The connection between cognition, eye, and brain remains inconclusive in Alzheimer's disease (AD) spectrum disorders. To explore the relationship between cognitive function, retinal biometrics, and brain alterations in the AD spectrum. Prospective. Healthy control (HC) (n = 16), subjective cognitive decline (SCD) (n = 35), mild cognitive impairment (MCI) (n = 18), and AD group (n = 7). 3-T, 3D T1-weighted Brain Volume (BRAVO) and resting-state functional MRI (fMRI). In all subgroups, cortical thickness was measured from BRAVO and segmented using the Desikan-Killiany-Tourville (DKT) atlas. The fractional amplitude of low-frequency fluctuations (FALFF) and regional homogeneity (ReHo) were measured in fMRI using voxel-based analysis. The eye was imaged by optical coherence tomography angiography (OCTA), with the deep learning model FARGO segmenting the foveal avascular zone (FAZ) and retinal vessels. FAZ area and perimeter, retinal blood vessels curvature (RBVC), thicknesses of the retinal nerve fiber layer (RNFL) and ganglion cell layer-inner plexiform layer (GCL-IPL) were calculated. Cognition-eye-brain associations were compared across the HC group and each AD spectrum stage using multivariable linear regression. Multivariable linear regression analysis. Statistical significance was set at p < 0.05 with FWE correction for fMRI and p < 1/62 (Bonferroni-corrected) for structural analyses. Reductions of FALFF in temporal regions, especially the left superior temporal gyrus (STG) in MCI patients, were linked to decreased RNFL thickness and increased FAZ area significantly. In AD patients, reduced ReHo values in occipital regions, especially the right middle occipital gyrus (MOG), were significantly associated with an enlarged FAZ area. The SCD group showed widespread cortical thickening significantly associated with all aforementioned retinal biometrics, with notable thickening in the right fusiform gyrus (FG) and right parahippocampal gyrus (PHG) correlating with reduced GCL-IPL thickness. Brain function and structure may be associated with cognition and retinal biometrics across the AD spectrum. Specifically, cognition-eye-brain connections may be present in SCD. 2. 3.

Cerebral perivascular spaces (PVS) are involved in cerebrospinal fluid (CSF) circulation and clearance of metabolic waste in adult humans. A high number of PVS has been reported in autism spectrum disorder (ASD) but its relationship with CSF and disease severity is unclear. To quantify PVS in children with ASD through MRI. Retrospective. Sixty six children with ASD (mean age: 4.7 ± 1.5 years; males/females: 59/7). 3T, 3D T1-weighted GRE and 3D T2-weighted turbo spin echo sequences. PVS were segmented using a weakly supervised PVS algorithm. PVS count, white matter-perivascular spaces (WM-PVS_tot) and normalized volume (WM-PVS_voln) were analyzed in the entire white matter. Six regions: frontal, parietal, limbic, occipital, temporal, and deep WM (WM-PVS_sr). WM, GM, CSF, and extra-axial CSF (eaCSF) volumes were also calculated. Autism Diagnostic Observation Schedule, Wechsler Intelligence Scale, and Griffiths Mental Developmental scales were used to assess clinical severity and developmental quotient (DQ). Kendall correlation analysis (continuous variables) and Friedman (categorical variables) tests were used to compare medians of PVS variables across different WM regions. Post hoc pairwise comparisons with Wilcoxon tests were used to evaluate distributions of PVS in WM regions. Generalized linear models were employed to assess DQ, clinical severity, age, and eaCSF volume in relation to PVS variables. A p-value < 0.05 indicated statistical significance. Severe DQ (β = 0.0089), mild form of autism (β = -0.0174), and larger eaCSF (β = 0.0082) volume was significantly associated with greater WM-PVS_tot count. WM-PVS_voln was predominantly affected by normalized eaCSF volume (eaCSF_voln) (β = 0.0242; adjusted for WM volumes). The percentage of WM-PVS_sr was higher in the frontal areas (32%) and was lowest in the temporal regions (11%). PVS count and volume in ASD are associated with eaCSF_voln. PVS count is related to clinical severity and DQ. PVS count was higher in frontal regions and lower in temporal regions. 4. Stage 3.

Segmentation of brain structures from MRI is crucial for evaluating brain morphology, yet existing CNN and transformer-based methods struggle to delineate complex structures accurately. While current diffusion models have shown promise in image segmentation, they are inadequate when applied directly to brain MRI due to neglecting anatomical information. To address this, we propose Collaborative Anatomy Diffusion (CA-Diff), a framework integrating spatial anatomical features to enhance segmentation accuracy of the diffusion model. Specifically, we introduce distance field as an auxiliary anatomical condition to provide global spatial context, alongside a collaborative diffusion process to model its joint distribution with anatomical structures, enabling effective utilization of anatomical features for segmentation. Furthermore, we introduce a consistency loss to refine relationships between the distance field and anatomical structures and design a time adapted channel attention module to enhance the U-Net feature fusion procedure. Extensive experiments show that CA-Diff outperforms state-of-the-art (SOTA) methods.

Understanding brain dynamics through functional Magnetic Resonance Imaging (fMRI) remains a fundamental challenge in neuroscience, particularly in capturing how the brain transitions between various functional states. Recently, metastability, which refers to temporarily stable brain states, has offered a promising paradigm to quantify complex brain signals into interpretable, discretized representations. In particular, compared to cluster-based machine learning approaches, tokenization approaches leveraging vector quantization have shown promise in representation learning with powerful reconstruction and predictive capabilities. However, most existing methods ignore brain transition dependencies and lack a quantification of brain dynamics into representative and stable embeddings. In this study, we propose a Hierarchical State space-based Tokenization network, termed HST, which quantizes brain states and transitions in a hierarchical structure based on a state space-based model. We introduce a refined clustered Vector-Quantization Variational AutoEncoder (VQ-VAE) that incorporates quantization error feedback and clustering to improve quantization performance while facilitating metastability with representative and stable token representations. We validate our HST on two public fMRI datasets, demonstrating its effectiveness in quantifying the hierarchical dynamics of the brain and its potential in disease diagnosis and reconstruction performance. Our method offers a promising framework for the characterization of brain dynamics, facilitating the analysis of metastability.

Collecting pixel-level labels for medical datasets can be a laborious and expensive process, and enhancing segmentation performance with a scarcity of labeled data is a crucial challenge. This work introduces AugPaint, a data augmentation framework that utilizes inpainting to generate image-label pairs from limited labeled data. AugPaint leverages latent diffusion models, known for their ability to generate high-quality in-domain images with low overhead, and adapts the sampling process for the inpainting task without need for retraining. Specifically, given a pair of image and label mask, we crop the area labeled with the foreground and condition on it during reversed denoising process for every noise level. Masked background area would gradually be filled in, and all generated images are paired with the label mask. This approach ensures the accuracy of match between synthetic images and label masks, setting it apart from existing dataset generation methods. The generated images serve as valuable supervision for training downstream segmentation models, effectively addressing the challenge of limited annotations. We conducted extensive evaluations of our data augmentation method on four public medical image segmentation datasets, including CT, MRI, and skin imaging. Results across all datasets demonstrate that AugPaint outperforms state-of-the-art label-efficient methodologies, significantly improving segmentation performance.

Transcranial Doppler (TCD) ultrasound has significant clinical value for assessing cerebral hemodynamics, but its reliance on operator expertise limits broader clinical adoption. In this work, we present a lightweight real-time deep learning-based approach capable of automatically identifying the middle cerebral artery (MCA) in TCD Color Doppler images. Two state-of-the-art object detection models, YOLOv10 and Real-Time Detection Transformers (RT-DETR), were investigated for automated MCA detection in real-time. TCD Color Doppler data (41 subjects; 365 videos; 61,611 frames) were collected from neurologically healthy individuals (n = 31) and stroke patients (n = 10). MCA bounding box annotations were performed by clinical experts on all frames. Model training consisted of pretraining utilizing a large abdominal ultrasound dataset followed by subsequent fine-tuning on acquired TCD data. Detection performance at the instance and frame levels, and inference speed were assessed through four-fold cross-validation. Inter-rater agreement between model and two human expert readers was assessed using distance between bounding boxes and inter-rater variability was quantified using the individual equivalence coefficient (IEC) metric. Both YOLOv10 and RT-DETR models showed comparable frame level accuracy for MCA presence, with F1 scores of 0.884 ± 0.023 and 0.884 ± 0.019 respectively. YOLOv10 outperformed RT-DETR for instance-level localization accuracy (AP: 0.817 vs. 0.780) and had considerably faster inference speed on a desktop CPU (11.6 ms vs. 91.14 ms). Furthermore, YOLOv10 showed an average inference time of 36 ms per frame on a tablet device. The IEC was -1.08 with 95 % confidence interval: [-1.45, -0.19], showing that the AI predictions deviated less from each reader than the readers' annotations deviated from each other. Real-time automated detection of the MCA is feasible and can be implemented on mobile platforms, potentially enabling wider clinical adoption by less-trained operators in point-of-care settings.

Radiomics is transforming medical imaging by extracting complex features that enhance disease diagnosis, prognosis, and treatment evaluation. However, traditional approaches face significant challenges, such as the need for manual feature engineering, high dimensionality, and limited sample sizes. This paper presents Radio DINO, a novel family of deep learning foundation models that leverage self-supervised learning (SSL) techniques from DINO and DINOV2, pretrained on the RadImageNet dataset. The novelty of our approach lies in (1) developing Radio DINO to capture rich semantic embeddings, enabling robust feature extraction without manual intervention, (2) demonstrating superior performance across various clinical tasks on the MedMNISTv2 dataset, surpassing existing models, and (3) enhancing the interpretability of the model by providing visualizations that highlight its focus on clinically relevant image regions. Our results show that Radio DINO has the potential to democratize advanced radiomics tools, making them accessible to healthcare institutions with limited resources and ultimately improving diagnostic and prognostic outcomes in radiology.

Infant meningitis can be a life-threatening disease and requires prompt and accurate diagnosis to prevent severe outcomes or death. Gold-standard diagnosis requires lumbar puncture (LP) to obtain and analyze cerebrospinal fluid (CSF). Despite being standard practice, LPs are invasive, pose risks for the patient and often yield negative results, either due to contamination with red blood cells from the puncture itself or because LPs are routinely performed to rule out a life-threatening infection, despite the disease's relatively low incidence. Furthermore, in low-income settings where incidence is the highest, LPs and CSF exams are rarely feasible, and suspected meningitis cases are generally treated empirically. There is a growing need for non-invasive, accurate diagnostic methods. We developed a three-stage deep learning framework using Neosonics ultrasound technology for 30 infants with suspected meningitis and a permeable fontanelle at three Spanish University Hospitals (from 2021 to 2023). In stage 1, 2194 images were processed for quality control using a vessel/non-vessel model, with a focus on vessel identification and manual removal of images exhibiting artifacts such as poor coupling and clutter. This refinement process resulted in a final cohort comprising 16 patients-6 cases (336 images) and 10 controls (445 images), yielding 781 images for the second stage. The second stage involved the use of a deep learning model to classify images based on a white blood cell count threshold (set at 30 cells/mm³) into control or meningitis categories. The third stage integrated explainable artificial intelligence (XAI) methods, such as Grad-CAM visualizations, alongside image statistical analysis, to provide transparency and interpretability of the model's decision-making process in our artificial intelligence-driven screening tool. Our approach achieved 96% accuracy in quality control and 93% precision and 92% accuracy in image-level meningitis detection, with an overall patient-level accuracy of 94%. It identified 6 meningitis cases and 10 controls with 100% sensitivity and 90% specificity, demonstrating only a single misclassification. The use of gradient-weighted class activation mapping-based XAI significantly enhanced diagnostic interpretability, and to further refine our insights we incorporated a statistics-based XAI approach. By analyzing image metrics such as entropy and standard deviation, we identified texture variations in the images attributable to the presence of cells, which improved the interpretability of our diagnostic tool. This study supports the efficacy of a multi-stage deep learning model for non-invasive screening of infant meningitis and its potential to guide the need for LPs. It also highlights the transformative potential of artificial intelligence in medical diagnostic screening for neonatal health care, paving the way for future research and innovations.

This study evaluates the performance of Iterative and AI-based Reconstruction algorithms in CT imaging for brain, chest, and upper abdomen assessments. Using a 320-slice CT scanner, phantom images were analysed through quantitative metrics such as Noise, Contrast-to-Noise-Ratio and Target Transfer Function. Additionally, five radiologists performed subjective evaluations on real patient images by scoring clinical parameters related to anatomical structures across the three body sites. The study aimed to relate results obtained with the typical approach related to parameters involved in medical physics using a Catphan physical phantom, with the evaluations assigned by the radiologists to the clinical parameters chosen in this study, and to determine whether the physical approach alone can ensure the implementation of new procedures and the optimization in clinical practice. AI-based algorithms demonstrated superior performance in chest and abdominal imaging, enhancing parenchymal and vascular detail with notable reductions in noise. However, their performance in brain imaging was less effective, as the aggressive noise reduction led to excessive smoothing, which affected diagnostic interpretability. Iterative reconstruction methods provided balanced results for brain imaging, preserving structural details and maintaining diagnostic clarity. The findings emphasize the need for region-specific optimization of reconstruction protocols. While AI-based methods can complement traditional IR techniques, they should not be assumed to inherently improve outcomes. A critical and cautious introduction of AI-based techniques is essential, ensuring radiologists adapt effectively without compromising diagnostic accuracy.

This review was written to inform practicing clinical rheumatologists about recent advances in artificial intelligence (AI) based research in rheumatoid arthritis (RA), using accessible and practical language. We highlight developments from 2023 to early 2025 across diagnostic imaging, treatment prediction, drug discovery, and patient-facing tools. Given the increasing clinical interest in AI and its potential to augment care delivery, this article aims to bridge the gap between technical innovation and real-world rheumatology practice. Several AI models have demonstrated high accuracy in early RA detection using imaging modalities such as thermal imaging and nuclear scans. Predictive models for treatment response have leveraged routinely collected electronic health record (EHR) data, moving closer to practical application in clinical workflows. Patient-facing tools like mobile symptom checkers and large language models (LLMs) such as ChatGPT show promise in enhancing education and engagement, although accuracy and safety remain variable. AI has also shown utility in identifying novel biomarkers and accelerating drug discovery. Despite these advances, as of early 2025, no AI-based tools have received FDA approval for use in rheumatology, in contrast to other specialties. Artificial intelligence holds tremendous promise to enhance clinical care in RA-from early diagnosis to personalized therapy. However, clinical adoption remains limited due to regulatory, technical, and implementation challenges. A streamlined regulatory framework and closer collaboration between clinicians, researchers, and industry partners are urgently needed. With thoughtful integration, AI can serve as a valuable adjunct in addressing clinical complexity and workforce shortages in rheumatology.