These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

340 related items for PubMed ID: 29095675

  • 21. Generalizability and Bias in a Deep Learning Pediatric Bone Age Prediction Model Using Hand Radiographs.
    Beheshtian E, Putman K, Santomartino SM, Parekh VS, Yi PH.
    Radiology; 2023 Feb; 306(2):e220505. PubMed ID: 36165796
    [Abstract] [Full Text] [Related]

  • 22.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 23. Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment.
    Lee KC, Lee KH, Kang CH, Ahn KS, Chung LY, Lee JJ, Hong SJ, Kim BH, Shim E.
    Korean J Radiol; 2021 Dec; 22(12):2017-2025. PubMed ID: 34668353
    [Abstract] [Full Text] [Related]

  • 24.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 25. The applicability of Greulich and Pyle atlas to assess skeletal age for four ethnic groups.
    Mansourvar M, Ismail MA, Raj RG, Kareem SA, Aik S, Gunalan R, Antony CD.
    J Forensic Leg Med; 2014 Feb; 22():26-9. PubMed ID: 24485416
    [Abstract] [Full Text] [Related]

  • 26.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 27. Infant bone age estimation based on fibular shaft length: model development and clinical validation.
    Tsai A, Stamoulis C, Bixby SD, Breen MA, Connolly SA, Kleinman PK.
    Pediatr Radiol; 2016 Mar; 46(3):342-56. PubMed ID: 26637315
    [Abstract] [Full Text] [Related]

  • 28. Rethinking Greulich and Pyle: A Deep Learning Approach to Pediatric Bone Age Assessment Using Pediatric Trauma Hand Radiographs.
    Pan I, Baird GL, Mutasa S, Merck D, Ruzal-Shapiro C, Swenson DW, Ayyala RS.
    Radiol Artif Intell; 2020 Jul; 2(4):e190198. PubMed ID: 33937834
    [Abstract] [Full Text] [Related]

  • 29. Deeplasia: deep learning for bone age assessment validated on skeletal dysplasias.
    Rassmann S, Keller A, Skaf K, Hustinx A, Gausche R, Ibarra-Arrelano MA, Hsieh TC, Madajieu YED, Nöthen MM, Pfäffle R, Attenberger UI, Born M, Mohnike K, Krawitz PM, Javanmardi B.
    Pediatr Radiol; 2024 Jan; 54(1):82-95. PubMed ID: 37953411
    [Abstract] [Full Text] [Related]

  • 30.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 31. Digital hand atlas and web-based bone age assessment: system design and implementation.
    Cao F, Huang HK, Pietka E, Gilsanz V.
    Comput Med Imaging Graph; 2000 Jan; 24(5):297-307. PubMed ID: 10940607
    [Abstract] [Full Text] [Related]

  • 32. [Comparison of Three CNN Models Applied in Bone Age Assessment of Pelvic Radiographs of Adolescents].
    Peng LQ, Wan L, Wang MW, Li Z, Wang P, Liu TA, Wang YH, Zhao H.
    Fa Yi Xue Za Zhi; 2020 Oct; 36(5):622-630. PubMed ID: 33295161
    [Abstract] [Full Text] [Related]

  • 33. Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography.
    Cheng PM, Tran KN, Whang G, Tejura TK.
    AJR Am J Roentgenol; 2019 Feb; 212(2):342-350. PubMed ID: 30476452
    [Abstract] [Full Text] [Related]

  • 34. Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.
    Zucker EJ, Barnes ZA, Lungren MP, Shpanskaya Y, Seekins JM, Halabi SS, Larson DB.
    J Cyst Fibros; 2020 Jan; 19(1):131-138. PubMed ID: 31056440
    [Abstract] [Full Text] [Related]

  • 35. Automatic analysis of hand radiographs for the assessment of skeletal age: a subsymbolic approach.
    Rucci M, Coppini G, Nicoletti I, Cheli D, Valli G.
    Comput Biomed Res; 1995 Jun; 28(3):239-56. PubMed ID: 7554858
    [Abstract] [Full Text] [Related]

  • 36. A comparison of skeletal maturity assessed by radiological and ultrasonic methods.
    Utczas K, Muzsnai A, Cameron N, Zsakai A, Bodzsar EB.
    Am J Hum Biol; 2017 Jul 08; 29(4):. PubMed ID: 28094893
    [Abstract] [Full Text] [Related]

  • 37. Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels.
    Kim PH, Yoon HM, Kim JR, Hwang JY, Choi JH, Hwang J, Lee J, Sung J, Jung KH, Bae B, Jung AY, Cho YA, Shim WH, Bak B, Lee JS.
    Korean J Radiol; 2023 Nov 08; 24(11):1151-1163. PubMed ID: 37899524
    [Abstract] [Full Text] [Related]

  • 38. The BoneXpert method for automated determination of skeletal maturity.
    Thodberg HH, Kreiborg S, Juul A, Pedersen KD.
    IEEE Trans Med Imaging; 2009 Jan 08; 28(1):52-66. PubMed ID: 19116188
    [Abstract] [Full Text] [Related]

  • 39. Skeletal bone age assessments for young children based on regression convolutional neural networks.
    Hao PY, Chokuwa S, Xie XH, Wu FL, Wu J, Bai C.
    Math Biosci Eng; 2019 Jul 12; 16(6):6454-6466. PubMed ID: 31698572
    [Abstract] [Full Text] [Related]

  • 40. An Abbreviated Scale for the Assessment of Skeletal Bone Age Using Radiographs of the Knee.
    Tang X, Lu Y, Pang M, Nhan DT, Klyce W, Fritz J, Lee RJ.
    Orthopedics; 2018 Sep 01; 41(5):e676-e680. PubMed ID: 30052264
    [Abstract] [Full Text] [Related]

    Page: [Previous] [Next] [New Search]
    of 17.