This paper is in the following e-collection/theme issue:

Machine Learning (1434) Clinical Information and Decision Making (1374) Imaging Informatics (190) Nails and Musculoskeletal (24)

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

Authors of this article:

Ana Luiza Dallora1 Author Orcid Image ;   Ola Kvist2 Author Orcid Image ;   Johan Sanmartin Berglund1 Author Orcid Image ;   Sandra Diaz Ruiz2 Author Orcid Image ;   Martin Boldt3 Author Orcid Image ;   Carl-Erik Flodmark4 Author Orcid Image ;   Peter Anderberg1 Author Orcid Image
Article Authors Cited by (6) Tweetations (1) Metrics

Journals

  1. Opoku Asare K, Terhorst Y, Vega J, Peltonen E, Lagerspetz E, Ferreira D. Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study. JMIR mHealth and uHealth 2021;9(7):e26540 View
  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
  6. Bergeron M, Côté J, Cumming S, Purcell R, Armstrong N, Basilico L, Burrows K, Charrin J, Felix A, Groesswang H, Iwasaki Y, Kocher M, Martowicz M, McConnell K, Moran J, Holm Moseid C, Mountjoy M, Soligard T, Tetelbaum E, Thiel A, Vertommen T, Viseras G, Budgett R, Engebretsen L, Erdener U. IOC consensus statement on elite youth athletes competing at the Olympic Games: essentials to a healthy, safe and sustainable paradigm. British Journal of Sports Medicine 2024;58(17):946 View