We developed a deep learning radiomics nomogram (DLRN) using CT scans to improve clinical decision-making and risk stratification for early recurrence of hepatocellular carcinoma (HCC) after transplantation, which typically has a poor prognosis. In this two-center study, 245 HCC patients who had contrast-enhanced CT before liver transplantation were split into a training set (n = 184) and a validation set (n = 61). We extracted radiomics and deep learning features from tumor and peritumor areas on preoperative CT images. The DLRN was created by combining these features with significant clinical variables using multivariate logistic regression. Its performance was validated against four traditional risk criteria to assess its additional value. The DLRN model showed strong predictive accuracy for early HCC recurrence post-transplant, with AUCs of 0.884 and 0.829 in training and validation groups. High DLRN scores significantly increased relapse risk by 16.370 times (95% CI: 7.100-31.690; p  < 0.001). Combining DLRN with Metro-Ticket 2.0 criteria yielded the best prediction (AUC: training/validation: 0.936/0.863). The CT-based DLRN offers a non-invasive method for predicting early recurrence following liver transplantation in patients with HCC. Furthermore, it provides substantial additional predictive value with traditional prognostic scoring systems. AI-driven predictive models utilizing preoperative CT imaging enable accurate identification of early HCC recurrence risk following liver transplantation, facilitating risk-stratified surveillance protocols and optimized post-transplant management. A CT-based DLRN for predicting early HCC recurrence post-transplant was developed. The DLRN predicted recurrence with high accuracy (AUC: 0.829) and 16.370-fold increased recurrence risk. Combining DLRN with Metro-Ticket 2.0 criteria achieved optimal prediction (AUC: 0.863).

This study aimed to develop a deep learning (DL) model for automatic detection and diagnosis of gouty arthritis (GA) in the first metatarsophalangeal joint (MTPJ) using ultrasound (US) images. A retrospective study included individuals who underwent first MTPJ ultrasonography between February and July 2023. A five-fold cross-validation method (training set = 4:1) was employed. A deep residual convolutional neural network (CNN) was trained, and Gradient-weighted Class Activation Mapping (Grad-CAM) was used for visualization. Different ResNet18 models with varying residual blocks (2, 3, 4, 6) were compared to select the optimal model for image classification. Diagnostic decisions were based on a threshold proportion of abnormal images, determined from the training set. A total of 2401 US images from 260 patients (149 gout, 111 control) were analyzed. The model with 3 residual blocks performed best, achieving an AUC of 0.904 (95% CI: 0.887~0.927). Visualization results aligned with radiologist opinions in 2000 images. The diagnostic model attained an accuracy of 91.1% (95% CI: 90.4%~91.8%) on the testing set, with a diagnostic threshold of 0.328.  The DL model demonstrated excellent performance in automatically detecting and diagnosing GA in the first MTPJ.

This study aimed to develop and validate machine learning models integrating clinicoradiological and radiomic features from 2-[18 F]-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) to predict pathological high invasiveness in cT1-sized (tumor size ≤ 3 cm) non-small cell lung cancer (NSCLC). We retrospectively reviewed 1459 patients with NSCLC (633 with pathological high invasiveness and 826 with pathological non-high invasiveness) from two medical centers. Patients with cT1-sized NSCLC were included. 1145 radiomic features were extracted per modality (PET and CT) from each patient. Optimal predictors were selected to construct a radiomics score (Rad-score) for the PET/CT radiomics model. A combined model incorporating significant clinicoradiological features and the Rad-score was developed. Logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) algorithms were used to train the combined model. Model performance was assessed the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA). Shapley Additive Explanations (SHAP) was applied to visualize the prediction process. The radiomics model was built using 11 radiomic features, achieving AUCs of 0.851 (training), 0.859 (internal validation), and 0.829 (external validation). Among all models, the XGBoost combined model demonstrated the best predictive performance, with AUCs of 0.958, 0.919, and 0.903, respectively, along with good calibration and high net benefit. The XGBoost combined model showed strong performance in predicting pathological high invasiveness in cT1-sized NSCLC.

Accurately predicting post-transplant renal function is essential for optimizing donor-recipient matching and improving long-term outcomes in kidney transplantation (KT). Traditional models using only structured clinical data often fail to account for complex biological and anatomical factors. This study aimed to develop and validate a multimodal deep learning model that integrates computed tomography (CT) imaging, radiology report text, and structured clinical variables to predict 1-year estimated glomerular filtration rate (eGFR) in living donor kidney transplantation (LDKT) recipients. A retrospective cohort of 1,937 LDKT recipients was selected from 3,772 KT cases. Exclusions included deceased donor KT, immunologic high-risk recipients (n = 304), missing CT imaging, early graft complications, and anatomical abnormalities. eGFR at 1 year post-transplant was classified into four categories: > 90, 75-90, 60-75, and 45-60 mL/min/1.73 m2. Radiology reports were embedded using BioBERT, while CT videos were encoded using a CLIP-based visual extractor. These were fused with structured clinical features and input into ensemble classifiers including XGBoost. Model performance was evaluated using cross-validation and SHapley Additive exPlanations (SHAP) analysis. The full multimodal model achieved a macro F1 score of 0.675, micro F1 score of 0.704, and weighted F1 score of 0.698-substantially outperforming the clinical-only model (macro F1 = 0.292). CT imaging contributed more than text data (clinical + CT macro F1 = 0.651; clinical + text = 0.486). The model showed highest accuracy in the >90 (F1 = 0.7773) and 60-75 (F1 = 0.7303) categories. SHAP analysis identified donor age, BMI, and donor sex as key predictors. Dimensionality reduction confirmed internal feature validity. Multimodal deep learning integrating clinical, imaging, and textual data enhances prediction of post-transplant renal function. This framework offers a robust and interpretable approach for individualized risk stratification in LDKT, supporting precision medicine in transplantation.

Deep learning has significantly advanced automatic medical diagnostics, releasing human resources from clinical pressure, yet the persistent challenge of data scarcity in this area hampers its further improvements and applications. To address this gap, we introduce a novel ensemble framework called 'Efficient Transfer and Self-supervised Learning based Ensemble Framework' (ETSEF). ETSEF leverages features from multiple pre-trained deep learning models to efficiently learn powerful representations from a limited number of data samples. To the best of our knowledge, ETSEF is the first strategy that combines two pre-training methodologies (Transfer Learning and Self-supervised Learning) with ensemble learning approaches. Various data enhancement techniques, including data augmentation, feature fusion, feature selection, and decision fusion, have also been deployed to maximise the efficiency and robustness of the ETSEF model. Five independent medical imaging tasks, including endoscopy, breast cancer detection, monkeypox detection, brain tumour detection, and glaucoma detection, were tested to demonstrate ETSEF's effectiveness and robustness. Facing limited sample numbers and challenging medical tasks, ETSEF has demonstrated its effectiveness by improving diagnostic accuracy by up to 13.3% compared to strong ensemble baseline models and up to 14.4% compared with recent state-of-the-art methods. Moreover, we emphasise the robustness and trustworthiness of the ETSEF method through various vision-explainable artificial intelligence techniques, including Grad-CAM, SHAP, and t-SNE. Compared to large-scale deep learning models, ETSEF can be flexibly deployed and maintain superior performance for challenging medical imaging tasks, demonstrating potential for application in areas lacking training data. The code is available at Github ETSEF.

Tyrosine kinase inhibitors (TKIs) combined with immunotherapy regimens are now widely used for treating advanced hepatocellular carcinoma (HCC), but their clinical efficacy is limited to a subset of patients. Considering that the vast majority of advanced HCC patients lose the opportunity for liver resection and thus cannot provide tumor tissue samples, we leveraged the clinical and image data to construct a multimodal convolutional neural network (CNN)-Transformer model for predicting and analyzing tumor response to TKI-immunotherapy. An automatic liver tumor segmentation system, based on a two-stage 3D U-Net framework, delineates lesions by first segmenting the liver parenchyma and then precisely localizing the tumor. This approach effectively addresses the variability in clinical data and significantly reduces bias introduced by manual intervention. Thus, we developed a clinical model using only pre-treatment clinical information, a CNN model using only pre-treatment magnetic resonance imaging data, and an advanced multimodal CNN-Transformer model that fused imaging and clinical parameters using a training cohort (n = 181) and then validated them using an independent cohort (n = 30). In the validation cohort, the area under the curve (95% confidence interval) values were 0.720 (0.710-0.731), 0.695 (0.683-0.707), and 0.785 (0.760-0.810), respectively, indicating that the multimodal model significantly outperformed the single-modality baseline models across validations. Finally, single-cell sequencing with the surgical tumor specimens reveals tumor ecosystem diversity associated with treatment response, providing a preliminary biological validation for the prediction model. In summary, this multimodal model effectively integrates imaging and clinical features of HCC patients, has a superior performance in predicting tumor response to TKI-immunotherapy, and provides a reliable tool for optimizing personalized treatment strategies.

Image registration is a fundamental task in medical image analysis. Deformations are often closely related to the morphological characteristics of tissues, making accurate feature extraction crucial. Recent weakly supervised methods improve registration by incorporating anatomical priors such as segmentation masks or landmarks, either as inputs or in the loss function. However, such weak labels are often not readily available, limiting their practical use. Motivated by the strong representation learning ability of visual foundation models, this paper introduces SAMIR, an efficient medical image registration framework that utilizes the Segment Anything Model (SAM) to enhance feature extraction. SAM is pretrained on large-scale natural image datasets and can learn robust, general-purpose visual representations. Rather than using raw input images, we design a task-specific adaptation pipeline using SAM's image encoder to extract structure-aware feature embeddings, enabling more accurate modeling of anatomical consistency and deformation patterns. We further design a lightweight 3D head to refine features within the embedding space, adapting to local deformations in medical images. Additionally, we introduce a Hierarchical Feature Consistency Loss to guide coarse-to-fine feature matching and improve anatomical alignment. Extensive experiments demonstrate that SAMIR significantly outperforms state-of-the-art methods on benchmark datasets for both intra-subject cardiac image registration and inter-subject abdomen CT image registration, achieving performance improvements of 2.68% on ACDC and 6.44% on the abdomen dataset. The source code will be publicly available on GitHub following the acceptance of this paper.

The aim of this study was to assess measurements of the maxillary canines using Cone Beam Computed Tomography (CBCT) and develop a machine learning model for sex estimation. CBCT scans from 610 patients were screened. The maxillary canines were examined to measure total tooth length, average enamel thickness, and mesiodistal width. Various supervised machine learning algorithms were employed to construct predictive models, including Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Logistic Regression, Multi-Layer Perceptron (MLP), Random Forest Classifier, Support Vector Machine (SVM), XGBoost, LightGBM, and CatBoost. Validation of each model was performed using a 10-fold cross-validation approach. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were computed, with ROC curves generated for visualization. The total length of the tooth proved to be the variable with the highest predictive power. The algorithms that demonstrated superior performance in terms of AUC were LightGBM and Logistic Regression, achieving AUC values of 0.77 [CI95% = 0.65-0.89] and 0.75 [CI95% = 0.62-0.86] for the test data, and 0.74 [CI95% = 0.70-0.80] and 0.75 [CI95% = 0.70-0.79] in cross-validation, respectively. Both models also showed high precision values. The use of maxillary canine measurements, combined with supervised machine learning techniques, has proven to be viable for sex estimation. The machine learning approach combined with is a low-cost option as it relies solely on a single anatomical structure.

Medical image segmentation plays a vital role in diagnosis, treatment planning, and disease monitoring. However, endoscopic and dermoscopic images often exhibit blurred boundaries and low contrast, presenting a significant challenge for precise segmentation. Moreover, single encoder-decoder architectures suffer from inherent limitations, resulting in the loss of either fine-grained details or global context. Some dual-encoder models yield inaccurate results due to mismatched receptive fields and overly simplistic fusion strategies. To overcome these issues, we present the Dual Backbone Cascaded Multi-Convolutional Segmentation Network (DBCM-Net). Our approach employs a Multi-Axis Vision Transformer and a Vision Mamba encoder to extract semantic features at multiple scales, with a cascaded design that enables information sharing between the two backbones. We introduce the Global and Local Fusion Attention Block (GLFAB) to generate attention masks that seamlessly integrate global context with local detail, producing more precise feature maps. Additionally, we incorporate a Depthwise Separable Convolution Attention Module (DSCAM) within the encoders to strengthen the model's ability to capture critical features. A Feature Refinement Fusion Block (FRFB) is further applied to refine these feature maps before subsequent processing. The cascaded network architecture synergistically combines the complementary strengths of both encoders. We rigorously evaluated our model on three distinct datasets, achieving Dice coefficients of 94.93% on the CVC-ClinicDB polyp dataset, 91.93% on ISIC2018, and 92.73% on ACDC, each surpassing current state-of-the-art methods. Extensive experiments demonstrate that the proposed method excels in segmentation accuracy and preserves edge details effectively.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content</i>. Purpose To develop a three-stage, age-and modality-conditioned framework to synthesize longitudinal infant brain MRI scans, and account for rapid structural and contrast changes during early brain development. Materials and Methods This retrospective study used T1- and T2-weighted MRI scans (848 scans) from 139 infants in the Baby Connectome Project, collected since September 2016. The framework models three critical image cues related: volumetric expansion, cortical folding, and myelination, predicting missing time points with age and modality as predictive factors. The method was compared with LGAN, CounterSyn, and Diffusion-based approach using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and the Dice similarity coefficient (DSC). Results The framework was trained on 119 participants (average age: 11.25 ± 6.16 months, 60 female, 59 male) and tested on 20 (average age: 12.98 ± 6.59 months, 11 female, 9 male). For T1-weighted images, PSNRs were 25.44 ± 1.95 and 26.93 ± 2.50 for forward and backward MRI synthesis, and SSIMs of 0.87 ± 0.03 and 0.90 ± 0.02. For T2-weighted images, PSNRs were 26.35 ± 2.30 and 26.40 ± 2.56, with SSIMs of 0.87 ± 0.03 and 0.89 ± 0.02, significantly outperforming competing methods (<i>P</i> < .001). The framework also excelled in tissue segmentation (<i>P</i> < .001) and cortical reconstruction, achieving DSC of 0.85 for gray matter and 0.86 for white matter, with intraclass correlation coefficients exceeding 0.8 in most cortical regions. Conclusion The proposed three-stage framework effectively synthesized age-specific infant brain MRI scans, outperforming competing methods in image quality and tissue segmentation with strong performance in cortical reconstruction, demonstrating potential for developmental modeling and longitudinal analyses. ©RSNA, 2025.