Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis. Deep learning has increasingly been developed to assist in diagnosis and assessment, which can help improve clinical management and provide new ideas for clinical research. To support the development and testing of deep learning models for cervical spondylosis, we have publicly shared a multi-modal and multi-view imaging dataset of cervical spondylosis, named MMCSD. This dataset comprises MRI and CT images from 250 patients. It includes axial bone and soft tissue window CT scans, sagittal T1-weighted and T2-weighted MRI, as well as axial T2-weighted MRI. Neck pain is one of the most common symptoms of cervical spondylosis. We use the MMCSD to develop a deep learning model for predicting postoperative neck pain in patients with cervical spondylosis, thereby validating its usability. We hope that the MMCSD will contribute to the advancement of neural network models for cervical spondylosis and neck pain, further optimizing clinical diagnostic assessments and treatment decision-making for these conditions.

The most common types of kidneys and liver cancer are renal cell carcinoma (RCC) and hepatic cell carcinoma (HCC), respectively. Accurate grading of these carcinomas is essential for determining the most appropriate treatment strategies, including surgery or pharmacological interventions. Traditional deep learning methods often struggle with the intricate and complex patterns seen in histopathology images of RCC and HCC, leading to inaccuracies in classification. To enhance the grading accuracy for liver and renal cell carcinoma, this research introduces a novel feature selection and fusion framework inspired by economic theories, incorporating attention mechanisms into three Convolutional Neural Network (CNN) architectures-MobileNetV2, DenseNet121, and InceptionV3-as foundational models. The attention mechanisms dynamically identify crucial image regions, leveraging each CNN's unique strengths. Additionally, a Gini-based feature selection method is implemented to prioritize the most discriminative features, and the extracted features from each network are optimally combined using a fusion technique modeled after a linear production function, maximizing each model's contribution to the final prediction. Experimental evaluations demonstrate that this proposed approach outperforms existing state-of-the-art models, achieving high accuracies of 93.04% for RCC and 98.24% for LCC. This underscores the method's robustness and effectiveness in accurately grading these types of cancers. The code of our method is publicly available in https://github.com/GHOSTCALL983/GRADE-CLASSIFICATION .

Ankylosing Spondylitis (AS), commonly known as Bechterew's disease, is a complex, potentially disabling disease that develops slowly over time and progresses to radiographic sacroiliitis. The etiology of this disease is poorly understood, making it difficult to diagnose. Therefore, treatment is also delayed. This study aims to diagnose AS with an automated system that classifies axial magnetic resonance imaging (MRI) sequences of AS patients. Recently, the application of deep learning neural networks (DLNNs) for MRI classification has become widespread. The implementation of this process on computer-independent end devices is advantageous due to its high computational power and low latency requirements. In this research, an MRI dataset containing images from 527 individuals was used. A deep learning architecture on a Field Programmable Gate Array (FPGA) card was implemented and analyzed. The results show that the classification performed on FPGA in AS diagnosis yields successful results close to the classification performed on CPU.

Early detection of lung cancer through low-dose CT lung cancer screening in a high-risk population has proven to reduce lung cancer-specific mortality. Nodule management plays a pivotal role in early detection and further diagnostic approaches. The European Society of Thoracic Imaging (ESTI) has established a nodule management recommendation to improve the handling of pulmonary nodules detected during screening. For solid nodules, the primary method for assessing the likelihood of malignancy is to monitor nodule growth using volumetry software. For subsolid nodules, the aggressiveness is determined by measuring the solid part. The ESTI-recommendation enhances existing protocols but puts a stronger focus on lesion aggressiveness. The main goals are to minimise the overall number of follow-up examinations while preventing the risk of a major stage shift and reducing the risk of overtreatment. KEY POINTS: Question Assessment of nodule growth and management according to guidelines is essential in lung cancer screening. Findings Assessment of nodule aggressiveness defines follow-up in lung cancer screening. Clinical relevance The ESTI nodule management recommendation aims to reduce follow-up examinations while preventing major stage shift and overtreatment.

To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective study included TNBC patients from the First Affiliated Hospital of Soochow University and Jiangsu Province Hospital (2016-2023). We analyzed clinical characteristics and radiomic features from T2-weighted MRI. Using LASSO regression for feature selection, we applied Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) to build prediction models. A total of 163 patients, with a median age of 53 years (range: 24-73), were divided into a training group (n = 115) and a validation group (n = 48). Among them, 54 (33.13%) had ALN metastasis, and 109 (66.87%) were non-metastasis. Nottingham grade (P = 0.005), tumor size (P = 0.016) were significant difference between non-metastasis cases and metastasis cases. In the validation set, the LR-based combined model achieved the highest AUC (0.828, 95%CI: 0.706-0.950) with excellent sensitivity (0.813) and accuracy (0.812). Although the RF-based model had the highest AUC in the training set and the highest specificity (0.906) in the validation set, its performance was less consistent compared to the LR model. MRI-T2WI radiomic features predict ALN metastasis in TNBC, with integration into clinical models enhancing preoperative predictions and personalizing management.

Deep learning models require large amounts of annotated data, which are hard to obtain in the medical field, as the annotation process is laborious and depends on expert knowledge. This data scarcity hinders a model's ability to generalise effectively on unseen data, and recently, foundation models pretrained on large datasets have been proposed as a promising solution. RadiologyNET is a custom medical dataset that comprises 1,902,414 medical images covering various body parts and modalities of image acquisition. We used the RadiologyNET dataset to pretrain several popular architectures (ResNet18, ResNet34, ResNet50, VGG16, EfficientNetB3, EfficientNetB4, InceptionV3, DenseNet121, MobileNetV3Small and MobileNetV3Large). We compared the performance of ImageNet and RadiologyNET foundation models against training from randomly initialiased weights on several publicly available medical datasets: (i) Segmentation-LUng Nodule Analysis Challenge, (ii) Regression-RSNA Pediatric Bone Age Challenge, (iii) Binary classification-GRAZPEDWRI-DX and COVID-19 datasets, and (iv) Multiclass classification-Brain Tumor MRI dataset. Our results indicate that RadiologyNET-pretrained models generally perform similarly to ImageNet models, with some advantages in resource-limited settings. However, ImageNet-pretrained models showed competitive performance when fine-tuned on sufficient data. The impact of modality diversity on model performance was tested, with the results varying across tasks, highlighting the importance of aligning pretraining data with downstream applications. Based on our findings, we provide guidelines for using foundation models in medical applications and publicly release our RadiologyNET-pretrained models to support further research and development in the field. The models are available at https://github.com/AIlab-RITEH/RadiologyNET-TL-models .

This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Additionally, this current study compared the performance of CNN models with the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS) and Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) ultrasound-based malignancy risk stratification systems. A total of 327 eligible patients with FTC and FTA who underwent preoperative thyroid ultrasound examination were retrospectively enrolled between August 2017, and August 2024. Patients were randomly assigned to a training cohort (n = 263) and a test cohort (n = 64) in an 8:2 ratio using stratified sampling. Five CNN models, including VGG16, ResNet101, MobileNetV2, ResNet152, and ResNet50, pre-trained with ImageNet, were developed and tested to distinguish FTC from FTA. The CNN models exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) ranging from 0.64 to 0.77. The ResNet152 model demonstrated the highest AUC (0.77; 95% CI, 0.67-0.87) for distinguishing between FTC and FTA. Decision curve and calibration curve analyses demonstrated the models' favorable clinical value and calibration. Furthermore, when comparing the performance of the developed models with that of the C-TIRADS and ACR-TIRADS systems, the models developed in this study demonstrated superior performance. This can potentially guide appropriate management of FTC in patients with follicular neoplasms.

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with early detection playing a crucial role in improving survival rates. Artificial intelligence (AI), particularly in medical image analysis, has emerged as a potential tool for HCC diagnosis and surveillance. Recent advancements in deep learning-driven medical imaging have demonstrated significant potential in enhancing early HCC detection, particularly in ultrasound (US)-based surveillance. This review provides a comprehensive analysis of the current landscape, challenges, and future directions of AI in HCC surveillance, with a specific focus on the application in US imaging. Additionally, it explores AI's transformative potential in clinical practice and its implications for improving patient outcomes. We examine various AI models developed for HCC diagnosis, highlighting their strengths and limitations, with a particular emphasis on deep learning approaches. Among these, convolutional neural networks have shown notable success in detecting and characterising different focal liver lesions on B-mode US often outperforming conventional radiological assessments. Despite these advancements, several challenges hinder AI integration into clinical practice, including data heterogeneity, a lack of standardisation, concerns regarding model interpretability, regulatory constraints, and barriers to real-world clinical adoption. Addressing these issues necessitates the development of large, diverse, and high-quality data sets to enhance the robustness and generalisability of AI models. Emerging trends in AI for HCC surveillance, such as multimodal integration, explainable AI, and real-time diagnostics, offer promising advancements. These innovations have the potential to significantly improve the accuracy, efficiency, and clinical applicability of AI-driven HCC surveillance, ultimately contributing to enhanced patient outcomes.

Introduction Contrast enema (CE) is widely used in the evaluation of suspected Hirschsprung disease (HD). Deep learning is a promising tool to standardize image assessment and support clinical decision-making. This study assesses the diagnostic performance of a deep neural network (DNN), with and without clinical data, and compares its interpretation with that of pediatric surgeons and radiologists. Materials and Methods In this retrospective study, 1471 contrast enema images from patients <15 years were analysed, with 218 images used for testing. A deep neural network, pediatric radiologists, and surgeons independently reviewed the testing set, with and without clinical data. Diagnostic performance was assessed using ROC and PR curves, and interobserver agreement was evaluated using Fleiss' kappa. Results The deep neural network achieved high diagnostic accuracy (AUC-ROC = 0.87) in contrast enema interpretation, with improved performance when combining anteroposterior and lateral images (AUC-ROC = 0.92). Clinical data integration further enhanced model sensitivity and negative predictive value. The super-surgeon (majority voting of colorectal surgeons) outperformed most individual clinicians (sensitivity 81.8%, specificity 79.1%), while the super-radiologist (majority voting of radiologist) showed moderate accuracy. Interobserver analysis revealed strong agreement between the model and surgeons (Cohen's kappa = 0.73), and overall consistency among experts and the model (Fleiss' kappa = 0.62). Conclusions AI-assisted CE interpretation achieved higher specificity and comparable sensitivity to those of the clinicians. Its consistent performance and substantial agreement with experts support its potential role in improving CE assessment in HD.

BackgroundThe precise pneumoconiosis staging suffers from progressive pair label noise (PPLN) in chest X-ray datasets, because adjacent stages are confused due to unidentifialble and diffuse opacities in the lung fields. As deep neural networks are employed to aid the disease staging, the performance is degraded under such label noise.ObjectiveThis study improves the effectiveness of pneumoconiosis staging by mitigating the impact of PPLN through network architecture refinement and sample selection mechanism adjustment.MethodsWe propose a novel multi-branch architecture that incorporates the dual-threshold sample selection. Several auxiliary branches are integrated in a two-phase module to learn and predict the <i>progressive feature tendency</i>. A novel difference-based metric is introduced to iteratively obtained the instance-specific thresholds as a complementary criterion of dynamic sample selection. All the samples are finally partitioned into <i>clean</i> and <i>hard</i> sets according to dual-threshold criteria and treated differently by loss functions with penalty terms.ResultsCompared with the state-of-the-art, the proposed method obtains the best metrics (accuracy: 90.92%, precision: 84.25%, sensitivity: 81.11%, F1-score: 82.06%, and AUC: 94.64%) under real-world PPLN, and is less sensitive to the rise of synthetic PPLN rate. An ablation study validates the respective contributions of critical modules and demonstrates how variations of essential hyperparameters affect model performance.ConclusionsThe proposed method achieves substantial effectiveness and robustness against PPLN in pneumoconiosis dataset, and can further assist physicians in diagnosing the disease with a higher accuracy and confidence.