The development of artificial intelligence has revolutionized the field of dentistry. Medical image segmentation is a vital part of AI applications in dentistry. This technique can assist medical practitioners in accurately diagnosing diseases. The detection of the maxillary sinus (MS), such as dental implants, tooth extraction, and endoscopic surgery, is important in the surgical field. The accurate segmentation of MS in radiological images is a prerequisite for diagnosis and treatment planning. This study aims to investigate the feasibility of applying a CNN algorithm based on the U-Net architecture to facilitate MS segmentation of individuals from the Chinese population. A total of 300 CBCT images in the axial, coronal, and sagittal planes were used in this study. These images were divided into a training set and a test set at a ratio of 8:2. The marked regions (maxillary sinus) were labelled for training and testing in the original images. The training process was performed for 40 epochs using a learning rate of 0.00001. Computation was performed on an RTX GeForce 3060 GPU. The best model was retained for predicting MS in the test set and calculating the model parameters. The trained U-Net model achieved yield segmentation accuracy across the three imaging planes. The IoU values were 0.942, 0.937 and 0.916 in the axial, sagittal and coronal planes, respectively, with F1 scores across all planes exceeding 0.95. The accuracies of the U-Net model were 0.997, 0.998, and 0.995 in the axial, sagittal and coronal planes, respectively. The trained U-Net model achieved highly accurate segmentation of MS across three planes on the basis of 2D CBCT images among the Chinese population. The AI model has shown promising application potential for daily clinical practice. Not applicable.

Although brain atrophy is a prevalent finding in Wilson disease (WD), its role as a contributing factor to clinical symptoms, especially cognitive decline, remains unclear. The objective of this study was to investigate different neuroimaging biomarkers related to grey matter atrophy and their relationship with neurological and cognitive impairment in WD. In this study, 30 WD patients and 30 age- and sex-matched healthy controls were enrolled prospectively and underwent structural magnetic resonance imaging (MRI). Regional atrophy was evaluated using established linear radiological measurements and the automated workflow volumetric estimation of gross atrophy and brain age longitudinally (veganbagel) for age- and sex-specific estimations of regional brain volume changes. Brain Age Gap Estimate (BrainAGE), defined as the discrepancy between machine learning predicted brain age from structural MRI and chronological age, was assessed using an established model. Atrophy markers and clinical scores were compared between 19 WD patients with a neurological phenotype (neuro-WD), 11 WD patients with a hepatic phenotype (hep-WD), and a healthy control group using Welch's ANOVA or Kruskal-Wallis test. Correlations between atrophy markers and neurological and neuropsychological scores were investigated using Spearman's correlation coefficients. Patients with neuro-WD demonstrated increased third ventricle width and bicaudate index, along with significant striatal-thalamic atrophy patterns that correlated with global cognitive function, mental processing speed, and verbal memory. Median BrainAGE was significantly higher in patients with neuro-WD (8.97 years, interquartile range [IQR] = 5.62-15.73) compared to those with hep-WD (4.72 years, IQR = 0.00-5.48) and healthy controls (0.46 years, IQR = - 4.11-4.24). Striatal-thalamic atrophy and BrainAGE were significantly correlated with neurological symptom severity. Our findings indicate advanced predicted brain age and substantial striatal-thalamic atrophy patterns in patients with neuro-WD, which serve as promising neuroimaging biomarkers for neurological and cognitive functions in treated, chronic WD.

Meningiomas are among the most common intracranial tumors, and challenges still remain in terms of tumor classification, treatment, and management. With the popularization of artificial intelligence technology, radiomics has been further developed and more extensively applied in the study of meningiomas. This objective and quantitative technique has played an important role in the identification, classification, grading, pathology, treatment, and prognosis of meningiomas, although new problems have also emerged. This review examines the application of magnetic resonance imaging (MRI) techniques in meningioma research. A database search was conducted for articles published between November 2017 and April 2025, with a total of 87 studies included after screening. These studies were summarized in detail, and the risk of bias and the certainty of the evidence were assessed using the Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and radiomics quality scores (RQS). All the studies were retrospective, with most being single-center studies. Contrast-enhanced T1-weighted imaging (T1C) and T2-weighted imaging (T2WI) are the most commonly used MRI sequences. Current research focuses on five topics, namely, differentiation, grade and subtypes, molecular pathology, biological behavior, treatment, and complications, with 14, 32, 14, 12, and 19 studies addressing these topics (some of which are multiple topics). Combined imaging features with clinical or pathological features often outperform traditional clinical models. Most studies show a low to moderate risk of bias. Large, prospective, multicenter studies are needed to validate the performance of radiomic models in diverse patient populations before their clinical implementation can be considered.

Deep learning image reconstruction (DLIR) technology effectively improves the image quality while maintaining spatial resolution. The impact of DLIR on the quantification of coronary artery calcium (CAC) is still unclear. The purpose of this study was to investigate the effect of DLIR on the quantification of coronary calcium in high-risk populations. A retrospective study was conducted on patients who underwent coronary artery CT angiography (CCTA) at our hospital(China) from February 2022 to September 2022. Raw data were reconstructed with filtered back projection (FBP) reconstruction, 40% and 80% level adaptive statistical iterative reconstruction-veo (ASiR-V 40%, ASiR-V 80%) and low, medium and high-level deep learning algorithm (DLIR-L, DLIR-M, and DLIR-H). Calculate and compare the signal-to-noise and contrast-to-noise ratio, volumetric score, mass scores, and Agaston score of 6 sets of images. There were 178 patients, female (107), mean age (62.43 ± 9.26), and mean BMI (25.33 ± 3.18) kg/m². Compared with FBP, the image noise of ASiR-V and DLIR was significantly reduced (P < 0.001). There was no significant difference in Agaston score, volumetric score, and mass scores among the six reconstruction algorithms (all P > 0.05). Bland-Altman diagram indicated that the Agatston scores of the five reconstruction algorithms showed good agreement with FBP, with DLIR-L(AUC, 110.08; 95% CI: 26.48, 432.92;)and ASIR-V40% (AUC,110.96; 95% CI: 26.23, 431.34;) having the highest consistency with FBP. Compared with FBP, DLIR and ASiR-V improve CT image quality to varying degrees while having no impact on Agatston score-based risk stratification. CACS is a powerful tool for cardiovascular risk stratification, and DLIR can improve image quality without affecting CACS, making it widely applicable in clinical practice.

This study aims to construct a survival prognosis prediction model for muscle-invasive bladder cancer based on CT imaging features. A total of 91 patients with muscle-invasive bladder cancer were sourced from the TCGA and TCIA dataset and were divided into a training group (64 cases) and a validation group (27 cases). Additionally, 54 patients with muscle-invasive bladder cancer were retrospectively collected from our hospital to serve as an external test group; their enhanced CT imaging data were analyzed and processed to identify the most relevant radiomic features. Five distinct machine learning methods were employed to develop the optimal radiomics model, which was then combined with clinical data to create a nomogram model aimed at accurately predicting the overall survival (OS) of patients with muscle-invasive bladder cancer. The model's performance was ultimately assessed using various evaluation methods, including the ROC curve, calibration curve, decision curve, and Kaplan-Meier (KM) analysis. Eight radiomic features were identified for modeling analysis. Among the models evaluated, the Gradient Boosting Machine (GBM) In the prediction of OS performed the best. the 2-year AUCs were 0.859, 95% CI (0.767-0.952) for the training group, 0.850, 95% CI (0.705-0.995) for the validation group, and 0.700, 95% CI (0.520-0.880) for the external test group. The 3-year AUCs were 0.809, 95% CI (0.704-0.913) for the training group, 0.895, 95% CI (0.768-1.000) for the validation group, and 0.730, 95% CI (0.569-0.891) for the external test group. The nomogram model incorporating clinical data achieved superior results, the AUCs for predicting 2-year OS were 0.913 (95% CI: 0.83-0.98) for the training group, 0.86 (95% CI: 0.78-0.96) for the validation group, and 0.778 (95% CI: 0.69-0.94) for the external test group; for predicting 3-year OS, the AUCs were 0.837 (95% CI: 0.83-0.98) for the training group, 0.982 (95% CI: 0.84-1.0) for the validation group, and 0.785 (95% CI: 0.75-0.96) for the external test group. The calibration curve demonstrated excellent calibration of the model, while the decision curve and KM analysis indicated that the model possesses substantial clinical utility. The GBM model, based on the radiomic features of enhanced CT imaging, holds significant potential for predicting the prognosis of patients with muscle-invasive bladder cancer. Furthermore, the combined model, which incorporates clinical features, demonstrates enhanced performance and is beneficial for clinical decision-making.

Despite the widespread use of stereotactic radiosurgery (SRS) to treat cerebral arteriovenous malformations (AVMs), this procedure can lead to radiation-induced changes (RICs) in the surrounding brain tissue. Volumetric assessment of RICs is crucial for therapy planning and monitoring. RICs that appear as hyper-dense areas in magnetic resonance T2-weighted (T2w) images are clearly identifiable; however, physicians lack tools for the segmentation and quantification of these areas. This paper presents an algorithm to calculate the volume of RICs in patients with AVMs following SRS. The algorithm could be used to predict the course of RICs and facilitate clinical management. We trained a Mask Region-based Convolutional Neural Network (Mask R-CNN) as an alternative to manual pre-processing in designating regions of interest. We also applied transfer learning to the DeepMedic deep learning model to facilitate the automatic segmentation and quantification of AVM edema regions in T2w images. The resulting quantitative findings were used to explore the effects of SRS treatment among 28 patients with unruptured AVMs based on 139 regularly tracked T2w scans. The actual range of RICs in the T2w images was labeled manually by a clinical radiologist to serve as the gold standard in supervised learning. The trained model was tasked with segmenting the test set for comparison with the manual segmentation results. The average Dice similarity coefficient in these comparisons was 71.8%. The proposed segmentation algorithm achieved results on par with conventional manual calculations in determining the volume of RICs, which were shown to peak at the end of the first year after SRS and then gradually decrease. These findings have the potential to enhance clinical decision-making. Not applicable.

<b>Objective:</b>To development a deep learning（DL） model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma（NPC） and nasopharyngeal lymphoma（NPL）. <b>Methods:</b>The retrospective study included 142 patients with NPL and 292 patients with NPC who underwent conventional MRI at Renmin Hospital of Wuhan University from June 2012 to February 2023. MRI from 80 patients were manually segmented to train the segmentation model. The automatically segmented regions of interest（ROIs） formed four datasets: T1 weighted images（T1WI）, T2 weighted images（T2WI）, T1 weighted contrast-enhanced images（T1CE）, and a combination of T1WI and T2WI. The ImageNet-pretrained ResNet101 model was fine-tuned for the classification task. Statistical analysis was conducted using SPSS 22.0. The Dice coefficient loss was used to evaluate performance of segmentation task. Diagnostic performance was assessed using receiver operating characteristic（ROC） curves. Gradient-weighted class activation mapping（Grad-CAM） was imported to visualize the model's function. <b>Results:</b>The DICE score of the segmentation model reached 0.876 in the testing set. The AUC values of classification models in testing set were as follows: T1WI: 0.78（95%<i>CI</i> 0.67-0.81）, T2WI: 0.75（95%<i>CI</i> 0.72-0.86）, T1CE: 0.84（95%<i>CI</i> 0.76-0.87）, and T1WI+T2WI: 0.93（95%<i>CI</i> 0.85-0.94）. The AUC values for the two clinicians were 0.77（95%<i>CI</i> 0.72-0.82） for the junior, and 0.84（95%<i>CI</i> 0.80-0.89） for the senior. Grad-CAM analysis revealed that the central region of the tumor was highly correlated with the model's classification decisions, while the correlation was lower in the peripheral regions. <b>Conclusion:</b>The deep learning model performed well in differentiating NPC from NPL based on conventional MRI. The T1WI+T2WI combination model exhibited the best performance. The model can assist in the early diagnosis of NPC and NPL, facilitating timely and standardized treatment, which may improve patient prognosis.

Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research. We investigated 92 SSc recipients (mean age 51 years ± 10) who underwent bilateral LTx between 2007 and 2020. PGD was defined as grade 3 PGD at 72 h post-LTx. A comprehensive set of CT image features was automatically computed from recipient chest CT scans using deep learning algorithms. Volumetric analysis of recipients' lungs and chest cavity was used to estimate lung-size matching. Four machine learning (ML) algorithms were developed to predict PGD, including multivariate logistic regression, support vector machine (SVM), random forest classifier (RFC), and multilayer perceptron (MLP). PGD was significantly associated with BMI >30 kg/m² (p = 0.009), African American race (p = 0.011), lower Preop FEV1 (p = 0.002) and FVC (p = 0.004), longer waitlist time (p = 0.014), higher lung allocation score (LAS) (p = 0.028), and interstitial lung disease (p = 0.050). From CT analysis, PGD was significantly associated with decreased lung volume (p < 0.001), increased heart-chest cavity volume ratio (p < 0.001), epicardial (p = 0.033) and total heart (p = 0.049) adipose tissue, and five cardiopulmonary features (p < 0.050). Oversized donor allografts estimated using CT analysis were significantly associated with PGD (p < 0.050). The MLP model achieved a maximum AUROC of 0.85 (95% CI: 0.81-0.88) in predicting PGD with four features: Preop FEV1, heart-chest cavity volume ratio, waitlist time, and donor to recipient chest cavity volume ratio. CT-derived features are significantly associated with PGD, and models incorporating these features can predict PGD in SSc recipients.