The location of the hemorrhagic of spontaneous intracerebral hemorrhage (sICH) is clinically pivotal for both identifying its etiology and prognosis, but comprehensive and quantitative modeling approach has yet to be thoroughly explored. We employed lesion-symptom mapping to extract the location features of sICH. We registered patients' non-contrast computed tomography image and hematoma masks with standard human brain templates to identify specific affected brain regions. Then, we generated hemorrhage probabilistic maps of different etiologies and prognoses. By integrating radiomics and clinical features into multiple logistic regression models, we developed and validated optimal etiological and prognostic models across three centers, comprising 1162 sICH patients. Hematomas of different etiology have unique spatial distributions. The location-based features demonstrated robust classification of the etiology of spontaneous intracerebral hemorrhage (sICH), with a mean area under the curve (AUC) of 0.825 across diverse datasets. These features provided significant incremental value when integrated into predictive models (fusion model mean AUC = 0.915), outperforming models relying solely on clinical features (mean AUC = 0.828). In prognostic assessments, both hematoma location (mean AUC = 0.762) and radiomic features (mean AUC = 0.837) contributed substantial incremental predictive value, as evidenced by the fusion model's mean AUC of 0.873, compared to models utilizing clinical features alone (mean AUC = 0.771). Our results show that location features were more intrinsically robust, generalizable relative, strong interpretability to the complex modeling of radiomics, our approach demonstrated a novel interpretable, streamlined, comprehensive etiologic classification and prognostic prediction framework for sICH.

To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans. Patients who underwent chest CT for thoracic surgery between April 2017 and December 2020 were retrospectively analyzed. ILA quantification was performed using deep learning-based automated software. Cases were categorized as ILA or non-ILA according to the Fleischner Society's definition, based on the quantification results or radiologist assessment (reference standard). Measurement variability, agreement, and diagnostic performance between the pre- and post-contrast scans were evaluated. In 1134 included patients, post-contrast scans quantified a slightly larger volume of nonfibrotic ILA (mean difference: -0.2%), due to increased ground-glass opacity and reticulation volumes (-0.2% and -0.1%), whereas the fibrotic ILA volume remained unchanged (0.0%). ILA was diagnosed in 15 (1.3%), 22 (1.9%), and 40 (3.5%) patients by pre- and post-contrast scans and radiologists, respectively. The agreement between the pre- and post-contrast scans was substantial (κ = 0.75), but both pre-contrast (κ = 0.46) and post-contrast (κ = 0.54) scans demonstrated moderate agreement with the radiologist. The sensitivity for ILA (32.5% vs. 42.5%, p = 0.221) and specificity for non-ILA (99.8% vs. 99.5%, p = 0.248) were comparable between pre- and post-contrast scans. Radiologist's reclassification for equivocal ILA due to unilateral abnormalities increased the sensitivity for ILA (67.5% and 75.0%, respectively) in both pre- and post-contrast scans. Applying automated quantification on post-contrast scans appears to be acceptable in terms of agreement and diagnostic performance; however, radiologists may need to improve sensitivity reclassifying equivocal ILA. Question The effect of contrast enhancement on the automated quantification of interstitial lung abnormality (ILA) remains unknown. Findings Automated quantification measured slightly larger ground-glass opacity and reticulation volumes on post-contrast scans than on pre-contrast scans; however, contrast enhancement did not affect the sensitivity for interstitial lung abnormality. Clinical relevance Applying automated quantification on post-contrast scans appears to be acceptable in terms of agreement and diagnostic performance.

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.

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.

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.

Cancer is among the most dangerous diseases contributing to rising global mortality rates. Lung cancer, particularly adenocarcinoma, is one of the deadliest forms and severely impacts human life. Early diagnosis and appropriate treatment significantly increase patient survival rates. Computed Tomography (CT) is a preferred imaging modality for detecting lung cancer, as it offers detailed visualization of tumor structure and growth. With the advancement of deep learning, the automated identification of lung cancer from CT images has become increasingly effective. This study proposes a novel lung cancer detection framework using a Flower Pollination Algorithm (FPA)-based weighted ensemble of three high-performing pretrained Convolutional Neural Networks (CNNs): VGG16, ResNet101V2, and InceptionV3. Unlike traditional ensemble approaches that assign static or equal weights, the FPA adaptively optimizes the contribution of each CNN based on validation performance. This dynamic weighting significantly enhances diagnostic accuracy. The proposed FPA-based ensemble achieved an impressive accuracy of 98.2%, precision of 98.4%, recall of 98.6%, and an F1 score of 0.985 on the test dataset. In comparison, the best individual CNN (VGG16) achieved 94.6% accuracy, highlighting the superiority of the ensemble approach. These results confirm the model's effectiveness in accurate and reliable cancer diagnosis. The proposed study demonstrates the potential of deep learning and neural networks to transform cancer diagnosis, helping early detection and improving treatment outcomes.

Ultra-high-resolution (UHR) photon-counting-detector (PCD) CT improves image resolution but increases noise, necessitating the use of smoother reconstruction kernels that reduce resolution below the 0.125-mm maximum spatial resolution. A denoising convolutional neural network (CNN) was developed to reduce noise in images reconstructed with the available sharpest reconstruction kernel while preserving resolution for enhanced temporal bone visualization to address this issue. With institutional review board approval, the CNN was trained on 6 patient cases of clinical temporal bone imaging (1885 images) and tested on 20 independent cases using a dual-source PCD-CT (NAEOTOM Alpha). Images were reconstructed using quantum iterative reconstruction at strength 3 (QIR3) with both a clinical routine kernel (Hr84) and the sharpest available head kernel (Hr96). The CNN was applied to images reconstructed with Hr96 and QIR1 kernel. For each case, three series of images (Hr84-QIR3, Hr96-QIR3, and Hr96-CNN) were randomized for review by 2 neuroradiologists assessing the overall quality and delineating the modiolus, stapes footplate, and incudomallear joint. The CNN reduced noise by 80% compared with Hr96-QIR3 and by 50% relative to Hr84-QIR3, while maintaining high resolution. Compared with the conventional method at the same kernel (Hr96-QIR3), Hr96-CNN significantly decreased image noise (from 204.63 to 47.35 HU) and improved its structural similarity index (from 0.72 to 0.99). Hr96-CNN images ranked higher than Hr84-QIR3 and Hr96-QIR3 in overall quality (<i>P</i> < .001). Readers preferred Hr96-CNN for all 3 structures. The proposed CNN significantly reduced image noise in UHR PCD-CT, enabling the use of the sharpest kernel. This combination greatly enhanced diagnostic image quality and anatomic visualization.