Apical periodontitis is a prevalent oral pathology that presents significant public health challenges. Despite advances in automated diagnostic systems across various medical fields, the development of Computer-Aided Diagnosis (CAD) applications for apical periodontitis is still constrained by the lack of a large-scale, high-quality annotated dataset. To address this issue, we release a large-scale panoramic radiograph benchmark called "PerioXrays", comprising 3,673 images and 5,662 meticulously annotated instances of apical periodontitis. To the best of our knowledge, this is the first benchmark dataset for automated apical periodontitis diagnosis. This paper further proposes a clinical-oriented apical periodontitis detection (PerioDet) paradigm, which jointly incorporates Background-Denoising Attention (BDA) and IoU-Dynamic Calibration (IDC) mechanisms to address the challenges posed by background noise and small targets in automated detection. Extensive experiments on the PerioXrays dataset demonstrate the superiority of PerioDet in advancing automated apical periodontitis detection. Additionally, a well-designed human-computer collaborative experiment underscores the clinical applicability of our method as an auxiliary diagnostic tool for professional dentists.

Radiography imaging protocols target on specific anatomical regions, resulting in highly consistent images with recurrent structural patterns across patients. Recent advances in medical anomaly detection have demonstrated the effectiveness of CNN- and transformer-based approaches. However, CNNs exhibit limitations in capturing long-range dependencies, while transformers suffer from quadratic computational complexity. In contrast, Mamba-based models, leveraging superior long-range modeling, structural feature extraction, and linear computational efficiency, have emerged as a promising alternative. To capitalize on the inherent structural regularity of medical images, this study introduces SP-Mamba, a spatial-perception Mamba framework for unsupervised medical anomaly detection. The window-sliding prototype learning and Circular-Hilbert scanning-based Mamba are introduced to better exploit consistent anatomical patterns and leverage spatial information for medical anomaly detection. Furthermore, we excavate the concentration and contrast characteristics of anomaly maps for improving anomaly detection. Extensive experiments on three diverse medical anomaly detection benchmarks confirm the proposed method's state-of-the-art performance, validating its efficacy and robustness. The code is available at https://github.com/Ray-RuiPan/SP-Mamba.

Artificial intelligence is increasingly leveraged across various domains to automate decision-making processes that significantly impact human lives. In medical image analysis, deep learning models have demonstrated remarkable performance. However, their inherent complexity makes them black box systems, raising concerns about reliability and interpretability. Counterfactual explanations provide comprehensible insights into decision processes by presenting hypothetical "what-if" scenarios that alter model classifications. By examining input alterations, counterfactual explanations provide patterns that influence the decision-making process. Despite their potential, generating plausible counterfactuals that adhere to similarity constraints providing human-interpretable explanations remains a challenge. In this paper, we investigate this challenge by a model-specific optimization approach. While deep generative models such as variational autoencoders (VAEs) exhibit significant generative power, probabilistic models like sum-product networks (SPNs) efficiently represent complex joint probability distributions. By modeling the likelihood of a semi-supervised VAE's latent space with an SPN, we leverage its dual role as both a latent space descriptor and a classifier for a given discrimination task. This formulation enables the optimization of latent space counterfactuals that are both close to the original data distribution and aligned with the target class distribution. We conduct experimental evaluation on the cheXpert dataset. To evaluate the effectiveness of the integration of SPNs, our SPN-guided latent space manipulation is compared against a neural network baseline. Additionally, the trade-off between latent variable regularization and counterfactual quality is analyzed.

This investigation delves into the diagnosis of COVID-19, using X-ray images generated by way of an effective deep learning model. In terms of assessing the COVID-19 diagnosis learning model, the methods currently employed tend to focus on the accuracy rate level, while neglecting several significant assessment parameters. These parameters, which include precision, sensitivity and specificity, significantly, F1-score, and ROC-AUC influence the performance level of the model. In this paper, we have improved the InceptionResNet and called Enhanced InceptionResNet with restructured parameters termed, "Enhanced InceptionResNet," which incorporates depth-wise separable convolutions to enhance the efficiency of feature extraction and minimize the consumption of computational resources. For this investigation, three residual network (ResNet) models, namely Res- Net, InceptionResNet model, and the Enhanced InceptionResNet with restructured parameters, were employed for a medical image classification assignment. The performance of each model was evaluated on a balanced dataset of 2600 X-ray images. The models were subsequently assessed for accuracy and loss, as well subjected to a confusion matrix analysis. The Enhanced InceptionResNet consistently outperformed ResNet and InceptionResNet in terms of validation and testing accuracy, recall, precision, F1-score, and ROC-AUC demonstrating its superior capacity for identifying pertinent information in the data. In the context of validation and testing accuracy, our Enhanced InceptionRes- Net repeatedly proved to be more reliable than ResNet, an indication of the former's capacity for the efficient identification of pertinent information in the data (99.0% and 98.35%, respectively), suggesting enhanced feature extraction capabilities. The Enhanced InceptionResNet excelled in COVID-19 diagnosis from chest X-rays, surpassing ResNet and Default InceptionResNet in accuracy, precision, and sensitivity. Despite computational demands, it shows promise for medical image classification. Future work should leverage larger datasets, cloud platforms, and hyperparameter optimisation to improve performance, especially for distinguishing normal and pneumonia cases.

To develop a standardized, real-time feedback system for monitoring urinary stone fragmentation during shockwave lithotripsy (SWL), thereby optimizing treatment efficacy and minimizing patient risk. A two-pronged approach was implemented to quantify stone fragmentation in C-arm X-ray images. First, the initial pre-treatment stone image was compared to subsequent images to measure stone area loss. Second, a Convolutional Neural Network (CNN) was trained to estimate the probability that an image contains a urinary stone. These two criteria were integrated to create a real-time signaling system capable of evaluating shockwave efficacy during SWL. The system was developed using data from 522 shockwave treatments encompassing 4,057 C-arm X-ray images. The combined area-loss metric and CNN output enabled consistent real-time assessment of stone fragmentation, providing actionable feedback to guide SWL in diverse clinical contexts. The proposed system offers a novel and reliable method for monitoring of urinary stone fragmentation during SWL. By helping to balance treatment efficacy with patient safety, it holds significant promise for semi-automated SWL platforms, particularly in resource-limited or remote environments such as arid regions and extended space missions.

Forearm fractures constitute a significant proportion of emergency department presentations in pediatric population. The treatment goal is to restore length and alignment between the distal and proximal bone fragments. While immobilization through splinting or casting is enough for non-displaced and minimally displaced fractures. However, moderately or severely displaced fractures often require reduction for realignment. However, appropriate treatment in current practices has challenges due to the lack of resources required for specialized pediatric care leading to delayed and unnecessary transfers between medical centers, which potentially create treatment complications and burdens. The purpose of this study is to build a machine learning model for analyzing forearm fractures to assist clinical centers that lack surgical expertise in pediatric orthopedics. X-ray scans from 1250 children were curated, preprocessed, and manually annotated at our clinical center. Several machine learning models were fine-tuned using a pretraining strategy leveraging self-supervised learning model with vision transformer backbone. We further employed strategies to identify the most important region related to fractures within the forearm X-ray. The model performance was evaluated with and without region of interest (ROI) detection to find an optimal model for forearm fracture analyses. Our proposed strategy leverages self-supervised pretraining (without labels) followed by supervised fine-tuning (with labels). The fine-tuned model using regions cropped with ROI identification resulted in the highest classification performance with a true-positive rate (TPR) of 0.79, true-negative rate (TNR) of 0.74, AUROC of 0.81, and AUPR of 0.86 when evaluated on the testing data. The results showed the feasibility of using machine learning models in predicting the appropriate treatment for forearm fractures in pediatric cases. With further improvement, the algorithm could potentially be used as a tool to assist non-specialized orthopedic providers in diagnosing and providing treatment.

A chest X-ray radiology report describes abnormal findings not only from X-ray obtained at a given examination, but also findings on disease progression or change in device placement with reference to the X-ray from previous examination. Majority of the efforts on automatic generation of radiology report pertain to reporting the former, but not the latter, type of findings. To the best of the authors' knowledge, there is only one work dedicated to generating summary of the latter findings, i.e., follow-up radiology report summary. In this study, we propose a transformer-based framework to tackle this task. Motivated by our observations on the significance of medical lexicon on the fidelity of report summary generation, we introduce two mechanisms to bestow clinical insight to our model, namely disease probability soft guidance and masked entity modeling loss. The former mechanism employs a pretrained abnormality classifier to guide the presence level of specific abnormalities, while the latter directs the model's attention toward medical lexicon. Extensive experiments were conducted to demonstrate that the performance of our model exceeded the state-of-the-art.


Clinical drivers for real-time head and neck (H&N) tumor tracking during radiation therapy (RT) are accounting for motion caused by changes to the immobilization mask fit, and to reduce mask-related patient distress by replacing the masks with patient motion management methods. The purpose of this paper is to investigate a deep learning-based method to segment H&N tumors in patient kilovoltage (kV) x-ray images to enable real-time H&N tumor tracking during RT.
Approach: An ethics-approved clinical study collected data from 17 H&N cancer patients undergoing conventional H&N RT. For each patient, personalized conditional Generative Adversarial Networks (cGANs) were trained to segment H&N tumors in kV x-ray images. Network training data were derived from each patient's planning CT and contoured gross tumor volumes (GTV). For each training epoch, the planning CT and GTV were deformed and forward projected to create the training dataset. The testing data consisted of kV x-ray images used to reconstruct the pre-treatment CBCT volume for the first, middle and end fractions. The ground truth tumor locations were derived by deformably registering the planning CT to the pre-treatment CBCT and then deforming the GTV and forward projecting the deformed GTV. The generated cGAN segmentations were compared to ground truth tumor segmentations using the absolute magnitude of the centroid error and the mean surface distance (MSD) metrics.
Main Results:
The centroid error for the nasopharynx (n=4), oropharynx (n=9) and larynx (n=4) patients was 1.5±0.9mm, 2.4±1.6mm, 3.5±2.2mm respectively and the MSD was 1.5±0.3mm, 1.9±0.9mm and 2.3±1.0mm respectively. There was a weak correlation between the centroid error and the LR tumor location (r=0.41), which was higher for oropharynx patients (r=0.77).
Significance: The paper reports on markerless H&N tumor detection accuracy using kV images. Accurate tracking of H&N tumors can enable more precise RT leading to mask-free RT enabling better patient outcomes.&#xD.

Pulmonary diseases have become one of the main reasons for people's health decline, impacting millions of people worldwide. Rapid advancement of deep learning has significantly impacted medical image analysis by improving diagnostic accuracy and efficiency. Timely and precise diagnosis of these diseases proves to be invaluable for effective treatment procedures. Chest X-rays (CXR) perform a pivotal role in diagnosing various respiratory diseases by offering valuable insights into the chest and lung regions. This study puts forth a hybrid approach for classifying CXR images into four classes namely COVID-19, tuberculosis, pneumonia, and normal (healthy) cases. The presented method integrates a machine learning method, Support Vector Machine (SVM), with a pre-trained deep learning model for improved classification accuracy and reduced training time. Data from a number of public sources was used in this study, which represents a wide range of demographics. Class weights were implemented during training to balance the contribution of each class in order to address the class imbalance. Several pre-trained architectures, namely DenseNet, MobileNet, EfficientNetB0, and EfficientNetB3, have been investigated, and their performance was evaluated. Since MobileNet achieved the best classification accuracy of 94%, it was opted for the hybrid model, which combines MobileNet with SVM classifier, increasing the accuracy to 97%. The results suggest that this approach is reliable and holds great promise for clinical applications.&#xD.

The integration of artificial intelligence (AI) into medical image analysis has transformed healthcare, offering unprecedented precision in diagnosis, treatment planning, and disease monitoring. However, its adoption within the Internet of Medical Things (IoMT) raises significant challenges related to transparency, trustworthiness, and security. This paper introduces a novel Explainable AI (XAI) framework tailored for Medical Cyber-Physical Systems (MCPS), addressing these challenges by combining deep neural networks with symbolic knowledge reasoning to deliver clinically interpretable insights. The framework incorporates an Enhanced Dynamic Confidence-Weighted Attention (Enhanced DCWA) mechanism, which improves interpretability and robustness by dynamically refining attention maps through adaptive normalization and multi-level confidence weighting. Additionally, a Resilient Observability and Detection Engine (RODE) leverages sparse observability principles to detect and mitigate adversarial threats, ensuring reliable performance in dynamic IoMT environments. Evaluations conducted on benchmark datasets, including CheXpert, RSNA Pneumonia Detection Challenge, and NIH Chest X-ray Dataset, demonstrate significant advancements in classification accuracy, adversarial robustness, and explainability. The framework achieves a 15% increase in lesion classification accuracy, a 30% reduction in robustness loss, and a 20% improvement in the Explainability Index compared to state-of-the-art methods.