We developed and evaluated a skeletal muscle deep-learning (SMDL) model using skeletal muscle computed tomography (CT) imaging to predict the survival of patients with gastric cancer (GC). This multicenter retrospective study included patients who underwent curative resection of GC between April 2008 and December 2020. Preoperative CT images at the third lumbar vertebra were used to develop a Transformer-based SMDL model for predicting recurrence-free survival (RFS) and disease-specific survival (DSS). The predictive performance of the SMDL model was assessed using the area under the curve (AUC) and benchmarked against both alternative artificial intelligence models and conventional body composition parameters. The association between the model score and survival was assessed using Cox regression analysis. An integrated model combining SMDL signature with clinical variables was constructed, and its discrimination and fairness were evaluated. A total of 1242, 311, and 94 patients were assigned to the training, internal, and external validation cohorts, respectively. The Transformer-based SMDL model yielded AUCs of 0.791-0.943 for predicting RFS and DSS across all three cohorts and significantly outperformed other models and body composition parameters. The model score was a strong independent prognostic factor for survival. Incorporating the SMDL signature into the clinical model resulted in better prognostic prediction performance. The false-negative and false-positive rates of the integrated model were similar across sex and age subgroups, indicating robust fairness. The Transformer-based SMDL model could accurately predict survival of GC and identify patients at high risk of recurrence or death, thereby assisting clinical decision-making.

Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-free environment. The Virtual Lung Screening Trial (VLST), an in silico study inspired by the National Lung Screening Trial (NLST), illustrates the potential of VITs to expedite clinical trials, minimize risks to participants, and promote optimal use of imaging technologies in healthcare. This study aimed to show that a virtual imaging trial platform could investigate some key elements of a major clinical trial, specifically the NLST, which compared Computed tomography (CT) and chest radiography (CXR) for lung cancer screening. With simulated cancerous lung nodules, a virtual patient cohort of 294 subjects was created using XCAT human models. Each virtual patient underwent both CT and CXR imaging, with deep learning models, the AI CT-Reader and AI CXR-Reader, acting as virtual readers to perform recall patients with suspicion of lung cancer. The primary outcome was the difference in diagnostic performance between CT and CXR, measured by the Area Under the Curve (AUC). The AI CT-Reader showed superior diagnostic accuracy, achieving an AUC of 0.92 (95 % CI: 0.90-0.95) compared to the AI CXR-Reader's AUC of 0.72 (95 % CI: 0.67-0.77). Furthermore, at the same 94 % CT sensitivity reported by the NLST, the VLST specificity of 73 % was similar to the NLST specificity of 73.4 %. This CT performance highlights the potential of VITs to replicate certain aspects of clinical trials effectively, paving the way toward a safe and efficient method for advancing imaging-based diagnostics.

Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret diverse datasets within a cohesive analytical framework. In our research, we combine functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) for joint analysis. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from multiple modalities - functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI - within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating an amalgamation of multimodal imaging data. This technique enhances interpretability at the connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved prediction accuracy and uncovers crucial anatomical features and neural connections, deepening our understanding of brain structure and function. This study not only advances multimodal neuroimaging analytics by offering a novel method for integrative analysis of diverse imaging modalities but also improves the understanding of intricate relationships between brain's structural and functional networks and cognitive development.

Multimodal medical images reveal different characteristics of the same anatomy or lesion, offering significant clinical value. Deep learning has achieved widespread success in medical image analysis with large-scale labeled datasets. However, annotating medical images is expensive and labor-intensive for doctors, and the variations between different modalities further increase the annotation cost for multimodal images. This study aims to minimize the annotation cost for multimodal medical image analysis. We proposes a novel active learning framework MDAL based on modality differences for multimodal medical images. MDAL quantifies the sample-wise modality differences through pointwise mutual information estimated by multimodal contrastive learning. We hypothesize that samples with larger modality differences are more informative for annotation and further propose two sampling strategies based on these differences: MaxMD and DiverseMD. Moreover, MDAL could select informative samples in one shot without initial labeled data. We evaluated MDAL on public brain glioma and meningioma segmentation datasets and an in-house ovarian cancer classification dataset. MDAL outperforms other advanced active learning competitors. Besides, when using only 20%, 20%, and 15% of labeled samples in these datasets, MDAL reaches 99.6%, 99.9%, and 99.3% of the performance of supervised training with full labeled dataset, respectively. The results show that our proposed MDAL could significantly reduce the annotation cost for multimodal medical image analysis. We expect MDAL could be further extended to other multimodal medical data for lower annotation costs.

To evaluate the radiation and contrast dose reduction potential of combining 70 kV with deep learning image reconstruction (DLIR) in coronary computed tomography angiography (CCTA) for slender patients with body-mass-index (BMI) ≤25 kg/m2. Sixty patients for CCTA were randomly divided into 2 groups: group A with 120 kV and contrast agent dose of 0.8 mL/kg, and group B with 70 kV and contrast agent dose of 0.5 mL/kg. Group A used adaptive statistical iterative reconstruction-V (ASIR-V) with 50% strength level (50%ASIR-V) while group B used 50% ASIR-V, DLIR of low level (DLIR-L), DLIR of medium level (DLIR-M), and DLIR of high level (DLIR-H) for image reconstruction. The CT values and SD values of coronary arteries and pericardial fat were measured, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The image quality was subjectively evaluated by 2 radiologists using a five-point scoring system. The effective radiation dose (ED) and contrast dose were calculated and compared. Group B significantly reduced radiation dose by 75.6% and contrast dose by 32.9% compared to group A. Group B exhibited higher CT values of coronary arteries than group A, and DLIR-L, DLIR-M, and DLIR-H in group B provided higher SNR values and CNR values and subjective scores, among which DLIR-H had the lowest noise and highest subjective scores. Using 70 kV combined with DLIR significantly reduces radiation and contrast dose while improving image quality in CCTA for slender patients with DLIR-H having the best effect on improving image quality. The 70 kV and DLIR-H may be used in CCTA for slender patients to significantly reduce radiation dose and contrast dose while improving image quality.

Coronary artery disease (CAD) is a prevalent cardiovascular condition with profound health implications. Digital subtraction angiography (DSA) remains the gold standard for diagnosing vascular disease, but its invasiveness and procedural demands underscore the need for alternative diagnostic approaches. Coronary computed tomography angiography (CCTA) has emerged as a promising non-invasive method for accurately classifying and localizing coronary artery stenosis. However, the complexity of CCTA images and their dependence on manual interpretation highlight the essential role of artificial intelligence in supporting clinicians in stenosis detection. This paper introduces a novel framework, Coronaryproposal-based Graph Convolutional Networks (Coronary p-Graph), designed for the automated detection of coronary stenosis from CCTA scans. The framework transforms CCTA data into curved multi-planar reformation (CMPR) images that delineate the coronary artery centerline. After aligning the CMPR volume along this centerline, the entire vasculature is analyzed using a convolutional neural network (CNN) for initial feature extraction. Based on predefined criteria informed by prior knowledge, the model generates candidate stenotic segments, termed "proposals," which serve as graph nodes. The spatial relationships between nodes are then modeled as edges, constructing a graph representation that is processed using a graph convolutional network (GCN) for precise classification and localization of stenotic segments. All CCTA images were rigorously annotated by three expert radiologists, using DSA reports as the reference standard. This novel methodology offers diagnostic performance equivalent to invasive DSA based solely on non-invasive CCTA, potentially reducing the need for invasive procedures. The proposed method was evaluated on a retrospective dataset comprising 259 cases, each with paired CCTA and corresponding DSA reports. Quantitative analyses demonstrated the superior performance of our approach compared to existing methods, with the following metrics: accuracy of 0.844, specificity of 0.910, area under the receiver operating characteristic curve (AUC) of 0.74, and mean absolute error (MAE) of 0.157.

Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was conducted on 91 patients who underwent ultrasonography examinations between January 2016 and December 2023 and were subsequently diagnosed with pleomorphic adenoma or Warthin's tumor based on postoperative pathological findings. A total of 526 ultrasonography images were collected for analysis. Convolutional neural network (CNN) models, including ResNet18, MobileNetV3Small, and InceptionV3, were trained and validated using these images for the differentiation of pleomorphic adenoma and Warthin's tumor. Performance evaluation metrics such as receiver operating characteristic (ROC) curves, area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predictive value were utilized. Two ultrasound physicians, with varying levels of expertise, conducted independent evaluations of the ultrasound images. Subsequently, a comparative analysis was performed between the diagnostic outcomes of the ultrasound physicians and the results obtained from the best-performing model. Inter-rater agreement between routine ultrasonography interpretation by the two expert ultrasonographers and the automatic identification diagnosis of the best model in relation to pathological results was assessed using kappa tests. The deep learning models achieved favorable performance in differentiating pleomorphic adenoma from Warthin's tumor. The ResNet18, MobileNetV3Small, and InceptionV3 models exhibited diagnostic accuracies of 82.4% (AUC: 0.932), 87.0% (AUC: 0.946), and 77.8% (AUC: 0.811), respectively. Among these models, MobileNetV3Small demonstrated the highest performance. The experienced ultrasonographer achieved a diagnostic accuracy of 73.5%, with sensitivity, specificity, positive predictive value, and negative predictive value of 73.7%, 73.3%, 77.8%, and 68.8%, respectively. The less-experienced ultrasonographer achieved a diagnostic accuracy of 69.0%, with sensitivity, specificity, positive predictive value, and negative predictive value of 66.7%, 71.4%, 71.4%, and 66.7%, respectively. The kappa test revealed strong consistency between the best-performing deep learning model and postoperative pathological diagnoses (kappa value: .778, <i>p</i>-value < .001). In contrast, the less-experienced ultrasonographer demonstrated poor consistency in image interpretations (kappa value: .380, <i>p</i>-value < .05). The diagnostic accuracy of the best deep learning model was significantly higher than that of the ultrasonographers, and the experienced ultrasonographer exhibited higher diagnostic accuracy than the less-experienced one. This study demonstrates the promising performance of a deep learning-based method utilizing ultrasonography images for the differentiation of pleomorphic adenoma and Warthin's tumor. The approach reduces subjective errors, provides decision support for clinicians, and improves diagnostic consistency.

Computed tomography (CT)-derived ventilation estimation, also known as CT ventilation imaging (CTVI), is emerging as a potentially crucial tool for designing functional avoidance radiotherapy treatment plans and evaluating therapy responses. However, most conventional CTVI methods are highly dependent on deformation fields from image registration to track volume variations, making them susceptible to registration errors and resulting in low estimation accuracy. In addition, existing deep learning-based CTVI methods typically have the issue of requiring a large amount of labeled data and cannot fully utilize temporal characteristics of 4D CT images. To address these issues, we propose a semi-supervised temporal attention (S²TA) network for lung 4D CT ventilation estimation. Specifically, the semi-supervised learning framework involves a teacher model for generating pseudo-labels from unlabeled 4D CT images, to train a student model that takes both labeled and unlabeled 4D CT images as input. The teacher model is updated as the moving average of the instantly trained student, to prevent it from being abruptly impacted by incorrect pseudo-labels. Furthermore, to fully exploit the temporal information of 4D CT images, a temporal attention architecture is designed to effectively capture the temporal relationships across multiple phases in 4D CT image sequence. Extensive experiments on three publicly available thoracic 4D CT datasets show that our proposed method can achieve higher estimation accuracy than state-of-the-art methods, which could potentially be used for lung functional avoidance radiotherapy and treatment response modeling.

Unsupervised anomaly detection (UAD) is crucial in low-dose computed tomography (LDCT). Recent AI technologies, leveraging global features, have enabled effective UAD with minimal training data of normal patients. However, this approach, devoid of utilizing local features, exhibits vulnerability in detecting deep lesions within the lungs. In other words, while the conventional use of global features can achieve high specificity, it often comes with limited sensitivity. Developing a UAD AI model with high sensitivity is essential to prevent false negatives, especially in screening patients with diseases demonstrating high mortality rates. We have successfully pioneered a new LDCT UAD AI model that leverages local features, achieving a previously unattainable increase in sensitivity compared to global methods (17.5% improvement). Furthermore, by integrating this approach with conventional global-based techniques, we have successfully consolidated the advantages of each model - high sensitivity from the local model and high specificity from the global model - into a single, unified, trained model (17.6% and 33.5% improvement, respectively). Without the need for additional training, we anticipate achieving significant diagnostic efficacy in various LDCT applications, where both high sensitivity and specificity are essential, using our fixed model. Code is available at https://github.com/kskim-phd/Fusion-UADL.

This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations for dual-energy X-ray absorptiometry (DXA) scans from chest low-dose computed tomography (LDCT). The framework was developed using data from 321 individuals and validated with an independent test cohort of 186 individuals. It addresses two classification tasks: (1) distinguishing normal from abnormal bone mineral density (BMD) and (2) differentiating osteoporosis from non-osteoporosis. Three field-of-view (FOV) settings-fitFOV (entire vertebra), halfFOV (vertebral body only), and largeFOV (fitFOV + 20 %)-were analyzed to assess their impact on model performance. Model predictions were weighted and combined to enhance classification accuracy, and visualizations were generated to improve decision transparency. DXA scans were recommended for individuals classified as having abnormal BMD or osteoporosis. The ensemble framework significantly outperformed individual models in both classification tasks (McNemar test, p < 0.001). In the development cohort, it achieved 91.6 % accuracy for task 1 with largeFOV (area under the receiver operating characteristic curve [AUROC]: 0.97) and 86.0 % accuracy for task 2 with fitFOV (AUROC: 0.94). In the test cohort, it demonstrated 86.6 % accuracy for task 1 (AUROC: 0.93) and 76.9 % accuracy for task 2 (AUROC: 0.99). DXA recommendation accuracy was 91.6 % and 87.1 % in the development and test cohorts, respectively, with notably high accuracy for osteoporosis detection (98.7 % and 100 %). This combined ViT-CNN framework effectively assesses bone status from LDCT images, particularly when utilizing fitFOV and largeFOV settings. By visualizing classification confidence and vertebral abnormalities, the proposed framework enhances decision transparency and supports clinicians in making informed DXA recommendations following opportunistic osteoporosis screening.