Models B to D, (b) unseen data performance is comparable to seen data for both HCM and AMI datasets, and (c) TDG-Xmer outperforms NT Xmer and SCARF-Xmer. Results: Model A achieved a mean accuracy superior to Models B, C, and D by 4.01%, 10%, and 23.95%, respectively. The Mean Area-under-the-curve of Models A, B, C, and D were 0.96, 0.95, 0.91, and 0.80, respectively. Performance decline on unseen cohorts remained below 10%, meeting regulatory criteria. 87% of high-risk genes were consistently identified by all three novel Xmers and by DEA. TDG-Xmer outperformed NT-Xmer and SCARF-Xmer by 0.5% and 5.26%, respectively. Conclusions: The proposed Xmers provide a robust and scientifically validated framework for accurate CVD risk stratification.

Background/Objectives: UNet-based models dominate medical image segmentation. Transformers have been added as an internal variant to these UNet-based architectures to improve feature learning. However, they have limitations in generalization and computational efficiency. Motivated by this idea, we have designed a two-stage novel hybrid segmentation framework, where tuners are added in cascade to the base architectures. Methods: Three sets of base UNets were designed, namely: B1: UNet1p, B2: UNet2p, and B3:UNet3p, and four sets of transformer-based tuners were designed, namely: T1:Transformer-augmented UNet, T2: Attention-guided UNet, T3: Swin Transformer-based UNet, and T4: Pyramid-based network, leading to 12 fused systems that combine three base UNets and four Tuners, namely: F1: B1 + T1, F2: B1 + T2, F3: B1 + T3, F4: B1 + T4; F5: B2 + T1, F6: B2 + T2, F7: B2 + T3, F8: B2 + T4, F9: B3 + T1, F10: B3 + T2, F11: B3 + T3, F12: B3 + T4. Results: The two-hybrid segmentation models are more effective and reliable than the single-stage UNet architecture. B3 + T4 achieved a Dice of 94.14% and Jaccard of 88.7%, surpassing prior baselines by 4.2% and 6.8%. It reduced cIMTE to 0.014 mm, a 36% improvement and the lowest reported to date, with cLIE and cMAE errors lowered by 40%. Conclusions: All 12 hybrid automated transformer-based models are highly accurate and reliable for wall segmentation in carotid ultrasound; they are a powerful paradigm for cardiovascular risk.

BACKGROUND AND MOTIVATION:Classifying diseases like heart problems using gene expression data depends on selecting important genes. Traditional machine learning (ML) often uses simple feature selection (FS) techniques, which can limit accuracy. In our research, we combine deep learning (DL) with gene-focused methods like differential expression analysis (DEA) to improve classification performance significantly. METHOD:We thoroughly and rigorously evaluated ML and DL classifiers using two gene expression datasets (GSE36961 and GSE57345). We tested four hypotheses using feature selection methods such as chi-square, DEA. We applied principal component analysis (PCA) to reduce the number of features. To ensure the reliability of our findings, we applied k-fold cross-validation, hyperparameter tuning, block effect analysis, and assessed data augmentation and generalization. Statistical tests, including paired t-test and Mann-Whitney U test, and Wilcoxon signed-rank test were performed to compare model performances rigorously. RESULTS:Our experiments on two gene expression datasets (GSE36961, GSE57345) not only confirmed all four hypotheses (H1, H2, H3, and H4) but also revealed significant performance improvements. For H1, without FS, DL outperformed ML models by a substantial margin. For H2, with FS, DL outperformed ML models by a significant percentage. In H3, ML with FS improved over ML without FS by a considerable margin. For H4, DL with FS outperformed DL without FS by a noticeable percentage. Among FS methods, DEA consistently yielded the best results for both ML and DL, further underlining the significance of our findings. CONCLUSIONS:Combining DL with biological feature selection, especially DEA, improves gene expression classification and enables gene ranking and biomarker identification. This integrative approach balances modeling power with biological relevance, providing a reproducible framework for robust biomarker-based classification.

Background: Globally, diabetes mellitus is a major health challenge with high morbidity and significant costs. Traditional methods rely on invasive biomarkers like glycated hemoglobin and lack consistency, necessitating more robust approaches. Methodology: This study uses attention-based deep learning for enhanced diabetes risk stratification. We focus on exploring recurrent neural networks with attention mechanisms. We used K-fold (K = 5) cross-validation and implemented 14 models for robustness. Further, we integrate an explainability paradigm by validating model outputs through reliability-focused statistical tests. Finally, we present the training time comparison between different hardware. Results: The attention-based models employed demonstrated superior performance in handling multidimensional data, resulting in highly accurate diabetes risk stratification predictions. We went on to evaluate these models and benchmarked them against classical methods, proving significant improvements over traditional ones with metrics such as the area under the curve scores reaching 0.99 for attention models. The percentage improvement over non attention-based models was 3.67%. Also, the models were able to show generalization at 60% of training data. Conclusion: The attention-based models employed in this study substantially enhance diabetes risk stratification, offering a promising tool for healthcare professionals. They allow for early and precise detection of diabetes risk stratification, thereby potentially improving patient outcomes through timely and tailored interventions. This research underscores the potential of sophisticated deep learning models in transforming the landscape of chronic disease management.

Background: Carotid plaque can be used to predict the risk of cardiovascular disease (CVD). Earlier machine learning solutions were not reliable, or accurate. The authors hypothesize that (i) attention-based unidirectional or bidirectional hybrid deep learning (HDL) is superior to non-attention-based unidirectional or bidirectional hybrid deep learning and (ii) attention-based bidirectional hybrid deep learning models are superior to attention-based unidirectional HDL paradigms. The proposed design, AtheroEdge (TM) 3.0(att-HDL) (AtheroPoint (TM), Roseville, CA, USA), shows how effectively characteristics of the carotid plaque in attention-based hybrid deep learning systems predict the risk of CVD more accurately and reliably. Methodology: The study involved 500 participants who underwent targeted carotid B-mode ultrasonography along with coronary angiography. Six hybrid models (four attention types) were used, totaling 6x4 = 24 models. These were benchmarked against the machine learning models. Mann-Whitney U test, Wilcoxon test, and paired T-test were used for the statistical and reliability tests. The scientific validation was performed using the unseen data. The area-under-the-curve and p-values were used for the performance evaluation of AtheroEdge (TM) 3.0(att-HDL). Results: The best attention-based bidirectional HDL model showed a mean improvement of 36.11 %, 5.37 %, 5.37 %, and 1.04 % over Random Forest, unidirectional LSTM, bidirectional LSTM, and best attention-based unidirectional HDL models, respectively. As per the reliability and statistical test findings, the bidirectional AtheroEdge (TM) 3.0(att-HDL) had a p-value of less than 0.001, while the unidirectional AtheroEdge (TM) 3.0(att-HDL) also complied with regulations having a p-value < 0.005. Conclusions: The hypothesis was scientifically validated, assessed for reliability and stability, and deemed suitable for clinical application.

Women are disproportionately affected by chronic autoimmune diseases (AD) like systemic lupus erythematosus (SLE), scleroderma, rheumatoid arthritis (RA), and Sjögren's syndrome. Traditional evaluations often underestimate the associated cardiovascular disease (CVD) and stroke risk in women having AD. Vitamin D deficiency increases susceptibility to these conditions. CVD risk prediction in AD can benefit from surrogate biomarker for coronary artery disease (CAD), such as carotid ultrasound. Due to non-linearity in the CVD risk stratification, we use artificial intelligence-based system using AD biomarkers and carotid ultrasound. Investigate the relationship between AD and CVD/stroke markers including autoantibody-influenced plaque load. Second, to study the surrogate biomarkers for the CAD and gather radiomics-based features such as carotid intima-media thickness (cIMT), and plaque area (PA). Third and final, explore the automated CVD/stroke risk identification using advanced machine learning (ML) and deep learning (DL) paradigms. Analysed biomarker data from women with AD, including carotid ultrasonography imaging, clinical parameters, autoantibody profiles, and vitamin D levels. Proposed artificial intelligence (AI) models to predict CVD/stroke risk accurately in AD for women. There is a strong association between AD duration and elevated cIMT/PA, with increased CVD risk linked to higher rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACPAs) levels. AI models outperformed conventional methods by integrating imaging data and disorder-specific factors. Interdisciplinary collaboration is crucial for managing CVD/stroke in women with chronic autoimmune diseases. AI-based assisted risk stratification methods may improve treatment decision-making and cardiovascular outcomes.

Financial time series prediction requires researchers to detect dynamic market indicator relationships while also predicting short-term market trends that are either positive or negative. Conventional LSTM (cLSTM) models face limitations when identifying multiple complex relationships between their input data points. Our work adds Price-to-Earnings (PE) and Price-to-Book (PB), Volatility Index (VIX), and Sentiment Score to the standard Open, Close, Volume features to enhance prediction accuracy levels. We introduce two enhanced Extended Long Short-Term Memory (xLSTM) architectures: (i) xLSTMcg, which implements cross-gating, and (ii) xLSTMeg, which employs exponential gating to improve information flow between feature channels. We compare categorical versus feature-based loss functions and apply ensemble techniques—bagging, boosting, and stacking—to optimize our bidirectional fused models. Furthermore, our study also evaluates the stability of models on validation dataset. The bidirectional configurations produced superior results compared to unidirectional configuration by 2

Personalized medicine is needed since standard breast cancer diagnosis and treatment are not always exact or customized. To revolutionize breast cancer diagnosis, this paper examines the clinical utility of artificial intelligence (AI), specifically deep learning (DL). The accuracy with which AI can analyze vast medical databases—genetic data, clinical background, and images— enabling improved accuracy in diagnosis, staging, and treatment planning through integration of multimodal data.Recent studies have shown that DL algorithms have already attained 94–98 % diagnostic accuracies in recognizing breast cancer subtypes from histopathological images, against the overall accuracy of approximately 88 % of an average human pathologist. Additionally, CNNs have achieved AUC values of >0.95 in distinguishing between malignant and benign breast lesions based on mammography, substantiating the fact that AI enhances the diagnostic accuracy with a significant improvement.The study highlights a few case studies, such as fibroadenoma, invasive lobular carcinoma (ILC), lobular carcinoma in situ (LCIS), and sclerosing adenosis (SAD), to illustrate how DL algorithms outperform human experts in early detection and correct diagnosis. Incorporating DL into personalized medicine not only guarantees more specialized and effective treatment plans but also guarantees a future where every patient with breast cancer will be treated with personalized care. Other than referencing AI's revolutionizing impact on breast cancer screening, this research offers an unassailable case for adopting AI and signals a new epoch of accurate, patient-centered medicine.

Background: The leading global cause of death is coronary artery disease (CAD), necessitating early and precise diagnosis. Intravascular ultrasound (IVUS) is a sophisticated imaging technique that provides detailed visualization of coronary arteries. However, the methods for segmenting walls in the IVUS scan into internal wall structures and quantifying plaque are still evolving. This study explores the use of transformers and attention-based models to improve diagnostic accuracy for wall segmentation in IVUS scans. Thus, the objective is to explore the application of transformer models for wall segmentation in IVUS scans to assess their inherent biases in artificial intelligence systems for improving diagnostic accuracy. Methods: By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we pinpointed and examined the top strategies for coronary wall segmentation using transformer-based techniques, assessing their traits, scientific soundness, and clinical relevancy. Coronary artery wall thickness is determined by using the boundaries (inner: lumen-intima and outer: media-adventitia) through cross-sectional IVUS scans. Additionally, it is the first to investigate biases in deep learning (DL) systems that are associated with IVUS scan wall segmentation. Finally, the study incorporates explainable AI (XAI) concepts into the DL structure for IVUS scan wall segmentation. Findings: Because of its capacity to automatically extract features at numerous scales in encoders, rebuild segmented pictures via decoders, and fuse variations through skip connections, the UNet and transformer-based model stands out as an efficient technique for segmenting coronary walls in IVUS scans. Conclusions: The investigation underscores a deficiency in incentives for embracing XAI and pruned AI (PAI) models, with no UNet systems attaining a bias-free configuration. Shifting from theoretical study to practical usage is crucial to bolstering clinical evaluation and deployment.

BackgroundThe field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).MethodsWe conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.FindingsA total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.InterpretationThe systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.FundingNo funding received.

A substantial proportion of patients diagnosed with rheumatologic and musculoskeletal diseases (RMDs) exhibit resistance to conventional therapies or experience recurrent symptoms. These diseases, which include autoimmune disorders such as multiple sclerosis, rheumatoid arthritis, and systemic lupus erythematosus, are marked by the presence of autoreactive B cells that play a critical role in their pathogenesis. The persistence of these autoreactive B cells within lymphatic organs and inflamed tissues impairs the effectiveness of B-cell-depleting monoclonal antibodies like rituximab.A promising therapeutic approach involves using T cells genetically engineered to express chimeric antigen receptors (CARs) that target specific antigens. This strategy has demonstrated efficacy in treating B-cell malignancies by achieving long-term depletion of malignant and normal B cells. Preliminary data from patients with RMDs, particularly those with lupus erythematosus and dermatomyositis, suggest that CAR T-cells targeting CD19 can induce rapid and sustained depletion of circulating B cells, leading to complete clinical and serological responses in cases that were previously unresponsive to conventional therapies.This review will provide an overview of the current state of preclinical and clinical studies on the use of CAR T-cells and other cellular therapies for RMDs. Additionally, it will explore potential future applications of these innovative treatment modalities for managing patients with refractory and recurrent manifestations of these diseases.

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. Methodology: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. Results: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. Conclusions: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

AbstractBackground & Motivation:The field of personalized medicine endeavors to transform the healthcare industry by advancing individualized strategies for diagnosis, treatment modalities, and prognostic assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Medical practitioners can use this strategy to tailor early interventions for each patient's explicit treatment or preventative requirements. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).Methods:In this comprehensive analysis, we conducted a detailed examination of the term "personalized medicine," delving into its fundamental principles, the obstacles it encounters as an emerging subject, and its potentially revolutionary implications in the domain of CVD. A total of 228 studies were selected using the PRISMA methodology.Findings and Conclusions: Herein, we provide a scoping review highlighting the role of AI, particularly DL, in personalized risk assessment for CVDs. It underscores the prospect for AI-driven personalized medicine to significantly improve the accuracy and efficiency of controlling CVD, revolutionizing patient outcomes. The article also presents examples from real-world case studies and outlines potential areas for future research.

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™–aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.