Surgeons treating skeletally immature patients use skeletal age to determine appropriate surgical strategies. Traditional bone age estimation methods utilizing hand radiographs are time-consuming. To develop highly accurate/reliable deep learning (DL) models for determination of accurate skeletal age from hand radiographs. Cohort Study. The authors utilized 3 publicly available hand radiograph data sets for model development/validation from (1) the Radiological Society of North America (RSNA), (2) the Radiological Hand Pose Estimation (RHPE) data set, and (3) the Digital Hand Atlas (DHA). All 3 data sets report corresponding sex and skeletal age. The RHPE and DHA also contain chronological age. After image preprocessing, a ConvNeXt model was trained first on the RSNA data set using sex/skeletal age as inputs using 5-fold cross-validation, with subsequent training on the RHPE with addition of chronological age. Final model validation was performed on the DHA and an institutional data set of 200 images. The first model, trained on the RSNA, achieved a mean absolute error (MAE) of 3.68 months on the RSNA test set and 5.66 months on the DHA. This outperformed the 4.2 months achieved on the RSNA test set by the best model from previous work (12.4% improvement) and 3.9 months by the open-source software Deeplasia (5.6% improvement). After incorporation of chronological age from the RHPE in model 2, this error improved to an MAE of 4.65 months on the DHA, again surpassing the best previously published models (19.8% improvement). Leveraging newer DL technologies trained on >20,000 hand radiographs across 3 distinct, diverse data sets, this study developed a robust model for predicting bone age. Utilizing features extracted from an RSNA model, combined with chronological age inputs, this model outperforms previous state-of-the-art models when applied to validation data sets. These results indicate that the models provide a highly accurate/reliable platform for clinical use to improve confidence about appropriate surgical selection (eg, physeal-sparing procedures) and time savings for orthopaedic surgeons/radiologists evaluating skeletal age. Development of an accurate DL model for determination of bone age from the hand reduces the time required for age estimation. Additionally, streamlined skeletal age estimation can aid practitioners in determining optimal treatment strategies and may be useful in research settings to decrease workload and improve reporting.

The present study examined an integrated multiple neuroimaging modality (T1 structural, Diffusion Tensor Imaging (DTI), and resting-state FMRI (rsFMRI)) to predict aphasia severity using Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) in 76 individuals with post-stroke aphasia. We employed Support Vector Regression (SVR) and Random Forest (RF) models with supervised feature selection and a stacked feature prediction approach. The SVR model outperformed RF, achieving an average root mean square error (RMSE) of 16.38±5.57, Pearson's correlation coefficient (r) of 0.70±0.13, and mean absolute error (MAE) of 12.67±3.27, compared to RF's RMSE of 18.41±4.34, r of 0.66±0.15, and MAE of 14.64±3.04. Resting-state neural activity and structural integrity emerged as crucial predictors of aphasia severity, appearing in the top 20% of predictor combinations for both SVR and RF. Finally, the feature selection method revealed that functional connectivity in both hemispheres and between homologous language areas is critical for predicting language outcomes in patients with aphasia. The statistically significant difference in performance between the model using only single modality and the optimal multi-modal SVR/RF model (which included both resting-state connectivity and structural information) underscores that aphasia severity is influenced by factors beyond lesion location and volume. These findings suggest that integrating multiple neuroimaging modalities enhances the prediction of language outcomes in aphasia beyond lesion characteristics alone, offering insights that could inform personalized rehabilitation strategies.

Accurate tissue motion tracking is critical to ensure treatment outcome and safety in 2D-Cine MRI-guided radiotherapy. This is typically achieved by registration of sequential images, but existing methods often face challenges with large misalignments and lack of interpretability. In this paper, we introduce DINOMotion, a novel deep learning framework based on DINOv2 with Low-Rank Adaptation (LoRA) layers for robust, efficient, and interpretable motion tracking. DINOMotion automatically detects corresponding landmarks to derive optimal image registration, enhancing interpretability by providing explicit visual correspondences between sequential images. The integration of LoRA layers reduces trainable parameters, improving training efficiency, while DINOv2's powerful feature representations offer robustness against large misalignments. Unlike iterative optimization-based methods, DINOMotion directly computes image registration at test time. Our experiments on volunteer and patient datasets demonstrate its effectiveness in estimating both linear and nonlinear transformations, achieving Dice scores of 92.07% for the kidney, 90.90% for the liver, and 95.23% for the lung, with corresponding Hausdorff distances of 5.47 mm, 8.31 mm, and 6.72 mm, respectively. DINOMotion processes each scan in approximately 30ms and consistently outperforms state-of-the-art methods, particularly in handling large misalignments. These results highlight its potential as a robust and interpretable solution for real-time motion tracking in 2D-Cine MRI-guided radiotherapy.

This study evaluated the performance of artificial intelligence (AI) algorithms in predicting best-corrected visual acuity (BCVA) for patients with multiple retinal diseases, using multimodal medical imaging including macular optical coherence tomography (OCT), optic disc OCT and fundus images. The goal was to enhance clinical BCVA evaluation efficiency and precision. A retrospective study used data from 2545 patients (4028 eyes) for training, 896 (1006 eyes) for testing and 196 (200 eyes) for internal validation, with an external prospective dataset of 741 patients (1381 eyes). Single-modality analyses employed different backbone networks and feature fusion methods, while multimodal fusion combined modalities using average aggregation, concatenation/reduction and maximum feature selection. Predictive accuracy was measured by mean absolute error (MAE), root mean squared error (RMSE) and R² score. Macular OCT achieved better single-modality prediction than optic disc OCT, with MAE of 3.851 vs 4.977 and RMSE of 7.844 vs 10.026. Fundus images showed an MAE of 3.795 and RMSE of 7.954. Multimodal fusion significantly improved accuracy, with the best results using average aggregation, achieving an MAE of 2.865, RMSE of 6.229 and R² of 0.935. External validation yielded an MAE of 8.38 and RMSE of 10.62. Multimodal fusion provided the most accurate BCVA predictions, demonstrating AI's potential to improve clinical evaluation. However, challenges remain regarding disease diversity and applicability in resource-limited settings.

This study presents a novel approach using graph neural networks to predict the risk of internal bleeding using vessel maps derived from patient CT and MRI scans, aimed at enhancing the realism of surgical simulators for emergency scenarios such as trauma, where rapid detection of internal bleeding can be lifesaving. First, medical images are segmented and converted into graph representations of the vasculature, where nodes represent vessel branching points with spatial coordinates and edges encode vessel features such as length and radius. Due to no existing dataset directly labeling bleeding risks, we calculate the bleeding probability for each vessel node using a physics-based heuristic, peripheral vascular resistance via the Hagen-Poiseuille equation. A graph attention network is then trained to regress these probabilities, effectively learning to predict hemorrhage risk from the graph-structured imaging data. The model is trained using a tenfold cross-validation on a combined dataset of 1708 vessel graphs extracted from four public image datasets (MSD, KiTS, AbdomenCT, CT-ORG) with optimization via the Adam optimizer, mean squared error loss, early stopping, and L2 regularization. Our model achieves a mean R-squared of 0.86, reaching up to 0.9188 in optimal configurations and low mean training and validation losses of 0.0069 and 0.0074, respectively, in predicting bleeding risk, with higher performance on well-connected vascular graphs. Finally, we integrate the trained model into an immersive virtual reality environment to simulate intra-abdominal bleeding scenarios for immersive surgical training. The model demonstrates robust predictive performance despite the inherent sparsity of real-life datasets.

Cervical cancer is a significant global health issue, and high-dose-rate brachytherapy (HDR-BT) is crucial for its treatment. However, manually creating HDR-BT plans is time-consuming and heavily relies on the planner's expertise, making standardization difficult. This study introduces two advanced deep learning models to address this need: Bi-branch Cross-Attention UNet (BiCA-UNet) and Dendrite Cross-Attention UNet (DCA-UNet). BiCA-UNet enhances the correlation between the CT scan and segmentation maps of the clinical target volume (CTV), applicator, bladder, and rectum. It uses two branches: one processes the stacked input of CT scans and segmentations, and the other focuses on the CTV segmentation. A cross-attention mechanism integrates these branches, improving the model's understanding of the CTV region for accurate dose predictions. Building on BiCA-UNet, DCA-UNet further introduces a primary branch of stacked inputs and three secondary branches for CTV, bladder, and rectum segmentations forming a dendritic structure. Cross attention with bladder and rectum segmentation helps the model understand the regions of organs at risk (OAR), refining dose prediction. Evaluation of these models using multiple metrics indicates that both BiCA-UNet and DCA-UNet significantly improve HDR-BT dose prediction accuracy for various applicator types. The cross-attention mechanisms enhance the feature representation of critical anatomical regions, leading to precise and reliable treatment plans. This research highlights the potential of BiCA-UNet and DCA-UNet in advancing HDR-BT planning, contributing to the standardization of treatment plans, and offering promising directions for future research to improve patient outcomes in the source data.

Image registration is essential for medical image applications where alignment of voxels across multiple images is needed for qualitative or quantitative analysis. With recent advances in deep neural networks and parallel computing, deep learning-based medical image registration methods become competitive with their flexible modeling and fast inference capabilities. However, compared to traditional optimization-based registration methods, the speed advantage may come at the cost of registration performance at inference time. Besides, deep neural networks ideally demand large training datasets while optimization-based methods are training-free. To improve registration accuracy and data efficiency, we propose a novel image registration method, termed Recurrent Inference Image Registration (RIIR) network. RIIR is formulated as a meta-learning solver for the registration problem in an iterative manner. RIIR addresses the accuracy and data efficiency issues, by learning the update rule of optimization, with implicit regularization combined with explicit gradient input. We extensively evaluated RIIR on brain MRI, lung CT, and quantitative cardiac MRI datasets, in terms of both registration accuracy and training data efficiency. Our experiments showed that RIIR outperformed a range of deep learning-based methods, even with only 5% of the training data, demonstrating high data efficiency. Key findings from our ablation studies highlighted the important added value of the hidden states introduced in the recurrent inference framework for meta-learning. Our proposed RIIR offers a highly data-efficient framework for deep learning-based medical image registration.

Proton therapy is commonly used for treating hepatocellular carcinoma (HCC); however, its feasibility can be challenging to assess in large tumors or those adjacent to critical organs at risk (OARs), which are typically assessed only after planning computed tomography (CT) acquisition. This study aimed to predict proton dose distributions using diagnostic CT (dCT) and diagnostic MRI (dMRI) with a convolutional neural network (CNN), enabling early treatment feasibility assessments. Dose distributions and dose-volume histograms (DVHs) were calculated for 118 patients with HCC using intensity-modulated proton therapy (IMPT) and passive proton therapy. A CPU-based CNN model was used to predict DVHs and 3D dose distributions from diagnostic images. Prediction accuracy was evaluated using mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and gamma passing rate with a 3 mm/3% criterion. The predicted DVHs and dose distributions showed high agreement with actual values. MAE remained below 3.0%, with passive techniques achieving 1.2-1.8%. MSE was below 0.004 in all cases. PSNR ranged from 24 to 28 dB, and SSIM exceeded 0.94 in most conditions. Gamma passing rates averaged 82-83% for IMPT and 92-93% for passive techniques. The model achieved comparable accuracy when using dMRI and dCT. This study demonstrates that early dose distribution prediction from diagnostic imaging is feasible and accurate using a lightweight CNN model. Despite anatomical variability between diagnostic and planning images, this approach provides timely insights into treatment feasibility, potentially supporting insurance pre-authorization, reducing unnecessary imaging, and optimizing clinical workflows for HCC proton therapy.

Predicting brain age from T1-weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns about robust and accurate generalization in new data limit their clinical applicability. The large number of trainable parameters, combined with limited medical imaging training data, contributes to this challenge, often resulting in a generalization gap where there is a significant discrepancy between model performance on training data versus unseen data. In this study, we assess a deep model, SFCN-reg, based on the VGG-16 architecture, and address the generalization gap through comprehensive preprocessing, extensive data augmentation, and model regularization. Using training data from the UK Biobank, we demonstrate substantial improvements in model performance. Specifically, our approach reduces the generalization MAE by 47% (from 5.25 to 2.79 years) in the Alzheimer's Disease Neuroimaging Initiative dataset and by 12% (from 4.35 to 3.75 years) in the Australian Imaging, Biomarker and Lifestyle dataset. Furthermore, we achieve up to 13% reduction in scan-rescan error (from 0.80 to 0.70 years) while enhancing the model's robustness to registration errors. Feature importance maps highlight anatomical regions used to predict age. These results highlight the critical role of high-quality preprocessing and robust training techniques in improving accuracy and narrowing the generalization gap, both necessary steps toward the clinical use of brain age prediction models. Our study makes valuable contributions to neuroimaging research by offering a potential pathway to improve the clinical applicability of deep learning models.

Machine learning models can assist with the selection of electrode arrays required for optimal insertion angles. Cochlea implantation is a successful therapy in patients with severe to profound hearing loss. The effectiveness of a cochlea implant depends on precise insertion and positioning of electrode array within the cochlea, which is known for its variability in shape and size. Preoperative imaging like CT or MRI plays a significant role in evaluating cochlear anatomy and planning the surgical approach to optimize outcomes. In this study, preoperative and postoperative CT and CBCT data of 558 cochlea-implant patients were analyzed in terms of the influence of anatomical factors and insertion depth onto the resulting insertion angle. Machine learning models can predict insertion depths needed for optimal insertion angles, with performance improving by including cochlear dimensions in the models. A simple linear regression using just the insertion depth explained 88% of variability, whereas adding cochlear length or diameter and width further improved predictions up to 94%.