Cerebrovascular diseases (CVD) can lead to stroke and dementia. Stroke is the second leading cause of death world wide and dementia incidence is increasing by the year. There are several markers of CVD that are visible on brain imaging, including: white matter hyperintensities (WMH), acute and chronic ischaemic stroke lesions (ISL), lacunes, enlarged perivascular spaces (PVS), acute and chronic haemorrhagic lesions, and cerebral microbleeds (CMB). Brain atrophy also occurs in CVD. These markers are important for patient management and intervention, since they indicate elevated risk of future stroke and dementia. We systematically reviewed automated systems designed to support radiologists reporting on these CVD imaging findings. We considered commercially available software and research publications which identify at least two CVD markers. In total, we included 29 commercial products and 13 research publications. Two distinct types of commercial support system were available: those which identify acute stroke lesions (haemorrhagic and ischaemic) from computed tomography (CT) scans, mainly for the purpose of patient triage; and those which measure WMH and atrophy regionally and longitudinally. In research, WMH and ISL were the markers most frequently analysed together, from magnetic resonance imaging (MRI) scans; lacunes and PVS were each targeted only twice and CMB only once. For stroke, commercially available systems largely support the emergency setting, whilst research systems consider also follow-up and routine scans. The systems to quantify WMH and atrophy are focused on neurodegenerative disease support, where these CVD markers are also of significance. There are currently no openly validated systems, commercially, or in research, performing a comprehensive joint analysis of all CVD markers (WMH, ISL, lacunes, PVS, haemorrhagic lesions, CMB, and atrophy).

Schizophrenia (SZ) is a complex mental disorder characterized by a profound disruption in cognition and emotion, often resulting in a distorted perception of reality. Magnetic resonance imaging (MRI) is an essential tool for diagnosing SZ which helps to understand the organization of the brain. Functional MRI (fMRI) is a specialized imaging technique to measure and map brain activity by detecting changes in blood flow and oxygenation. The proposed paper correlates the results using an explainable deep learning approach to identify the significant regions of SZ patients using group-level analysis for both structural MRI (sMRI) and fMRI data. The study found that the heat maps for Grad-CAM show clear visualization in the frontal lobe for the classification of SZ and CN with a 97.33% accuracy. The group difference analysis reveals that sMRI data shows intense voxel activity in the right superior frontal gyrus of the frontal lobe in SZ patients. Also, the group difference between SZ and CN during n-back tasks of fMRI data indicates significant voxel activation in the frontal cortex of the frontal lobe. These findings suggest that the frontal lobe plays a crucial role in the diagnosis of SZ, aiding clinicians in planning the treatment.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects a child's cognitive and social skills, often diagnosed only after symptoms appear around age 2. Leveraging MRI for early ASD detection can improve intervention outcomes. This study proposes a framework for autism detection and region localization using an optimized deep learning approach with attention mechanisms. The pipeline includes MRI image collection, pre-processing (bias field correction, histogram equalization, artifact removal, and non-local mean filtering), and autism classification with a Symmetric Structured MobileNet with Attention Mechanism (SSM-AM). Enhanced by Refreshing Awareness-aided Election-Based Optimization (RA-EBO), SSM-AM achieves robust classification. Abnormality region localization utilizes a Multiscale Dilated Attention-based Adaptive U-Net (MDA-AUnet) further optimized by RA-EBO. Experimental results demonstrate that our proposed model outperforms existing methods, achieving an accuracy of 97.29%, sensitivity of 97.27%, specificity of 97.36%, and precision of 98.98%, significantly improving classification and localization performance. These results highlight the potential of our approach for early ASD diagnosis and targeted interventions. The datasets utilized for this work are publicly available at https://fcon_1000.projects.nitrc.org/indi/abide/.

Accurate brain tumor segmentation from MRI images is critical in the medical industry, directly impacts the efficacy of diagnostic and treatment plans. Accurate segmentation of tumor region can be challenging, especially when noise and abnormalities are present. This research provides a systematic review of automatic brain tumor segmentation techniques, with a specific focus on the design of network architectures. The review categorizes existing methods into unsupervised and supervised learning techniques, as well as machine learning and deep learning approaches within supervised techniques. Deep learning techniques are thoroughly reviewed, with a particular focus on CNN-based, U-Net-based, transfer learning-based, transformer-based, and hybrid transformer-based methods. This survey encompasses a broad spectrum of automatic segmentation methodologies, from traditional machine learning approaches to advanced deep learning frameworks. It provides an in-depth comparison of performance metrics, model efficiency, and robustness across multiple datasets, particularly the BraTS dataset. The study further examines multi-modal MRI imaging and its influence on segmentation accuracy, addressing domain adaptation, class imbalance, and generalization challenges. The analysis highlights the current challenges in Computer-aided Diagnostic (CAD) systems, examining how different models and imaging sequences impact performance. Recent advancements in deep learning, especially the widespread use of U-Net architectures, have significantly enhanced medical image segmentation. This review critically evaluates these developments, focusing the iterative improvements in U-Net models that have driven progress in brain tumor segmentation. Furthermore, it explores various techniques for improving U-Net performance for medical applications, focussing on its potential for improving diagnostic and treatment planning procedures. The efficiency of these automated segmentation approaches is rigorously evaluated using the BraTS dataset, a benchmark dataset, part of the annual Multimodal Brain Tumor Segmentation Challenge (MICCAI). This evaluation provides insights into the current state-of-the-art and identifies key areas for future research and development.

Global aging raises concerns about cognitive health, metabolic disorders, and sarcopenia. Prevention of reversible decline and diseases in middle-aged individuals is essential for promoting healthy aging. We hypothesize that changes in body composition, specifically muscle mass and visceral fat, and metabolic indices are associated with accelerated brain aging. To explore these relationships, we employed a brain age model to investigate the links between the brain age gap (BAG), body composition, and metabolic markers. Using T1-weighted anatomical brain MRIs, we developed a machine learning model to predict brain age from gray matter features, trained on 2,675 healthy individuals aged 18-92 years. This model was then applied to a separate cohort of 458 Taiwanese adults (57.8 years ± 11.6; 280 men) to assess associations between BAG, body composition quantified by MRI, and metabolic markers. Our model demonstrated reliable generalizability for predicting individual age in the clinical dataset (MAE = 6.11 years, r = 0.900). Key findings included significant correlations between larger BAG and reduced total abdominal muscle area (r = -0.146, p = 0.018), lower BMI-adjusted skeletal muscle indices, (r = -0.134, p = 0.030), increased systemic inflammation, as indicated by high-sensitivity C-reactive protein levels (r = 0.121, p = 0.048), and elevated fasting glucose levels (r = 0.149, p = 0.020). Our findings confirm that muscle mass and metabolic health decline are associated with accelerated brain aging. Interventions to improve muscle health and metabolic control may mitigate adverse effects of brain aging, supporting healthier aging trajectories.

Focal cortical dysplasia (FCD) is a common cause of pharmacoresistant epilepsy. However, it can be challenging to detect FCD using MRI alone. This study aimed to review and analyze studies that used machine learning and artificial neural networks (ANN) methods as an additional tool to enhance MRI findings in FCD patients. A systematic search was conducted in four databases (Embase, PubMed, Scopus, and Web of Science). The quality of the studies was assessed using QUADAS-AI, and a bivariate random-effects model was used for analysis. The main outcome analyzed was the sensitivity and specificity of patient-wise outcomes. Heterogeneity among studies was assessed using I². A total of 41 studies met the inclusion criteria, including 24 ANN-based studies and 17 machine learning studies. Meta-analysis of internal validation datasets showed a pooled sensitivity of 0.81 and specificity of 0.92 for AI-based models in detecting FCD lesions. Meta-analysis of external validation datasets yielded a pooled sensitivity of 0.73 and specificity of 0.66. There was moderate heterogeneity among studies in the external validation dataset, but no significant publication bias was found. Although there is an increasing number of machine learning and ANN-based models for FCD detection, their clinical applicability remains limited. Further refinement and optimization, along with longitudinal studies, are needed to ensure their integration into clinical practice. Addressing the identified limitations and intensifying research efforts will improve their relevance and reliability in real medical scenarios.

Transcranial photoacoustic computed tomography (PACT) holds significant potential as a neuroimaging modality. However, compensating for skull-induced aberrations in reconstructed images remains a challenge. Although optimization-based image reconstruction methods (OBRMs) can account for the relevant wave physics, they are computationally demanding and generally require accurate estimates of the skull's viscoelastic parameters. To circumvent these issues, a learning-based image reconstruction method was investigated for three-dimensional (3D) transcranial PACT. The method was systematically assessed in virtual imaging studies that involved stochastic 3D numerical head phantoms and applied to experimental data acquired by use of a physical head phantom that involved a human skull. The results demonstrated that the learning-based method yielded accurate images and exhibited robustness to errors in the assumed skull properties, while substantially reducing computational times compared to an OBRM. To the best of our knowledge, this is the first demonstration of a learned image reconstruction method for 3D transcranial PACT.

Glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) are common malignant brain tumors with similar radiological features, while the accurate and non-invasive dialgnosis is essential for selecting appropriate treatment plans. This study develops a deep learning model, FoTNet, to improve the automatic diagnosis accuracy of these tumors, particularly for the relatively rare PCNSL tumor. The model integrates a frequency-based channel attention layer and the focal loss to address the class imbalance issue caused by the limited samples of PCNSL. A multi-center MRI dataset was constructed by collecting and integrating data from Sir Run Run Shaw Hospital, along with public datasets from UPENN and TCGA. The dataset includes T1-weighted contrast-enhanced (T1-CE) MRI images from 58 GBM, 82 PCNSL, and 269 BM cases, which were divided into training and testing sets with a 5:2 ratio. FoTNet achieved a classification accuracy of 92.5 % and an average AUC of 0.9754 on the test set, significantly outperforming existing machine learning and deep learning methods in distinguishing among GBM, PCNSL, and BM. Through multiple validations, FoTNet has proven to be an effective and robust tool for accurately classifying these brain tumors, providing strong support for preoperative diagnosis and assisting clinicians in making more informed treatment decisions.

Magnetic resonance imaging (MRI), combined with artificial intelligence techniques, has improved our understanding of brain structural change and enabled the estimation of brain age. Neurodegenerative disorders, such as Alzheimer's disease (AD), have been linked to accelerated brain aging. In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology. In this study, we aimed to develop a deep-learning framework that processes and integrates MRI images to more accurately predict brain age, cognitive function, and amyloid pathology.We collected over 10,000 T1-weighted MRI scans from more than 7,000 individuals across six cohorts. We designed a multi-modal deep-learning framework that employs 3D convolutional neural networks to analyze MRI and additional neural networks to evaluate demographic data. Our initial model focused on predicting brain age, serving as a foundational model from which we developed separate models for cognition function and amyloid plaque prediction through transfer learning. The brain age prediction model achieved the mean absolute error (MAE) for cognitive normal population in the ADNI (test) datasets of 3.302 years. The gap between predicted brain age and chronological age significantly increases while cognition declines. The cognition prediction model exhibited a root mean square error (RMSE) of 0.334 for the Clinical Dementia Rating (CDR) regression task, achieving an area under the curve (AUC) of approximately 0.95 in identifying ing dementia patients. Dementia related brain regions, such as the medial temporal lobe, were identified by our model. Finally, amyloid plaque prediction model was trained to predict amyloid plaque, and achieved an AUC about 0.8 for dementia patients. These findings indicate that the present predictive models can identify subtle changes in brain structure, enabling precise estimates of brain age, cognitive status, and amyloid pathology. Such models could facilitate the use of MRI as a non-invasive diagnostic tool for neurodegenerative diseases, including AD.

This paper presents MSLesSeg, a new, publicly accessible MRI dataset designed to advance research in Multiple Sclerosis (MS) lesion segmentation. The dataset comprises 115 scans of 75 patients including T1, T2 and FLAIR sequences, along with supplementary clinical data collected across different sources. Expert-validated annotations provide high-quality lesion segmentation labels, establishing a reliable human-labeled dataset for benchmarking. Part of the dataset was shared with expert scientists with the aim to compare the last automatic AI-based image segmentation solutions with an expert-biased handmade segmentation. In addition, an AI-based lesion segmentation of MSLesSeg was developed and technically validated against the last state-of-the-art methods. The dataset, the detailed analysis of researcher contributions, and the baseline results presented here mark a significant milestone for advancing automated MS lesion segmentation research.