In acute neck infections, magnetic resonance imaging (MRI) shows retropharyngeal edema (RPE), which is a prognostic imaging biomarker for a severe course of illness. This study aimed to develop a deep learning-based algorithm for the automated detection of RPE. We developed a deep neural network consisting of two parts using axial T2-weighted water-only Dixon MRI images from 479 patients with acute neck infections annotated by radiologists at both slice and patient levels. First, a convolutional neural network (CNN) classified individual slices; second, an algorithm classified patients based on a stack of slices. Model performance was compared with the radiologists' assessment as a reference standard. Accuracy, sensitivity, specificity, and area under receiver operating characteristic curve (AUROC) were calculated. The proposed CNN was compared with InceptionV3, and the patient-level classification algorithm was compared with traditional machine learning models. Of the 479 patients, 244 (51%) were positive and 235 (49%) negative for RPE. Our model achieved accuracy, sensitivity, specificity, and AUROC of 94.6%, 83.3%, 96.2%, and 94.1% at the slice level, and 87.4%, 86.5%, 88.2%, and 94.8% at the patient level, respectively. The proposed CNN was faster than InceptionV3 but equally accurate. Our patient classification algorithm outperformed traditional machine learning models. A deep learning model, based on weakly annotated data and computationally manageable training, achieved high accuracy for automatically detecting RPE on MRI in patients with acute neck infections. Our automated method for detecting relevant MRI findings was efficiently trained and might be easily deployed in practice to study clinical applicability. This approach might improve early detection of patients at high risk for a severe course of acute neck infections. Deep learning automatically detected retropharyngeal edema on MRI in acute neck infections. Areas under the receiver operating characteristic curve were 94.1% at the slice level and 94.8% at the patient level. The proposed convolutional neural network was lightweight and required only weakly annotated data.

Academic integrity is crucial in dental education, especially during practical exams assessing competencies. Traditional oversight may not detect sophisticated academic dishonesty methods like radiograph substitution or tampering. This study aimed to develop and evaluate a novel artificial intelligence (AI) model utilizing a Siamese neural network to detect inconsistencies in radiographic images taken for root canal treatment (RCT) procedures in preclinical endodontic courses, thereby enhancing educational integrity. A Siamese neural network was designed to compare radiographs from different RCT procedures. The model was trained on 3390 radiographs, with data augmentation applied to improve generalizability. The dataset was split into training, validation, and testing subsets. Performance metrics included accuracy, precision, sensitivity (recall), and F1-score. Cross-validation and hyperparameter tuning optimized the model. Our AI model achieved an accuracy of 89.31%, a precision of 76.82%, a sensitivity of 84.82%, and an F1-score of 80.50%. The optimal similarity threshold was 0.48, where maximum accuracy was observed. The confusion matrix indicated a high rate of correct classifications, and cross-validation confirmed the model's robustness with a standard deviation of 1.95% across folds. The AI-driven Siamese neural network effectively detects radiographic inconsistencies in RCT preclinical procedures. Implementing this novel model will serve as an objective tool to uphold academic integrity in dental education, enhance the fairness and reliability of assessments, promote a culture of honesty amongst students, and reduce the administrative burden on educators.

BackgroundLimited-angle Computed Tomography imaging suffers from severe artifacts in the reconstructed image due to incomplete projection data. Deep learning methods have been developed currently to address the challenges of robustness and low contrast of the limited-angle CT reconstruction with a relatively effective way.ObjectiveTo improve the low contrast of the current limited-angle CT reconstruction image, enhance the robustness of the reconstruction method and the contrast of the limited-angle image.MethodIn this paper, we proposed a limited-angle CT reconstruction method that combining the Fourier domain reweighting and wavelet domain enhancement, which fused information from different domains, thereby getting high-resolution reconstruction images.ResultsWe verified the feasibility and effectiveness of the proposed solution through experiments, and the reconstruction results are improved compared with the state-of-the-art methods.ConclusionsThe proposed method enhances some features of the original image domain data from different domains, which is beneficial to the reasonable diffusion and restoration of diffuse detail texture features.

As the frontline data for cancer diagnosis, microscopic pathology images are fundamental for providing patients with rapid and accurate treatment. However, despite their practical value, the deep learning community has largely overlooked their usage. This paper proposes a novel approach to classifying microscopy images as time series data, addressing the unique challenges posed by their manual acquisition and weakly labeled nature. The proposed method fits image sequences of varying lengths to a fixed-length target by leveraging Dynamic Time-series Warping (DTW). Attention-based pooling is employed to predict the class of the case simultaneously. We demonstrate the effectiveness of our approach by comparing performance with various baselines and showcasing the benefits of using various inference strategies in achieving stable and reliable results. Ablation studies further validate the contribution of each component. Our approach contributes to medical image analysis by not only embracing microscopic images but also lifting them to a trustworthy level of performance.

<b>Background/Objectives</b>: Different sized calcified masses called pulp stones are often detected in dental pulp and can impact dental procedures. The current research was conducted with the aim of measuring the ability of artificial intelligence algorithms to accurately diagnose pulp and pulp stone calcifications on panoramic radiographs. <b>Methods</b>: We used 713 panoramic radiographs, on which a minimum of one pulp stone was detected, identified retrospectively, and included in the study-4675 pulp stones and 5085 pulps were marked on these radiographs using CVAT v1.7.0 labeling software. <b>Results</b>: In the test dataset, the AI model segmented 462 panoramic radiographs for pulp stone and 220 panoramic radiographs for pulp. The dice coefficient and Intersection over Union (IoU) recorded for the Pulp Segmentation model were 0.84 and 0.758, respectively. Precision and recall were computed to be 0.858 and 0.827, respectively. The Pulp Stone Segmentation model achieved a dice coefficient of 0.759 and an IoU of 0.686, with precision and recall of 0.792 and 0.773, respectively. <b>Conclusions</b>: Pulp and pulp stones can successfully be identified using artificial intelligence algorithms. This study provides evidence that artificial intelligence software using deep learning algorithms can be valuable adjunct tools in aiding clinicians in radiographic diagnosis. Further research in which larger datasets are examined are needed to enhance the capability of artificial intelligence models to make accurate diagnoses.

<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 evaluate diagnostic accuracy of various large language models (LLMs) when answering radiology-specific questions with and without access to additional online, up-to-date information via retrieval-augmented generation (RAG). Materials and Methods The authors developed Radiology RAG (RadioRAG), an end-to-end framework that retrieves data from authoritative radiologic online sources in real-time. RAG incorporates information retrieval from external sources to supplement the initial prompt, grounding the model's response in relevant information. Using 80 questions from the RSNA Case Collection across radiologic subspecialties and 24 additional expert-curated questions with reference standard answers, LLMs (GPT-3.5-turbo, GPT-4, Mistral-7B, Mixtral-8 × 7B, and Llama3 [8B and 70B]) were prompted with and without RadioRAG in a zero-shot inference scenario (temperature ≤ 0.1, top- <i>P</i> = 1). RadioRAG retrieved context-specific information from www.radiopaedia.org. Accuracy of LLMs with and without RadioRAG in answering questions from each dataset was assessed. Statistical analyses were performed using bootstrapping while preserving pairing. Additional assessments included comparison of model with human performance and comparison of time required for conventional versus RadioRAG-powered question answering. Results RadioRAG improved accuracy for some LLMs, including GPT-3.5-turbo [74% (59/80) versus 66% (53/80), FDR = 0.03] and Mixtral-8 × 7B [76% (61/80) versus 65% (52/80), FDR = 0.02] on the RSNA-RadioQA dataset, with similar trends in the ExtendedQA dataset. Accuracy exceeded (FDR ≤ 0.007) that of a human expert (63%, (50/80)) for these LLMs, while not for Mistral-7B-instruct-v0.2, Llama3-8B, and Llama3-70B (FDR ≥ 0.21). RadioRAG reduced hallucinations for all LLMs (rates from 6-25%). RadioRAG increased estimated response time fourfold. Conclusion RadioRAG shows potential to improve LLM accuracy and factuality in radiology question answering by integrating real-time domain-specific data. ©RSNA, 2025.

This study develops a self-learning method to denoise MR images for use in ultralow field (ULF) applications. We propose use of a self-learning neural network for denoising 3D MRI obtained from two acquisitions (dual scan), which are utilized as training pairs. Based on the self-learning method Noise2Noise, an effective data augmentation method and integrated learning strategy for enhancing model performance are proposed. Experimental results demonstrate that (1) the proposed model can produce exceptional denoising results and outperform the traditional Noise2Noise method subjectively and objectively; (2) magnitude images can be effectively denoised comparing with several state-of-the-art methods on synthetic and real ULF data; and (3) the proposed method can yield better results on phase images and quantitative imaging applications than other denoisers due to the self-learning framework. Theoretical and experimental implementations show that the proposed self-learning model achieves improved performance on magnitude image denoising with synthetic and real-world data at ULF. Additionally, we test our method on calculated phase and quantification images, demonstrating its superior performance over several contrastive methods.

Cancer is an abnormal growth with potential to invade locally and metastasize to distant organs. Accurate auto-segmentation of the tumor and surrounding normal tissues is required for radiotherapy treatment plan optimization. Recent AI-based segmentation models are generally trained on large public datasets, which lack the heterogeneity of local patient populations. While these studies advance AI-based medical image segmentation, research on local datasets is necessary to develop and integrate AI tumor segmentation models directly into hospital software for efficient and accurate oncology treatment planning and execution. This study enhances tumor segmentation using computationally efficient hybrid UNet-Transformer models on magnetic resonance imaging (MRI) datasets acquired from a local hospital under strict privacy protection. We developed a robust data pipeline for seamless DICOM extraction and preprocessing, followed by extensive image augmentation to ensure model generalization across diverse clinical settings, resulting in a total dataset of 6080 images for training. Our novel architecture integrates UNet-based convolutional neural networks with a transformer bottleneck and complementary attention modules, including efficient attention, Squeeze-and-Excitation (SE) blocks, Convolutional Block Attention Module (CBAM), and ResNeXt blocks. To accelerate convergence and reduce computational demands, we used a maximum batch size of 8 and initialized the encoder with pretrained ImageNet weights, training the model on dual NVIDIA T4 GPUs via checkpointing to overcome Kaggle's runtime limits. Quantitative evaluation on the local MRI dataset yielded a Dice similarity coefficient of 0.764 and an Intersection over Union (IoU) of 0.736, demonstrating competitive performance despite limited data and underscoring the importance of site-specific model development for clinical deployment.

Multimodal medical image fusion (MMIF) extracts the most meaningful information from multiple source images, enabling a more comprehensive and accurate diagnosis. Achieving high-quality fusion results requires a careful balance of brightness, color, contrast, and detail; this ensures that the fused images effectively display relevant anatomical structures and reflect the functional status of the tissues. However, existing MMIF methods have limited capacity to capture detailed features during conventional training and suffer from insufficient cross-modal feature interaction, leading to suboptimal fused image quality. To address these issues, this study proposes a two-stage diffusion model-based fusion network (DM-FNet) to achieve unified MMIF. In Stage I, a diffusion process trains UNet for image reconstruction. UNet captures detailed information through progressive denoising and represents multilevel data, providing a rich set of feature representations for the subsequent fusion network. In Stage II, noisy images at various steps are input into the fusion network to enhance the model's feature recognition capability. Three key fusion modules are also integrated to process medical images from different modalities adaptively. Ultimately, the robust network structure and a hybrid loss function are integrated to harmonize the fused image's brightness, color, contrast, and detail, enhancing its quality and information density. The experimental results across various medical image types demonstrate that the proposed method performs exceptionally well regarding objective evaluation metrics. The fused image preserves appropriate brightness, a comprehensive distribution of radioactive tracers, rich textures, and clear edges. The code is available at https://github.com/HeDan-11/DM-FNet.

Few-shot learning alleviates the heavy dependence of medical image segmentation on large-scale labeled data, but it shows strong performance gaps when dealing with new tasks compared with traditional deep learning. Existing methods mainly learn the class knowledge of a few known (support) samples and extend it to unknown (query) samples. However, the large distribution differences between the support image and the query image lead to serious deviations in the transfer of class knowledge, which can be specifically summarized as two segmentation challenges: Intra-class inconsistency and Inter-class similarity, blurred and confused boundaries. In this paper, we propose a new interactive prototype learning and self-learning network to solve the above challenges. First, we propose a deep encoding-decoding module to learn the high-level features of the support and query images to build peak prototypes with the greatest semantic information and provide semantic guidance for segmentation. Then, we propose an interactive prototype learning module to improve intra-class feature consistency and reduce inter-class feature similarity by conducting mid-level features-based mean prototype interaction and high-level features-based peak prototype interaction. Last, we propose a query features-guided self-learning module to separate foreground and background at the feature level and combine low-level feature maps to complement boundary information. Our model achieves competitive segmentation performance on benchmark datasets and shows substantial improvement in generalization ability.