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.

Myocardial infarction (MI) continues to be a leading cause of death worldwide. The precise quantification of infarcted tissue is crucial to diagnosis, therapeutic management, and post-MI care. Late gadolinium enhancement-cardiac magnetic resonance (LGE-CMR) is regarded as the gold standard for precise infarct tissue localization in MI patients. A fundamental limitation of LGE-CMR is the invasive intravenous introduction of gadolinium-based contrast agents that present potential high-risk toxicity, particularly for individuals with underlying chronic kidney diseases. Herein, a completely non-invasive methodology is developed to identify the location and extent of an infarct region in the left ventricle via a machine learning (ML) model using only cardiac strains as inputs. In this transformative approach, the remarkable performance of a multi-fidelity ML model is demonstrated, which combines rodent-based in-silico-generated training data (low-fidelity) with very limited patient-specific human data (high-fidelity) in predicting LGE ground truth. The results offer a new paradigm for developing feasible prognostic tools by augmenting synthetic simulation-based data with very small amounts of in vivo human data. More broadly, the proposed approach can significantly assist with addressing biomedical challenges in healthcare where human data are limited.

Interleukin-18 has broad immune regulatory functions. Genomic data and enhanced Magnetic Resonance Imaging data related to LGG patients were downloaded from The Cancer Genome Atlas and Cancer Imaging Archive, and the constructed model was externally validated using hospital MRI enhanced images and clinical pathological features. Radiomic feature extraction was performed using "PyRadiomics", feature selection was conducted using Maximum Relevance Minimum Redundancy and Recursive Feature Elimination methods, and a model was built using the Gradient Boosting Machine algorithm to predict the expression status of IL18. The constructed radiomics model achieved areas under the receiver operating characteristic curve of 0.861, 0.788, and 0.762 in the TCIA training dataset (n = 98), TCIA validation dataset (n = 41), and external validation dataset (n = 50). Calibration curves and decision curve analysis demonstrated the calibration and high clinical utility of the model. The radiomics model based on enhanced MRI can effectively predict the expression status of IL18 and the prognosis of LGG.

Photon-counting detector CT myelography (PCD-CTM) is a recently described technique used for detecting spinal CSF leaks, including CSF-venous fistulas. Various image reconstruction techniques, including smoother-versus-sharper kernels and virtual monoenergetic images, are available with photon-counting CT. Moreover, denoising algorithms have shown promise in improving sharp kernel images. No prior studies have compared image quality of these different reconstructions on photon-counting CT myelography. Here, we sought to compare several image reconstructions using various parameters important for the detection of CSF-venous fistulas. We performed a retrospective review of all consecutive decubitus PCD-CTM between February 1, 2022, and August 1, 2024, at 1 institution. We included patients whose studies had the following reconstructions: Br48-40 keV virtual monoenergetic reconstruction, Br56 low-energy threshold (T3D), Qr89-T3D denoised with quantum iterative reconstruction, and Qr89-T3D denoised with a convolutional neural network algorithm. We excluded patients who had extradural CSF on preprocedural imaging or a technically unsatisfactory myelogram-. All 4 reconstructions were independently reviewed by 2 neuroradiologists. Each reviewer rated spatial resolution, noise, the presence of artifacts, image quality, and diagnostic confidence (whether positive or negative) on a 1-5 scale. These metrics were compared using the Friedman test. Additionally, noise and contrast were quantitatively assessed by a third reviewer and compared. The Qr89 reconstructions demonstrated higher spatial resolution than their Br56 or Br48-40keV counterparts. Qr89 with convolutional neural network denoising had less noise, better image quality, and improved diagnostic confidence compared with Qr89 with quantum iterative reconstruction denoising. The Br48-40keV reconstruction had the highest contrast-to-noise ratio quantitatively. In our study, the sharpest quantitative kernel (Qr89-T3D) with convolutional neural network denoising demonstrated the best performance regarding spatial resolution, noise level, image quality, and diagnostic confidence for detecting or excluding the presence of a CSF-venous fistula.

Tractography uses diffusion Magnetic Resonance Imaging (dMRI) to noninvasively reconstruct brain white matter (WM) tracts, with Convolutional Neural Network (CNNs) like U-Net significantly advancing accuracy in medical image segmentation. This work proposes a metaheuristic optimization algorithm-based CNN architecture. This architecture combines the Inception-ResNet-V2 module and the densely connecting convolutional module (DI) into the Spatial Attention U-Net (SAU-Net) architecture for segmenting WM fiber tracts and analyzing the brain's structural connectivity. The proposed network model (DISAU-Net) consists of the following parts are; First, the Inception-ResNet-V2 block is used to replace the standard convolutional layers and expand the network's width; Second, the Dense-Inception block is used to extract features and deepen the network without the need for any additional parameters; Third, the down-sampling block is used to speed up training by decreasing the size of feature maps, and the up-sampling block is used to increase the maps' resolution. In addition, the parameter existing in the classifiers is randomly selected with the Gray Wolf Optimization (GWO) technique to boost the performance of the CNN architecture. We validated our method by segmenting WM tracts on dMRI scans of 280 subjects from the human connectome project (HCP) database. The proposed method is far more efficient than current methods. It offers unprecedented quantitative evaluation with high tract segmentation consistency, achieving accuracy of 97.10%, dice score of 96.88%, recall 95.74%, f1-score 94.79% for fiber tracts. The results showed that the proposed method is a potential approach for segmenting WM fiber tracts and analyzing the brain's structural connectivity.

High-resolution computed tomography (HRCT) is helpful for diagnosing interstitial lung diseases (ILD), but it largely depends on the experience of physicians. Herein, our study aims to develop a deep-learning-based classification model to differentiate the three common types of ILD, so as to provide a reference to help physicians make the diagnosis and improve the accuracy of ILD diagnosis. Patients were selected from four tertiary Grade A hospitals in Kunming based on inclusion and exclusion criteria. HRCT scans of 130 patients were included. The imaging manifestations were usual interstitial pneumonia (UIP), non-specific interstitial pneumonia (NSIP), and organizing pneumonia (OP). Additionally, 50 chest HRCT cases without imaging abnormalities during the same period were selected.Construct a data set. Conduct the training, validation, and testing of the Parallel Multi-scale Feature Fusion Network (PMFF-Net) deep learning model. Utilize Python software to generate data and charts pertaining to model performance. Assess the model's accuracy, precision, recall, and F1-score, and juxtapose its diagnostic efficacy against that of physicians across various hospital levels, with differing levels of seniority, and from various departments. The PMFF -Net deep learning model is capable of classifying imaging types such as UIP, NSIP, and OP, as well as normal imaging. In a mere 105 s, it makes the diagnosis for 18 HRCT images with a diagnostic accuracy of 92.84 %, precision of 91.88 %, recall of 91.95 %, and an F1 score of 0.9171. The diagnostic accuracy of senior radiologists (83.33 %) and pulmonologists (77.77 %) from tertiary hospitals is higher than that of internists from secondary hospitals (33.33 %). Meanwhile, the diagnostic accuracy of middle-aged radiologists (61.11 %) and pulmonologists (66.66 %) are higher than junior radiologists (38.88 %) and pulmonologists (44.44 %) in tertiary hospitals, whereas junior and middle-aged internists at secondary hospitals were unable to complete the tests. This study found that the PMFF-Net model can effectively classify UIP, NSIP, OP imaging types, and normal imaging, which can help doctors of different hospital levels and departments make clinical decisions quickly and effectively.

Accurate segmentation of vascular networks from sparse CT scan slices remains a significant challenge in medical imaging, particularly due to the thin, branching nature of vessels and the inherent sparsity between imaging planes. Existing deep learning approaches, based on binary voxel classification, often struggle with structural continuity and geometric fidelity. To address this challenge, we present VesselSDF, a novel framework that leverages signed distance fields (SDFs) for robust vessel reconstruction. Our method reformulates vessel segmentation as a continuous SDF regression problem, where each point in the volume is represented by its signed distance to the nearest vessel surface. This continuous representation inherently captures the smooth, tubular geometry of blood vessels and their branching patterns. We obtain accurate vessel reconstructions while eliminating common SDF artifacts such as floating segments, thanks to our adaptive Gaussian regularizer which ensures smoothness in regions far from vessel surfaces while producing precise geometry near the surface boundaries. Our experimental results demonstrate that VesselSDF significantly outperforms existing methods and preserves vessel geometry and connectivity, enabling more reliable vascular analysis in clinical settings.

The high computational demands of Vision Transformers (ViTs), in processing a huge number of tokens, often constrain their practical application in analyzing medical images. This research proposes an adaptive prompt-guided pruning method to selectively reduce the processing of irrelevant tokens in the segmentation pipeline. The prompt-based spatial prior helps to rank the tokens according to their relevance. Tokens with low-relevance scores are down-weighted, ensuring that only the relevant ones are propagated for processing across subsequent stages. This data-driven pruning strategy facilitates end-to-end training, maintains gradient flow, and improves segmentation accuracy by focusing computational resources on essential regions. The proposed framework is integrated with several state-of-the-art models to facilitate the elimination of irrelevant tokens; thereby, enhancing computational efficiency while preserving segmentation accuracy. The experimental results show a reduction of $\sim$ 35-55\% tokens; thus reducing the computational costs relative to the baselines. Cost-effective medical image processing, using our framework, facilitates real-time diagnosis by expanding its applicability in resource-constrained environments.

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.