The automated segmentation of computed tomography (CT) images has made their opportunistic use more feasible, yet, the association of muscle area and density from multiple anatomical regions with functional outcomes and mortality risk in older adults has not been fully explored. We aimed to determine if muscle area and density at the L1 and L3 vertebra and right and left proximal thigh were similarly related to functional outcomes and 10-year mortality risk. Men from the Osteoporotic Fractures in Men (MrOS) study who had CT images, measures of grip strength, 6 m walking speed, and leg power (Nottingham Power Rig) at the baseline visit were included in the analyses (n = 3290, 73.7 ± 5.8 years). CT images were automatically segmented to derive muscle area and muscle density. Deaths were centrally adjudicated over a 10-year follow-up. Linear regression and proportional hazards were used to model relationships of CT muscle metrics with functional outcomes and mortality, respectively, while adjusting for covariates. Muscle area and density were positively related to functional outcomes regardless of anatomical region, with the most variance explained in leg power (adjusted R² = 0.40-0.46), followed by grip strength (adjusted R² = 0.25-0.29) and walking speed (adjusted R² = 0.18-0.20). A one-unit SD increase in muscle area and density was associated with a 5%-13% and 8%-21% decrease in the risk of all-cause mortality, respectively, with the strongest associations observed at the right and left thigh. Automated measures of CT muscle area and density are related to functional outcomes and risk of mortality in older men, regardless of CT anatomical region.

Accurate tumor delineation is foundational to radiotherapy. In the era of deep learning, the automation of this labor-intensive and variation-prone process is increasingly tractable. We developed a deep neural network model to segment gross tumor volumes (GTVs) in the lung and propagate them across 4D CT images to generate an internal target volume (ITV), capturing tumor motion during respiration. Using a multicenter cohort-based registry from 9 clinics across 2 health systems, we trained a 3D UNet model (iSeg) on pre-treatment CT images and corresponding GTV masks (n = 739, 5-fold cross-validation) and validated it on two independent cohorts (n = 161; n = 102). The internal cohort achieved a median Dice (DSC) of 0.73 [IQR: 0.62-0.80], with comparable performance in external cohorts (DSC = 0.70 [0.52-0.78] and 0.71 [0.59-79]), indicating multi-site validation. iSeg matched human inter-observer variability and was robust to image quality and tumor motion (DSC = 0.77 [0.68-0.86]). Machine-generated ITVs were significantly smaller than physician delineated contours (p < 0.0001), indicating more precise delineation. Notably, higher false positive voxel rate (regions segmented by the machine but not the human) were associated with increased local failure (HR: 1.01 per voxel, p = 0.03), suggesting the clinical relevance of these discordant regions. These results mark a leap in automated target volume segmentation and suggest that machine delineation can enhance the accuracy, reproducibility, and efficiency of this core task in radiotherapy.

This study aims to evaluate the success of artificial intelligence models developed using convolutional neural network-based algorithms on CBCT images. Labeling was done by segmentation method for 15 different conditions including caries, restorative filling material, root-canal filling material, dental implant, implant supported crown, crown, pontic, impacted tooth, supernumerary tooth, residual root, osteosclerotic area, periapical lesion, radiolucent jaw lesion, radiopaque jaw lesion, and mixed appearing jaw lesion on the data set consisting of 300 CBCT images. In model development, the Mask R-CNN architecture and ResNet 101 model were used as a transfer learning method. The success metrics of the model were calculated with the confusion matrix method. When the F1 scores of the developed models were evaluated, the most successful dental implant was found to be 1, and the lowest F1 score was found to be a mixed appearing jaw lesion. F1 scores were respectively dental implant, root canal filling material, implant supported crown, restorative filling material, radiopaque jaw lesion, crown, pontic, impacted tooth, caries, residual tooth root, radiolucent jaw lesion, osteosclerotic area, periapical lesion, supernumerary tooth, for mixed appearing jaw lesion; 1 is 0.99, 0.98, 0.98, 0.97, 0.96, 0.96, 0.95, 0.94, 0.94, 0.94, 0.90, 0.90, 0.87, and 0.8. Interpreting CBCT images is a time-consuming process and requires expertise. In the era of digital transformation, artificial intelligence-based systems that can automatically evaluate images and convert them into report format as a decision support mechanism will contribute to reducing the workload of physicians, thus increasing the time allocated to the interpretation of pathologies.

Automated radiology report generation (RRG) aims to produce detailed textual reports from clinical imaging, such as computed tomography (CT) scans, to improve the accuracy and efficiency of diagnosis and provision of management advice. RRG is complicated by two key challenges: (1) inherent complexity in extracting relevant information from imaging data under resource constraints, and (2) difficulty in objectively evaluating discrepancies between model-generated and expert-written reports. To address these challenges, we propose $\mu^2$LLM, a $\underline{\textbf{mu}}$ltiscale $\underline{\textbf{mu}}$ltimodal large language models for RRG tasks. The novel ${\mu}^2$Tokenizer, as an intermediate layer, integrates multi-modal features from the multiscale visual tokenizer and the text tokenizer, then enhances report generation quality through direct preference optimization (DPO), guided by GREEN-RedLlama. Experimental results on four large CT image-report medical datasets demonstrate that our method outperforms existing approaches, highlighting the potential of our fine-tuned $\mu^2$LLMs on limited data for RRG tasks. At the same time, for prompt engineering, we introduce a five-stage, LLM-driven pipeline that converts routine CT reports into paired visual-question-answer triples and citation-linked reasoning narratives, creating a scalable, high-quality supervisory corpus for explainable multimodal radiology LLM. All code, datasets, and models will be publicly available in our official repository. https://github.com/Siyou-Li/u2Tokenizer

Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from image artifacts and loss of detail due to quantum and electronic noise, potentially impacting diagnostic accuracy. Transformer combined with diffusion models has been a promising approach for image generation. Nevertheless, existing methods exhibit limitations in preserving finegrained image details. To address this issue, frequency domain-directed diffusion transformer (FD-DiT) is proposed for LDCT reconstruction. FD-DiT centers on a diffusion strategy that progressively introduces noise until the distribution statistically aligns with that of LDCT data, followed by denoising processing. Furthermore, we employ a frequency decoupling technique to concentrate noise primarily in high-frequency domain, thereby facilitating effective capture of essential anatomical structures and fine details. A hybrid denoising network is then utilized to optimize the overall data reconstruction process. To enhance the capability in recognizing high-frequency noise, we incorporate sliding sparse local attention to leverage the sparsity and locality of shallow-layer information, propagating them via skip connections for improving feature representation. Finally, we propose a learnable dynamic fusion strategy for optimal component integration. Experimental results demonstrate that at identical dose levels, LDCT images reconstructed by FD-DiT exhibit superior noise and artifact suppression compared to state-of-the-art methods.

This study proposes Scout-Dose-TCM for direct, prospective estimation of organ-level doses under tube current modulation (TCM) and compares its performance to two established methods. We analyzed contrast-enhanced chest-abdomen-pelvis CT scans from 130 adults (120 kVp, TCM). Reference doses for six organs (lungs, kidneys, liver, pancreas, bladder, spleen) were calculated using MC-GPU and TotalSegmentator. Based on these, we trained Scout-Dose-TCM, a deep learning model that predicts organ doses corresponding to discrete cosine transform (DCT) basis functions, enabling real-time estimates for any TCM profile. The model combines a feature learning module that extracts contextual information from lateral and frontal scouts and scan range with a dose learning module that output DCT-based dose estimates. A customized loss function incorporated the DCT formulation during training. For comparison, we implemented size-specific dose estimation per AAPM TG 204 (Global CTDIvol) and its organ-level TCM-adapted version (Organ CTDIvol). A 5-fold cross-validation assessed generalizability by comparing mean absolute percentage dose errors and r-squared correlations with benchmark doses. Average absolute percentage errors were 13% (Global CTDIvol), 9% (Organ CTDIvol), and 7% (Scout-Dose-TCM), with bladder showing the largest discrepancies (15%, 13%, and 9%). Statistical tests confirmed Scout-Dose-TCM significantly reduced errors vs. Global CTDIvol across most organs and improved over Organ CTDIvol for the liver, bladder, and pancreas. It also achieved higher r-squared values, indicating stronger agreement with Monte Carlo benchmarks. Scout-Dose-TCM outperformed Global CTDIvol and was comparable to or better than Organ CTDIvol, without requiring organ segmentations at inference, demonstrating its promise as a tool for prospective organ-level dose estimation in CT.

Accurate stratification of the activity index of Crohn's disease (CD) using computed tomography enterography (CTE) radiomics and serum markers can aid in predicting disease progression and assist physicians in personalizing therapeutic regimens for patients with CD. This retrospective study enrolled 233 patients diagnosed with CD between January 2019 and August 2024. Patients were divided into training and testing cohorts at a ratio of 7:3 and further categorized into remission, mild active phase, and moderate-severe active phase groups based on simple endoscopic score for CD (SEC-CD). Radiomics features were extracted from CTE venous images, and T-test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The serum markers were selected based on the variance analysis. We also developed a random forest (RF) model for multi-class stratification of CD. The model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and quantified the contribution of each feature in the dataset to CD activity via Shapley additive exPlanations (SHAP) values. Finally, we enrolled gender, radiomics scores, and serum scores to develop a nomogram model to verify the effectiveness of feature extraction. 14 non-zero coefficient radiomics features and six serum markers with significant differences (P<0.01) were ultimately selected to predict CD activity. The AUC (micro/macro) for the ensemble machine learning model combining the radiomics features and serum markers is 0.931/0.928 for three-class. The AUC for the remission phase, the mild active phase, and the moderate-severe active phase were 0.983, 0.852, and 0.917, respectively. The mean AUC for the nomogram model was 0.940. A radiomics model was developed by integrating radiomics and serum markers of CD patients, achieving enhanced consistency with SEC-CD in grade CD. This model has the potential to assist clinicians in accurate diagnosis and treatment.

Artificial intelligence (AI) has enabled accurate and fast plaque quantification from coronary computed tomography angiography (CCTA). However, AI detects any coronary plaque in up to 97% of patients. To avoid overdiagnosis, a plaque burden safety cut-off for future coronary events is needed. Percent atheroma volume (PAV) was quantified with AI-guided quantitative computed tomography in a blinded fashion. Safety cut-off derivation was performed in the Turku CCTA registry (Finland), and pre-defined as ≥90% sensitivity for acute coronary syndrome (ACS). External validation was performed in the Amsterdam CCTA registry (the Netherlands). In the derivation cohort, 100/2271 (4.4%) patients experienced ACS (median follow-up 6.9 years). A threshold of PAV ≥ 2.6% was derived with 90.0% sensitivity and negative predictive value (NPV) of 99.0%. In the validation cohort 27/568 (4.8%) experienced ACS (median follow-up 6.7 years) with PAV ≥ 2.6% showing 92.6% sensitivity and 99.0% NPV for ACS. In the derivation cohort, 45.2% of patients had PAV < 2.6 vs. 4.3% with PAV 0% (no plaque) (P < 0.001) (validation cohort: 34.3% PAV < 2.6 vs. 2.6% PAV 0%; P < 0.001). Patients with PAV ≥ 2.6% had higher adjusted ACS rates in the derivation [Hazard ratio (HR) 4.65, 95% confidence interval (CI) 2.33-9.28, P < 0.001] and validation cohort (HR 7.31, 95% CI 1.62-33.08, P = 0.010), respectively. This study suggests that PAV up to 2.6% quantified by AI is associated with low-ACS risk in two independent patient cohorts. This cut-off may be helpful for clinical application of AI-guided CCTA analysis, which detects any plaque in up to 96-97% of patients.

This research aimed to compare the classification accuracy of three machine learning (ML) methods (random forest (RF), support vector machines (SVM), linear discriminant analysis (LDA)) for sex estimation of sub-adults using cranial computed tomography (CCT) images. A total of 521 CCT scans from sub-adult Malaysians aged 0 to 20 were analysed using Mimics software (Materialise Mimics Ver. 21). Plane-to-plane (PTP) protocol was used for measuring 14 chosen craniometric parameters. A trio of machine learning algorithms RF, SVM, and LDA with GridSearchCV was used to produce classification models for sex estimation. In addition, performance was measured in the form of accuracy, precision, recall, and F1-score, among others. RF produced testing accuracy of 73%, with the best hyperparameters of max_depth = 6, max_samples = 40, and n_estimators = 45. SVM obtained an accuracy of 67% with the best hyperparameters: learning rate (C) = 10, gamma = 0.01, and kernel = radial basis function (RBF). LDA obtained the lowest accuracy of 65% with shrinkage of 0.02. Among the tested ML methods, RF showed the highest testing accuracy in comparison to SVM and LDA. This is the first AI-based classification model that can be used for estimating sex in sub-adults using CCT scans.