2026-08-29T10:23:30Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/268392025-12-18T13:01:56Zcom_20.500.12105_19586com_20.500.12105_2202col_20.500.12105_19587
Repisalud author Del Cerro, C F author Giménez, R C author García-Blas, J author Sosenko, K author Ortega, J M author Desco, M author Abella, M funder Ministerio de Ciencia e Innovación (España) funder Agencia Estatal de Investigación (España) funder Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) funder Unión Europea. Comisión Europea. NextGenerationEU funder Instituto de Salud Carlos III funder Unión Europea. Comisión Europea. H2020 funder Fundación ProCNIC 2025-07-23T10:51:17Z 2025-07-23T10:51:17Z 2025-06 J Imaging Inform Med. 2025 Jun;38(3):1661-1668. Journal of Imaging Informatics in Medicine 39402356 https://hdl.handle.net/20.500.12105/26839 Radiation dose and image quality in radiology are influenced by the X-ray prime factors: KVp, mAs, and source-detector distance. These parameters are set by the X-ray technician prior to the acquisition considering the radiographic position. A wrong setting of these parameters may result in exposure errors, forcing the test to be repeated with the increase of the radiation dose delivered to the patient. This work presents a novel approach based on deep learning that automatically estimates the radiographic position from a photograph captured prior to X-ray exposure, which can then be used to select the optimal prime factors. We created a database using 66 radiographic positions commonly used in clinical settings, prospectively obtained during 2022 from 75 volunteers in two different X-ray facilities. The architecture for radiographic position classification was a lightweight version of ConvNeXt trained with fine-tuning, discriminative learning rates, and a one-cycle policy scheduler. Our resulting model achieved an accuracy of 93.17% for radiographic position classification and increased to 95.58% when considering the correct selection of prime factors, since half of the errors involved positions with the same KVp and mAs values. Most errors occurred for radiographic positions with similar patient pose in the photograph. Results suggest the feasibility of the method to facilitate the acquisition workflow reducing the occurrence of exposure errors while preventing unnecessary radiation dose delivered to patients. eng Classification Deep learning Prime factors Radiographic position Radiography Deep Learning-Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors. research article 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 URL https://repisalud.isciii.es/bitstreams/5ff0e44b-8937-4ca3-a742-fc8139cd940f/download File MD5 e3974d34e6bc5981bf8bbc25ae08a814 1708330 application/pdf Deep Learning-Based Estimation_J Imaging Inform Med_2025.pdf