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Large Language Model Approach for Zero-Shot Information Extraction and Clustering of Japanese Radiology Reports: Algorithm Development and Validation.
JMIR Cancer
January 2025
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Background: The application of natural language processing in medicine has increased significantly, including tasks such as information extraction and classification. Natural language processing plays a crucial role in structuring free-form radiology reports, facilitating the interpretation of textual content, and enhancing data utility through clustering techniques. Clustering allows for the identification of similar lesions and disease patterns across a broad dataset, making it useful for aggregating information and discovering new insights in medical imaging.
View Article and Find Full Text PDF18F-Sodium Fluoride PET/CT as a Tool to Assess Enthesopathies in X-Linked Hypophosphatemia.
Calcif Tissue Int
January 2025
Endocrinology Department, School of Medicine, Pontificia Universidad Católica de Chile, Av. Diagonal Paraguay 262, Cuarto Piso, Santiago, Chile.
X-linked hypophosphatemia (XLH) is a rare metabolic disorder characterized by elevated FGF23 and chronic hypophosphatemia, leading to impaired skeletal mineralization and enthesopathies that are associated with pain, stiffness, and diminished quality of life. The natural history of enthesopathies in XLH remains poorly defined, partly due to absence of a sensitive quantitative tool for assessment and monitoring. This study investigates the utility of 18F-NaF PET/CT scans in characterizing enthesopathies in XLH subjects.
View Article and Find Full Text PDFAutomated spinopelvic measurements on radiographs with artificial intelligence: a multi-reader study.
Radiol Med
January 2025
Department of Radiology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Purpose: To develop an artificial intelligence (AI) algorithm for automated measurements of spinopelvic parameters on lateral radiographs and compare its performance to multiple experienced radiologists and surgeons.
Methods: On lateral full-spine radiographs of 295 consecutive patients, a two-staged region-based convolutional neural network (R-CNN) was trained to detect anatomical landmarks and calculate thoracic kyphosis (TK), lumbar lordosis (LL), sacral slope (SS), and sagittal vertical axis (SVA). Performance was evaluated on 65 radiographs not used for training, which were measured independently by 6 readers (3 radiologists, 3 surgeons), and the median per measurement was set as the reference standard.
Correlations between spinopelvic parameters and health-related quality of life in degenerative lumbar scoliosis patients before and after long -level fusion surgery.
BMC Musculoskelet Disord
January 2025
Department of Orthopedics, Peking University Third Hospital, No. 49. North Garden Street, Hai Dian District, Beijing, 100191, People's Republic of China.
Background: For degenerative lumbar scoliosis (DLS), prior studies mainly focused on the preoperative relationship between spinopelvic parameters and health-related quality of life (HRQoL), lacking an exhaustive evaluation of the postoperative situation. Therefore, the postoperative parameters most closely bonded with clinical outcomes has not yet been well-defined in DLS patients. The objective of this study was to comprehensively assess the correlation between radiographic parameters and HRQoL before and after surgery, and to identified the most valuable spinopelvic parameters for postoperative curative effect.
View Article and Find Full Text PDFA multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.
NPJ Digit Med
January 2025
Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
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