In many contemporary laboratories a completely normal stress perfusion SPECT-MPI is required for rest imaging cancelation. We hypothesized that an artificial intelligence (AI)-derived CAC score of 0 from computed tomography attenuation correction (CTAC) scans obtained during hybrid SPECT/CT, may identify additional patients at low risk of MACE who could be selected for stress-only imaging. Patients without known coronary artery disease who underwent SPECT/CT MPI and had stress total perfusion deficit (TPD) <5% were included. Stress TPD was categorized as no abnormality (stress TPD 0%) or minimal abnormality (stress TPD 1-4%). CAC was automatically quantified from the CTAC scans. We evaluated associations with major adverse cardiovascular events (MACE). In total, 6,884 patients (49.4% males and median age 63 years) were included. Of these, 9.7% experienced MACE (15% non-fatal MI, 2.7% unstable angina, 38.5% coronary revascularization and 43.8% deaths). Compared to patients with TPD 0%, those with TPD 1-4% and CAC 0 had lower MACE risk (hazard ratio [HR] 0.58; 95% confidence interval [CI] 0.45-0.76), while those with TPD 1-4% and CAC score>0 had a higher MACE risk (HR 1.90; 95%CI 1.56-2.30). Compared to canceling rest scans only in patients with normal perfusion (TPD 0%), by canceling rest scans in patients with CAC 0, more than twice as many rest scans (55% vs 25%) could be cancelled. Using AI-derived CAC 0 on CT scans with hybrid SPECT/CT in patients with a stress TPD<5% can double the proportion of patients in whom stress-only procedures could be safely performed.

Sensory experience impacts brain development. In the mouse somatosensory cortex, sensory deprivation via whisker trimming induces reductions in the perineuronal net, the size of neuronal cell bodies, the size and orientation of dendritic arbors, the density of dendritic spines, and the level of myelination, among other effects. Using a custom-developed laboratory diffractometer, we measured the X-ray diffraction patterns of mouse brain tissue to establish a novel method for examining nanoscale brain structures. Two groups of mice were examined: a control group and one that underwent 30 days of whisker-trimming from birth an established method of sensory deprivation that affects the mouse barrel cortex (whisker sensory processing region of the primary somatosensory cortex). Mice were perfused, and primary somatosensory cortices were isolated for immunocytochemistry and X-ray diffraction imaging. X-ray images were characterized using a specially developed machine-learning approach, and the clusters that correspond to the two groups are well separated in principal components space. We obtained the perfect values for sensitivity and specificity, as well as for the receiver operator curve classifier. New machine-learning approaches allow for the first time x-ray diffraction to identify cortex that has undergone sensory deprivation without the use of stains. We hypothesize that our results are related to the alteration of different nanoscale structural components in the brains of sensory deprived mice. The effects of these nanoscale structural formations can be reflective of changes in the micro- and macro-scale structures and assemblies with the neocortex.

This study focused on developing and validating a composite model that integrates radiomic and dosiomic features based on a lung biologically equivalent dose segmentation approach to predict symptomatic radiation pneumonitis (SRP) following lung SBRT. A dual-centered cohorts of 182 lung cancer patients treated with SBRT were divided into training, validation, and external testing sets. Radiomic and dosiomic features were extracted from two distinct regions of interest (ROIs) in the planning computed tomography (CT) images and 3D dose distribution maps, which encompassed both the entire lung and biologically equivalent dose (BED) regions. Feature selection involved correlation filters and LASSO regularization. Five machine learning algorithms generated three individual models (dose-volume histogram [DVH], radiomic [R], dosiomic [D]) and three combined models (R + DVH, R + D, R + D + DVH). Performance was evaluated via ROC analysis, calibration, and decision curve analysis. Among the clinical and dosimetric factors, V_BED70 (α/β = 3 Gy) of the lung was recognized as an independent risk factor for SRP. BED-based radiomic and dosiomic models outperformed whole-lung models (AUCs: 0.806 vs. 0.674 and 0.821 vs. 0.647, respectively). The R + D + DVH trio model achieved the highest predictive accuracy (AUC: 0.889, 95 % CI: 0.701-0.956), with robust calibration and clinical utility. The R + D + DVH trio model based on lung biologically equivalent dose segmentation approach outperforms other models in predicting SRP across various SBRT fractionation schemes.

Neoadjuvant chemoradiotherapy (nCRT) followed by esophagectomy remains standard for locally advanced esophageal squamous cell carcinoma (ESCC). However, accurately predicting pathological complete response (pCR) and treatment outcomes remains challenging. This study aimed to develop and validate a multidimensional deep ensemble learning model (DELRN) using pretreatment CT imaging to predict pCR and stratify prognostic risk in ESCC patients undergoing nCRT. In this multicenter, retrospective cohort study, 485 ESCC patients were enrolled from four hospitals (May 2009-August 2023, December 2017-September 2021, May 2014-September 2019, and March 2013-July 2019). Patients were divided into a discovery cohort (n = 194), an internal cohort (n = 49), and three external validation cohorts (n = 242). A multidimensional deep ensemble learning model (DELRN) integrating radiomics and 3D convolutional neural networks was developed based on pretreatment CT images to predict pCR and clinical outcomes. The model's performance was evaluated by discrimination, calibration, and clinical utility. Kaplan-Meier analysis assessed overall survival (OS) and disease-free survival (DFS) at two follow-up centers. The DELRN model demonstrated robust predictive performance for pCR across the discovery, internal, and external validation cohorts, with area under the curve (AUC) values of 0.943 (95 % CI: 0.912-0.973), 0.796 (95 % CI: 0.661-0.930), 0.767 (95 % CI: 0.646-0.887), 0.829 (95 % CI: 0.715-0.942), and 0.782 (95 % CI: 0.664-0.900), respectively, surpassing single-domain radiomics or deep learning models. DELRN effectively stratified patients into high-risk and low-risk groups for OS (log-rank P = 0.018 and 0.0053) and DFS (log-rank P = 0.00042 and 0.035). Multivariate analysis confirmed DELRN as an independent prognostic factor for OS and DFS. The DELRN model demonstrated promising clinical potential as an effective, non-invasive tool for predicting nCRT response and treatment outcome in ESCC patients, enabling personalized treatment strategies and improving clinical decision-making with future prospective multicenter validation.

The variability and biases in the real-world performance benchmarking of deep learning models for medical imaging compromise their trustworthiness for real-world deployment. The common approach of holding out a single fixed test set fails to quantify the variance in the estimation of test performance metrics. This study introduces NACHOS (Nested and Automated Cross-validation and Hyperparameter Optimization using Supercomputing) to reduce and quantify the variance of test performance metrics of deep learning models. NACHOS integrates Nested Cross-Validation (NCV) and Automated Hyperparameter Optimization (AHPO) within a parallelized high-performance computing (HPC) framework. NACHOS was demonstrated on a chest X-ray repository and an Optical Coherence Tomography (OCT) dataset under multiple data partitioning schemes. Beyond performance estimation, DACHOS (Deployment with Automated Cross-validation and Hyperparameter Optimization using Supercomputing) is introduced to leverage AHPO and cross-validation to build the final model on the full dataset, improving expected deployment performance. The findings underscore the importance of NCV in quantifying and reducing estimation variance, AHPO in optimizing hyperparameters consistently across test folds, and HPC in ensuring computational feasibility. By integrating these methodologies, NACHOS and DACHOS provide a scalable, reproducible, and trustworthy framework for DL model evaluation and deployment in medical imaging. To maximize public availability, the full open-source codebase is provided at https://github.com/thepanlab/NACHOS.

BackgroundEarly detection of autism spectrum disorder (ASD) improves outcomes, yet clinical assessment is time-intensive. Artificial intelligence (AI) may support screening in preschool children by analysing behavioural, neurophysiological, imaging, and biomarker data. AimTo synthesise studies that applied AI in ASD assessment and evaluate whether the underlying data and AI approaches can distinguish ASD characteristics in early childhood. MethodsA systematic search of 15 databases was conducted on 30 November 2024 using predefined terms. Inclusion criteria were empirical studies applying AI to ASD detection in children aged 0-7 years. Reporting followed PRISMA 2020. ResultsTwelve studies met criteria. Reported performance (AUC) ranged from 0.65 to 0.997. Modalities included behavioural (eye-tracking, home videos), motor (tablet/reaching), EEG, diffusion MRI, and blood/epigenetic biomarkers. The largest archival dataset (M-CHAT-R) achieved near-perfect AUC with neural networks. Common limitations were small samples, male-skewed cohorts, and limited external validation. ConclusionsAI can aid early ASD screening in infants and preschoolers, but larger and more diverse datasets, rigorous external validation, and multimodal integration are needed before clinical deployment.

Lung adenocarcinoma remains a leading cause of cancer-related mortality, and the diagnostic performance of computed tomography (CT) is limited when dependent solely on human interpretation. This study aimed to develop and evaluate an interpretable deep learning framework using an attention-enhanced Squeeze-and-Excitation Residual Network (SE-ResNet) to improve automated classification of lung adenocarcinoma from thoracic CT images. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to enhance model interpretability and assist in the visual localization of tumor regions. A total of 3800 chest CT axial slices were collected from 380 subjects (190 patients with lung adenocarcinoma and 190 controls, with 10 slices extracted from each case). This dataset was used to train and evaluate the baseline ResNet50 model as well as the proposed SE-ResNet50 model. Performance was compared using accuracy, Area Under the Curve (AUC), precision, recall, and F1-score. Grad-CAM visualizations were generated to assess the alignment between the model's attention and radiologically confirmed tumor locations. The SE-ResNet model achieved a classification accuracy of 94% and an AUC of 0.941, significantly outperforming the baseline ResNet50, which had an 85% accuracy and an AUC of 0.854. Grad-CAM heatmaps produced from the SE-ResNet demonstrated superior localization of tumor-relevant regions, confirming the enhanced focus provided by the attention mechanism. The proposed SE-ResNet framework delivers high accuracy and interpretability in classifying lung adenocarcinoma from CT images. It shows considerable potential as a decision-support tool to assist radiologists in diagnosis and may serve as a valuable clinical tool with further validation.

This mini-review explores the transformative potential of artificial intelligence (AI) in improving the diagnosis, management, and long-term care of congenital heart diseases (CHDs). AI offers significant advancements across the spectrum of CHD care, from prenatal screening to postnatal management and long-term monitoring. Using AI algorithms, enhanced fetal echocardiography, and genetic tests improves prenatal diagnosis and risk stratification. Postnatally, AI revolutionizes diagnostic imaging analysis, providing more accurate and efficient identification of CHD subtypes and severity. Compared with traditional methods, advanced signal processing techniques enable a more precise assessment of hemodynamic parameters. AI-driven decision support systems tailor treatment strategies, thereby optimizing therapeutic interventions and predicting patient outcomes with greater accuracy. This personalized approach leads to better clinical outcomes and reduced morbidity. Furthermore, AI-enabled remote monitoring and wearable devices facilitate ongoing surveillance, thereby enabling early detection of complications and provision of prompt interventions. This continuous monitoring is crucial in the immediate postoperative period and throughout the patient's life. Despite the immense potential of AI, challenges remain. These include the need for standardized datasets, the development of transparent and understandable AI algorithms, ethical considerations, and seamless integration into existing clinical workflows. Overcoming these obstacles through collaborative data sharing and responsible implementation will unlock the full potential of AI to improve the lives of patients with CHD, ultimately leading to better patient outcomes and improved quality of life.

Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance. Our study aims to assess the diagnostic accuracy of ML in identifying myocarditis using CMR. A systematic search was performed using PubMed, Embase, Web of Science, Cochrane, and Scopus to identify studies reporting the diagnostic accuracy of ML in the detection of myocarditis using CMR. The included studies evaluated both image-based and report-based assessments using various ML models. Diagnostic accuracy was estimated using a Random-Effects model (R software). We found a total of 141 ML model results from a total of 12 studies, which were included in the systematic review. The best models achieved 0.93 (95% Confidence Interval (CI) 0.88-0.96) sensitivity and 0.95 (95% CI 0.89-0.97) specificity. Pooled area under the curve was 0.97 (95% CI 0.93-0.98). Comparisons with human physicians showed comparable results for diagnostic accuracy of myocarditis. Quality assessment concerns and heterogeneity were present. CMR augmented using ML models with advanced algorithms can provide high diagnostic accuracy for myocarditis, even surpassing novice CMR radiologists. However, high heterogeneity, quality assessment concerns, and lack of information on cost-effectiveness may limit the clinical implementation of ML. Future investigations should explore cost-effectiveness and minimize biases in their methodologies.

Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis. Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected. Radiomics, habitat radiomics, and combined models were constructed using machine learning classifiers, including KNN, DT, LR, and SVM. The AUC in the test set was used to evaluate the optimal predictive model. DCA curve and calibration curve were employed to evaluate the predictive performance of the models. SHAP analysis was utilized to visualize the contribution of each feature in the optimal model. For the radiomics-based models, the Combined radiomics model constructed by LR demonstrated better performance, with the AUC of 0.8779 (95% CI: 0.8171-0.9386) in the training set and 0.7166 (95% CI: 0.497-0.9361) in the test set. The Habitat radiomics model (SVM) based on T1-CE showed an AUC of 0.7446 (95% CI: 0.6503- 0.8388) in the training set and 0.7433 (95% CI: 0.5322-0.9545) in the test set. Finally, the Combined all model exhibited the highest predictive performance: LR achieved AUC values of 0.8962 (95% CI: 0.8299-0.9625) and 0.8289 (95% CI: 0.6785-0.9793) in training and test sets, respectively. The Combined all model developed in this study can provide effective reference value in predicting the DEL status of PCNSL, and habitat radiomics significantly enhances the predictive efficacy.