Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing significant challenges in providing appropriate care. As the number of dementia cases continues to rise, delivering optimal care becomes more complex. Machine learning (ML) plays a crucial role in addressing this challenge by utilizing medical data to enhance care planning and management for individuals at risk of various types of dementia. Magnetic resonance imaging (MRI) is a commonly used method for analyzing neurological disorders. Recent evidence highlights the benefits of integrating artificial intelligence (AI) techniques with MRI, significantly enhancing the diagnostic accuracy for different forms of dementia. This paper explores the use of AI in the automated detection and classification of dementia, aiming to streamline early diagnosis and improve patient outcomes. Integrating ML models into clinical practice can transform dementia care by enabling early detection, personalized treatment plans, and more effectual monitoring of disease progression. In this study, an Enhancing Automated Detection and Classification of Dementia in Thinking Inability Persons using Artificial Intelligence Techniques (EADCD-TIPAIT) technique is presented. The goal of the EADCD-TIPAIT technique is for the detection and classification of dementia in individuals with cognitive impairment using MRI imaging. The EADCD-TIPAIT method performs preprocessing to scale the input data using z-score normalization to obtain this. Next, the EADCD-TIPAIT technique performs a binary greylag goose optimization (BGGO)-based feature selection approach to efficiently identify relevant features that distinguish between normal and dementia-affected brain regions. In addition, the wavelet neural network (WNN) classifier is employed to detect and classify dementia. Finally, the improved salp swarm algorithm (ISSA) is implemented to choose the WNN technique's hyperparameters optimally. The stimulation of the EADCD-TIPAIT technique is examined under a Dementia prediction dataset. The performance validation of the EADCD-TIPAIT approach portrayed a superior accuracy value of 95.00% under diverse measures.

Stroke is a leading cause of mortality and disability worldwide, with approximately 15 million people suffering strokes annually. Machine learning (ML) techniques have emerged as powerful tools for stroke prediction, enabling early identification of risk factors through data-driven approaches. However, the clinical utility and performance characteristics of these approaches require systematic evaluation. To systematically review and analyze ML techniques used for stroke prediction, systematically synthesize performance metrics across different prediction targets and data sources, evaluate their clinical applicability, and identify research trends focusing on patient population characteristics and stroke prevalence patterns. A systematic review was conducted following PRISMA guidelines. Five databases (Google Scholar, Lens, PubMed, ResearchGate, and Semantic Scholar) were searched for open-access publications on ML-based stroke prediction published between January 2013 and December 2024. Data were extracted on publication characteristics, datasets, ML methodologies, evaluation metrics, prediction targets (stroke occurrence vs. outcomes), data sources (EHR, imaging, biosignals), patient demographics, and stroke prevalence. Descriptive synthesis was performed due to substantial heterogeneity precluding quantitative meta-analysis. Fifty-eight studies were included, with peak publication output in 2021 (21 articles). Studies targeted three main prediction objectives: stroke occurrence prediction (n = 52, 62.7 %), stroke outcome prediction (n = 19, 22.9 %), and stroke type classification (n = 12, 14.4 %). Data sources included electronic health records (n = 48, 57.8 %), medical imaging (n = 21, 25.3 %), and biosignals (n = 14, 16.9 %). Systematic analysis revealed ensemble methods consistently achieved highest accuracies for stroke occurrence prediction (range: 90.4-97.8 %), while deep learning excelled in imaging-based applications. African populations, despite highest stroke mortality rates globally, were represented in fewer than 4 studies. ML techniques show promising results for stroke prediction. However, significant gaps exist in representation of high-risk populations and real-world clinical validation. Future research should prioritize population-specific model development and clinical implementation frameworks.

Deep learning (DL) has shown promise in glioma imaging tasks using magnetic resonance imaging (MRI) and histopathology images, yet their complexity demands greater transparency in artificial intelligence (AI) systems. This is noticeable when users must understand the model output for a clinical application. In this systematic review, 65 post-hoc eXplainable AI (XAI), or interpretable AI studies, have been reviewed that provide an understanding of why a system generated a given output for tasks related to glioma imaging. A framework of post-hoc XAI methods, such as Gradient-based XAI (G-XAI) and Perturbation-based XAI (P-XAI), is introduced to evaluate deep models and explain their application in gliomas. The papers on XAI techniques in gliomas are surveyed and categorized by their specific aims such as grading, genetic biomarker detection, localization, intra-tumoral heterogeneity assessment, and survival analysis, and their XAI approach. This review highlights the growing integration of XAI in glioma imaging, demonstrating their role in bridging AI decision-making and medical diagnostics. The co-occurrence analysis emphasizes their role in enhancing model transparency and trust and guiding future research toward more reliable clinical applications. Finally, the current challenges associated with DL and XAI approaches and their clinical integration are discussed with an outlook on future opportunities from clinical users' perspectives and upcoming trends in XAI.

Accurate prediction of stroke risk at an early stage is essential for timely intervention and prevention, especially given the serious health consequences and economic burden that strokes can cause. In this study, we proposed a class-balanced and data-augmented (CBDA-ResNet50) deep learning model to improve the prediction accuracy of the well-known ResNet50 architecture for stroke risk. Our approach uses advanced techniques such as class balancing and data augmentation to address common challenges in medical imaging datasets, such as class imbalance and limited training examples. In most cases, these problems lead to biased or less reliable predictions. To address these issues, the proposed model assures that the predictions are still accurate even when some stroke risk factors are absent in the data. The performance of CBDA-ResNet50 improves by using the Adam optimizer and the ReduceLROnPlateau scheduler to adjust the learning rate. The application of weighted cross entropy removes the imbalance between classes and significantly improves the results. It achieves an accuracy of 97.87% and a balanced accuracy of 98.27%, better than many of the previous best models. This shows that we can make more reliable predictions by combining modern deep-learning models with advanced data-processing techniques. CBDA-ResNet50 has the potential to be a model for early stroke prevention, aiming to improve patient outcomes and reduce healthcare costs.

Reducing radiation dose from PET imaging is essential to minimize cancer risks; however, it often leads to increased noise and degraded image quality, compromising diagnostic reliability. Recent advances in deep learning have shown promising results in addressing these limitations through effective denoising. However, existing networks trained on specific noise levels often fail to generalize across diverse acquisition conditions. Moreover, training multiple models for different noise levels is impractical due to data and computational constraints. This study aimed to develop a supervised Swin Transformer-based unified noise-aware (ST-UNN) network that handles diverse noise levels and reconstructs high-quality images in low-dose PET imaging. We present a Swin Transformer-based Noise-Aware Network (ST-UNN), which incorporates multiple sub-networks, each designed to address specific noise levels ranging from 1 % to 10 %. An adaptive weighting mechanism dynamically integrates the outputs of these sub-networks to achieve effective denoising. The model was trained and evaluated using PET/CT dataset encompassing the entire head and malignant lesions in the head and neck region. Performance was assessed using a combination of structural and statistical metrics, including the Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), Standardized Uptake Value (SUV) mean bias, SUV_max bias, and Root Mean Square Error (RMSE). This comprehensive evaluation ensured reliable results for both global and localized regions within PET images. The ST-UNN consistently outperformed conventional networks, particularly in ultra-low-dose scenarios. At 1 % count level, it achieved a PSNR of 34.77, RMSE of 0.05, and SSIM of 0.97, notably surpassing the baseline networks. It also achieved the lowest SUV_mean bias (0.08) and RMSE lesion (0.12) at this level. Across all count levels, ST-UNN maintained high performance and low error, demonstrating strong generalization and diagnostic integrity. ST-UNN offers a scalable, transformer-based solution for low-dose PET imaging. By dynamically integrating sub-networks, it effectively addresses noise variability and provides superior image quality, thereby advancing the capabilities of low-dose and dynamic PET imaging.

Brain lesions are abnormalities or injuries in brain tissue that are often detectable using magnetic resonance imaging (MRI), which reveals structural changes in the affected areas. This broad definition of brain lesions includes areas of the brain that are irreversibly damaged, as well as areas of brain tissue that are deformed as a result of lesion growth or swelling. Despite the importance of differentiating between damaged and deformed tissue, existing lesion segmentation methods overlook this distinction, labeling both of them as a single anomaly. In this work, we introduce a diffusion model-based framework for analyzing and reversing the brain lesion process. Our pipeline first segments abnormal regions in the brain, then estimates and reverses tissue deformations by restoring displaced tissue to its original position, isolating the core lesion area representing the initial damage. Finally, we inpaint the core lesion area to arrive at an estimation of the pre-lesion healthy brain. This proposed framework reverses a forward lesion growth process model that is well-established in biomechanical studies that model brain lesions. Our results demonstrate improved accuracy in lesion segmentation, characterization, and brain labeling compared to traditional methods, offering a robust tool for clinical and research applications in brain lesion analysis. Since pre-lesion healthy versions of abnormal brains are not available in any public dataset for validation of the reverse process, we simulate a forward model to synthesize multiple lesioned brain images.

Accurate MR signal simulation, including microvascular structures and water diffusion, is crucial for MRI techniques like fMRI BOLD modeling and MR vascular Fingerprinting (MRF), which use susceptibility effects on MR signals for tissue characterization. However, integrating microvascular features and diffusion remains computationally challenging, limiting the accuracy of the estimates. Using advanced modeling and deep neural networks, we propose a novel simulation tool that efficiently accounts for susceptibility and diffusion effects. We used dimension reduction of magnetic field inhomogeneity matrices combined with deep learning methodology to accelerate the simulations while maintaining their accuracy. We validated our results through an in silico study against a reference method and in vivo MRF experiments. This approach accelerates MR signal generation by a factor of almost 13 000 compared to previously used simulation methods while preserving accuracy. The MR-WAVES method allows fast generation of MR signals accounting for microvascular structures and water-diffusion contribution.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with intracranial large vessel occlusion, yet individual benefits can vary widely. As demand for thrombectomy rises and geographical disparities in stroke care access persist, there is a growing need for predictive models that quantify individual benefits. However, current imaging methods for estimating outcomes may not fully capture the dynamic nature of cerebral ischaemia and lack a patient-specific assessment of thrombectomy benefits. Our study introduces a deep learning approach to predict individual responses to thrombectomy in acute ischaemic stroke patients. The proposed models provide predictions for both tissue and clinical outcomes under two scenarios: one assuming successful reperfusion and another assuming unsuccessful reperfusion. The resulting simulations of penumbral salvage and difference in National Institutes of Health Stroke Scale (NIHSS) at discharge quantify the potential individual benefits of the intervention. Our models were developed on an extensive dataset from routine stroke care, which included 405 ischaemic stroke patients who underwent thrombectomy. We used acute data for training (n = 304), including multimodal CT imaging and clinical characteristics, along with post hoc markers such as thrombectomy success, final infarct localization and NIHSS at discharge. We benchmarked our tissue outcome predictions under the observed reperfusion scenario against a thresholding-based clinical method and a generalized linear model. Our deep learning model showed significant superiority, with a mean Dice score of 0.48 on internal test data (n = 50) and 0.52 on external test data (n = 51), versus 0.26/0.36 and 0.34/0.35 for the baselines, respectively. The NIHSS sum score prediction achieved median absolute errors of 1.5 NIHSS points on the internal test dataset and 3.0 NIHSS points on the external test dataset, outperforming other machine learning models. By predicting the patient-specific response to thrombectomy for both tissue and clinical outcomes, our approach offers an innovative biomarker that captures the dynamics of cerebral ischaemia. We believe this method holds significant potential to enhance personalized therapeutic strategies and to facilitate efficient resource allocation in acute stroke care.

To assess the impact of a commercially available computed tomography (CT)-based artificial intelligence (AI) software for detecting acute intracranial hemorrhage (AIH) on radiologists' diagnostic performance and workflow in a real-world clinical setting. This retrospective study included a total of 956 non-contrast brain CT scans obtained over a 70-day period, interpreted independently by 2 board-certified general radiologists. Of these, 541 scans were interpreted during the initial 35 days before the implementation of AI software, and the remaining 415 scans were interpreted during the subsequent 35 days, with reference to AIH probability scores generated by the software. To assess the software's impact on radiologists' performance in detecting AIH, performance before and after implementation was compared. Additionally, to evaluate the software's effect on radiologists' workflow, Kendall's Tau was used to assess the correlation between the daily chronological order of CT scans and the radiologists' reading order before and after implementation. The early diagnosis rate for AIH (defined as the proportion of AIH cases read within the first quartile by radiologists) and the median reading order of AIH cases were also compared before and after implementation. A total of 956 initial CT scans from 956 patients [mean age: 63.14 ± 18.41 years; male patients: 447 (47%)] were included. There were no significant differences in accuracy [from 0.99 (95% confidence interval: 0.99-1.00) to 0.99 (0.98-1.00), <i>P</i> = 0.343], sensitivity [from 1.00 (0.99-1.00) to 1.00 (0.99-1.00), <i>P</i> = 0.859], or specificity [from 1.00 (0.99-1.00) to 0.99 (0.97-1.00), <i>P</i> = 0.252] following the implementation of the AI software. However, the daily correlation between the chronological order of CT scans and the radiologists' reading order significantly decreased [Kendall's Tau, from 0.61 (0.48-0.73) to 0.01 (0.00-0.26), <i>P</i> < 0.001]. Additionally, the early diagnosis rate significantly increased [from 0.49 (0.34-0.63) to 0.76 (0.60-0.93), <i>P</i> = 0.013], and the daily median reading order of AIH cases significantly decreased [from 7.25 (Q1-Q3: 3-10.75) to 1.5 (1-3), <i>P</i> < 0.001] after the implementation. After the implementation of CT-based AI software for detecting AIH, the radiologists' daily reading order was considerably reprioritized to allow more rapid interpretation of AIH cases without compromising diagnostic performance in a real-world clinical setting. With the increasing number of CT scans and the growing burden on radiologists, optimizing the workflow for diagnosing AIH through CT-based AI software integration may enhance the prompt and efficient treatment of patients with AIH.

Accurate segmentation of brain tumors from multimodal Magnetic Resonance Imaging (MRI) plays a critical role in diagnosis, treatment planning, and disease monitoring in neuro-oncology. Traditional methods of tumor segmentation, often manual and labour-intensive, are prone to inconsistencies and inter-observer variability. Recently, deep learning models, particularly Convolutional Neural Networks (CNNs), have shown great promise in automating this process. However, these models face challenges in terms of generalization across diverse datasets, accurate tumor boundary delineation, and uncertainty estimation. To address these challenges, we propose AG-MS3D-CNN, an attention-guided multiscale 3D convolutional neural network for brain tumor segmentation. Our model integrates local and global contextual information through multiscale feature extraction and leverages spatial attention mechanisms to enhance boundary delineation, particularly in complex tumor regions. We also introduce Monte Carlo dropout for uncertainty estimation, providing clinicians with confidence scores for each segmentation, which is crucial for informed decision-making. Furthermore, we adopt a multitask learning framework, which enables the simultaneous segmentation, classification, and volume estimation of tumors. To ensure robustness and generalizability across diverse MRI acquisition protocols and scanners, we integrate a domain adaptation module into the network. Extensive evaluations on the BraTS 2021 dataset and additional external datasets, such as OASIS, ADNI, and IXI, demonstrate the superior performance of AG-MS3D-CNN compared to existing state-of-the-art methods. Our model achieves high Dice scores and shows excellent robustness, making it a valuable tool for clinical decision support in neuro-oncology.