Published on in Vol 8, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18846, first published .
Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach

Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach

Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach

Journals

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  2. Zech J, Carotenuto G, Jaramillo D. Inferring pediatric knee skeletal maturity from MRI using deep learning. Skeletal Radiology 2022;51(8):1671 View
  3. Giannitto N, Militi A, Sapienza D, Scurria S, Gualniera P, Mondello C, Spagnolo E, Terranova A, Portelli M, Cervino G, Fiorillo L, Meto A, Alibrandi A, Asmundo A. Application of Third Molar Maturity Index (I3M) for Assessing Adult Age of 18 Years in a Southern Italian Population Sample. European Journal of Dentistry 2023;17(01):200 View
  4. Ording Muller L, Adolfsson J, Forsberg L, Bring J, Dahlgren J, Domeij H, Gornitzki C, Wernersson E, Odeberg J. Magnetic resonance imaging of the knee for chronological age estimation—a systematic review. European Radiology 2023;33(8):5258 View
  5. Ciftci R, Secgin Y, Oner Z, Toy S, Oner S. Age Estimation Using Machine Learning Algorithms with Parameters Obtained from X-ray Images of the Calcaneus. Nigerian Journal of Clinical Practice 2024;27(2):209 View