Coronary artery disease (CAD) remains the leading cause of death globally, with computed tomography coronary angiography (CTCA) serving as a key diagnostic tool. However, coronary arterial analysis using CTCA, such as identifying artery-specific features from computational modelling, is labour-intensive and time-consuming. Automated anatomical labelling of coronary arteries offers a potential solution, yet the inherent anatomical variability of coronary trees presents a significant challenge. Traditional knowledge-based labelling methods fall short in leveraging data-driven insights, while recent deep-learning approaches often demand substantial computational resources and overlook critical clinical knowledge. To address these limitations, we propose a lightweight method that integrates anatomical knowledge with rule-based topology constraints for effective coronary artery labelling. Our approach achieves state-of-the-art performance on benchmark datasets, providing a promising alternative for automated coronary artery labelling.

Brain stroke is an illness which affects almost every age group, particularly people over 65. There are two significant kinds of strokes: ischemic and hemorrhagic strokes. Blockage of brain vessels causes an ischemic stroke, while cracks in blood vessels in or around the brain cause a hemorrhagic stroke. In the prompt analysis of brain stroke, patients can live an easier life. Recognizing strokes using medical imaging is crucial for early diagnosis and treatment planning. Conversely, access to innovative imaging methods is restricted, particularly in emerging states, so it is challenging to analyze brain stroke cases of disabled people appropriately. Hence, the development of more accurate, faster, and more reliable diagnostic models for the timely recognition and efficient treatment of ischemic stroke is greatly needed. Artificial intelligence technologies, primarily deep learning (DL), have been widely employed in medical imaging, utilizing automated detection methods. This paper presents an Enhanced Brain Stroke Detection and Classification using Artificial Intelligence with Feature Fusion Technologies (EBSDC-AIFFT) model. This paper aims to develop an enhanced brain stroke detection system for individuals with disabilities, utilizing biomedical images to improve diagnostic accuracy. Initially, the image pre-processing stage involves various steps, including resizing, normalization, data augmentation, and data splitting, to enhance image quality. In addition, the EBSDC-AIFFT model combines the Inception-ResNet-v2 model, the convolutional block attention module-ResNet18 method, and the multi-axis vision transformer technique for feature extraction. Finally, the variational autoencoder (VAE) model is implemented for the classification process. The performance validation of the EBSDC-AIFFT technique is performed under the brain stroke CT image dataset. The comparison study of the EBSDC-AIFFT technique demonstrated a superior accuracy value of 99.09% over existing models.

Accurate delineation of the Gross Tumor Volume (GTV) and the Internal Target Volume (ITV) in early-stage lung tumors is crucial in Stereotactic Body Radiation Therapy (SBRT). Traditionally, the ITVs, which account for breathing motion, are generated by manually contouring GTVs across all breathing phases (BPs), a time-consuming process. This research aims to streamline this workflow by developing a deep learning algorithm to automatically delineate GTVs in all four-dimensional computed tomography (4D-CT) BPs for early-stage Non-Small Cell Lung Cancer Patients (NSCLC). A dataset of 214 early-stage NSCLC patients treated with SBRT was used. Each patient had a 4D-CT scan containing ten reconstructed BPs. The data were divided into a training set (75 %) and a testing set (25 %). Three models SwinUNetR and Dynamic UNet (DynUnet), and a hybrid model combining both (Swin + Dyn)were trained and evaluated using the Dice Similarity Coefficient (DSC), 3 mm Surface Dice Similarity Coefficient (SDSC), and the 95^th percentile Hausdorff distance (HD95). The best performing model was used to delineate GTVs in all test set BPs, creating the ITVs using two methods: all 10 phases and the maximum inspiration/expiration phases. The ITVs were compared to the ground truth ITVs. The Swin + Dyn model achieved the highest performance, with a test set SDSC of 0.79 ± 0.14 for GTV 50 %. For the ITVs, the SDSC was 0.79 ± 0.16 using all 10 BPs and 0.77 ± 0.14 using 2 BPs. At the voxel level, the Swin + DynNet network achieved a sensitivity of 0.75 ± 0.14 and precision of 0.84 ± 0.10 for the ITV 2 breathing phases, and a sensitivity of 0.79 ± 0.12 and precision of 0.80 ± 0.11 for the 10 breathing phases. The Swin + Dyn Net algorithm, trained on the maximum expiration CT-scan effectively delineated gross tumor volumes in all breathing phases and the resulting ITV showed a good agreement with the ground truth (surface DSC = 0.79 ± 0.16 using all 10 BPs and 0.77 ± 0.14 using 2 BPs.). The proposed approach could reduce delineation time and inter-performer variability in the tumor contouring process for NSCLC SBRT workflows.

To compare the lesion detectability of hypovascular liver metastases between 70-keV and 40-keV images from dual energy-computed tomography (CT) reconstructed with deep-learning image reconstruction (DLIR). This multi-institutional, retrospective study included adult patients both pre- and post-treatment for gastrointestinal adenocarcinoma. All patients underwent contrast-enhanced CT with reconstruction at 40-keV and 70-keV. Liver metastases were confirmed using gadoxetic acid-enhanced magnetic resonance imaging. Four radiologists independently assessed lesion conspicuity (per-patient and per-lesion) using a 5-point scale. A radiologic technologist measured image noise, tumor-to-liver contrast, and contrast-to-noise ratio (CNR). Quantitative and qualitative results were compared between 70-keV and 40-keV images. The study included 138 patients (mean age, 69 ± 12 years; 80 men) with 208 liver metastases. Seventy-one patients had liver metastases, while 67 did not. Primary cancer sites included 68 cases of pancreas, 50 colorectal, 12 stomach, and 8 gallbladder/bile duct. No significant difference in per-patient lesion detectability was found between 70-keV images (sensitivity, 71.8-90.1%; specificity, 61.2-85.1%; accuracy, 73.9-79.7%) and 40-keV images (sensitivity, 76.1-90.1%; specificity, 53.7-82.1%; accuracy, 71.7-79.0%) (p = 0.18-> 0.99). Similarly, no significant difference in per-lesion lesion detectability was observed between 70-keV (sensitivity, 67.3-82.2%) and 40-keV images (sensitivity, 68.8-81.7%) (p = 0.20-> 0.99). However, Image noise was significantly higher at 40 keV, along with greater tumor-to-liver contrast and CNRs for both hepatic parenchyma and tumors (p < 0.01). There was no significant difference in hypovascular liver metastases detectability between 70-keV and 40-keV images using the DLIR technology.

To assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) with motion reduction algorithm (SR-DLR-M) in mitigating aorta motion artifacts compared to SR-DLR and deep learning reconstruction with motion reduction algorithm (DLR-M). This retrospective study included 86 patients (mean age, 65.0 ± 14.1 years; 53 males) who underwent contrast-enhanced CT including the chest region. CT images were reconstructed with SR-DLR-M, SR-DLR, and DLR-M. Circular or ovoid regions of interest were placed on the aorta, and the standard deviation of the CT attenuation was recorded as quantitative noise. From the CT attenuation profile along a line region of interest that intersected the left common carotid artery wall, edge rise slope and edge rise distance were calculated. Two readers assessed the images based on artifact, sharpness, noise, structure depiction, and diagnostic acceptability (for aortic dissection). Quantitative noise was 7.4/5.4/8.3 Hounsfield unit (HU) in SR-DLR-M/SR-DLR/DLR-M. Significant differences were observed between SR-DLR-M vs. SR-DLR and DLR-M (p < 0.001). Edge rise slope and edge rise distance were 107.1/108.8/85.8 HU/mm and 1.6/1.5/2.0 mm, respectively, in SR-DLR-M/SR-DLR/DLR-M. Statistically significant differences were detected between SR-DLR-M vs. DLR-M (p ≤ 0.001 for both). Two readers scored artifacts in SR-DLR-M as significantly better than those in SR-DLR (p < 0.001). Scores for sharpness, noise, and structure depiction in SR-DLR-M were significantly better than those in DLR-M (p ≤ 0.005). Diagnostic acceptability in SR-DLR-M was significantly better than that in SR-DLR and DLR-M (p < 0.001). SR-DLR-M provided significantly better CT images in diagnosing aortic dissection compared to SR-DLR and DLR-M.

It is unclear whether coronary artery stenosis, plaque burden, and composition differ between major epicardial arteries supplying ischemic myocardial territories. We studied 837 symptomatic patients undergoing coronary computed tomography angiography (CTA) and ¹⁵O-water PET myocardial perfusion imaging for suspected obstructive coronary artery disease. Coronary CTA was analyzed using Artificial Intelligence-Guided Quantitative Computed Tomography (AI-QCT) to assess stenosis and atherosclerotic plaque characteristics. Myocardial ischemia was defined by regional PET perfusion in the left anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) territories. Among arteries supplying ischemic territories, the LAD exhibited significantly higher stenosis and both absolute and normalized plaque volumes compared to LCX and RCA (p<0.001 for all). Multivariable logistic regression showed diameter stenosis (p=0.001-0.015), percent atheroma volume (PAV; p<0.001), and percent non-calcified plaque volume (p=0.001-0.017) were associated with ischemia across all three arteries. Percent calcified plaque volume was associated with ischemia only in the RCA (p=0.001). The degree of stenosis and atherosclerotic burden are significantly higher in LAD as compared to LCX and RCA, both in epicardial coronary arteries supplying non-ischemic or ischemic myocardial territories. In all the three main coronary arteries both luminal narrowing and plaque burden are independent predictors of ischemia, where the plaque burden is mainly driven by non-calcified plaque. However, many vessels supplying ischemic territories have relatively low stenosis degree and plaque burden, especially in the LCx and RCA, limiting the ability of diameter stenosis and PAV to predict myocardial ischemia.

We developed a machine learning model comprising morphological characteristics, enhancement dynamics, and extracellular volume (ECV) fractions for distinguishing malignant and benign small renal masses (SRMs), supporting personalised management. This retrospective analysis involved 230 patients who underwent SRM resection with preoperative imaging, including 185 internal and 45 external cases. The internal cohort was split into training (n=136) and validation (n=49) sets. Histopathological evaluation categorised the lesions as renal cell carcinomas (n=183) or benign masses (n=47). Eleven multiphasic contrast-enhanced computed tomography (CT) parameters, including the ECV fraction, were manually measured, along with clinical and laboratory data. Feature selection involved univariate analysis and least absolute shrinkage and selection operator regularisation. Feature selection informed various machine learning classifiers, and performance was evaluated using receiver operating characteristic curves and classification tests. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). The analysis included 183 carcinoma and 47 benign SRM cases. Feature selection identified seven discriminative parameters, including the ECV fraction, which informed multiple machine learning models. The Extreme Gradient Boosting model incorporating ECV exhibited optimal performance in distinguishing malignant and benign SRMs, achieving area under the curve values of 0.993 (internal training set), 0.986 (internal validation set), and 0.951 (external test set). SHAP analysis confirmed ECV as the top contributor to SRM characterisation. The integration of multiphase contrast-enhanced CT-derived ECV fraction with conventional contrast-enhanced CT parameters demonstrated diagnostic efficacy in differentiating malignant and benign SRMs.

Bronchial artery chemoembolization (BACE) is a new treatment method for lung cancer. This study aimed to investigate the ability of dual-energy computed tomography (DECT) to predict early recurrence (ER) after BACE among patients with non-small cell lung cancer (NSCLC) who failed first-line therapy. Clinical and imaging data from NSCLC patients undergoing BACE at Wenzhou Medical University Affiliated Fifth *** Hospital (10/2023-06/2024) were retrospectively analyzed. Logistic regression (LR) machine learning models were developed using 5 arterial-phase (AP) virtual monoenergetic images (VMIs; 40, 70, 100, 120, and 150 keV), while deep learning models utilized ResNet50/101/152 architectures with iodine maps. A combined model integrating optimal Rad-score, DL-score, and clinical features was established. Model performance was assessed via area under the receiver operating characteristic curve analysis (AUC), with SHapley Additive exPlanations (SHAP) framework applied for interpretability. A total of 196 patients were enrolled in this study (training cohort: n=158; testing cohort: n=38). The 100 keV machine learning model demonstrated superior performance (AUC=0.751) compared to other VMIs. The deep learning model based on the ResNet101 method (AUC=0.791) performed better than other approaches. The hybrid model combining Rad-score-100keV-A, Rad-score-100keV-V, DL-score-ResNet101-A, DL-score-ResNet101-V, and clinical features exhibited the best performance (AUC=0.798) among all models. DECT holds promise for predicting ER after BACE among NSCLC patients who have failed first-line therapy, offering valuable guidance for clinical treatment planning.

To develop and validate a deep learning model for automated 3D segmentation of rotator cuff muscles on longitudinal CT scans to quantify muscle volume and fat fraction in patients undergoing total shoulder arthroplasty (TSA). The proposed segmentation models adopted DeepLabV3 + with ResNet50 as the backbone. The models were trained, validated, and tested on preoperative or minimum 2-year follow-up CT scans from 53 TSA subjects. 3D Dice similarity scores, average symmetric surface distance (ASSD), 95th percentile Hausdorff distance (HD95), and relative absolute volume difference (RAVD) were used to evaluate the model performance on hold-out test sets. The trained models were applied to a cohort of 172 patients to quantify rotator cuff muscle volumes and fat fractions across preoperative and minimum 2- and 5-year follow-ups. Compared to the ground truth, the models achieved mean Dice of 0.928 and 0.916, mean ASSD of 0.844 mm and 1.028 mm, mean HD95 of 3.071 mm and 4.173 mm, and mean RAVD of 0.025 and 0.068 on the hold-out test sets for the pre-operative and the minimum 2-year follow-up CT scans, respectively. This study developed accurate and reliable deep learning models for automated 3D segmentation of rotator cuff muscles on clinical CT scans in TSA patients. These models substantially reduce the time required for muscle volume and fat fraction analysis and provide a practical tool for investigating how rotator cuff muscle health relates to surgical outcomes. This has the potential to inform patient selection, rehabilitation planning, and surgical decision-making in TSA and RCR.

To analyze the methodological quality of studies on deep learning (DL) in rib fracture imaging with the Must AI Criteria-10 (MAIC-10) checklist, and to report insights and experiences regarding the applicability of the MAIC-10 checklist. An electronic literature search was conducted on the PubMed database. After selection of articles, three radiologists independently rated the articles according to MAIC-10. Differences of the MAIC-10 score for each checklist item were assessed using the Fleiss' kappa coefficient. A total of 25 original articles discussing DL applications in rib fracture imaging were identified. Most studies focused on fracture detection (n = 21, 84%). In most of the research papers, internal cross-validation of the dataset was performed (n = 16, 64%), while only six studies (24%) conducted external validation. The mean MAIC-10 score of the 25 studies was 5.63 (SD, 1.84; range 1-8), with the item "clinical need" being reported most consistently (100%) and the item "study design" being most frequently reported incompletely (94.8%). The average inter-rater agreement for the MAIC-10 score was 0.771. The MAIC-10 checklist is a valid tool for assessing the quality of AI research in medical imaging with good inter-rater agreement. With regard to rib fracture imaging, items such as "study design", "explainability", and "transparency" were often not comprehensively addressed. AI in medical imaging has become increasingly common. Therefore, quality control systems of published literature such as the MAIC-10 checklist are needed to ensure high quality research output. Quality control systems are needed for research on AI in medical imaging. The MAIC-10 checklist is a valid tool to assess AI in medical imaging research quality. Checklist items such as "study design", "explainability", and "transparency" are frequently addressed incomprehensively.