Functional magnetic resonance imaging (fMRI) has opened new frontiers in neuroscience by instrumentally driving our understanding of brain function and development. Despite its substantial successes, fMRI studies persistently encounter obstacles stemming from inherent, unavoidable physiological confounds. The adverse effects of these confounds are especially noticeable with higher magnetic fields, which have been gaining momentum in fMRI experiments. This review focuses on the four major physiological confounds impacting fMRI studies: low-frequency fluctuations in both breathing depth and rate, low-frequency fluctuations in the heart rate, thoracic movements, and cardiac pulsatility. Over the past three decades, numerous correction techniques have emerged to address these challenges. Correction methods have effectively enhanced the detection of task-activated voxels and minimized the occurrence of false positives and false negatives in functional connectivity studies. While confound correction methods have merit, they also have certain limitations. For instance, model-based approaches require externally recorded physiological data that is often unavailable in fMRI studies. Methods reliant on independent component analysis, on the other hand, need prior knowledge about the number of components. Machine learning techniques, although showing potential, are still in the early stages of development and require additional validation. This article reviews the mechanics of physiological confound correction methods, scrutinizes their performance and limitations, and discusses their impact on fMRI studies.

Mild traumatic brain injury (mTBI) caused by sports-related incidents in children and youth often leads to prolonged cognitive impairments but remains difficult to diagnose. In order to identify clinically relevant imaging and behavioral biomarkers associated concussion, a closed-head mTBI was induced in adolescent pigs. Twelve (n = 4 male and n = 8 female), 16-week old Yucatan pigs were tested; n = 6 received mTBI and n = 6 received a sham procedure. T1-weighted imaging was used to assess volumetric alterations in different regions of the brain and diffusion tensor imaging (DTI) to examine microstructural damage in white matter. The pigs were imaged at 1- and 3-month post-injury. Neuropsychological screening for executive function and anxiety were performed before and in the months after the injury. The volumetric analysis showed significant longitudinal changes in pigs with mTBI compared with sham, which may be attributed to swelling and neuroinflammation. Fractional anisotropy (FA) values derived from DTI images demonstrated a 21% increase in corpus callosum from 1 to 3 months in mTBI pigs, which is significantly higher than in sham pigs (4.8%). Additionally, comparisons of the left and right internal capsules revealed a decrease in FA in the right internal capsule for mTBI pigs, which may indicate demyelination. The neuroimaging results suggest that the injury had disrupted the maturation of white and gray matter in the developing brain. Behavioral testing showed that compare to sham pigs, mTBI pigs exhibited 23% increased activity in open field tests, 35% incraesed escape attempts, along with a 65% decrease in interaction with the novel object, suggesting possible memory impairments and cognitive deficits. The correlation analysis showed an associations between volumetric features and behavioral metrics. Furthermore, a machine learning model, which integrated FA, volumetric features and behavioral test metrics, achieved 67% accuracy, indicating its potential to differentiate the two groups. Thus, the imaging biomarkers were indicative of long-term behavioral impairments and could be crucial to the clinical management of concussion in youth.

Steatotic liver disease (SLD) is the most common liver disease worldwide, affecting 30% of the global population. It is strongly associated with the interplay of genetic and lifestyle-related risk factors. The genetic variant accounting for the largest fraction of SLD heritability is PNPLA3 I148M, which is carried by 23% of the western population and increases the risk of SLD two to three-fold. However, identification of variant carriers is not part of routine clinical care and prevents patients from receiving personalised care. We analysed MRI images and common genetic variants in PNPLA3, TM6SF2, MTARC1, HSD17B13 and GCKR from a cohort of 45 603 individuals from the UK Biobank. Proton density fat fraction (PDFF) maps were generated using a water-fat separation toolbox, applied to the magnitude and phase MRI data. The liver region was segmented using a U-Net model trained on 600 manually segmented ground truth images. The resulting liver masks and PDFF maps were subsequently used to calculate liver PDFF values. Individuals with (PDFF ≥ 5%) and without SLD (PDFF < 5%) were selected as the study cohort and used to train and test a Vision Transformer classification model with five-fold cross validation. We aimed to differentiate individuals who are homozygous for the PNPLA3 I148M variant from non-carriers, as evaluated by the area under the receiver operating characteristic curve (AUROC). To ensure a clear genetic contrast, all heterozygous individuals were excluded. To interpret our model, we generated attention maps that highlight the regions that are most predictive of the outcomes. Homozygosity for the PNPLA3 I148M variant demonstrated the best predictive performance among five variants with AUROC of 0.68 (95% CI: 0.64-0.73) in SLD patients and 0.57 (95% CI: 0.52-0.61) in non-SLD patients. The AUROCs for the other SNPs ranged from 0.54 to 0.57 in SLD patients and from 0.52 to 0.54 in non-SLD patients. The predictive performance was generally higher in SLD patients compared to non-SLD patients. Attention maps for PNPLA3 I148M carriers showed that fat deposition in regions adjacent to the hepatic vessels, near the liver hilum, plays an important role in predicting the presence of the I148M variant. Our study marks novel progress in the non-invasive detection of homozygosity for PNPLA3 I148M through the application of deep learning models on MRI images. Our findings suggest that PNPLA3 I148M might affect the liver fat distribution and could be used to predict the presence of PNPLA3 variants in patients with fatty liver. The findings of this research have the potential to be integrated into standard clinical practice, particularly when combined with clinical and biochemical data from other modalities to increase accuracy, enabling easier identification of at-risk individuals and facilitating the development of tailored interventions for PNPLA3 I148M-associated liver disease.

In recent years, machine learning and deep learning have shown potential for improving Parkinson's disease (PD) diagnosis, one of the most common neurodegenerative diseases. This comprehensive analysis examines machine learning and deep learning-based Parkinson's disease diagnosis using MRI, speech, and handwriting datasets. To thoroughly analyze PD, this study collected data from scientific literature, experimental investigations, publicly accessible datasets, and global health reports. This study examines the worldwide historical setting of Parkinson's disease, focusing on its increasing prevalence and inequities in treatment access across various regions. A comprehensive summary consolidates essential findings from clinical investigations and pertinent datasets related to Parkinson's disease management. The worldwide context, prospective treatments, therapies, and drugs for Parkinson's disease have been thoroughly examined. This analysis identifies significant research deficiencies and suggests future methods, emphasizing the necessity for more extensive and diverse datasets and improved model accessibility. The current study proposes the Meta-Park model for diagnosing Parkinson's disease, achieving training, testing, and validation accuracy of 97.67 %, 95 %, and 94.04 %. This method provides a dependable and scalable way to improve clinical decision-making in managing Parkinson's disease. This research seeks to provide innovative, data-driven decisions for early diagnosis and effective treatment by merging the proposed method with a thorough examination of existing interventions, providing renewed hope to patients and the medical community.

Undersampling accelerates signal acquisition at the expense of introducing artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimposed exponential functions, deep learning has achieved fast and high-fidelity signal reconstruction by training a mapping from the undersampled exponentials to the fully sampled ones. However, the mismatch, such as undersampling rates (25 % vs. 50 %), anatomical region (knee vs. brain), and contrast configurations (PDw vs. T₂w), between the training and target data will heavily compromise the reconstruction. To overcome this limitation, we propose Alternating Deep Low-Rank (ADLR), which combines deep learning solvers and classic optimization solvers. Experimental validation on the reconstruction of synthetic and real-world biomedical magnetic resonance signals demonstrates that ADLR can effectively alleviate the mismatch issue and achieve lower reconstruction errors than state-of-the-art methods.

Despite their exposure to potentially traumatic stressors, the majority of World Trade Center (WTC) responders-those who worked on rescue, recovery, and cleanup efforts on or following September 11, 2001-have shown psychological resilience, never developing long-term psychopathology. Psychological resilience may be protective against the earlier age-related cognitive changes associated with posttraumatic stress disorder (PTSD) in this cohort. In the current study, we calculated the difference between estimated brain age from structural magnetic resonance imaging (MRI) data and chronological age in WTC responders who participated in a parent functional MRI study of resilience (<i>N</i> = 97). We hypothesized that highly resilient responders would show the least brain aging and explored associations between brain aging and psychological and cognitive measures. WTC responders screened for the absence of cognitive impairment were classified into 3 groups: a WTC-related PTSD group (<i>n</i> = 32), a Highly Resilient group without lifetime psychopathology despite high WTC-related exposure (<i>n</i> = 34), and a Lower WTC-Exposed control group also without lifetime psychopathology (<i>n</i> = 31). We used <i>BrainStructureAges</i>, a deep learning algorithm that estimates voxelwise age from T1-weighted MRI data to calculate decelerated (or accelerated) brain aging relative to chronological age. Globally, brain aging was decelerated in the Highly Resilient group and accelerated in the PTSD group, with a significant group difference (<i>p</i> = .021, Cohen's <i>d</i> = 0.58); the Lower WTC-Exposed control group exhibited no significant brain age gap or group difference. Lesser brain aging was associated with resilience-linked factors including lower emotional suppression, greater optimism, and better verbal learning. Cognitively healthy WTC responders show differences in brain aging related to resilience and PTSD.

The present study aimed to develop and validate a fusion model based on multi-phase contrast-enhanced computed tomography (CECT) radiomics features combined with clinical features to preoperatively predict the expression levels of Ki-67 in patients with gastric cancer (GC). A total of 164 patients with GC who underwent surgical treatment at our hospital between September 2015 and September 2023 were retrospectively included and were randomly divided into a training set (n=114) and a testing set (n=50). Using Pyradiomics, radiomics features were extracted from multi-phase CECT images and were combined with significant clinical features through various machine learning algorithms [support vector machine (SVM), random forest (RandomForest), K-nearest neighbors (KNN), LightGBM and XGBoost] to build a fusion model. Receiver operating characteristic, area under the curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate, validate and compare the predictive performance and clinical utility of the model. Among the three single-phase models, for the arterial phase model, the SVM radiomics model had the highest AUC value in the training set, which was 0.697; and the RandomForest radiomics model had the highest AUC value in the testing set, which was 0.658. For the venous phase model, the SVM radiomics model had the highest AUC value in the training set, which was 0.783; and the LightGBM radiomics model had the highest AUC value in the testing set, which was 0.747. For the delayed phase model, the KNN radiomics model had the highest AUC value in the training set, which was 0.772; and the SVM radiomics model had the highest AUC in the testing set, which was 0.719. The clinical feature model had the lowest AUC values in both the training set and the testing set, which were 0.614 and 0.520, respectively. Notably, the multi-phase model and the fusion model, which were constructed by combining the clinical features and the multi-phase features, demonstrated excellent discriminative performance, with the fusion model achieving AUC values of 0.933 and 0.817 in the training and testing sets, thus outperforming other models (DeLong test, both P<0.05). The calibration curve showed that the fusion model had goodness of fit (Hosmer-Lemeshow test, >0.5 in the training and validation sets). The DCA showed that the net benefit of the fusion model in identifying high expression of Ki-67 was improved compared with that of other models. Furthermore, the fusion model achieved an AUC value of 0.805 in the external validation data from The Cancer Imaging Archive. In conclusion, the fusion model established in the present study was revealed to have excellent performance and is expected to serve as a non-invasive tool for predicting Ki-67 status and guiding clinical treatment.

Accurate adult age estimation (AAE) is critical for forensic and anthropological applications, yet traditional methods relying on bone mineral density (BMD) face significant challenges due to biological variability and methodological limitations. This study aims to develop an end-to-end Deep Learning (DL) based pipeline for automated AAE using BMD from proximal femoral CT scans. The main objectives are to construct a large-scale dataset of 5151 CT scans from real-world clinical and cadaver cohorts, fine-tune the Segment Anything Model (SAM) for accurate femoral bone segmentation, and evaluate multiple convolutional neural networks (CNNs) for precise age estimation based on segmented BMD data. Model performance was assessed through cross-validation, internal clinical testing, and external post-mortem validation. SAM achieved excellent segmentation performance with a Dice coefficient of 0.928 and an average intersection over union (mIoU) of 0.869. The CNN models achieved an average mean absolute error (MAE) of 5.20 years in cross-validation (male: 5.72; female: 4.51), which improved to 4.98 years in the independent clinical test set (male: 5.32; female: 4.56). External validation on the post-mortem dataset revealed an MAE of 6.91 years, with 6.97 for males and 6.69 for females. Ensemble learning further improved accuracy, reducing MAE to 4.78 years (male: 5.12; female: 4.35) in the internal test set, and 6.58 years (male: 6.64; female: 6.37) in the external validation set. These findings highlight the feasibility of dl-driven AAE and its potential for forensic applications, offering a fully automated framework for robust age estimation.

Recent advancements in diagnostic technologies have significantly transformed the landscape of respiratory medicine, aiming for early detection, improved specificity and personalized therapeutic strategies. Innovations in imaging such as multi-slice computed tomography (CT) scanners, high-resolution CT and magnetic resonance imaging (MRI) have revolutionized our ability to visualize and assess the structural and functional aspects of the respiratory system. These techniques are complemented by breakthroughs in molecular biology that have identified specific biomarkers and genetic determinants of respiratory diseases, enabling targeted diagnostic approaches. Additionally, functional tests including spirometry and exercise testing continue to provide valuable insights into pulmonary function and capacity. The integration of artificial intelligence is poised to further refine these diagnostic tools, enhancing their accuracy and efficiency. The present narrative review explores these developments and their impact on the management and outcomes of respiratory conditions, underscoring the ongoing shift towards more precise and less invasive diagnostic modalities in respiratory medicine.

This article explores the transformative potential of artificial intelligence (AI) in critical care ultrasound AI technologies, notably deep learning and convolutional neural networks, now assisting in image acquisition, interpretation, and quality assessment, streamlining workflow and reducing operator variability. By automating routine tasks, AI enhances diagnostic accuracy and bridges training gaps, potentially democratizing advanced ultrasound techniques. Furthermore, AI's integration into tele-ultrasound systems shows promise in extending expert-level diagnostics to underserved areas, significantly broadening access to quality care. The article highlights the ongoing need for explainable AI systems to gain clinician trust and facilitate broader adoption.