Del Cerro, C FGiménez, R CGarcía-Blas, JSosenko, KOrtega, J MDesco, MAbella, M2025-07-232025-07-232025-06J Imaging Inform Med. 2025 Jun;38(3):1661-1668.https://hdl.handle.net/20.500.12105/26839Radiation 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.This work was supported by Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, and co-funded by the European Regional Development Fund, “A way of making Europe”: PDC2021-121656-I00 (MULTIRAD), funded by MCIN/AEI/10.13039/501100011033 and by the European Union “NextGenerationEU”/PRTR. This is also funded by Instituto de Salud Carlos III through the projects PMPTA22/00121 and PMPTA22/00118, cofunded by the European Union “NextGenerationEU”/PRTR, and by the ASPIDE Project funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant 801091. The CNIC is supported by Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación, and the Pro CNIC Foundation.engVoRhttp://creativecommons.org/licenses/by/4.0/ClassificationDeep learningPrime factorsRadiographic positionRadiographyAttribution 4.0 International39402356Journal of Imaging Informatics in Medicineopen access