Postoperative papillary thyroid cancer (PTC) patients often have enlarged cervical lymph nodes due to inflammation or hyperplasia, which complicates the assessment of recurrence or metastasis. This study aimed to explore the diagnostic capabilities of computed tomography (CT) imaging and radiomic analysis to distinguish the recurrence of cervical lymph nodes in patients with PTC postoperatively. A retrospective analysis of 194 PTC patients who underwent total thyroidectomy was conducted, with 98 cases of cervical lymph node recurrence and 96 cases without recurrence. Using 3D Slicer software, Regions of Interest (ROI) were delineated on enhanced venous phase CT images, analyzing 302 positive and 391 negative lymph nodes. These nodes were randomly divided into training and validation sets in a 3:2 ratio. Python was used to extract radiomic features from the ROIs and to develop radiomic models. Univariate and multivariate analyses identified statistically significant risk factors for cervical lymph node recurrence from clinical data, which, when combined with radiomic scores, formed a nomogram to predict recurrence risk. The diagnostic efficacy and clinical utility of the models were assessed using ROC curves, calibration curves, and Decision Curve Analysis (DCA). This study analyzed 693 lymph nodes (302 positive and 391 negative) and identified 35 significant radiomic features through dimensionality reduction and selection. The three machine learning models, including the Lasso regression, Support Vector Machine (SVM), and RF radiomics models, showed.

This clinically oriented review explores the technical advancements of simultaneous PET/magnetic resonance (MR) imaging to provide an overview of the addressed obstacles over time, current challenges, and future trends in the field. In particular, advanced attenuation and motion correction techniques and MR-guided PET reconstruction frameworks were reviewed, and the state-of-the-art PET/MR systems and their strengths were discussed. Overall, PET/MR holds great potential in various clinical applications, including oncology, neurology, and cardiology. However, it requires continued optimization in hardware, algorithms, and clinical protocols to achieve broader adoption and be included in the routine clinical standards.

Medicine is one of the most sensitive fields in which artificial intelligence (AI) is extensively used, spanning from medical image analysis to clinical support. Specifically, in medicine, where every decision may severely affect human lives, the issue of ensuring that AI systems operate ethically and produce results that align with ethical considerations is of great importance. In this work, we investigate the combination of several key parameters on the performance of artificial neural networks (ANNs) used for medical image analysis in the presence of data corruption or errors. For this purpose, we examined five different ANN architectures (AlexNet, LeNet 5, VGG16, ResNet-50, and Vision Transformers - ViT), and for each architecture, we checked its performance under varying combinations of training dataset sizes and percentages of images that are corrupted through mislabeling. The image mislabeling simulates deliberate or nondeliberate changes to the dataset, which may cause the AI system to produce unreliable results. We found that the five ANN architectures produce different results for the same task, both for cases with and without dataset modification, which implies that the selection of which ANN architecture to implement may have ethical aspects that need to be considered. We also found that label corruption resulted in a mixture of performance metrics tendencies, indicating that it is difficult to conclude whether label corruption has occurred. Our findings demonstrate the relation between ethics in AI and ANN architecture implementation and AI computational parameters used therefor, and raise awareness of the need to find appropriate ways to determine whether label corruption has occurred.

The core role of medical images in disease diagnosis makes their quality directly affect the accuracy of clinical judgment. However, due to factors such as low-dose scanning, equipment limitations and imaging artifacts, medical images are often accompanied by non-uniform noise interference, which seriously affects structure recognition and lesion detection. This paper proposes a medical image adaptive denoising model (MI-ND) that integrates multi-scale convolutional and Transformer architecture, introduces a noise level estimator (NLE) and a noise adaptive attention module (NAAB), and realizes channel-spatial attention regulation and cross-modal feature fusion driven by noise perception. Systematic testing is carried out on multimodal public datasets. Experiments show that this method significantly outperforms the comparative methods in image quality indicators such as PSNR, SSIM, and LPIPS, and improves the F1 score and ROC-AUC in downstream diagnostic tasks, showing strong prac-tical value and promotional potential. The model has outstanding benefits in structural recovery, diagnostic sensitivity, and cross-modal robustness, and provides an effective solution for medical image enhancement and AI-assisted diagnosis and treatment.

The core role of medical images in disease diagnosis makes their quality directly affect the accuracy of clinical judgment. However, due to factors such as low-dose scanning, equipment limitations and imaging artifacts, medical images are often accompanied by non-uniform noise interference, which seriously affects structure recognition and lesion detection. This paper proposes a medical image adaptive denoising model (MI-ND) that integrates multi-scale convolutional and Transformer architecture, introduces a noise level estimator (NLE) and a noise adaptive attention module (NAAB), and realizes channel-spatial attention regulation and cross-modal feature fusion driven by noise perception. Systematic testing is carried out on multimodal public datasets. Experiments show that this method significantly outperforms the comparative methods in image quality indicators such as PSNR, SSIM, and LPIPS, and improves the F1 score and ROC-AUC in downstream diagnostic tasks, showing strong prac-tical value and promotional potential. The model has outstanding benefits in structural recovery, diagnostic sensitivity, and cross-modal robustness, and provides an effective solution for medical image enhancement and AI-assisted diagnosis and treatment.

Generative artificial intelligence models facilitate open-data sharing by proposing synthetic data as surrogates of real patient data. Despite the promise for healthcare, some of these models are susceptible to patient data memorization, where models generate patient data copies instead of novel synthetic samples, resulting in patient re-identification. Here we assess memorization in unconditional latent diffusion models by training them on a variety of datasets for synthetic data generation and detecting memorization with a self-supervised copy detection approach. We show a high degree of patient data memorization across all datasets, with approximately 37.2% of patient data detected as memorized and 68.7% of synthetic samples identified as patient data copies. Latent diffusion models are more susceptible to memorization than autoencoders and generative adversarial networks, and they outperform non-diffusion models in synthesis quality. Augmentation strategies during training, small architecture size and increasing datasets can reduce memorization, while overtraining the models can enhance it. These results emphasize the importance of carefully training generative models on private medical imaging datasets and examining the synthetic data to ensure patient privacy.

Retrospective cross-sectional study. To develop a multi-view fusion framework that effectively identifies suspect keratoconus cases and facilitates the possibility of early clinical intervention. A total of 573 corneal topography maps representing eyes classified as normal, suspect, or keratoconus. We designed the Corneal Multi-View Fusion Transformer (CMVFT), which integrates features from seven standard corneal topography maps. A pretrained ResNet-50 extracts single-view representations that are further refined by a custom-designed Multi-Scale Attention Module (MSAM). This integrated design specifically compensates for the representation gap commonly encountered when applying Transformers to small-sample corneal topography datasets by dynamically bridging local convolution-based feature extraction with global self-attention mechanisms. A subsequent fusion Transformer then models long-range dependencies across views for comprehensive multi-view feature integration. The primary measure was the framework's ability to differentiate suspect cases from normal and keratoconus cases, thereby creating a pathway for early clinical intervention. Experimental evaluation demonstrated that CMVFT effectively distinguishes suspect cases within a feature space characterized by overlapping attributes. Ablation studies confirmed that both the MSAM and the fusion Transformer are essential for robust multi-view feature integration, successfully compensating for potential representation shortcomings in small datasets. This study is the first to apply a Transformer-driven multi-view fusion approach in corneal topography analysis. By compensating for the representation gap inherent in small-sample settings, CMVFT shows promise in enabling the identification of suspect keratoconus cases and supporting early intervention strategies, with prospective implications for early clinical intervention.

Accelerating Magnetic Resonance Imaging (MRI) reduces scan time but often degrades image quality. While Implicit Neural Representations (INRs) show promise for MRI reconstruction, they struggle at high acceleration factors due to weak prior constraints, leading to structural loss and aliasing artefacts. To address this, we propose PrIINeR, an INR-based MRI reconstruction method that integrates prior knowledge from pre-trained deep learning models into the INR framework. By combining population-level knowledge with instance-based optimization and enforcing dual data consistency, PrIINeR aligns both with the acquired k-space data and the prior-informed reconstruction. Evaluated on the NYU fastMRI dataset, our method not only outperforms state-of-the-art INR-based approaches but also improves upon several learning-based state-of-the-art methods, significantly improving structural preservation and fidelity while effectively removing aliasing artefacts.PrIINeR bridges deep learning and INR-based techniques, offering a more reliable solution for high-quality, accelerated MRI reconstruction. The code is publicly available on https://github.com/multimodallearning/PrIINeR.

Optic disc and cup segmentation is a crucial subfield of computer vision, playing a pivotal role in automated pathological image analysis. It enables precise, efficient, and automated diagnosis of ocular conditions, significantly aiding clinicians in real-world medical applications. However, due to the scarcity of medical segmentation data and the insufficient integration of global contextual information, the segmentation accuracy remains suboptimal. This issue becomes particularly pronounced in optic disc and cup cases with complex anatomical structures and ambiguous boundaries.In order to address these limitations, this paper introduces a self-supervised training strategy integrated with a newly designed network architecture to improve segmentation accuracy.Specifically,we initially propose a non-local dual deformable convolutional block,which aims to capture the irregular image patterns(i.e. boundary).Secondly,we modify the traditional vision transformer and design an adaptive K-Nearest Neighbors(KNN) transformation block to extract the global semantic context from images. Finally,an initialization strategy based on self-supervised training is proposed to reduce the burden on the network on labeled data.Comprehensive experimental evaluations demonstrate the effectiveness of our proposed method, which outperforms previous networks and achieves state-of-the-art performance,with IOU scores of 0.9577 for the optic disc and 0.8399 for the optic cup on the REFUGE dataset.

Nanotheranostics has garnered significant interest for its capacity to improve customized healthcare via targeted and efficient treatment alternatives. Nanotheranostics promises an innovative approach to precision medicine by integrating therapeutic and diagnostic capabilities into nanoscale devices. Nanotheranostics provides an integrated approach that improves diagnosis and facilitates real-time, tailored treatment, revolutionizing patient care. Through the application of nanotheranostic devices, outcomes can be modified for patients on an individualized therapeutic level by taking into consideration individual differences in disease manifestation as well as treatment response. In this review, no aspect of imaging in nanotheranostics is excluded, thus including MRI and CT as well as PET and OI, which are essential for comprehensive analysis needed in medical decision making. Integration of AI and ML into theranostics facilitates predicting treatment outcomes and personalizing the approaches to the methods, which significantly enhances reproducibility in medicine. In addition, several nanoparticles such as lipid-based and polymeric particles, iron oxide, quantum dots, and mesoporous silica have shown promise in diagnosis and targeted drug delivery. These nanoparticles are capable of treating multiple diseases such as cancers, some other neurological disorders, and infectious diseases. While having potential, the field of nanotheranostics still encounters issues regarding clinical applicability, alongside some regulatory hurdles pertaining to new therapeutic agents. Advanced research in this sphere is bound to enhance existing perspectives and fundamentally aid the integration of nanomedicine into conventional health procedures, especially relating to efficacy and the growing emphasis on safe, personalized healthcare.