Published on in Vol 7, No 4 (2019): Oct-Dec

Age Assessment of Youth and Young Adults Using Magnetic Resonance Imaging of the Knee: A Deep Learning Approach

Age Assessment of Youth and Young Adults Using Magnetic Resonance Imaging of the Knee: A Deep Learning Approach

Age Assessment of Youth and Young Adults Using Magnetic Resonance Imaging of the Knee: A Deep Learning Approach

Journals

  1. Lu T, Shi L, Zhan M, Fan F, Peng Z, Zhang K, Deng Z. Age estimation based on magnetic resonance imaging of the ankle joint in a modern Chinese Han population. International Journal of Legal Medicine 2020;134(5):1843 View
  2. Dallora A, Kvist O, Berglund J, Ruiz S, Boldt M, Flodmark C, Anderberg P. Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects: Machine Learning Multifactorial Approach. JMIR Medical Informatics 2020;8(9):e18846 View
  3. Sabottke C, Breaux M, Spieler B. Estimation of age in unidentified patients via chest radiography using convolutional neural network regression. Emergency Radiology 2020;27(5):463 View
  4. Mauer M, Well E, Herrmann J, Groth M, Morlock M, Maas R, Säring D. Automated age estimation of young individuals based on 3D knee MRI using deep learning. International Journal of Legal Medicine 2021;135(2):649 View
  5. Morid M, Borjali A, Del Fiol G. A scoping review of transfer learning research on medical image analysis using ImageNet. Computers in Biology and Medicine 2021;128:104115 View
  6. Lee B, Lee M. Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment. Korean Journal of Radiology 2021;22(5):792 View
  7. Upalananda W, Wantanajittikul K, Na Lampang S, Janhom A. Semi-automated technique to assess the developmental stage of mandibular third molars for age estimation. Australian Journal of Forensic Sciences 2023;55(1):23 View
  8. Lu T, Qiu L, Ren B, Shi L, Fan F, Deng Z. Forensic age estimation based on magnetic resonance imaging of the proximal humeral epiphysis in Chinese living individuals. International Journal of Legal Medicine 2021;135(6):2437 View
  9. Martin D, Tong E, Kelly B, Yeom K, Yedavalli V. Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview. Frontiers in Radiology 2021;1 View
  10. Shan W, Sun Y, Hu L, Qiu J, Huo M, Zhang Z, Lei Y, Chen Q, Zhang Y, Yue X. Boosting algorithm improves the accuracy of juvenile forensic dental age estimation in southern China population. Scientific Reports 2022;12(1) View
  11. Demircioğlu A, Quinsten A, Forsting M, Umutlu L, Nassenstein K. Pediatric age estimation from radiographs of the knee using deep learning. European Radiology 2022;32(7):4813 View
  12. Ieki H, Ito K, Saji M, Kawakami R, Nagatomo Y, Takada K, Kariyasu T, Machida H, Koyama S, Yoshida H, Kurosawa R, Matsunaga H, Miyazawa K, Ozaki K, Onouchi Y, Katsushika S, Matsuoka R, Shinohara H, Yamaguchi T, Kodera S, Higashikuni Y, Fujiu K, Akazawa H, Iguchi N, Isobe M, Yoshikawa T, Komuro I. Deep learning-based age estimation from chest X-rays indicates cardiovascular prognosis. Communications Medicine 2022;2(1) View
  13. Li N, Cheng B, Zhang J. A Cascade Model with Prior Knowledge for Bone Age Assessment. Applied Sciences 2022;12(15):7371 View
  14. Deng X, Lu T, Liu G, Fan F, Peng Z, Chen X, Chen T, Zhan M, Shi L, Luo S, Zhang X, Liu M, Qiu S, Cong B, Deng Z. Forensic age prediction and age classification for critical age thresholds via 3.0T magnetic resonance imaging of the knee in the Chinese Han population. International Journal of Legal Medicine 2022;136(3):841 View
  15. ATASEVER S, AZGINOGLU N, TERZI D, TERZI R. A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning. Clinical Imaging 2023;94:18 View
  16. 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
  17. Matijaš T, Pinjuh A, Dolić K, Radović D, Galić T, Božić Štulić D, Mihanović F. Improving the Age Estimation Efficiency by Calculation of the Area Ratio Index Using Semi-Automatic Segmentation of Knee MRI Images. Biomedicines 2023;11(7):2046 View
  18. Donia M, El-Behaidy W, Youssif A. Impulsive Aggression Break, Based on Early Recognition Using Spatiotemporal Features. Big Data and Cognitive Computing 2023;7(3):150 View
  19. Kumar R, Sood P, Nirala R, Ade R, Sonaji A. Uses of AI in Field of Radiology- What is State of Doctor & Pateints Communication in Different Disease for Diagnosis Purpose. Journal for Research in Applied Sciences and Biotechnology 2023;2(5):51 View
  20. Zolotenkov D, Trufanov M, Solodovnikov V. Individual age determination based on computed tomography knee analysis using artificial neural networks and computer vision: Preliminary results. Russian Journal of Forensic Medicine 2024;9(4):403 View
  21. Fan F, Liu H, Dai X, Liu G, Liu J, Deng X, Peng Z, Wang C, Zhang K, Chen H, Yin C, Zhan M, Deng Z. Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics. International Journal of Legal Medicine 2024;138(3):927 View
  22. He L, Zhu H, Deng X, Fan F, Liao P, Du W, Chen H, Deng Z, Zhang Y. FAM-Former: Global Transformer and Feature Aggregation Module for Knee MRI Age Estimation. IEEE Transactions on Instrumentation and Measurement 2024;73:1 View

Books/Policy Documents

  1. Kadar M, Botnari A. Proceedings of Seventh International Congress on Information and Communication Technology. View
  2. Mouloodi S, Rahmanpanah H, Burvill C, Martin C, Gohery S, Davies H. Biomedical Visualisation. View