This study aims to investigate whether non-contrast computed tomography radiomics can effectively reflect split renal function and to develop a radiomics model for its assessment. This retrospective study included kidneys from the study center and split them into training (70%) and testing (30%) sets. Renal dynamic imaging was used as the reference standard for measuring split renal function. Based on chronic kidney disease staging, kidneys were categorized into three groups according to glomerular filtration rate: > 45 ml/min/1.73 m², 30-45 ml/min/1.73 m², and < 30 ml/min/1.73 m².Features were selected based on feature importance ranking from a tree model, and a random forest radiomics model was built. A total of 543 kidneys were included, with 381 in the training set and 162 in the testing set. In the training set, 16 features identified as most important for distinguishing between the groups were ultimately included to develop the random forest model. The model demonstrated good discriminatory ability in the testing set. The AUC for the > 45 ml/min/1.73 m², 30-45 ml/min/1.73 m², and < 30 ml/min/1.73 m² categories were 0.859 (95% CI 0.804-0.910), 0.679 (95% CI 0.589-0.760), and 0.901 (95% CI 0.848-0.946), respectively. The calibration curves for the kidneys in each group closely align with the diagonal, with Hosmer-Lemeshow test P-values of 0.124, 0.241, and 0.199 for the three groups, respectively (all P > 0.05). The decision curve analysis confirmed the radiomics model's clinical utility, demonstrating significantly higher net benefit than both treat-all and treat-none strategies at clinically relevant probability thresholds: 1-69% and 71-75% for the > 45 ml/min/1.73 m² group, 15-d50% for the 30-45 ml/min/1.73 m² group, and 0-99% for the < 30 ml/min/1.73 m² group. Non-contrast computed tomography radiomics can effectively reflect split renal function information, and the model developed based on it can accurately assess split renal function, holding great potential for clinical application.

Retrospective study. To develop and validate a computed tomography-based deep learning(DL) model for diagnosing cervical spinal stenosis(CSS). Although magnetic resonance imaging (MRI) is widely used for diagnosing CSS, its inherent limitations, including prolonged scanning time, limited availability in resource-constrained settings, and contraindications for patients with metallic implants, make computed tomography (CT) a critical alternative in specific clinical scenarios. The development of CT-based DL models for CSS detection holds promise in transcending the diagnostic efficacy limitations of conventional CT imaging, thereby serving as an intelligent auxiliary tool to optimize healthcare resource allocation. Paired CT/MRI images were collected. CT images were divided into training, validation, and test sets in an 8:1:1 ratio. The two-stage model architecture employed: (1) a Faster R-CNN-based detection model for localization, annotation, and extraction of regions of interest (ROI); (2) comparison of 16 convolutional neural network (CNN) models for stenosis classification to select the best-performing model. The evaluation metrics included accuracy, F1-score, and Cohen's κ coefficient, with comparisons made against diagnostic results from physicians with varying years of experience. In the multiclass classification task, four high-performing models (DL1-b0, DL2-121, DL3-101, and DL4-26d) achieved accuracies of 88.74%, 89.40%, 89.40%, and 88.08%, respectively. All models demonstrated >80% consistency with senior physicians and >70% consistency with junior physicians.In the binary classification task, the models achieved accuracies of 94.70%, 96.03%, 96.03%, and 94.70%, respectively. All four models demonstrated consistency rates slightly below 90% with junior physicians. However, when compared with senior physicians, three models (excluding DL4-26d) exhibited consistency rates exceeding 90%. The DL model developed in this study demonstrated high accuracy in CT image analysis of CSS, with a diagnostic performance comparable to that of senior physicians.

Evaluating the minimum distance (dTICA) between the internal carotid artery (ICA) and tonsillar tumors (TT) on imaging is essential for preoperative planning; we propose a tool to automatically extract dTICA. CT scans of 96 patients with TT were selected from the cancer imaging archive. nnU-Net, a deep learning framework, was implemented to automatically segment both the TT and ICA from these scans. Dice similarity coefficient (DSC) and average hausdorff distance (AHD) were used to evaluate the performance of the nnU-Net. Thereafter, an automated tool was built to calculate the magnitude of dTICA from these segmentations. The average DSC and AHD were 0.67, 2.44 mm, and 0.83, 0.49 mm for the TT and ICA, respectively. The mean dTICA was 6.66 mm and statistically varied by tumor T stage (p = 0.00456). The proposed pipeline can accurately and automatically capture dTICA, potentially assisting clinicians in preoperative evaluation.

The objective of this study was to assess the performance of an artificial intelligence (AI) algorithm in detecting intracranial haemorrhages (ICHs) on non-contrast CT scans (NCCT). Another objective was to gauge the department's acceptance of said algorithm. Surveys conducted at three and nine months post-implementation revealed an increase in radiologists' acceptance of the AI tool with an increasing performance. However, a significant portion still preferred an additional physician given comparable cost. Our findings emphasize the importance of careful software implementation into a robust IT architecture.

Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitive modality for dementia-type diagnosis. To address this clinical challenge, we introduce a neuropathology-based, data-driven, multi-label deep-learning framework to identify and quantify in vivo biomarkers for Alzheimer's disease (AD), vascular dementia (VD) and Lewy body dementia (LBD) using antemortem T1-weighted MRI scans of 423 demented and 361 control participants from National Alzheimer's Coordinating Center and Alzheimer's Disease Neuroimaging Initiative datasets. Based on the best-performing deep-learning model, explainable heat maps were extracted to visualize disease patterns, and the novel Deep Signature of Pathology Atrophy REcognition (DeepSPARE) indices were developed, where a higher DeepSPARE score indicates more brain alterations associated with that specific pathology. A substantial discrepancy in clinical and neuropathological diagnosis was observed in the demented patients: 71% had more than one pathology, but 67% were diagnosed clinically as AD only. Based on these neuropathological diagnoses and leveraging cross-validation principles, the deep-learning model achieved the best performance, with a balanced accuracy of 0.844, 0.839 and 0.623 for AD, VD and LBD, respectively, and was used to generate the explainable deep-learning heat maps and DeepSPARE indices. The explainable deep-learning heat maps revealed distinct neuroimaging brain alteration patterns for each pathology: (i) the AD heat map highlighted bilateral hippocampal regions; (ii) the VD heat map emphasized white matter regions; and (iii) the LBD heat map exposed occipital alterations. The DeepSPARE indices were validated by examining their associations with cognitive testing and neuropathological and neuroimaging measures using linear mixed-effects models. The DeepSPARE-AD index was associated with Mini-Mental State Examination, the Trail Making Test B, memory, hippocampal volume, Braak stages, Consortium to Establish a Registry for Alzheimer's Disease (CERAD) scores and Thal phases [false-discovery rate (FDR)-adjusted P < 0.05]. The DeepSPARE-VD index was associated with white matter hyperintensity volume and cerebral amyloid angiopathy (FDR-adjusted P < 0.001), and the DeepSPARE-LBD index was associated with Lewy body stages (FDR-adjusted P < 0.05). The findings were replicated in an out-of-sample Alzheimer's Disease Neuroimaging Initiative dataset by testing associations with cognitive, imaging, plasma and CSF measures. CSF and plasma tau phosphorylated at threonine-181 (pTau181) were significantly associated with DeepSPARE-AD in the AD and mild cognitive impairment amyloid-β positive (AD/MCIΑβ+) group (FDR-adjusted P < 0.001), and CSF α-synuclein was associated solely with DeepSPARE-LBD (FDR-adjusted P = 0.036). Overall, these findings demonstrate the advantages of our innovative deep-learning framework in detecting antemortem neuroimaging signatures linked to different pathologies. The newly deep-learning-derived DeepSPARE indices are precise, pathology-sensitive and single-valued non-invasive neuroimaging metrics, bridging the traditional widely available in vivo T1 imaging with histopathology.

The ability to predict the spreading of pathology in patients with frontotemporal dementia (FTD) is crucial for early diagnosis and targeted interventions. In this study, we examined the relationship between network vulnerability and longitudinal progression of atrophy in FTD patients, using the network diffusion model (NDM) of the spread of pathology. Thirty behavioural variant FTD (bvFTD), 13 semantic variant primary progressive aphasia (svPPA), 14 non-fluent variant primary progressive aphasia (nfvPPA) and 12 semantic behavioural variant FTD (sbvFTD) patients underwent longitudinal T1-weighted MRI. Fifty young controls (20-31 years of age) underwent multi-shell diffusion MRI scan. An NDM was developed to model progression of FTD pathology as a spreading process from a seed through the healthy structural connectome, using connectivity measures from fractional anisotropy and intracellular volume fraction in young controls. Four disease epicentres were initially identified from the peaks of atrophy of each FTD variant: left insula (bvFTD), left temporal pole (svPPA), right temporal pole (sbvFTD) and left supplementary motor area (nfvPPA). Pearson's correlations were calculated between NDM-predicted atrophy in young controls and the observed longitudinal atrophy in FTD patients over a follow-up period of 24 months. The NDM was then run for all 220 brain seeds to verify whether the four epicentres were among those that yielded the highest correlation. Using the NDM, predictive maps in young controls showed progression of pathology from the peaks of atrophy in svPPA, nfvPPA and sbvFTD over 24 months. svPPA exhibited early involvement of the left temporal and occipital lobes, progressing to extensive left hemisphere impairment. nfvPPA and sbvFTD spread in a similar manner bilaterally to frontal, sensorimotor and temporal regions, with sbvFTD additionally affecting the right hemisphere. Moreover, the NDM-predicted atrophy of each region was positively correlated with longitudinal real atrophy, with a greater effect in svPPA and sbvFTD. In bvFTD, the model starting from the left insula (the peak of atrophy) demonstrated a highly left-lateralized pattern, with pathology spreading to frontal, sensorimotor, temporal and basal ganglia regions, with minimal extension to the contralateral hemisphere by 24 months. However, unlike the atrophy peaks observed in the other three phenotypes, the left insula did not show the strongest correlation between the estimated and real atrophy. Instead, the bilateral superior frontal gyrus emerged as optimal seeds for modelling atrophy spread, showing the highest correlation ranking in both hemispheres. Overall, NDM applied on the intracellular volume fraction connectome yielded higher correlations relative to NDM applied on fractional anisotropy maps. The NDM implementation using the cross-sectional structural connectome is a valuable tool to predict patterns of atrophy and spreading of pathology in FTD clinical variants.

The socioeconomic burden of hip fractures, the most severe osteoporotic fracture outcome, is increasing and the current clinical risk assessment lacks sensitivity. This study aimed to develop a method for improved prediction of hip fracture by incorporating measurements of bone microstructure and composition derived from HR-pQCT. In a prospective cohort study of 3028 community-dwelling women aged 75-80, all participants answered questionnaires and underwent baseline examinations of anthropometrics and bone by DXA and HR-pQCT. Medical records, a regional x-ray archive, and registers were used to identify incident fractures and death. Prediction models for hip, major osteoporotic fracture (MOF), and any fracture were developed using Cox proportional hazards regression and machine learning algorithms (neural network, random forest, ensemble, and Extreme Gradient Boosting). In the 2856 (94.3%) women with complete HR-pQCT data at 2 tibia sites (distal and ultra-distal), the median follow-up period was 8.0 yr, and 217 hip, 746 MOF, and 1008 any type of incident fracture occurred. In Cox regression models adjusted for age, BMI, clinical risk factors (CRFs), and FN BMD, the strongest predictors of hip fracture were tibia total volumetric BMD and cortical thickness. The performance of the Cox regression-based prediction models for hip fracture was significantly improved by HR-pQCT (time-dependent AUC; area under receiver operating characteristic curve at 5 yr of follow-up 0.75 [0.64-0.85]), compared to a reference model including CRFs and FN BMD (AUC = 0.71 [0.58-0.81], p < .001) and a Fracture Risk Assessment Tool risk score model (AUC = 0.70 [0.60-0.80], p < .001). The Cox regression model for hip fracture had a significantly higher accuracy than the neural network-based model, the best-performing machine learning algorithm, at clinically relevant sensitivity levels. We conclude that the addition of HR-pQCT parameters improves the prediction of hip fractures in a cohort of older Swedish women.

Despite decades of advancements in diagnostic MRI, 30%-50% of temporal lobe epilepsy (TLE) patients remain categorized as 'non-lesional' (i.e. MRI negative) based on visual assessment by human experts. MRI-negative patients face diagnostic uncertainty and significant delays in treatment planning. Quantitative MRI studies have demonstrated that MRI-negative patients often exhibit a TLE-specific pattern of temporal and limbic atrophy that might be too subtle for the human eye to detect. This signature pattern could be translated successfully into clinical use via advances in artificial intelligence in computer-aided MRI interpretation, thereby improving the detection of brain 'lesional' patterns associated with TLE. Here, we tested this hypothesis by using a three-dimensional convolutional neural network applied to a dataset of 1178 scans from 12 different centres, which was able to differentiate TLE from healthy controls with high accuracy (85.9% ± 2.8%), significantly outperforming support vector machines based on hippocampal (74.4% ± 2.6%) and whole-brain (78.3% ± 3.3%) volumes. Our analysis focused subsequently on a subset of patients who achieved sustained seizure freedom post-surgery as a gold standard for confirming TLE. Importantly, MRI-negative patients from this cohort were accurately identified as TLE 82.7% ± 0.9% of the time, an encouraging finding given that clinically these were all patients considered to be MRI negative (i.e. not radiographically different from controls). The saliency maps from the convolutional neural network revealed that limbic structures, particularly medial temporal, cingulate and orbitofrontal areas, were most influential in classification, confirming the importance of the well-established TLE signature atrophy pattern for diagnosis. Indeed, the saliency maps were similar in MRI-positive and MRI-negative TLE groups, suggesting that even when humans cannot distinguish more subtle levels of atrophy, these MRI-negative patients are on the same continuum common across all TLE patients. As such, artificial intelligence can identify TLE lesional patterns, and artificial intelligence-aided diagnosis has the potential to enhance the neuroimaging diagnosis of TLE greatly and to redefine the concept of 'lesional' TLE.

The incidence of Alzheimer's disease is rising with the increasing elderly population worldwide. While no cure exists, early diagnosis can significantly slow disease progression. Computer-aided diagnostic systems are becoming critical tools for assisting in the early detection of Alzheimer's disease. In this systematic review, we aim to evaluate recent advancements in computer-aided decision support systems for Alzheimer's disease diagnosis, focusing on data modalities, machine learning methods, and performance metrics. We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies published between 2021 and 2024 were retrieved from PubMed, IEEEXplore and Web of Science, using search terms related to Alzheimer's disease classification, neuroimaging, machine learning, and diagnostic performance. A total of 39 studies met the inclusion criteria, focusing on the use of Magnetic Resonance Imaging, Positron Emission Tomography, and biomarkers for Alzheimer's disease classification using machine learning models. Multimodal approaches, combining Magnetic Resonance Imaging with Positron Emission Tomography and Cognitive assessments, outperformed single-modality studies in diagnostic accuracy reliability. Convolutional Neural Networks were the most commonly used machine learning models, followed by hybrid models and Random Forest. The highest accuracy reported for binary classification was 100%, while multi-class classification achieved up to 99.98%. Techniques like Synthetic Minority Over-sampling Technique and data augmentation were frequently employed to address data imbalance, improving model generalizability. Our review highlights the advantages of using multimodal data in computer-aided decision support systems for more accurate Alzheimer's disease diagnosis. However, we also identified several limitations, including data imbalance, small sample sizes, and the lack of external validation in most studies. Future research should utilize larger, more diverse datasets, incorporate longitudinal data, and validate models in real-world clinical trials. Additionally, there is a growing need for explainability in machine learning models to ensure they are interpretable and trusted in clinical settings. While computer-aided decision support systems show great promise in improving the early diagnosis of Alzheimer's disease, further work is needed to enhance their robustness, generalizability, and clinical applicability. By addressing these challenges, computer-aided decision support systems could play a pivotal role in the early detection and management of Alzheimer's disease, potentially improving patient outcomes and reducing healthcare costs.

Statistical Shape Models are machine learning tools in computational orthopedics that enable the study of anatomical variability and the creation of synthetic models for pathogenetic analysis and surgical planning. Current models of the glenohumeral joint either describe individual bones or are limited to non-pathologic datasets, failing to capture coupled shape variation in arthropathic anatomy. We aimed to develop a novel combined scapula-proximal-humerus model applicable to clinical populations. Preoperative computed tomography scans from 45 Reverse Total Shoulder Arthroplasty patients were used to generate three-dimensional models of the scapula and proximal humerus. Correspondence point clouds were combined into a two-body shape model using Principal Component Analysis. Individual scapula-only and proximal-humerus-only shape models were also created for comparison. The models were validated using compactness, specificity, generalization ability, and leave-one-out cross-validation. The modes of variation for each model were also compared. The combined model was described using eigenvector decomposition into single body models. The models were further compared in their ability to predict the shape of one body when given the shape of its counterpart, and the generation of diverse realistic synthetic pairs de novo. The scapula and proximal-humerus models performed comparably to previous studies with median average leave-one-out cross-validation errors of 1.08 mm (IQR: 0.359 mm), and 0.521 mm (IQR: 0.111 mm); the combined model was similar with median error of 1.13 mm (IQR: 0.239 mm). The combined model described coupled variations between the shapes equalling 43.2% of their individual variabilities, including the relationship between glenoid and humeral head erosions. The combined model outperformed the individual models generatively with reduced missing shape prediction bias (> 10%) and uniformly diverse shape plausibility (uniformity p-value < .001 vs. .59). This study developed the first two-body scapulohumeral shape model that captures coupled variations in arthropathic shoulder anatomy and the first proximal-humeral statistical model constructed using a clinical dataset. While single-body models are effective for descriptive tasks, combined models excel in generating joint-level anatomy. This model can be used to augment computational analyses of synthetic populations investigating shoulder biomechanics and surgical planning.