Brain tumors pose a significant risk to human life, making accurate grading essential for effective treatment planning and improved survival rates. Magnetic Resonance Imaging (MRI) plays a crucial role in this process. The objective of this study was to develop an automated brain tumor grading system utilizing deep learning techniques. A dataset comprising 293 MRI scans from patients was obtained from the Department of Radiology at Bahawal Victoria Hospital in Bahawalpur, Pakistan. The proposed approach integrates a specialized Convolutional Neural Network (CNN) with pre-trained models to classify brain tumors into low-grade (LGT) and high-grade (HGT) categories with high accuracy. To assess the model's robustness, experiments were conducted using various methods: (1) raw MRI slices, (2) MRI segments containing only the tumor area, (3) feature-extracted slices derived from the original images through the proposed CNN architecture, and (4) feature-extracted slices from tumor area-only segmented images using the proposed CNN. The MRI slices and the features extracted from them were labeled using machine learning models, including Support Vector Machine (SVM) and CNN architectures based on transfer learning, such as MobileNet, Inception V3, and ResNet-50. Additionally, a custom model was specifically developed for this research. The proposed model achieved an impressive peak accuracy of 99.45%, with classification accuracies of 99.56% for low-grade tumors and 99.49% for high-grade tumors, surpassing traditional methods. These results not only enhance the accuracy of brain tumor grading but also improve computational efficiency by reducing processing time and the number of iterations required.

Pseudo-healthy image inpainting is an essential preprocessing step for analyzing pathological brain MRI scans. Most current inpainting methods favor slice-wise 2D models for their high in-plane fidelity, but their independence across slices produces discontinuities in the volume. Fully 3D models alleviate this issue, but their high model capacity demands extensive training data for reliable, high-fidelity synthesis -- often impractical in medical settings. We address these limitations with a hierarchical diffusion framework by replacing direct 3D modeling with two perpendicular coarse-to-fine 2D stages. An axial diffusion model first yields a coarse, globally consistent inpainting; a coronal diffusion model then refines anatomical details. By combining perpendicular spatial views with adaptive resampling, our method balances data efficiency and volumetric consistency. Our experiments show our approach outperforms state-of-the-art baselines in both realism and volumetric consistency, making it a promising solution for pseudo-healthy image inpainting. Code is available at https://github.com/dou0000/3dMRI-Consistent-Inpaint.

Meningitis diagnosis requires a lumbar puncture (LP) to obtain cerebrospinal fluid (CSF) for a laboratory-based analysis. In high-income settings, LPs are part of the systematic approach to screen for meningitis, and most yield negative results. In low- and middle-income settings, LPs are seldom performed, and suspected cases are often treated empirically. The aim of this study was to validate a non-invasive transfontanellar white blood cell (WBC) counter in CSF to screen for meningitis. We conducted a prospective study across three Spanish hospitals, one Mozambican and one Moroccan hospital (2020-2023). We included patients under 24 months with suspected meningitis, an open fontanelle, and a LP performed within 24 h from recruitment. High-resolution-ultrasound (HRUS) images of the CSF were obtained using a customized probe. A deep-learning model was trained to classify CSF patterns based on LPs WBC counts, using a 30cells/mm³ threshold. The algorithm was applied to 3782 images from 76 patients. It correctly classified 17/18 CSFs with <math xmlns="http://www.w3.org/1998/Math/MathML"><mo>≥</mo></math> 30 WBC, and 55/58 controls (sensitivity 94.4%, specificity 94.8%). The only false negative was paired to a traumatic LP with 40 corrected WBC/mm³. This non-invasive device could be an accurate tool for screening meningitis in neonates and young infants, modulating LP indications. Our non-invasive, high-resolution ultrasound device achieved 94% accuracy in detecting elevated leukocyte counts in neonates and infants with suspected meningitis, compared to the gold standard (lumbar punctures and laboratory analysis). This first-in-class screening device introduces the first non-invasive method for neonatal and infant meningitis screening, potentially modulating lumbar puncture indications. This technology could substantially reduce lumbar punctures in low-suspicion cases and provides a viable alternative critically ill patients worldwide or in settings where lumbar punctures are unfeasible, especially in low-income countries).

Recent advancements in four-dimensional (4D) ultrasonography have enabled detailed observation of fetal behavior <i>in utero</i>, including facial movements, limb gestures, and stimulus responses. These developments have prompted renewed inquiry into whether such behaviors are merely reflexive or represent early signs of integrated neural function. However, the relationship between fetal movement patterns and conscious awareness remains scientifically uncertain and ethically contested. A systematic review was conducted in accordance with PRISMA 2020 guidelines. Four databases (PubMed, Scopus, Embase, Web of Science) were searched for English-language articles published from 2000 to 2025, using keywords including "fetal behavior," "4D ultrasound," "neurodevelopment," and "consciousness." Studies were included if they involved human fetuses, used 4D ultrasound or functional imaging modalities, and offered interpretation relevant to neurobehavioral or ethical analysis. A structured appraisal using AMSTAR-2 was applied to assess study quality. Data were synthesized narratively to map fetal behaviors onto developmental milestones and evaluate their interpretive limits. Seventy-four studies met inclusion criteria, with 23 rated as high-quality. Fetal behaviors such as yawning, hand-to-face movement, and startle responses increased in complexity between 24-34 weeks gestation. These patterns aligned with known neurodevelopmental events, including thalamocortical connectivity and cortical folding. However, no study provided definitive evidence linking observed behaviors to conscious experience. Emerging applications of artificial intelligence in ultrasound analysis were found to enhance pattern recognition but lack external validation. Fetal behavior observed via 4D ultrasound may reflect increasing neural integration but should not be equated with awareness. Interpretations must remain cautious, avoiding anthropomorphic assumptions. Ethical engagement requires attention to scientific limits, sociocultural diversity, and respect for maternal autonomy as imaging technologies continue to evolve.

Unsupervised anomaly detection (UAD) plays a crucial role in neuroimaging for identifying deviations from healthy subject data and thus facilitating the diagnosis of neurological disorders. In this work, we focus on Bayesian flow networks (BFNs), a novel class of generative models, which have not yet been applied to medical imaging or anomaly detection. BFNs combine the strength of diffusion frameworks and Bayesian inference. We introduce AnoBFN, an extension of BFNs for UAD, designed to: i) perform conditional image generation under high levels of spatially correlated noise, and ii) preserve subject specificity by incorporating a recursive feedback from the input image throughout the generative process. We evaluate AnoBFN on the challenging task of Alzheimer's disease-related anomaly detection in FDG PET images. Our approach outperforms other state-of-the-art methods based on VAEs (beta-VAE), GANs (f-AnoGAN), and diffusion models (AnoDDPM), demonstrating its effectiveness at detecting anomalies while reducing false positive rates.

Head-and-neck MRI faces inherent challenges, including motion artifacts and trade-offs between spatial resolution and acquisition time. We aimed to evaluate a dual-network deep learning (DL) super-resolution method for improving image quality and reducing scan time in T1- and T2-weighted head-and-neck MRI. In this prospective study, 97 patients with head-and-neck masses were enrolled at xx from August 2023 to August 2024. After exclusions, 58 participants underwent paired conventional and accelerated T1WI and T2WI MRI sequences, with the accelerated sequences being reconstructed using a dual-network DL framework for super-resolution. Image quality was assessed both quantitatively (signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], contrast ratio [CR]) and qualitatively by two blinded radiologists using a 5-point Likert scale for image sharpness, lesion conspicuity, structure delineation, and artifacts. Wilcoxon signed-rank tests were used to compare paired outcomes. Among 58 participants (34 men, 24 women; mean age 51.37 ± 13.24 years), DL reconstruction reduced scan times by 46.3% (T1WI) and 26.9% (T2WI). Quantitative analysis showed significant improvements in SNR (T1WI: 26.33 vs. 20.65; T2WI: 14.14 vs. 11.26) and CR (T1WI: 0.20 vs. 0.18; T2WI: 0.34 vs. 0.30; all p < 0.001), with comparable CNR (p > 0.05). Qualitatively, image sharpness, lesion conspicuity, and structure delineation improved significantly (p < 0.05), while artifact scores remained similar (all p > 0.05). The dual-network DL method significantly enhanced image quality and reduced scan times in head-and-neck MRI while maintaining diagnostic performance comparable to conventional methods. This approach offers potential for improved workflow efficiency and patient comfort.

This paper addresses the problem of detecting possible serious bacterial infection (pSBI) of infancy, i.e. a clinical presentation consistent with bacterial sepsis in newborn infants using cranial ultrasound (cUS) images. The captured image set for each patient enables multiview imagery: coronal and sagittal, with geometric overlap. To exploit this geometric relation, we develop a new learning framework, called the intersection-guided Crossview Local-and Image-level Fusion Network (CLIF-Net). Our technique employs two distinct convolutional neural network branches to extract features from coronal and sagittal images with newly developed multi-level fusion blocks. Specifically, we leverage the spatial position of these images to locate the intersecting region. We then identify and enhance the semantic features from this region across multiple levels using cross-attention modules, facilitating the acquisition of mutually beneficial and more representative features from both views. The final enhanced features from the two views are then integrated and projected through the image-level fusion layer, outputting pSBI and non-pSBI class probabilities. We contend that our method of exploiting multi-view cUS images enables a first of its kind, robust 3D representation tailored for pSBI detection. When evaluated on a dataset of 302 cUS scans from Mbale Regional Referral Hospital in Uganda, CLIF-Net demonstrates substantially enhanced performance, surpassing the prevailing state-of-the-art infection detection techniques.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that predominantly affects the elderly population and currently has no cure. Magnetic Resonance Imaging (MRI), as a non-invasive imaging technique, is essential for the early diagnosis of AD. MRI inherently contains both spatial and frequency information, as raw signals are acquired in the frequency domain and reconstructed into spatial images via the Fourier transform. However, most existing AD diagnostic models extract features from a single domain, limiting their capacity to fully capture the complex neuroimaging characteristics of the disease. While some studies have combined spatial and frequency information, they are mostly confined to 2D MRI, leaving the potential of dual-domain analysis in 3D MRI unexplored. To overcome this limitation, we propose Spatio-Frequency Network (SFNet), the first end-to-end deep learning framework that simultaneously leverages spatial and frequency domain information to enhance 3D MRI-based AD diagnosis. SFNet integrates an enhanced dense convolutional network to extract local spatial features and a global frequency module to capture global frequency-domain representations. Additionally, a novel multi-scale attention module is proposed to further refine spatial feature extraction. Experiments on the Alzheimer's Disease Neuroimaging Initiative (ANDI) dataset demonstrate that SFNet outperforms existing baselines and reduces computational overhead in classifying cognitively normal (CN) and AD, achieving an accuracy of 95.1%.

Cerebrovascular disease (CeVD) and cognitive impairment risk factors contribute to cognitive decline, but the role of brain age gap (BAG) in mediating this relationship remains unclear, especially in Southeast Asian populations. This study investigated the influence of cognitive impairment risk factors on cognition and examined how BAG mediates this relationship, particularly in individuals with varying CeVD burden. This cross-sectional study analyzed Singaporean community and memory clinic participants. Cognitive impairment risk factors were assessed using the Cognitive Impairment Scoring System (CISS), encompassing 11 sociodemographic and vascular factors. Cognition was assessed through a neuropsychological battery, evaluating global cognition and 6 cognitive domains: executive function, attention, memory, language, visuomotor speed, and visuoconstruction. Brain age was derived from structural MRI features using ensemble machine learning model. Propensity score matching balanced risk profiles between model training and the remaining sample. Structural equation modeling examined the mediation effect of BAG on CISS-cognition relationship, stratified by CeVD burden (high: CeVD+, low: CeVD-). The study included 1,437 individuals without dementia, with 646 in the matched sample (mean age 66.4 ± 6.0 years, 47% female, 60% with no cognitive impairment). Higher CISS was consistently associated with poorer cognitive performance across all domains, with the strongest negative associations in visuomotor speed (β = -2.70, <i>p</i> < 0.001) and visuoconstruction (β = -3.02, <i>p</i> < 0.001). Among the CeVD+ group, BAG significantly mediated the relationship between CISS and global cognition (proportion mediated: 19.95%, <i>p</i> = 0.01), with the strongest mediation effects in executive function (34.1%, <i>p</i> = 0.03) and language (26.6%, <i>p</i> = 0.008). BAG also mediated the relationship between CISS and memory (21.1%) and visuoconstruction (14.4%) in the CeVD+ group, but these effects diminished after statistical adjustments. Our findings suggest that BAG is a key intermediary linking cognitive impairment risk factors to cognitive function, particularly in individuals with high CeVD burden. This mediation effect is domain-specific, with executive function, language, and visuoconstruction being the most vulnerable to accelerated brain aging. Limitations of this study include the cross-sectional design, limiting causal inference, and the focus on Southeast Asian populations, limiting generalizability. Future longitudinal studies should verify these relationships and explore additional factors not captured in our model.

Parkinson's disease (PD) and progressive supranuclear palsy (PSP) present similar clinical symptoms, but their treatment options and clinical prognosis differ significantly. Therefore, we aimed to discriminate between PD and PSP based on multi-level indices of resting-state functional magnetic resonance imaging (rs-fMRI) via the machine learning approach. A total of 58 PD and 52 PSP patients were prospectively enrolled in this study. Participants were randomly allocated to a training set and a validation set in a 7:3 ratio. Various rs-fMRI indices were extracted, followed by a comprehensive feature screening for each index. We constructed fifteen distinct combinations of indices and selected four machine learning algorithms for model development. Subsequently, different validation templates were employed to assess the classification results and investigate the relationship between the most significant features and clinical assessment scales. The classification performance of logistic regression (LR) and support vector machine (SVM) models, based on multiple index combinations, was significantly superior to that of other machine learning models and combinations when utilizing automatic anatomical labeling (AAL) templates. This has been verified across different templates. The utilization of multiple rs-fMRI indices significantly enhances the performance of machine learning models and can effectively achieve the automatic identification of PD and PSP at the individual level.