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Editorial
. 2024 Jul;34(7):4284-4286.
doi: 10.1007/s00330-023-10555-w. Epub 2024 Jan 8.

Deep learning-based diagnostic models for bone lesions: is current research ready for clinical translation?

Jingyu Zhong  1
Affiliations
Editorial

Deep learning-based diagnostic models for bone lesions: is current research ready for clinical translation?

Jingyu Zhong. Eur Radiol. 2024 Jul.
No abstract available

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Conflict of interest statement

Dr. Jingyu Zhong acknowledges his position as a member of the Scientific Editorial Board of European Radiology and BMC Medical Imaging. He has therefore not taken part in the review or selection process of this paper.

Comment on

References

    1. Ribeiro GJ, Gillet R, Blum A, Teixeira PAG. Imaging report and data system (RADS) for bone tumors: where do we stand and future directions. Skeletal Radiol. 2023;52(2):151–156. doi: 10.1007/s00256-022-04179-2. - DOI - PubMed
    1. He Y, Pan I, Bao B, et al. Deep learning-based classification of primary bone tumors on radiographs: a preliminary study. EBioMedicine. 2020;62:103121. doi: 10.1016/j.ebiom.2020.103121. - DOI - PMC - PubMed
    1. Li J, Li S, Li X, et al. Primary bone tumor detection and classification in full-field bone radiographs via YOLO deep learning model. Eur Radiol. 2023;33(6):4237–4248. doi: 10.1007/s00330-022-09289-y. - DOI - PubMed
    1. von Schacky CE, Wilhelm NJ, Schäfer VS, et al. Multitask deep learning for segmentation and classification of primary bone tumors on radiographs. Radiology. 2021;301(2):398–406. doi: 10.1148/radiol.2021204531. - DOI - PubMed
    1. Yildiz Potter I, Yeritsyan D, Mahar S, et al. Automated bone tumor segmentation and classification as benign or malignant using computed tomographic imaging. J Digit Imaging. 2023;36(3):869–878. doi: 10.1007/s10278-022-00771-z. - DOI - PMC - PubMed

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