To determine the value of on-site deep learning-based CT-derived fractional flow reserve (CT-FFR) for therapeutic management and adverse clinical outcomes in patients suspected of coronary artery disease (CAD) compared with coronary CT angiography (CCTA) alone. This single-centre prospective study included consecutive patients suspected of CAD between June 2021 and September 2021 at our hospital. Four hundred and sixty-one patients were randomized into either CT-FFR+CCTA or CCTA-alone group. The first endpoint was the invasive coronary angiography (ICA) efficiency, defined as the ICA with nonobstructive disease (stenosis <50%) and the ratio of revascularization to ICA (REV-to-ICA ratio) within 90 days. The second endpoint was the incidence of major adverse cardiaovascular events (MACE) at 2 years. A total of 461 patients (267 [57.9%] men; median age, 64 [55-69]) were included. At 90 days, the rate of ICA with nonobstructive disease in the CT-FFR+CCTA group was lower than in the CCTA group (14.7% vs 34.0%, P=.047). The REV-to-ICA ratio in the CT-FFR+CCTA group was significantly higher than in the CCTA group (73.5% vs. 50.9%, P=.036). No significant difference in ICA efficiency was found in intermediate stenosis (25%-69%) between the 2 groups (all P>.05). After a median follow-up of 23 (22-24) months, MACE were observed in 11 patients in the CT-FFR+CCTA group and 24 in the CCTA group (5.9% vs 10.0%, P=.095). The on-site deep learning-based CT-FFR improved the efficiency of ICA utilization with a similarly low rate of MACE compared with CCTA alone. The on-site deep learning-based CT-FFR was superior to CCTA for therapeutic management.

Autism spectrum disorder (ASD) is a neurodevelopmental condition that affects a child's cognitive and social skills, often diagnosed only after symptoms appear around age 2. Leveraging MRI for early ASD detection can improve intervention outcomes. This study proposes a framework for autism detection and region localization using an optimized deep learning approach with attention mechanisms. The pipeline includes MRI image collection, pre-processing (bias field correction, histogram equalization, artifact removal, and non-local mean filtering), and autism classification with a Symmetric Structured MobileNet with Attention Mechanism (SSM-AM). Enhanced by Refreshing Awareness-aided Election-Based Optimization (RA-EBO), SSM-AM achieves robust classification. Abnormality region localization utilizes a Multiscale Dilated Attention-based Adaptive U-Net (MDA-AUnet) further optimized by RA-EBO. Experimental results demonstrate that our proposed model outperforms existing methods, achieving an accuracy of 97.29%, sensitivity of 97.27%, specificity of 97.36%, and precision of 98.98%, significantly improving classification and localization performance. These results highlight the potential of our approach for early ASD diagnosis and targeted interventions. The datasets utilized for this work are publicly available at https://fcon_1000.projects.nitrc.org/indi/abide/.

Genome-wide association studies (GWAS) facilitate construction of polygenic risk scores (PRSs) for rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF). We investigated associations of RA and IPF PRSs with RA and high-resolution chest computed tomography (HRCT) parenchymal lung abnormalities. Participants in COPDGene, a prospective multicenter cohort of current/former smokers, had chest HRCT at study enrollment. Using genome-wide genotyping, RA and IPF PRSs were constructed using GWAS summary statistics. HRCT imaging underwent visual inspection for interstitial lung abnormalities (ILA) and quantitative CT (QCT) analysis using a machine-learning algorithm that quantified percentage of normal lung, interstitial abnormalities, and emphysema. RA was identified through self-report and DMARD use. We investigated associations of RA and IPF PRSs with RA, ILA, and QCT features using multivariable logistic and linear regression. We analyzed 9,230 COPDGene participants (mean age 59.6 years, 46.4 % female, 67.2 % non-Hispanic White, 32.8 % Black/African American). In non-Hispanic White participants, RA PRS was associated with RA diagnosis (OR 1.32 per unit, 95 %CI 1.18-1.49) but not ILA or QCT features. Among non-Hispanic White participants, IPF PRS was associated with ILA (OR 1.88 per unit, 95 %CI 1.52-2.32) and quantitative interstitial abnormalities (adjusted β=+0.50 % per unit, p = 7.3 × 10^-8) but not RA. There were no statistically significant associations among Black/African American participants. RA and IPF PRSs were associated with their intended phenotypes among non-Hispanic White participants but performed poorly among Black/African American participants. PRS may have future application to risk stratify for RA diagnosis among patients with ILD or for ILD among patients with RA.

Scanner-referenced T1 (srT1) is a method for measuring pancreas T1 relaxation time. The purpose of this multi-centre study is 2-fold: (1) to evaluate the repeatability of manual ROI-based analysis of srT1, (2) to validate a semi-automated measurement method with an automatic quality control (QC) module to identify likely discrepancies between automated and manual measurements. Pancreatic MRI scans from a scan-rescan cohort (46 subjects) were used to evaluate the repeatability of manual analysis. Seven hundred and eight scans from a longitudinal multi-centre study of 466 subjects were divided into training, internal validation (IV), and external validation (EV) cohorts. A semi-automated method for measuring srT1 using machine learning is proposed and compared against manual analysis on the validation cohorts with and without automated QC. Inter-operator agreement between manual ROI-based method and semi-automated method had low bias (3.8 ms or 0.5%) and limits of agreement [-36.6, 44.1] ms. There was good agreement between the 2 methods without automated QC (IV: 3.2 [-47.1, 53.5] ms, EV: -0.5 [-35.2, 34.2] ms). After QC, agreement on the IV set improved, was unchanged in the EV set, and the agreement in both was within inter-operator bounds (IV: -0.04 [-33.4, 33.3] ms, EV: -1.9 [-37.6, 33.7] ms). The semi-automated method improved scan-rescan agreement versus manual analysis (manual: 8.2 [-49.7, 66] ms, automated: 6.7 [-46.7, 60.1] ms). The semi-automated method for characterization of standardized pancreatic T1 using MRI has the potential to decrease analysis time while maintaining accuracy and improving scan-rescan agreement. We provide intra-operator, inter-operator, and scan-rescan agreement values for manual measurement of srT1, a standardized biomarker for measuring pancreas fibro-inflammation. Applying a semi-automated measurement method improves scan-rescan agreement and agrees well with manual measurements, while reducing human effort. Adding automated QC can improve agreement between manual and automated measurements. We describe a method for semi-automated, standardized measurement of pancreatic T1 (srT1), which includes automated quality control. Measurements show good agreement with manual ROI-based analysis, with comparable consistency to inter-operator performance.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative potential of AI in veterinary diagnostic imaging and radiation oncology. This position statement outlines the guiding principles for the ethical development and integration of AI technologies to ensure patient safety and clinical effectiveness. Artificial intelligence systems must adhere to good machine learning practices, emphasizing transparency, error reporting, and the involvement of clinical experts throughout development. These tools should also include robust mechanisms for secure patient data handling and postimplementation monitoring. The position highlights the critical importance of maintaining a veterinarian in the loop, preferably a board-certified radiologist or radiation oncologist, to interpret AI outputs and safeguard diagnostic quality. Currently, no commercially available AI products for veterinary diagnostic imaging meet the required standards for transparency, validation, or safety. The ACVR and ECVDI advocate for rigorous peer-reviewed research, unbiased third-party evaluations, and interdisciplinary collaboration to establish evidence-based benchmarks for AI applications. Additionally, the statement calls for enhanced education on AI for veterinary professionals, from foundational training in curricula to continuing education for practitioners. Veterinarians are encouraged to disclose AI usage to pet owners and provide alternative diagnostic options as needed. Regulatory bodies should establish guidelines to prevent misuse and protect the profession and patients. The ACVR and ECVDI stress the need for a cautious, informed approach to AI adoption, ensuring these technologies augment, rather than compromise, veterinary care.

The skeletal muscles that athletes should train vary depending on their discipline and position. Therefore, individual skeletal muscle cross-sectional area assessment is important in the development of training strategies. To measure the cross-sectional area of skeletal muscle, manual segmentation of each muscle is performed using magnetic resonance (MR) imaging. This task is time-consuming and requires significant effort. Additionally, interobserver variability can sometimes be problematic. The purpose of this study was to develop an automated computerized method for semantic segmentation of individual thigh skeletal muscles from MR images of athletes. Our database consisted of 697 images from the thighs of 697 elite athletes. The images were randomly divided into a training dataset (70%), a validation dataset (10%), and a test dataset (20%). A label image was generated for each image by manually annotating 15 object classes: 12 different skeletal muscles, fat, bones, and vessels and nerves. Using the validation dataset, DeepLab v3+ was chosen from three different semantic segmentation models as a base model for segmenting individual thigh skeletal muscles. The feature extractor in DeepLab v3+ was also optimized to ResNet50. The mean Jaccard index and Dice index for the proposed method were 0.853 and 0.916, respectively, which were significantly higher than those from conventional DeepLab v3+ (Jaccard index: 0.810, p < .001; Dice index: 0.887, p < .001). The proposed method achieved a mean area error for 15 objective classes of 3.12%, useful in the assessment of skeletal muscle cross-sectional area from MR images.

Schizophrenia (SZ) is a complex mental disorder characterized by a profound disruption in cognition and emotion, often resulting in a distorted perception of reality. Magnetic resonance imaging (MRI) is an essential tool for diagnosing SZ which helps to understand the organization of the brain. Functional MRI (fMRI) is a specialized imaging technique to measure and map brain activity by detecting changes in blood flow and oxygenation. The proposed paper correlates the results using an explainable deep learning approach to identify the significant regions of SZ patients using group-level analysis for both structural MRI (sMRI) and fMRI data. The study found that the heat maps for Grad-CAM show clear visualization in the frontal lobe for the classification of SZ and CN with a 97.33% accuracy. The group difference analysis reveals that sMRI data shows intense voxel activity in the right superior frontal gyrus of the frontal lobe in SZ patients. Also, the group difference between SZ and CN during n-back tasks of fMRI data indicates significant voxel activation in the frontal cortex of the frontal lobe. These findings suggest that the frontal lobe plays a crucial role in the diagnosis of SZ, aiding clinicians in planning the treatment.

This study aimed to develop a predictive model for lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) patients by deep learning radiomic (DLRad) and clinical features. This study included 271 thyroid lobes from 228 PTC patients who underwent preoperative neck non-contrast CT at Center 1 (May 2021-April 2024). LNM status was confirmed via postoperative pathology, with each thyroid lobe labeled accordingly. The cohort was divided into training (n = 189) and validation (n = 82) cohorts, with additional temporal (n = 59 lobes, Center 1, May-August 2024) and external (n = 66 lobes, Center 2) test cohorts. Thyroid lobes were manually segmented from the isthmus midline, ensuring interobserver consistency (ICC ≥ 0.8). Deep learning and radiomics features were selected using LASSO algorithms to compute DLRad scores. Logistic regression identified independent predictors, forming DLRad, clinical, and combined models. Model performance was evaluated using AUC, calibration, decision curves, and the DeLong test, compared against radiologists' assessments. Independent predictors of LNM included age, gender, multiple nodules, tumor size group, and DLRad. The combined model demonstrated superior diagnostic performance with AUCs of 0.830 (training), 0.799 (validation), 0.819 (temporal test), and 0.756 (external test), outperforming the DLRad model (AUCs: 0.786, 0.730, 0.753, 0.642), clinical model (AUCs: 0.723, 0.745, 0.671, 0.660), and radiologist evaluations (AUCs: 0.529, 0.606, 0.620, 0.503). It also achieved the lowest Brier scores (0.167, 0.184, 0.175, 0.201) and the highest net benefit in decision-curve analysis at threshold probabilities > 20 %. The combined model integrating DLRad and clinical features exhibits good performance in predicting LNM in PTC patients.

Cerebrovascular diseases (CVD) can lead to stroke and dementia. Stroke is the second leading cause of death world wide and dementia incidence is increasing by the year. There are several markers of CVD that are visible on brain imaging, including: white matter hyperintensities (WMH), acute and chronic ischaemic stroke lesions (ISL), lacunes, enlarged perivascular spaces (PVS), acute and chronic haemorrhagic lesions, and cerebral microbleeds (CMB). Brain atrophy also occurs in CVD. These markers are important for patient management and intervention, since they indicate elevated risk of future stroke and dementia. We systematically reviewed automated systems designed to support radiologists reporting on these CVD imaging findings. We considered commercially available software and research publications which identify at least two CVD markers. In total, we included 29 commercial products and 13 research publications. Two distinct types of commercial support system were available: those which identify acute stroke lesions (haemorrhagic and ischaemic) from computed tomography (CT) scans, mainly for the purpose of patient triage; and those which measure WMH and atrophy regionally and longitudinally. In research, WMH and ISL were the markers most frequently analysed together, from magnetic resonance imaging (MRI) scans; lacunes and PVS were each targeted only twice and CMB only once. For stroke, commercially available systems largely support the emergency setting, whilst research systems consider also follow-up and routine scans. The systems to quantify WMH and atrophy are focused on neurodegenerative disease support, where these CVD markers are also of significance. There are currently no openly validated systems, commercially, or in research, performing a comprehensive joint analysis of all CVD markers (WMH, ISL, lacunes, PVS, haemorrhagic lesions, CMB, and atrophy).

To evaluate whether deep learning (DL) analysis of intratumor subregion based on dynamic contrast-enhanced MRI (DCE-MRI) can help predict Ki-67 expression level in breast cancer. A total of 290 breast cancer patients from two hospitals were retrospectively collected. A k-means clustering algorithm confirmed subregions of tumor. DL features of whole tumor and subregions were extracted from DCE-MRI images based on 3D ResNet18 pre-trained model. The logistic regression model was constructed after dimension reduction. Model performance was assessed using the area under the curve (AUC), and clinical value was demonstrated through decision curve analysis (DCA). The k-means clustering method clustered the tumor into two subregions (habitat 1 and habitat 2) based on voxel values. Both the habitat 1 model (validation set: AUC = 0.771, 95 %CI: 0.642-0.900 and external test set: AUC = 0.794, 95 %CI: 0.696-0.891) and the habitat 2 model (AUC = 0.734, 95 %CI: 0.605-0.862 and AUC = 0.756, 95 %CI: 0.646-0.866) showed better predictive capabilities for Ki-67 expression level than the whole tumor model (AUC = 0.686, 95 %CI: 0.550-0.823 and AUC = 0.680, 95 %CI: 0.555-0.804). The combined model based on the two subregions further enhanced the predictive capability (AUC = 0.808, 95 %CI: 0.696-0.921 and AUC = 0.842, 95 %CI: 0.758-0.926), and it demonstrated higher clinical value than other models in DCA. The deep learning model derived from subregion of tumor showed better performance for predicting Ki-67 expression level in breast cancer patients. Additionally, the model that integrated two subregions further enhanced the predictive performance.