Neuroimaging studies of brain function are important research methods widely applied to stroke patients. Currently, a large number of studies have focused on functional imaging of the gray matter cortex. Relevant research indicates that certain areas of the gray matter cortex in stroke patients exhibit abnormal brain activity during resting state. However, studies on brain function based on white matter remain insufficient. The changes in functional connectivity caused by stroke in white matter, as well as the repair or compensation mechanisms of white matter function after stroke, are still unclear. The aim of this study is to investigate and demonstrate the changes in brain functional connectivity activity in the white matter region of stroke patients. Revealing the recombination characteristics of white matter functional networks after stroke, providing potential biomarkers for rehabilitation therapy Provide new clinical insights for the rehabilitation and treatment of stroke patients. We recruited 36 stroke patients and 36 healthy controls for resting-state functional magnetic resonance imaging (rs-fMRI). Regional Homogeneity (ReHo) and Degree Centrality (DC), which are sensitive to white matter functional abnormalities, were selected as feature vectors. ReHo reflects local neuronal synchrony, while DC quantifies global network hub properties. The combination of both effectively characterizes functional changes in white matter. ReHo evaluates the functional consistency of different white matter regions by calculating the activity similarity between adjacent brain regions. Additionally, DC analysis of white matter was used to investigate the connectivity patterns and organizational principles of functional networks between white matter regions. This was achieved by calculating the number of connections in each brain region to identify changes in neural activation of white matter regions that significantly impact the brain network. Furthermore, ReHo and DC metrics were used as feature vectors for classification using machine learning algorithms. The results indicated significant differences in white matter DC and ReHo values between stroke patients and healthy controls. In the two-sample t-test analysis of white matter DC, stroke patients showed a significant reduction in DC values in the corpus callosum genu (GCC), corpus callosum body (BCC), and left anterior corona radiata (ACRL) regions (GCC: 0.143 vs. 1.024; BCC: 0.238 vs. 1.143; ACRL: 0.143 vs. 0.821, p < 0.001). However, an increase in DC values was observed in the left superior longitudinal fasciculus (SLF_L) region (1.190 vs. 0.190, p < 0.001). In the two-sample t-test analysis of white matter ReHo, stroke patients exhibited a decrease in ReHo values in the GCC and BCC regions (GCC: 0.859 vs. 1.375; BCC: 1.156 vs. 1.687, p < 0.001), indicating values lower than those in the healthy control group. Using leave-one-out cross-validation (LOOCV) to evaluate the white matter DC and ReHo feature values through SVM classification models for stroke patients and healthy controls, the DC classification AUC was 0.89, and the ReHo classification AUC reached 0.98. These results suggest that the features possess validity and discriminative power. These findings suggest alterations in functional connectivity in specific white matter regions following stroke. Specifically, we observed a weakening of functional connectivity in the genu of the corpus callosum (GCC), the body of the corpus callosum (BCC), and the left anterior corona radiata (ACR_L) regions, while compensatory functional connectivity was enhanced in the left superior longitudinal fasciculus (SLF_L) region. These findings reveal the reorganization characteristics of white matter functional networks after stroke, which may provide potential biomarkers for the rehabilitation treatment of stroke patients and offer new clinical insights for their rehabilitation and treatment.

Anterior cruciate ligament (ACL) injuries are among the most common knee injuries, affecting 1 in 3500 people annually. With rising rates of ACL tears, particularly in children, timely diagnosis is critical. This study evaluates artificial intelligence (AI) effectiveness in diagnosing and classifying ACL tears on MRI through a systematic review and meta-analysis, comparing AI performance with clinicians and assessing radiomic and non-radiomic models. Major databases were searched for AI models diagnosing ACL tears via MRIs. 36 studies, representing 52 models, were included. Accuracy, sensitivity, and specificity metrics were extracted. Pooled estimates were calculated using a random-effects model. Subgroup analyses compared MRI sequences, ground truths, AI versus clinician performance, and radiomic versus non-radiomic models. This study was conducted in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocols. AI demonstrated strong diagnostic performance, with pooled accuracy, sensitivity, and specificity of 87.37%, 90.73%, and 91.34%, respectively. Classification models achieved pooled metrics of 90.46%, 88.68%, and 94.08%. Radiomic models outperformed non-radiomic models, and AI demonstrated comparable performance to clinicians in key metrics. Three-dimensional (3D) proton density fat suppression (PDFS) sequences with < 2 mm slice depth yielded the most promising results, despite small sample sizes, favouring arthroscopic benchmarks. Despite high heterogeneity (I² > 90%). AI models demonstrate diagnostic performance comparable to clinicians and may serve as valuable adjuncts in ACL tear detection, pending prospective validation. However, substantial heterogeneity and limited interpretability remain key challenges. Further research and standardised evaluation frameworks are needed to support clinical integration. Question Is AI effective and accurate in diagnosing and classifying anterior cruciate ligament (ACL) tears on MRI? Findings AI demonstrated high accuracy (87.37%), sensitivity (90.73%), and specificity (91.34%) in ACL tear diagnosis, matching or surpassing clinicians. Radiomic models outperformed non-radiomic approaches. Clinical relevance AI can enhance the accuracy of ACL tear diagnosis, reducing misdiagnoses and supporting clinicians, especially in resource-limited settings. Its integration into clinical workflows may streamline MRI interpretation, reduce diagnostic delays, and improve patient outcomes by optimising management.

Current dental CBCT segmentation tools often lack accuracy, accessibility, or comprehensive anatomical coverage. To address this, we constructed a densely annotated dental CBCT dataset and developed a deep learning model, OraSeg, for tooth-level instance segmentation, which is then deployed as a one-click tool and made freely accessible for non-commercial use. We established a standardized annotated dataset covering 35 key oral anatomical structures and employed UNetR as the backbone network, combining Swin Transformer and the spatial Mamba module for multi-scale residual feature fusion. The OralSeg model was designed and optimized for precise instance segmentation of dental CBCT images, and integrated into the 3D Slicer platform, providing a graphical user interface for one-click segmentation. OralSeg had a Dice similarity coefficient of 0.8316 ± 0.0305 on CBCT instance segmentation compared to SwinUNETR and 3D U-Net. The model significantly improves segmentation performance, especially in complex oral anatomical structures, such as apical areas, alveolar bone margins, and mandibular nerve canals. The OralSeg model presented in this study provides an effective solution for instance segmentation of dental CBCT images. The tool allows clinical dentists and researchers with no AI background to perform one-click segmentation, and may be applicable in various clinical and research contexts. OralSeg can offer researchers and clinicians a user-friendly tool for tooth-level instance segmentation, which may assist in clinical diagnosis, educational training, and research, and contribute to the broader adoption of digital dentistry in precision medicine.

This paper examines the current challenges in computed tomography (CT), with a critical exploration of existing methodologies from a mathematical perspective. Specifically, it aims to identify research directions to enhance image quality in low-dose, cost-effective cone beam CT (CBCT) systems, which have recently gained widespread use in general dental clinics. Dental CBCT offers a substantial cost advantage over standard medical CT, making it affordable for local dental practices; however, this affordability brings significant challenges related to image quality degradation, further complicated by the presence of metallic implants, which are particularly common in older patients. This paper investigates metal-induced artefacts stemming from mismatches in the forward model used in conventional reconstruction methods and explains an alternative approach that bypasses the traditional Radon transform model. Additionally, it examines both the potential and limitations of deep learning-based methods in tackling these challenges, offering insights into their effectiveness in improving image quality in low-dose dental CBCT.This article is part of the theme issue 'Frontiers of applied inverse problems in science and engineering'.

Rectocele (RC) is a common pelvic organ prolapse (POP) that can cause obstructed defecation and reduced quality of life. Magnetic resonance defecography (MRD) offers high-resolution, radiation-free visualization of pelvic floor anatomy but relies on time-consuming, observer-dependent manual measurements. Our research constructs a nomogram model incorporating intra-ROI and habitat radiomics features to improve MRD-based RC diagnosis. We retrospectively analyzed 222 MRD patients (155 training, 67 testing). Clinical features were selected via univariate and multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied, and features with non-zero coefficients were retained to construct the radiomics signatures. A support vector machine (SVM) learning algorithm was used to construct the intra-ROI combined with the habitat radiomics model. Clinical features were then combined with radiomics features using a multivariable logistic regression algorithm to generate a clinical-radiomics nomogram. Model performance was assessed using receiver operating characteristic curve (ROC) and decision curve analysis (DCA). The combined intra-ROI and habitat radiomics model outperformed intra-ROI or habitat radiomics models alone, achieving areas under the curve (AUCs) of 0.913 (training) and 0.805 (testing). The nomogram integrating radiomics features and gender showed strong calibration and discrimination, with AUCs of 0.930 and 0.852 in the training and testing cohorts, respectively. Our findings suggest that integrating intra-ROI with habitat radiomics features can aid RC assessment. While the clinical-radiomics nomogram showed the highest internal performance, this single-center retrospective study lacks external validation and includes a relatively small test cohort. Therefore, risk of model overfitting cannot be excluded. Prospective, multi-center validation and larger cohorts are warranted before routine clinical deployment.

Neoadjuvant chemoimmunotherapy (nCIT) is gradually becoming an important treatment strategy for patients with locally advanced esophageal squamous cell carcinoma (LA-ESCC). This study aimed to predict the pathological complete response (pCR) of these patients using variational autoencoder (VAE)-based deep learning and radiomics technology. A total of 253 LA-ESCC patients who were treated with nCIT and underwent enhanced CT at our hospital between July 2019 and July 2023 were included in the training cohort. VAE-based deep learning and radiomics were utilized to construct deep learning (DL) models and deep learning radiomics (DLR) models. The models were trained and validated via 5-fold cross-validation among 253 patients. Forty patients were recruited from our institution between August 2023 and August 2024 as the test cohort. The AUCs of DL and DLR model were 0.935 (95% CI: 0.786-0.992) and 0.949 (95% CI: 0.910-0.986) in the validation cohort and 0.839 (95% CI: 0.726-0.853), 0.926 (95% CI: 0.886-0.934) in the test cohort. The performance gap between Precision and Recall of the DLR model was smaller than that of DL model. The F1 scores of the DL and DLR model were 0.726 (95% confidence interval [CI]: 0.476-0.842) and 0.766 (95% CI: 0.625-0.842) in the validation cohort and 0.727 (95% CI: 0.645-0.811), 0.836 (95% CI: 0.820-0.850) in the test cohort. We constructed a DLR model to predict pCR in nCIT treated LA-ESCC patients, which demonstrated superior performance compared to the DL model. We innovatively used VAE-based deep learning and radiomics to construct the DLR model for predicting pCR of LA-ESCC after nCIT.

Radiotherapy (RT) of head and neck cancer can cause severe toxicities. Early identification of individuals at risk could enable personalized treatment. This study evaluated whether convolutional neural networks (CNNs) applied to Magnetic Resonance (MR) images acquired early after irradiation can predict radiation-induced tissue changes associated with toxicity in mice. Patient/material and methods: Twenty-nine C57BL/6JRj mice were included (irradiated: n = 14; control: n = 15). Irradiated mice received 65 Gy of fractionated RT to the oral cavity, swallowing muscles and salivary glands. T2-weighted MR images were acquired 3-5 days post-irradiation. CNN models (VGG, MobileNet, ResNet, EfficientNet) were trained to classify sagittal slices as irradiated or control (n = 586 slices). Predicted class probabilities were correlated with five toxicity endpoints assessed 8-105 days post-irradiation. Model explainability was assessed with VarGrad heatmaps, to verify that predictions relied on clinically relevant image regions. The best-performing model (EfficientNet B3) achieved 83% slice-level accuracy (ACC) and correctly classified 28 of 29 mice. Higher predicted probabilities of the irradiated class were strongly associated with oral mucositis, dermatitis, reduced saliva production, late submandibular gland fibrosis and atrophy of salivary gland acinar cells. Explainability heatmaps confirmed that CNNs focused on irradiated regions. The high CNN classification ACC, the regions highlighted by the explainability analysis and the strong correlations between model predictions and toxicity suggest that CNNs, together with post-irradiation magnetic resonance imaging, may identify individuals at risk of developing toxicity.

Precise assessment of Graves` orbitopathy (GO) predicts therapeutic strategies. Various imaging techniques and different measurement methods are used, but there is a lack of standardization. Traditionally, the Clinical Activity Score (CAS) has been used for assessing GO, especially for evaluating disease activity to predict response to glucocorticoid (GC) therapy, but technological developments have led to a shift towards more objective imaging methods that offer accuracy. Imaging methods for Graves' orbitopathy assessment include ultrasonography (USG), computed tomography (CT), magnetic resonance imaging (MRI), and single photon emission computed tomography (SPECT). These can be divided into those that assess disease activity (MRI, SPECT) and those that assess disease severity (USG, CT, MRI, SPECT). USG is the accessible first-aid tool that provides non-invasive imaging of orbital structures, with a short time of examination making it highly suitable for initial evaluation and monitoring of GO. It does have limitations, particularly in visualizing the apex of the orbit. Initially, orbital CT was thought to provide more accurate morphological information, particularly in extraocular muscles, and superior visualization of bone structures compared to MRI, making it the imaging modality of choice prior to planned orbital decompression; however, it has difficulty in accurately assessing the inflammatory activity stages of GO. Although CT offers a better view of deeper-lying tissue, it is limited by radiation exposure. MRI is best suited for follow-up examinations because it offers superior soft tissue visualization and precise tissue differentiation. However, it is not specific for orbital changes, the examination is very expensive, and it is rarely available. Recent literature proposes that nuclear medicine imaging techniques may be the best discipline for assessing GO. SPECT fused with low-dose CT scans is now used to increase the diagnostic value of the investigation. It provides functional information on top of the anatomical images. The use of cost-effective radioisotope - technetium-99m (99mTc)-DTPA - gives great diagnostic results with short examination time, low radiation exposure, and satisfactory spatial resolution. Nowadays, 36 years after CAS development and with technological improvement, researchers aim to integrate artificial intelligence tools with SPECT/CT imaging to diagnose and stage GO activity more effectively.

<b>Aims/Background</b> Spinal disorders, such as herniated discs and scoliosis, are highly prevalent conditions with rising incidence in the aging global population. Accurate analysis of spinal anatomical structures is a critical prerequisite for achieving high-precision positioning with surgical navigation robots. However, traditional manual segmentation methods are limited by issues such as low efficiency and poor consistency. This work aims to develop a fully automated deep learning-based vertebral segmentation and labeling workflow to provide efficient and accurate preoperative analysis support for spine surgery navigation robots. <b>Methods</b> In the localization stage, the You Only Look Once version 7 (YOLOv7) network was utilized to predict the bounding boxes of individual vertebrae on computed tomography (CT) sagittal slices, transforming the 3D localization problem into a 2D one. Subsequently, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm was employed to aggregate the 2D detection results into 3D vertebral centers. This approach significantly reduces inference time and enhances localization accuracy. In the segmentation stage, a 3D U-Net model integrated with an attention mechanism was trained using the region of interest (ROI) based on the vertebral center as input, effectively extracting the 3D structural features of vertebrae to achieve precise segmentation. In the labeling stage, a vertebra labeling network was trained by combining deep learning architectures-ResNet and Transformer, which are capable of extracting rich intervertebral features, to obtain the final labeling results through post-processing based on positional logic analysis. To verify the effectiveness of this workflow, experiments were conducted on a dataset comprising 106 spinal CT datasets sourced from various devices, covering a wide range of clinical scenarios. <b>Results</b> The results demonstrate that the method performed excellently in the three key tasks of localization, segmentation, and labeling, with a Mean Localization Error (MLE) of 1.42 mm. The segmentation accuracy metrics included a Dice Similarity Coefficient (DSC) of 0.968 ± 0.014, Intersection over Union (IoU) of 0.879 ± 0.018, Pixel Accuracy (PA) of 0.988 ± 0.005, mean symmetric distance (MSD) of 1.09 ± 0.19 mm, and Hausdorff Distance (HD) of 5.42 ± 2.05 mm. The degree of classification accuracy reached up to 94.36%. <b>Conclusion</b> These quantitative assessments and visualizations confirm the effectiveness of our method (vertebra localization, vertebra segmentation and vertebra labeling), indicating its potential for deployment in spinal surgery navigation robots to provide accurate and efficient preoperative analysis and navigation support for spinal surgeries.

Intracoronary optical coherence tomography (OCT) can identify changes following drug/device treatment and high-risk plaques, but analysis requires expert clinician or core laboratory interpretation, while artifacts and limited sampling markedly impair reproducibility. Assistive technologies such as artificial intelligence-based analysis may therefore aid both detailed OCT interpretation and patient management. We determined if artificial intelligence-based OCT analysis (AutoOCT) can rapidly process, optimize and analyze OCT images, and identify plaque composition changes that predict drug success/failure and high-risk plaques. AutoOCT deep learning artificial intelligence modules were designed to correct segmentation errors from poor-quality or artifact-containing OCT images, identify tissue/plaque composition, classify plaque types, measure multiple parameters including lumen area, lipid and calcium arcs, and fibrous cap thickness, and output segmented images and clinically useful parameters. Model development used 36 212 frames (127 whole pullbacks, 106 patients). Internal validation of tissue and plaque classification and measurements used ex vivo OCT pullbacks from autopsy arteries, while external validation for plaque stabilization and identifying high-risk plaques used core laboratory analysis of IBIS-4 (Integrated Biomarkers and Imaging Study-4) high-intensity statin (83 patients) and CLIMA (Relationship Between Coronary Plaque Morphology of Left Anterior Descending Artery and Long-Term Clinical Outcome Study; 62 patients) studies, respectively. AutoOCT recovered images containing common artifacts with measurements and tissue and plaque classification accuracy of 83% versus histology, equivalent to expert clinician readers. AutoOCT replicated core laboratory plaque composition changes after high-intensity statin, including reduced lesion lipid arc (13.3° versus 12.5°) and increased minimum fibrous cap thickness (18.9 µm versus 24.4 µm). AutoOCT also identified high-risk plaque features leading to patient events including minimal lumen area <3.5 mm², Lipid arc >180°, and fibrous cap thickness <75 µm, similar to the CLIMA core laboratory. AutoOCT-based analysis of whole coronary artery OCT identifies tissue and plaque types and measures features correlating with plaque stabilization and high-risk plaques. Artificial intelligence-based OCT analysis may augment clinician or core laboratory analysis of intracoronary OCT images for trials of drug/device efficacy and identifying high-risk lesions.