Pneumonia is a serious respiratory disease that requires early and precise diagnosis to reduce morbidity and mortality. This study aims to develop an efficient deep learning model for the accurate classification of pneumonia, COVID-19, and normal cases using chest X-ray and CT images.

Non-invasive biomarkers of liver metabolism are essential for early detection of metabolic alterations. Choline plays a central role in hepatic function, yet its dietary intake and imaging correlates remain underexplored. This study evaluated the feasibility of proton Magnetic Resonance Spectroscopy (1H-MRS) at 3T for hepatic choline quantification and examined its correlation with dietary intake in young women, a population at risk of nutrient-sensitive liver conditions.

Life-threatening respiratory conditions such as COVID-19 and pneumonia demand rapid and accurate diagnosis. Chest X-rays (CXR) are widely used due to their accessibility and cost-effectiveness, but interpreting them remains clinically challenging, especially with overlapping radiological features.

This study introduces a novel fusion of 3D ResNet classification and Grad-CAM visualization to predict vaginal invasion in early-stage cervical cancer using T2WI-MRI, enhancing diagnostic accuracy while enabling anatomical localization of invasive lesions.

Anterior Inferior Cerebellar Artery (AICA) aneurysms are rare, accounting for 0.1% to 0.5% of posterior circulation aneurysms. They often present with diverse morphologies and clinical symptoms, challenging diagnosis and management.

This study aimed to investigate the clinical and MR imaging differences between dual-phenotype hepatocellular carcinoma (DPHCC) and conventional hepatocellular carcinoma (HCC).

Although airway size changes occur in patients with chronic asthma, HRCT has not yet been used to assess changes in the inner diameter of the airways.

The rapid advancement of computational technologies has significantly transformed medical diagnostics, particularly in the realm of neurological disorders. This review provides a comprehensive analysis of the current computational approaches employed for the diagnosis of five major neurological disorders: Alzheimer's disease, Parkinson's disease, Epilepsy, Huntington's disease, and Amyotrophic Lateral Sclerosis. By evaluating 140 peer-reviewed studies, we explored a diverse array of diagnostic methods, including machine learning algorithms, neuroimaging techniques, and electrophysiological signal analysis. Our review highlights the efficacy, accuracy, and limitations of these diagnostic methods, emphasizing their role in early detection and differential diagnosis. Furthermore, we discuss the integration of multimodal data and the potential of emerging technologies such as deep learning and artificial intelligence to enhance diagnostic practices. We also address the current challenges in clinical implementation and propose future research directions to improve diagnostic precision and patient outcomes. This review aims to serve as a valuable resource for researchers, clinicians, and stakeholders in the field of neurodiagnostics, fostering a deeper understanding of computational methodologies that shape the future of neurological disorder diagnosis.

Kidney abnormalities such as cysts, stones, tumors, and other structural disorders pose significant health risks and can lead to chronic kidney disease if not diagnosed in time.

We aim to enhance the accuracy of healthcare AI by generating realistic synthetic medical images using Exposed GANs. One potential issue with synthetic MIG using exposed GANs is that the generated images may not accurately reflect real medical images, which could lead to incorrect medical AI diagnostic decisions. The primary goal of this research is to examine the capacity of GANs for generating synthetic medical images, which can improve the accuracy of healthcare AI systems. It is preferable to collaborate with medical institutions or utilize publicly available datasets from the Medical Segmentation Decathlon (MSD) to obtain medical images for academic research. One well-known pre-processing method for medical image data is normalizing to ensure all pixel values fall within a certain range. In the meantime, the Exposed GAN architecture has been designed to incorporate adversarial aerial training, aiming to produce more realistic medical images by pitting the generator against the discriminator to enhance output quality while improving the discriminator's ability to distinguish between fake and real images. Customization is a more likely research strategy; one can optimize model input parameters and loss functions (or offset the increased computational task of acquiring conditional GANs) at the architecture level. Data augmentation techniques, including random transformations and domain-specific adjustments, are employed to leverage the integration of synthetic data models and enhance the realism and generalization capabilities of the generated images. To enhance the accuracy of healthcare AI using synthetic MIG with exposed GANs, Python code must be implemented to train the GAN model on medical image datasets. The output performances of the discriminator were as follows: discriminator accuracy was 0.6924 on the real data and 0.78789 on the fake data. The average accuracy rate, MPa, was 96.29%, which serves as an evaluation tool for the success of our single-generator GAN in encouraging fabrication applications. There is intense hope that we will be able to unify synthetic MIG-GAN techniques to promote other health AI algorithms for personal applications.

This study aimed to develop and validate a deep learning radiomics (DLR) model based on superb microvascular imaging (SMI) for the noninvasive assessment of the severity of arteriolosclerosis in patients with chronic kidney disease (CKD).

Although Mucoepidermoid Carcinoma (MEC) most commonly arises in the salivary glands, its precise etiological factors and pathogenic mechanisms remain elusive. Pancreatic involvement is an extremely uncommon manifestation, with only 15 documented cases in the medical literature to date. Owing to the absence of typical imaging features and tumor markers, the diagnosis of pancreatic MEC still relies on pathological examination.

Shoulder pain is among the most frequent musculoskeletal complaints and remains a significant therapeutic challenge in clinical practice. A wide spectrum of conditions may contribute to this symptom, including rotator cuff tendinosis or tears, calcific tendinopathy, labral or capsuloligamentous injuries and degenerative changes of the glenohumeral joint. Accurate diagnosis requires an integrated approach that combines clinical history, physical examination, and imaging. However, variability in examination technique and interpretation often limits the reliability of clinical assessment alone. Diagnostic imaging plays a crucial role in evaluating the shoulder joint and its surrounding soft-tissue structures. Magnetic resonance imaging has become the gold standard for shoulder evaluation due to its high resolution and superior soft-tissue contrast, allowing for a detailed assessment of tendons, muscles, cartilage, and bone marrow. Magnetic resonance arthrography further enhances sensitivity for labroligamentous and cartilage injuries, and remains essential in many clinical scenarios. Recent technological advancements, such as radial imaging, kinematic or cine-MRI, 3D acquisition and reconstruction, dynamic contrast-enhanced sequences, ultrashort time-to-echo imaging, T2 mapping, and fat quantification, are expanding the diagnostic capabilities of MRI and promoting a shift from qualitative to quantitative evaluation of tissue integrity. Additionally, demand for faster imaging has driven the development of accelerated acquisition techniques that retain diagnostic image quality with shorter acquisition times. Emerging artificial intelligence-driven tools are beginning to influence every stage of imaging, from protocol optimization to automated segmentation and the extraction of quantitative biomarkers. These innovations promise to improve diagnostic accuracy, streamline workflows, and usher in a new era of patient-specific care in shoulder pain imaging.

The current diagnostic criteria for noncompaction of the ventricular myocardium (NVM) remain inconsistent, and comprehensive cardiac magnetic resonance (CMR) imaging data on the disease are limited. Therefore, the purpose of this study is to evaluate the clinical utility of CMR imaging in the diagnosis and functional assessment of patients with NVM.

Transanal small bowel evisceration is an extremely rare and life-threatening surgical emergency that primarily occurs in debilitated elderly patients. Preoperative computed tomography (CT) can be useful for identifying the viability of eviscerated small bowel and other intra-abdominal pathologies.

The biexponential model of Intravoxel Incoherent Motion (IVIM) has been applied to estimate renal damage. However, the role of the biexponential and stretched exponential models in assessing early renal damage in Chronic Kidney Disease (CKD) is unclear.

This study aimed to systematically review the application of narrow band imaging (NBI) in the diagnosis, treatment, and follow-up of head and neck cancer.

Acute Aortic Dissection (AD) is of great concern due to its high mortality rate. The probability of young patients without underlying diseases developing acute aortic dissection is relatively low. In extreme regions such as high-altitude areas, for patients presenting with atypical chest pain, it is necessary to not only consider life-threatening diseases such as aortic dissection and acute coronary syndrome, but also to rule out the interference of emphysema in the diagnosis. This case provides experience in the diagnosis, evacuation, and treatment of aortic dissection patients in high-altitude areas.

Synchronous liver metastases (SLM) critically influence prognosis in rectal cancer, highlighting the need for accurate preoperative detection. This study aimed to compare the predictive performance of radiomic features extracted from T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) MRI sequences and to develop machine learning-based predictive models for the early detection of SLM in rectal cancer patients.

Behçet's disease (BD), a chronic multisystem inflammatory disorder, rarely involves the heart. Aortic regurgitation (AR) is the predominant valvular lesion. When AR precedes characteristic mucocutaneous symptoms, misdiagnosis and treatment delays often occur.

This study aimed to develop and validate a radiomics fusion model based on CT and MRI for distinguishing between spinal osteosarcoma and chondrosarcoma, and to compare the performance of models derived from different imaging modalities.