The panoramic radiograph is the most commonly used imaging modality for predicting maxillary canine impaction. Several prediction models have been constructed based on panoramic radiographs. This study aimed to compare the prediction accuracy of existing models in an external validation facilitated by an automatic landmark detection system based on deep learning. Patients aged 7-14 years who underwent panoramic radiographic examinations and received a diagnosis of impacted canines were included in the study. An automatic landmark localization system was employed to assist the measurement of geometric parameters on the panoramic radiographs, followed by the calculated prediction of the canine impaction. Three prediction models constructed by Arnautska, Alqerban et al, and Margot et al were evaluated. The metrics of accuracy, sensitivity, specificity, precision, and area under the receiver operating characteristic curve (AUC) were used to compare the performance of different models. A total of 102 panoramic radiographs with 102 impacted canines and 102 nonimpacted canines were analyzed in this study. The prediction outcomes indicated that the model by Margot et al achieved the highest performance, with a sensitivity of 95% and a specificity of 86% (AUC, 0.97), followed by the model by Arnautska, with a sensitivity of 93% and a specificity of 71% (AUC, 0.94). The model by Alqerban et al showed poor performance with an AUC of only 0.20. Two of the existing predictive models exhibited good diagnostic accuracy, whereas the third model demonstrated suboptimal performance. Nonetheless, even the most effective model is constrained by several limitations, such as logical and computational challenges, which necessitate further refinement.

In this research, we predict the output signal generated by iron oxide-based nanoparticles in Magnetic Resonance Imaging (MRI) using the physical properties of the nanoparticles and the MRI machine. The parameters considered include the size of the magnetic core of the nanoparticles, their magnetic saturation (Ms), the concentration of the nanoparticles (C), and the magnetic field (MF) strength of the MRI device. These parameters serve as input variables for the model, while the relaxation rate R₂ (s^-1) is taken as the output variable. To develop this model, we employed a machine learning approach based on a neural network known as SA-LOOCV-GRBF (SLG). In this study, we compared two different random selection patterns: SLG disperse random selection (DSLG) and SLG parallel random selection (PSLG). The sensitivity to neuron number in the hidden layers for DSLG was more pronounced compared to the PSLG pattern, and the mean square error (MSE) was calculated for this evaluation. It appears that the PSLG method demonstrated strong performance while maintaining less sensitivity to increasing neuron numbers. Consequently, the new pattern, PSLG, was selected for predicting MRI behavior.

Multiple Sclerosis (MS) is a chronic autoimmune disorder of the central nervous system that can lead to significant neurological disability. Retinal imaging--particularly Scanning Laser Ophthalmoscopy (SLO) and Optical Coherence Tomography (OCT)--provides valuable biomarkers for early MS diagnosis through non-invasive visualization of neurodegenerative changes. This study proposes a foundation-based bi-modal classification framework that integrates SLO images and OCT-derived retinal thickness maps for MS diagnosis. To facilitate this, we introduce two modality-specific foundation models--SLOFound and TMFound--fine-tuned from the RETFound-Fundus backbone using an independent dataset of 203 healthy eyes, acquired at Noor Ophthalmology Hospital with the Heidelberg Spectralis HRA+OCT system. This dataset, which contains only normal cases, was used exclusively for encoder adaptation and is entirely disjoint from the classification dataset. For the classification stage, we use a separate dataset comprising IR-SLO images from 32 MS patients and 70 healthy controls, collected at the Kashani Comprehensive MS Center in Isfahan, Iran. We first assess OCT-derived maps layer-wise and identify the Ganglion Cell-Inner Plexiform Layer (GCIPL) as the most informative for MS detection. All subsequent analyses utilize GCIPL thickness maps in conjunction with SLO images. Experimental evaluations on the MS classification dataset demonstrate that our foundation-based bi-modal model outperforms unimodal variants and a prior ResNet-based state-of-the-art model, achieving a classification accuracy of 97.37%, with perfect sensitivity (100%). These results highlight the effectiveness of leveraging pre-trained foundation models, even when fine-tuned on limited data, to build robust, efficient, and generalizable diagnostic tools for MS in medical imaging contexts where labeled datasets are often scarce.

To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasopharyngeal carcinoma (NPC) and evaluate its feasibility and clinical performance. Patients with NPC scheduled for MSI RT workflow were prospectively enrolled. This workflow integrates RT procedures from computed tomography (CT) scan to beam delivery, all performed with the patient on the treatment couch. Workflow performance, tumor response, patient-reported acute toxicities, and quality of life were evaluated. From March 2022 to October 2023, 120 newly diagnosed, nonmetastatic patients with NPC were enrolled. Of these, 117 completed the workflow with a median duration of 23.2 minutes (range, 16.3-45.8). Median translation errors were 0.2 mm (from CT scan to planning approval) and 0.1 mm (during beam delivery). AI-generated contours required minimal revision for the high-risk clinical target volume and organs at risk, minor revision for the involved cervical lymph nodes and low-risk clinical target volume (median Dice similarity coefficients (DSC), 0.98 and 0.94), and more revision for the gross tumor at the primary site and the involved retropharyngeal lymph nodes (median DSC, 0.84). Of 117 AI-generated plans, 108 (92.3%) passed after the first optimization, with ≥97.8% of target volumes receiving ≥100% of the prescribed dose. Dosimetric constraints were met for most organs at risk, except the thyroid and submandibular glands. One hundred and fifteen patients achieved a complete response at week 12 post-RT, while 14 patients reported any acute toxicity as "very severe" from the start of RT to week 12 post-RT. AI-empowered MSI RT workflow for patients with NPC is clinically feasible in a single institutional setting compared with standard, human-based RT workflow.

We propose a tumor tracking framework for 2D cine magnetic resonance imaging (MRI) based on a pair of deep learning (DL) models relying on patient-specific (PS) training. The chosen DL models are: (1) an image registration transformer and (2) an auto-segmentation convolutional neural network (CNN). We collected over 1,400,000 cine MRI frames from 219 patients treated on a 0.35 T MRI-linac plus 7500 frames from additional 35 patients that were manually labeled and subdivided into fine-tuning, validation, and testing sets. The transformer was first trained on the unlabeled data (without segmentations). We then continued training (with segmentations) either on the fine-tuning set or for PS models based on 8 randomly selected frames from the first 5 seconds of each patient's cine MRI. The PS auto-segmentation CNN was trained from scratch with the same 8 frames for each patient, without pre-training. Furthermore, we implemented B-spline image registration as a conventional model, as well as different baselines. Output segmentations of all models were compared on the testing set using the Dice similarity coefficient, the 50% and 95% Hausdorff distance (HD_50%/HD_95%), and the root-mean-square-error of the target centroid in superior-inferior direction. The PS transformer and CNN significantly outperformed all other models, achieving a median (interquartile range) dice similarity coefficient of 0.92 (0.03)/0.90 (0.04), HD_50% of 1.0 (0.1)/1.0 (0.4) mm, HD_95% of 3.1 (1.9)/3.8 (2.0) mm, and root-mean-square-error of the target centroid in superior-inferior direction of 0.7 (0.4)/0.9 (1.0) mm on the testing set. Their inference time was about 36/8 ms per frame and PS fine-tuning required 3 min for labeling and 8/4 min for training. The transformer was better than the CNN in 9/12 patients, the CNN better in 1/12 patients, and the 2 PS models achieved the same performance on the remaining 2/12 testing patients. For targets in the thorax, abdomen, and pelvis, we found 2 PS DL models to provide accurate real-time target localization during MRI-guided radiotherapy.

Transformers have revolutionized medical image restoration, but the quadratic complexity still poses limitations for their application to high-resolution medical images. The recent advent of the Receptance Weighted Key Value (RWKV) model in the natural language processing field has attracted much attention due to its ability to process long sequences efficiently. To leverage its advanced design, we propose Restore-RWKV, the first RWKV-based model for medical image restoration. Since the original RWKV model is designed for 1D sequences, we make two necessary modifications for modeling spatial relations in 2D medical images. First, we present a recurrent WKV (Re-WKV) attention mechanism that captures global dependencies with linear computational complexity. Re-WKV incorporates bidirectional attention as basic for a global 16 receptive field and recurrent attention to effectively model 2D dependencies from various scan directions. Second, we develop an omnidirectional token shift (Omni-Shift) layer that enhances local dependencies by shifting tokens from all directions and across a wide context range. These adaptations make the proposed Restore-RWKV an efficient and effective model for medical image restoration. Even a lightweight variant of Restore-RWKV, with only 1.16 million parameters, achieves comparable or even superior results compared to existing state-of-the-art (SOTA) methods. Extensive experiments demonstrate that the resulting Restore-RWKV achieves SOTA performance across a range of medical image restoration tasks, including PET image synthesis, CT image denoising, MRI image superresolution, and all-in-one medical image restoration. Code is available at: https://github.com/Yaziwel/Restore-RWKV.

Deformable image registration (DIR) is critical for accurate clinical diagnosis and effective treatment planning. However, patient movement, significant intensity differences, and large breathing deformations hinder accurate anatomical alignment in multi-modal image registration. These factors exacerbate the entanglement of anatomical and modality-specific style information, thereby severely limiting the performance of multi-modal registration. To address this, we propose a novel LAtent Diffusion-based Domain-Adaptive feature disentangling (LADDA) framework for unsupervised multi-modal medical image registration, which explicitly addresses the representation disentanglement. First, LADDA extracts reliable anatomical priors from the Latent Diffusion Model (LDM), facilitating downstream content-style disentangled learning. A Domain-Adaptive Feature Disentangling (DAFD) module is proposed to promote anatomical structure alignment further. This module disentangles image features into content and style information, boosting the network to focus on cross-modal content information. Next, a Neighborhood-Preserving Hashing (NPH) is constructed to further perceive and integrate hierarchical content information through local neighbourhood encoding, thereby maintaining cross-modal structural consistency. Furthermore, a Unilateral-Query-Frozen Attention (UQFA) module is proposed to enhance the coupling between upstream prior and downstream content information. The feature interaction within intra-domain consistent structures improves the fine recovery of detailed textures. The proposed framework is extensively evaluated on large-scale multi-center datasets, demonstrating superior performance across diverse clinical scenarios and strong generalization on out-of-distribution (OOD) data.

Currently, the application of convolutional neural networks (CNNs) in artificial intelligence (AI) for medical imaging diagnosis has emerged as a highly promising tool. In particular, AI-assisted diagnosis holds significant potential for orthopedic and emergency department physicians by improving diagnostic efficiency and enhancing the overall patient experience. This systematic review and meta-analysis has the objective of assessing the application of AI in diagnosing facial fractures and evaluating its diagnostic performance. This study adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and PRISMA-Diagnostic Test Accuracy (PRISMA-DTA). A comprehensive literature search was conducted in the PubMed, Cochrane Library, and Web of Science databases to identify original articles published up to December 2024. The risk of bias and applicability of the included studies were assessed using the QUADAS-2 tool. The results were analyzed using a Summary Receiver Operating Characteristic (SROC) curve. A total of 16 studies were included in the analysis, with contingency tables extracted from 11 of them. The pooled sensitivity was 0.889 (95% CI: 0.844-0.922), and the pooled specificity was 0.888 (95% CI: 0.834-0.926). The area under the Summary Receiver Operating Characteristic (SROC) curve was 0.911. In the subgroup analysis of nasal and mandibular fractures, the pooled sensitivity for nasal fractures was 0.851 (95% CI: 0.806-0.887), and the pooled specificity was 0.883 (95% CI: 0.862-0.902). For mandibular fractures, the pooled sensitivity was 0.905 (95% CI: 0.836-0.947), and the pooled specificity was 0.895 (95% CI: 0.824-0.940). AI can be developed as an auxiliary tool to assist clinicians in diagnosing facial fractures. The results demonstrate high overall sensitivity and specificity, along with a robust performance reflected by the high area under the SROC curve. This study has been prospectively registered on Prospero, ID:CRD42024618650, Creat Date:10 Dec 2024. https://www.crd.york.ac.uk/PROSPERO/view/CRD42024618650 .

The development of medical imaging techniques has made a significant contribution to clinical decision-making. However, the existence of suboptimal imaging quality, as indicated by irregular illumination or imbalanced intensity, presents significant obstacles in automating disease screening, analysis, and diagnosis. Existing approaches for natural image enhancement are mostly trained with numerous paired images, presenting challenges in data collection and training costs, all while lacking the ability to generalize effectively. Here, we introduce a pioneering training-free Diffusion Model for Universal Medical Image Enhancement, named UniMIE. UniMIE demonstrates its unsupervised enhancement capabilities across various medical image modalities without the need for any fine-tuning. It accomplishes this by relying solely on a single pre-trained model from ImageNet. We conduct a comprehensive evaluation on 13 imaging modalities and over 15 medical types, demonstrating better qualities, robustness, and accuracy than other modality-specific and data-inefficient models. By delivering high-quality enhancement and corresponding accuracy downstream tasks across a wide range of tasks, UniMIE exhibits considerable potential to accelerate the advancement of diagnostic tools and customized treatment plans. UniMIE represents a transformative approach to medical image enhancement, offering a versatile and robust solution that adapts to diverse imaging conditions. By improving image quality and facilitating better downstream analyses, UniMIE has the potential to revolutionize clinical workflows and enhance diagnostic accuracy across a wide range of medical applications.

The escalating adoption of high-resolution, large-field-of-view imagery amplifies the need for efficient compression methodologies. Conventional techniques frequently fail to preserve critical image details, while data-driven approaches exhibit limited generalizability. Implicit Neural Representations (INRs) present a promising alternative by learning continuous mappings from spatial coordinates to pixel intensities for individual images, thereby storing network weights rather than raw pixels and avoiding the generalization problem. However, INR-based compression of large images faces challenges including slow compression speed and suboptimal compression ratios. To address these limitations, we introduce COLI (Compressor for Large Images), a novel framework leveraging Neural Representations for Videos (NeRV). First, recognizing that INR-based compression constitutes a training process, we accelerate its convergence through a pretraining-finetuning paradigm, mixed-precision training, and reformulation of the sequential loss into a parallelizable objective. Second, capitalizing on INRs' transformation of image storage constraints into weight storage, we implement Hyper-Compression, a novel post-training technique to substantially enhance compression ratios while maintaining minimal output distortion. Evaluations across two medical imaging datasets demonstrate that COLI consistently achieves competitive or superior PSNR and SSIM metrics at significantly reduced bits per pixel (bpp), while accelerating NeRV training by up to 4 times.