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This publisher's note contains a correction to Opt. Lett.49, 4863 (2024)10.
View Article and Find Full Text PDFThis publisher's note contains a correction to Opt. Lett.47, 5397 (2022)10.
View Article and Find Full Text PDFJ Neuropsychiatry Clin Neurosci
January 2025
Editor, Journal of Neuropsychiatry and Clinical Neurosciences, and Cooper Neuropsychiatry Scholar and Professor, Department of Neurology, University of Colorado School of Medicine, Aurora.
Radiol Artif Intell
January 2025
Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104.
Purpose To evaluate the change in DBT-AI (digital breast tomosynthesis-artificial intelligence) case scores over sequential screens. Materials and Methods This retrospective review included 21,108 female patients (mean age, 58.1 ± [SD] 11.
View Article and Find Full Text PDFRadiol Artif Intell
January 2025
From the Department of Radiology (E.J.H., S.K., H.K., D. K., S.H.Y.) and Medical Research Collaborating Center (H.H.), Seoul National University Hospital, 101 Daehak- ro, Jongno-gu, Seoul 03080, Korea; Department of Radiology, Seoul National University College of Medicine (E.J.H., H.K., S.H.Y.), Seoul, Korea; Department of Radiology, Hanyang University Medical Center, Hanyang University College of Medicine (S-J.Y., Seoul, Korea).
Quantifying pleural effusion change on chest CT is important for evaluating disease severity and treatment response. The purpose of this study was to assess the accuracy of artificial intelligence (AI)-based volume quantification of pleural effusion change on CT images, using the volume of drained fluid as the reference standard. Seventy-nine participants (mean age, 65 ± [SD] 13 years; 47 male) undergoing thoracentesis were prospectively enrolled from October 2021 to September 2023.
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