Accurate identification of anal fistulas is essential, as it directly impacts the severity of subsequent perianal infections, prognostic indicators, and overall treatment outcomes. Traditional manual recognition methods are inefficient. In response, computer vision methods have been adopted to improve efficiency. Convolutional neural networks(CNNs) are the main basis for detecting anal fistulas in current computer vision techniques. However, these methods often struggle to capture long-range dependencies effectively, which results in inadequate handling of images of anal fistulas. This study proposes a new fusion model, CVT-HNet, that integrates MobileNet with vision transformer technology. This design utilizes CNNs to extract local features and Transformers to capture long-range dependencies. In addition, the MobileNetV2 with Coordinate Attention mechanism and encoder modules are optimized to improve the precision of detecting anal fistulas. Comparative experimental results show that CVT-HNet achieves an accuracy of 80.66% with significant robustness. It surpasses both pure Transformer architecture models and other fusion networks. Internal validation results demonstrate the reliability and consistency of CVT-HNet. External validation demonstrates that our model exhibits commendable transportability and generalizability. In visualization analysis, CVT-HNet exhibits a more concentrated focus on the region of interest in images of anal fistulas. Furthermore, the contribution of each CVT-HNet component module is evaluated by ablation experiments. The experimental results highlight the superior performance and practicality of CVT-HNet in detecting anal fistulas. By combining local and global information, CVT-HNet demonstrates strong performance. The model not only achieves high accuracy and robustness but also exhibits strong generalizability. This makes it suitable for real-world applications where variability in data is common.These findings emphasize its effectiveness in clinical contexts.

Deep learning techniques have become pivotal in medical image segmentation, but their success often relies on large, manually annotated datasets, which are expensive and labor-intensive to obtain. Additionally, different segmentation tasks frequently require retraining models from scratch, resulting in substantial computational costs. To address these limitations, we propose PDoRA, an innovative parameter-efficient fine-tuning method that leverages knowledge transfer from a pre-trained SwinUNETR model for a wide range of brain image segmentation tasks. PDoRA minimizes the reliance on extensive data annotation and computational resources by decomposing model weights into principal and residual weights. The principal weights are further divided into magnitude and direction, enabling independent fine-tuning to enhance the model's ability to capture task-specific features. The residual weights remain fixed and are later fused with the updated principal weights, ensuring model stability while enhancing performance. We evaluated PDoRA on three diverse medical image datasets for brain structure and metastasis segmentation. The results demonstrate that PDoRA consistently outperforms existing parameter-efficient fine-tuning methods, achieving superior segmentation accuracy and efficiency. Our code is available at https://github.com/Perfect199001/PDoRA/tree/main .

Three-dimensional magnetic resonance vessel wall imaging (3D MR-VWI) is critical for characterizing cerebrovascular pathologies, yet its clinical adoption is hindered by labor-intensive postprocessing. We developed VWI Assistant, a multi-sequence integrated deep learning platform trained on multicenter data (study cohorts 1981 patients and imaging datasets) to automate artery segmentation and reconstruction. The framework demonstrated robust performance across diverse patient populations, imaging protocols, and scanner manufacturers, achieving 92.9% qualified rate comparable to expert manual delineation. VWI Assistant reduced processing time by over 90% (10-12 min per case) compared to manual methods (p < 0.001) and improved inter-/intra-reader agreement. Real-world deployment (n = 1099 patients) demonstrated rapid clinical adoption, with utilization rates increasing from 10.8% to 100.0% within 12 months. By streamlining 3D MR-VWI workflows, VWI Assistant addresses scalability challenges in vascular imaging, offering a practical tool for routine use and large-scale research, significantly improving workflow efficiency while reducing labor and time costs.

Breast cancer is a relatively common carcinoma among women worldwide and remains a considerable public health concern. Consequently, the prompt identification of cancer is crucial, as research indicates that 96% of cancers are treatable if diagnosed prior to metastasis. Despite being considered the gold standard for breast cancer evaluation, conventional mammography possesses inherent drawbacks, including accessibility issues, especially in rural regions, and discomfort associated with the procedure. Therefore, there has been a surge in interest in non-invasive, radiation-free alternative diagnostic techniques, such as thermal imaging (thermography). Thermography employs infrared thermal sensors to capture and assess temperature maps of human breasts for the identification of potential tumours based on areas of thermal irregularity. This study proposes an advanced computer-aided diagnosis (CAD) system called Thermo-CAD to assess early breast cancer detection using thermal imaging, aimed at assisting radiologists. The CAD system employs a variety of deep learning techniques, specifically incorporating multiple convolutional neural networks (CNNs) to enhance diagnostic accuracy and reliability. To effectively integrate multiple deep features and diminish the dimensionality of features derived from each CNN, feature transformation and selection methods, including non-negative matrix factorization and Relief-F, are used leading to a reduction in classification complexity. The Thermo-CAD system is assessed utilising two datasets: the DMR-IR (Database for Mastology Research Infrared Images), for distinguishing between normal and abnormal breast tissues, and a novel thermography dataset to distinguish abnormal instances as benign or malignant. Thermo-CAD has proven to be an outstanding CAD system for thermographic breast cancer detection, attaining 100% accuracy on the DMR-IR dataset (normal versus abnormal breast cancer) using CSVM and MGSVM classifiers, and lower accuracy using LSVM and QSVM classifiers. However, it showed a lower ability to distinguish benign from malignant cases (second dataset), achieving an accuracy of 79.3% using CSVM. Yet, it remains a promising tool for early-stage cancer detection, especially in resource-constrained environments.

Autism spectrum disorder (ASD) diagnosis remains challenging and could benefit from objective imaging-based approaches. This study aimed to construct a prediction model using whole-brain imaging radiomics and machine learning to identify children with ASD. We analyzed 223 subjects (120 with ASD) from the ABIDE database, randomly divided into training and test sets (7:3 ratio), and an independent external test set of 87 participants from Georgetown University and University of Miami. Radiomics features were extracted from white matter, gray matter, and cerebrospinal fluid from whole-brain MR images. After feature dimensionality reduction, we screened clinical predictors using multivariate logistic regression and combined them with radiomics signatures to build machine learning models. Model performance was evaluated using ROC curves and by stratifying subjects into risk subgroups. Radiomics markers achieved AUCs of 0.78, 0.75, and 0.74 in training, test, and external test sets, respectively. Verbal intelligence quotient(VIQ) emerged as a significant ASD predictor. The decision tree algorithm with radiomics markers performed best, with AUCs of 0.87, 0.84, and 0.83; sensitivities of 0.89, 0.84, and 0.86; and specificities of 0.70, 0.63, and 0.66 in the three datasets, respectively. Risk stratification using a cut-off value of 0.4285 showed significant differences in ASD prevalence between subgroups across all datasets (training: χ²=21.325; test: χ²=5.379; external test: χ²=21.52m, P<0.05). A radiomics signature based on whole-brain MRI features can effectively identify ASD, with performance enhanced by incorporating VIQ data and using a decision tree algorithm, providing a potential adaptive strategy for clinical practice. ASD = Autism Spectrum Disorder; MRI = Magnetic Resonance Imaging; SVM = support vector machine; KNN = K-nearest neighbor; VIQ = Verbal intelligence quotient; FIQ = Full-Scale intelligence quotient; ROC = Receiver Operating Characteristic; AUC = Area under Curve.

Magnetic resonance imaging (MRI) is a cornerstone of neuroimaging, providing unparalleled soft-tissue contrast. However, its clinical utility is often limited by long acquisition times, which contribute to motion artifacts, patient discomfort, and increased costs. Although traditional acceleration techniques, such as parallel imaging and compressed sensing help reduce scan times, they may reduce signal-to-noise ratio (SNR) and introduce artifacts. The advent of deep learning-based image reconstruction (DLBIR) may help in several ways to reduce scan times while preserving or improving image quality. Various DLBIR techniques are currently available through different vendors, with claimed reductions in gradient times up to 85% while maintaining or enhancing lesion conspicuity, improved noise suppression and diagnostic accuracy. The evolution of DLBIR from 2D to 3D acquisitions, coupled with advancements in self-supervised learning, further expands its capabilities and clinical applicability. Despite these advancements, challenges persist in generalizability across scanners and imaging conditions, susceptibility to artifacts and potential alterations in pathology representation. Additionally, limited data on training, underlying algorithms and clinical validation of these vendor-specific closed-source algorithms pose barriers to end-user trust and widespread adoption. This review explores the current applications of DLBIR in neuroimaging, vendor-driven implementations, and emerging trends that may impact accelerated MRI acquisitions.ABBREVIATIONS: PI＝ parallel imaging; CS＝ compressed sensing; DLBIR = deep learning-based image reconstruction; AI= artificial intelligence; DR =. Deep resolve; ACS = Artificial-intelligence-assisted compressed sensing.

This study aimed to develop an interpretable machine learning model using magnetic resonance imaging (MRI) radiomics features to predict preoperative microscopic peritumoral small cancer foci (MSF) and explore its relationship with early recurrence in hepatocellular carcinoma (HCC) patients. A total of 1049 patients from three hospitals were divided into a training set (Hospital 1: 614 cases), a test set (Hospital 2: 248 cases), and a validation set (Hospital 3: 187 cases). Independent risk factors from clinical and MRI features were identified using univariate and multivariate logistic regression to build a clinicoradiological model. MRI radiomics features were then selected using methods like least absolute shrinkage and selection operator (LassoCV) and modeled with various machine learning algorithms, choosing the best-performing model as the radiomics model. The clinical and radiomics features were combined to form a fusion model. Model performance was evaluated by comparing receiver operating characteristic (ROC) curves, area under the curve (AUC) values, calibration curves, and decision curve analysis (DCA) curves. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) values assessed improvements in predictive efficacy. The model's prognostic value was verified using Kaplan-Meier analysis. SHapley Additive exPlanations (SHAP) was used to interpret how the model makes predictions. Three models were developed as follows: Clinical Radiology, XGBoost, and Clinical XGBoost. XGBoost was selected as the final model for predicting MSF, with AUCs of 0.841, 0.835, and 0.817 in the training, test, and validation sets, respectively. These results were comparable to the Clinical XGBoost model (0.856, 0.826, 0.837) and significantly better than the Clinical Radiology model (0.688, 0.561, 0.613). Additionally, the XGBoost model effectively predicted early recurrence in HCC patients. This study successfully developed an interpretable XGBoost machine learning model based on MRI radiomics features to predict preoperative MSF and early recurrence in HCC patients.

Diagnosing pulmonary tuberculosis (PTB) in children is challenging owing to paucibacillary disease, non-specific symptoms and signs and challenges in microbiological confirmation. Chest X-ray (CXR) interpretation is fundamental for diagnosis and classifying disease as severe or non-severe. In adults with PTB, there is substantial evidence showing the usefulness of artificial intelligence (AI) in CXR interpretation, but very limited data exist in children. A prospective two-stage study of children with presumed PTB in three sites (one in South Africa and two in Pakistan) will be conducted. In stage I, eligible children will be enrolled and comprehensively investigated for PTB. A CXR radiological reference standard (RRS) will be established by an expert panel of blinded radiologists. CXRs will be classified into those with findings consistent with PTB or not based on RRS. Cases will be classified as confirmed, unconfirmed or unlikely PTB according to National Institutes of Health definitions. Data from 300 confirmed and unconfirmed PTB cases and 250 unlikely PTB cases will be collected. An AI-CXR algorithm (qXR) will be used to process CXRs. The primary endpoint will be sensitivity and specificity of AI to detect confirmed and unconfirmed PTB cases (composite reference standard); a secondary endpoint will be evaluated for confirmed PTB cases (microbiological reference standard). In stage II, a multi-reader multi-case study using a cross-over design will be conducted with 16 readers and 350 CXRs to assess the usefulness of AI-assisted CXR interpretation for readers (clinicians and radiologists). The primary endpoint will be the difference in the area under the receiver operating characteristic curve of readers with and without AI assistance in correctly classifying CXRs as per RRS. The study has been approved by a local institutional ethics committee at each site. Results will be published in academic journals and presented at conferences. Data will be made available as an open-source database. PACTR202502517486411.

This work addresses the challenge of reliably measuring the muscles of the human tongue, which are difficult to quantify due to complex interwoven muscle types. We introduce a new semi-automated method, enabled by a manually curated dataset of MRI scans to accurately measure five key tongue muscles, combining AI-assisted, atlas-based, and manual segmentation approaches. The method was tested and validated in a dataset of 178 scans and included segmentation validation (n = 103) and clinical application (n = 132) in individuals with motor neuron disease. We show that people with speech and swallowing deficits tend to have smaller muscle volumes and present a normalisation strategy that removes confounding demographic factors, enabling broader application to large MRI datasets. As the tongue is generally covered in neuroimaging protocols, our multi-contrast pipeline will allow for the post-hoc analysis of a vast number of datasets. We expect this work to enable the investigation of tongue muscle morphology as a marker in a wide range of diseases that implicate tongue function, including neurodegenerative diseases and pathological speech disorders.

Currently, there is no guidance for personalized choice of induction chemotherapy (IC) regimens (TPF, docetaxel + cisplatin + 5-Fu; or GP, gemcitabine + cisplatin) for locoregionally advanced nasopharyngeal carcinoma (LA-NPC). This study aimed to develop deep learning models for IC response prediction in LA-NPC. For 1438 LA-NPC patients, pretreatment magnetic resonance imaging (MRI) scans and complete biological response (cBR) information after 3 cycles of IC were collected from two centers. All models were trained in 969 patients (TPF: 548, GP: 421), and internally validated in 243 patients (TPF: 138, GP: 105), then tested on an internal dataset of 226 patients (TPF: 125, GP: 101). MRI models for the TPF and GP cohorts were constructed to predict cBR from MRI using radiomics and graph convolutional network (GCN). The MRI-Clinical models were built based on both MRI and clinical parameters. The MRI models and MRI-Clinical models achieved high discriminative accuracy in both TPF cohorts (MRI model: AUC, 0.835; MRI-Clinical model: AUC, 0.838) and GP cohorts (MRI model: AUC, 0.764; MRI-Clinical model: AUC, 0.777). The MRI-Clinical models also showed good performance in the risk stratification. The survival curve revealed that the 3-year disease-free survival of the high-sensitivity group was better than that of the low-sensitivity group in both the TPF and GP cohorts. An online tool guiding personalized choice of IC regimen was developed based on MRI-Clinical models. Our radiomics and GCN-based IC response prediction tool has robust predictive performance and may provide guidance for personalized treatment.