Ankylosing Spondylitis (AS), commonly known as Bechterew's disease, is a complex, potentially disabling disease that develops slowly over time and progresses to radiographic sacroiliitis. The etiology of this disease is poorly understood, making it d...
To develop and validate a machine learning-based prediction model to predict axillary lymph node (ALN) metastasis in triple negative breast cancer (TNBC) patients using magnetic resonance imaging (MRI) and clinical characteristics. This retrospective...
Brain image segmentation plays a pivotal role in modern healthcare by enabling precise diagnosis and treatment planning. Federated Learning (FL) enables collaborative model training across institutions while safeguarding sensitive patient data. The i...
Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues. Therefore, accurately diagnosing of tears is important for the early and proper treatment. However, traditional diagnostic methods, such as clinical...
PURPOSE OF REVIEW: Diagnostic delay remains a critical challenge in axial spondyloarthritis (axSpA). This review highlights key clinical and imaging research from 2024 that addresses this persistent issue, with a focus on the evolving roles of MRI, a...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 30, 2025
Accurately and preoperatively predicting tumor response to transarterial chemoembolization (TACE) treatment is crucial for individualized treatment decision-making hepatocellular carcinoma (HCC). In this study, we propose a novel feature fusion netwo...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 30, 2025
Brain regional segmentation is an image-processing approach widely used in brain image analyses. Deep learning models that perform segmentation alone play an important role in medical fields such as automatic diagnosis and prognosis prediction. This ...
As technological innovation in computers has advanced, radiologists may now diagnose brain tumors (BT) with the use of artificial intelligence (AI). In the medical field, early disease identification enables further therapies, where the use of AI sys...
AIM: This study aimed to assess and compare the performance of nomograms and machine learning (ML) techniques using preoperative biomarkers for predicting side-specific extraprostatic extension (EPE) in prostate cancer, which is linked to poor outcom...
OBJECTIVES: To explore differences in quantitative parameters of subcortical nuclei using synthetic MRI across different motor subtypes of Parkinson's Disease (PD), and to develop an interpretable model for distinguishing PD subtypes.
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