Identifying amyloid-beta (Aβ)-positive patients is essential for Alzheimer's disease (AD) clinical trials and disease-modifying treatments but currently requires PET or cerebrospinal fluid sampling. Previous MRI-based deep learning models, using only T1-weighted (T1w) images, have shown moderate performance. Multi-contrast MRI and PET-based quantitative Aβ deposition were retrospectively obtained from three public datasets: ADNI, OASIS3, and A4. Aβ positivity was defined using each dataset's recommended centiloid threshold. Two EfficientNet models were trained to predict amyloid positivity: one using only T1w images and another incorporating both T1w and T2-FLAIR. Model performance was assessed using an internal held-out test set, evaluating AUC, accuracy, sensitivity, and specificity. External validation was conducted using an independent cohort from Stanford Alzheimer's Disease Research Center. DeLong's and McNemar's tests were used to compare AUC and accuracy, respectively. A total of 4,056 exams (mean [SD] age: 71.6 [6.3] years; 55% female; 55% amyloid-positive) were used for network development, and 149 exams were used for external testing (mean [SD] age: 72.1 [9.6] years; 58% female; 56% amyloid-positive). The multi-contrast model outperformed the single-modality model in the internal held-out test set (AUC: 0.67, 95% CI: 0.65-0.70, <i>P</i> < 0.001; accuracy: 0.63, 95% CI: 0.62-0.65, <i>P</i> < 0.001) compared to the T1w-only model (AUC: 0.61; accuracy: 0.59). Among cognitive subgroups, the highest performance (AUC: 0.71) was observed in mild cognitive impairment. The multi-contrast model also demonstrated consistent performance in the external test set (AUC: 0.65, 95% CI: 0.60-0.71, <i>P</i> = 0.014; accuracy: 0.62, 95% CI: 0.58- 0.65, <i>P</i> < 0.001). The use of multi-contrast MRI, specifically incorporating T2-FLAIR in addition to T1w images, significantly improved the predictive accuracy of PET-determined amyloid status from MRI scans using a deep learning approach. Aβ＝ amyloid-beta; AD＝ Alzheimer's disease; AUC＝ area under the receiver operating characteristic curve; CN＝ cognitively normal; MCI＝ mild cognitive impairment; T1w = T1-wegithed; T2-FLAIR = T2-weighted fluid attenuated inversion recovery; FBP＝¹⁸F-florbetapir; FBB＝¹⁸F-florbetaben; SUVR＝ standard uptake value ratio.

Early-stage cirrhosis frequently presents without symptoms, making timely identification of high-risk patients challenging. We aimed to develop a deep learning-based triple-modal fusion liver cirrhosis network (TMF-LCNet) for the prediction of adverse outcomes, offering a promising tool to enhance early risk assessment and improve clinical management strategies. This retrospective study included 243 patients with early-stage cirrhosis across two centers. Adverse outcomes were defined as the development of severe complications like ascites, hepatic encephalopathy and variceal bleeding. TMF-LCNet was developed by integrating three types of data: non-contrast abdominal CT images, radiomic features extracted from liver and spleen, and clinical text detailing laboratory parameters and adipose tissue composition measurements. TMF-LCNet was compared with conventional methods on the same dataset, and single-modality versions of TMF-LCNet were tested to determine the impact of each data type. Model effectiveness was measured using the area under the receiver operating characteristics curve (AUC) for discrimination, calibration curves for model fit, and decision curve analysis (DCA) for clinical utility. TMF-LCNet demonstrated superior predictive performance compared to conventional image-based, radiomics-based, and multimodal methods, achieving an AUC of 0.797 in the training cohort (n = 184) and 0.747 in the external test cohort (n = 59). Only TMF-LCNet exhibited robust model calibration in both cohorts. Of the three data types, the imaging modality contributed the most, as the image-only version of TMF-LCNet achieved performance closest to the complete version (AUC = 0.723 and 0.716, respectively; p > 0.05). This was followed by the text modality, with radiomics contributing the least, a pattern consistent with the clinical utility trends observed in DCA. TMF-LCNet represents an accurate and robust tool for predicting adverse outcomes in early-stage cirrhosis by integrating multiple data types. It holds potential for early identification of high-risk patients, guiding timely interventions, and ultimately improving patient prognosis.

In domain continual medical image segmentation, distillation-based methods mitigate catastrophic forgetting by continuously reviewing old knowledge. However, these approaches often exhibit biases towards both new and old knowledge simultaneously due to confounding factors, which can undermine segmentation performance. To address these biases, we propose the Causality-Adjusted Data Augmentation (CauAug) framework, introducing a novel causal intervention strategy called the Texture-Domain Adjustment Hybrid-Scheme (TDAHS) alongside two causality-targeted data augmentation approaches: the Cross Kernel Network (CKNet) and the Fourier Transformer Generator (FTGen). (1) TDAHS establishes a domain-continual causal model that accounts for two types of knowledge biases by identifying irrelevant local textures (L) and domain-specific features (D) as confounders. It introduces a hybrid causal intervention that combines traditional confounder elimination with a proposed replacement approach to better adapt to domain shifts, thereby promoting causal segmentation. (2) CKNet eliminates confounder L to reduce biases in new knowledge absorption. It decreases reliance on local textures in input images, forcing the model to focus on relevant anatomical structures and thus improving generalization. (3) FTGen causally intervenes on confounder D by selectively replacing it to alleviate biases that impact old knowledge retention. It restores domain-specific features in images, aiding in the comprehensive distillation of old knowledge. Our experiments show that CauAug significantly mitigates catastrophic forgetting and surpasses existing methods in various medical image segmentation tasks. The implementation code is publicly available at: https://github.com/PerceptionComputingLab/CauAug_DCMIS.

The impact of covert cerebrovascular disease on falls in the general population is not well-known. Here, we determine the time to a first fall following incidentally detected covert cerebrovascular disease during a clinical neuroimaging episode. This longitudinal cohort study assessed computed tomography (CT) and magnetic resonance imaging from 2009 to 2019 of patients aged >50 years registered with Kaiser Permanente Southern California which is a healthcare organization combining health plan coverage with coordinated medical services, excluding those with before stroke/dementia. We extracted evidence of incidental covert brain infarcts (CBI) and white matter hyperintensities/hypoattenuation (WMH) from imaging reports using natural language processing. We examined associations of CBI and WMH with falls requiring medical attention, using Cox proportional hazards regression models with adjustment for 12 variables including age, sex, ethnicity multimorbidity, polypharmacy, and incontinence. We assessed 241 050 patients, mean age 64.9 (SD, 10.42) years, 61.3% female, detecting covert cerebrovascular disease in 31.1% over a mean follow-up duration of 3.04 years. A recorded fall occurred in 21.2% (51 239/241 050) during follow-up. On CT, single fall incidence rate/1000 person-years (p-y) was highest in individuals with both CBI and WMH on CT (129.3 falls/1000 p-y [95% CI, 123.4-135.5]), followed by WMH (109.9 falls/1000 p-y [108.0-111.9]). On magnetic resonance imaging, the incidence rate was the highest with both CBI and WMH (76.3 falls/1000 p-y [95% CI, 69.7-83.2]), followed by CBI (71.4 falls/1000 p-y [95% CI, 65.9-77.2]). The adjusted hazard ratio for single index fall in individuals with CBI on CT was 1.13 (95% CI, 1.09-1.17); versus magnetic resonance imaging 1.17 (95% CI, 1.08-1.27). On CT, the risk for single index fall incrementally increased for mild (1.37 [95% CI, 1.32-1.43]), moderate (1.57 [95% CI, 1.48-1.67]), or severe WMH (1.57 [95% CI, 1.45-1.70]). On magnetic resonance imaging, index fall risk similarly increased with increasing WMH severity: mild (1.11 [95% CI, 1.07-1.17]), moderate (1.21 [95% CI, 1.13-1.28]), and severe WMH (1.34 [95% CI, 1.22-1.46]). In a large population with neuroimaging, CBI and WMH are independently associated with greater risks of an index fall. Increasing severities of WMH are associated incrementally with fall risk across imaging modalities.

Craniosynostosis, characterized by the premature fusion of cranial sutures, can lead to significant neurological and developmental complications, necessitating early diagnosis and precise treatment. Traditional cranial morphologic assessment has relied on CT scans, which expose infants to ionizing radiation. Recently, 3D stereophotogrammetry has emerged as a noninvasive alternative, but accurately aligning 3D photographs within standardized reference frames, such as the Sella-turcica-Nasion (S-N) frame, remains a challenge. This study proposes a novel method for predicting the Sella turcica (ST) coordinate from 3D cranial surface models using Principal Component Analysis (PCA) combined with a Feedforward Neural Network (FFNN). The accuracy of this method is compared with the conventional Computed Cranial Focal Point (CCFP) method, which has limitations, especially in cases of asymmetric cranial deformations like plagiocephaly. A data set of 153 CT scans, including 68 craniosynostosis subjects, was used to train and test the PCA-FFNN model. The results demonstrate that the PCA-FFNN approach outperforms CCFP, achieving significantly lower deviations in ST coordinate predictions (3.61 vs. 8.38 mm, P<0.001), particularly along the y-axes and z-axes. In addition, mesh realignment within the S-N reference frame showed improved accuracy with the PCA-FFNN method, evidenced by lower mean deviations and reduced dispersion in distance maps. These findings highlight the potential of the PCA-FFNN approach to provide a more reliable, noninvasive solution for cranial assessment, improving craniosynostosis follow-up and enhancing clinical outcomes.

New, intermediate-sized nodules in lung cancer screening undergo follow-up CT, but some of these will resolve. We evaluated the performance of a multi-view convolutional neural network (CNN) in distinguishing resolving and non-resolving new, intermediate-sized lung nodules. This retrospective study utilized data on 344 intermediate-sized nodules (50-500 mm³) in 250 participants from the NELSON (Dutch-Belgian Randomized Lung Cancer Screening) trial. We implemented four-fold cross-validation for model training and testing. A multi-view CNN model was developed by combining three two-dimensional (2D) CNN models and one three-dimensional (3D) CNN model. We used 2D, 2.5D, and 3D models for comparison. The models' performance was evaluated using sensitivity, specificity, and area under the ROC curve (AUC). Specificity, indicating what percentage of non-resolving nodules requiring follow-up can be correctly predicted, was maximized. Among all nodules, 18.3% (63) were resolving. The multi-view CNN model achieved an AUC of 0.81, with a mean sensitivity of 0.63 (SD, 0.15) and a mean specificity of 0.93 (SD, 0.02). The model significantly improved performance compared to 2D, 2.5D, or 3D models (p < 0.05). Under the premise of specificity greater than 90% (meaning < 10% of non-resolving nodules are incorrectly identified as resolving), follow-up CT in 14% of individuals could be prevented. The multi-view CNN model achieved high specificity in discriminating new intermediate nodules that would need follow-up CT by identifying non-resolving nodules. After further validation and optimization, this model may assist with decision-making when new intermediate nodules are found in lung cancer screening. The multi-view CNN-based model has the potential to reduce unnecessary follow-up scans when new nodules are detected, aiding radiologists in making earlier, more informed decisions. Predicting the resolution of new intermediate lung nodules in lung cancer screening CT is a challenge. Our multi-view CNN model showed an AUC of 0.81, a specificity of 0.93, and a sensitivity of 0.63 at the nodule level. The multi-view model demonstrated a significant improvement in AUC compared to the three 2D models, one 2.5D model, and one 3D model.

Coronary CT Angiography (CCTA) is essential for assessing atherosclerosis and coronary artery disease, aiding in early detection, risk prediction, and clinical assessment. However, traditional CCTA interpretation is limited by observer variability, time inefficiency, and inconsistent plaque characterization. AI has emerged as a transformative tool, enhancing diagnostic accuracy, workflow efficiency, and risk prediction for major adverse cardiovascular events (MACE). Studies show that AI improves stenosis detection by 27%, inter-reader agreement by 30%, and reduces reporting times by 40%, thereby addressing key limitations of manual interpretation. Integrating AI with multimodal imaging (e.g., FFR-CT, PET-CT) further enhances ischemia detection by 28% and lesion classification by 35%, providing a more comprehensive cardiovascular evaluation. This review synthesizes recent advancements in CCTA-AI automation, risk stratification, and precision diagnostics while critically analyzing data quality, generalizability, ethics, and regulation challenges. Future directions, including real-time AI-assisted triage, cloud-based diagnostics, and AI-driven personalized medicine, are explored for their potential to revolutionize clinical workflows and optimize patient outcomes.

This multicenter study aimed to develop a deep learning-based autosegmentation model for pancreatic cancer and surrounding anatomical structures using computed tomography (CT) to enhance surgical planning. We included patients with pancreatic cancer who underwent pancreatic surgery at three tertiary referral hospitals. A hierarchical Swin Transformer V2 model was implemented to segment the pancreas, pancreatic cancers, and peripancreatic structures from preoperative contrast-enhanced CT scans. Data was divided into training and internal validation sets at a 3:1 ratio (from one tertiary institution), with separately prepared external validation set (from two separate institutions). Segmentation performance was quantitatively assessed using the dice similarity coefficient (DSC) and qualitatively evaluated (complete vs partial vs absent). A total of 275 patients (51.6% male, mean age 65.8 ± 9.5 years) were included (176 training group, 59 internal validation group, and 40 external validation group). No significant differences in baseline characteristics were observed between the groups. The model achieved an overall mean DSC of 75.4 ± 6.0 and 75.6 ± 4.8 in the internal and external validation groups, respectively. It showed high accuracy particularly in the pancreas parenchyma (84.8 ± 5.3 and 86.1 ± 4.1) and lower accuracy in pancreatic cancer (57.0 ± 28.7 and 54.5 ± 23.5). The DSC scores for pancreatic cancer tended to increase with larger tumor sizes. Moreover, the qualitative assessments revealed high accuracy in the superior mesenteric artery (complete segmentation, 87.5%-100%), portal and superior mesenteric vein (97.5%-100%), pancreas parenchyma (83.1%-87.5%), but lower accuracy in cancers (62.7%-65.0%). The deep learning-based autosegmentation model for 3D visualization of pancreatic cancer and peripancreatic structures showed robust performance. Further improvement will enhance many promising applications in clinical research.

Early detection of glaucoma represents a vital factor in securing vision while the disease retains its position as one of the central causes of blindness worldwide. The current glaucoma screening strategies with expert interpretation depend on complex and time-consuming procedures which slow down both diagnosis processes and intervention timing. This research adopts a complex automated glaucoma diagnostic system that combines optimized segmentation solutions together with classification platforms. The proposed segmentation approach implements an enhanced version of U-Net++ using dynamic parameter control provided by GWO to segment optic disc and cup regions in retinal fundus images. Through the implementation of GWO the algorithm uses wolf-pack hunting strategies to adjust parameters dynamically which enables it to locate diverse textural patterns inside images. The system uses a CapsNet capsule network for classification because it maintains visual spatial organization to detect glaucoma-related patterns precisely. The developed system secures an evaluation accuracy of 95.1% in segmentation and classification tasks better than typical approaches. The automated system eliminates and enhances clinical diagnostic speed as well as diagnostic precision. The tool stands out because of its supreme detection accuracy and reliability thus making it an essential clinical early-stage glaucoma diagnostic system and a scalable healthcare deployment solution. To develop an advanced automated glaucoma diagnostic system by integrating an optimized U-Net++ segmentation model with a Capsule Network (CapsNet) classifier, enhanced through Grey Wolf Optimization Algorithm (GWOA), for precise segmentation of optic disc and cup regions and accurate glaucoma classification from retinal fundus images. This study proposes a two-phase computer-assisted diagnosis (CAD) framework. In the segmentation phase, an enhanced U-Net++ model, optimized by GWOA, is employed to accurately delineate the optic disc and cup regions in fundus images. The optimization dynamically tunes hyperparameters based on grey wolf hunting behavior for improved segmentation precision. In the classification phase, a CapsNet architecture is used to maintain spatial hierarchies and effectively classify images as glaucomatous or normal based on segmented outputs. The performance of the proposed model was validated using the ORIGA retinal fundus image dataset, and evaluated against conventional approaches. The proposed GWOA-UNet++ and CapsNet framework achieved a segmentation and classification accuracy of 95.1%, outperforming existing benchmark models such as MTA-CS, ResFPN-Net, DAGCN, MRSNet and AGCT. The model demonstrated robustness against image irregularities, including variations in optic disc size and fundus image quality, and showed superior performance across accuracy, sensitivity, specificity, precision, and F1-score metrics. The developed automated glaucoma detection system exhibits enhanced diagnostic accuracy, efficiency, and reliability, offering significant potential for early-stage glaucoma detection and clinical decision support. Future work will involve large-scale multi-ethnic dataset validation, integration with clinical workflows, and deployment as a mobile or cloud-based screening tool.

To develop a deep learning model based on Residual Networks (ResNet) for the automated and accurate identification of contrast phases in abdominal CT images. A dataset of 1175 abdominal contrast-enhanced CT scans was retrospectively collected for the model development, and another independent dataset of 215 scans from five hospitals was collected for external testing. Each contrast phase was independently annotated by two radiologists. A ResNet-based model was developed to automatically classify phases into the early arterial phase (EAP) or late arterial phase (LAP), portal venous phase (PVP), and delayed phase (DP). Strategy A identified EAP or LAP, PVP, and DP in one step. Strategy B used a two-step approach: first classifying images as arterial phase (AP), PVP, and DP, then further classifying AP images into EAP or LAP. Model performance and strategy comparison were evaluated. In the internal test set, the overall accuracy of the two-step strategy was 98.3% (283/288; p < 0.001), significantly higher than that of the one-step strategy (91.7%, 264/288; p < 0.001). In the external test set, the two-step model achieved an overall accuracy of 99.1% (639/645), with sensitivities of 95.1% (EAP), 99.4% (LAP), 99.5% (PVP), and 99.5% (DP). The proposed two-step ResNet-based model provides highly accurate and robust identification of contrast phases in abdominal CT images, outperforming the conventional one-step strategy. Automated and accurate identification of contrast phases in abdominal CT images provides a robust tool for improving image quality control and establishes a strong foundation for AI-driven applications, particularly those leveraging contrast-enhanced abdominal imaging data. Accurate identification of contrast phases is crucial in abdominal CT imaging. The two-step ResNet-based model achieved superior accuracy across internal and external datasets. Automated phase classification strengthens imaging quality control and supports precision AI applications.