Download
00330_2023_Article_10543.pdf 2,73MB
WeightNameValue
1000 Titel
  • Automated bone age assessment in a German pediatric cohort: agreement between an artificial intelligence software and the manual Greulich and Pyle method
1000 Autor/in
  1. Gräfe, Daniel |
  2. Beeskow, Anne Bettina |
  3. Pfäffle, Roland |
  4. Rosolowski, Maciej |
  5. Chung, Tek Sin |
  6. DiFranco, Matthew David |
1000 Verlag Springer Berlin Heidelberg
1000 Erscheinungsjahr 2023
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2023-12-28
1000 Erschienen in
1000 Quellenangabe
  • 34(7):4407-4413
1000 Copyrightjahr
  • 2023
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s00330-023-10543-0 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11213793/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:sec> <jats:title>Objectives</jats:title> <jats:p>This study aimed to evaluate the performance of artificial intelligence (AI) software in bone age (BA) assessment, according to the Greulich and Pyle (G&amp;P) method in a German pediatric cohort.</jats:p> </jats:sec><jats:sec> <jats:title>Materials and methods</jats:title> <jats:p>Hand radiographs of 306 pediatric patients aged 1–18 years (153 boys, 153 girls, 18 patients per year of life)—including a subgroup of patients in the age group for which the software is declared (243 patients)—were analyzed retrospectively. Two pediatric radiologists and one endocrinologist made independent blinded BA reads. Subsequently, AI software estimated BA from the same images. Both agreements, accuracy, and interchangeability between AI and expert readers were assessed.</jats:p> </jats:sec><jats:sec> <jats:title>Results</jats:title> <jats:p>The mean difference between the average of three expert readers and AI software was 0.39 months with a mean absolute difference (MAD) of 6.8 months (1.73 months for the mean difference and 6.0 months for MAD in the intended use subgroup). Performance in boys was slightly worse than in girls (MAD 6.3 months vs. 5.6 months). Regression analyses showed constant bias (slope of 1.01 with a 95% CI 0.99–1.02). The estimated equivalence index for interchangeability was − 14.3 (95% CI −27.6 to − 1.1).</jats:p> </jats:sec><jats:sec> <jats:title>Conclusion</jats:title> <jats:p>In terms of BA assessment, the new AI software was interchangeable with expert readers using the G&amp;P method.</jats:p> </jats:sec><jats:sec> <jats:title>Clinical relevance statement</jats:title> <jats:p>The use of AI software enables every physician to provide expert reader quality in bone age assessment.</jats:p> </jats:sec><jats:sec> <jats:title>Key Points</jats:title> <jats:p>• <jats:italic>A novel artificial intelligence–based software for bone age estimation has not yet been clinically validated.</jats:italic></jats:p> <jats:p>• <jats:italic>Artificial intelligence showed a good agreement and high accuracy with expert radiologists performing bone age assessment.</jats:italic></jats:p> <jats:p>• <jats:italic>Artificial intelligence showed to be interchangeable with expert readers.</jats:italic></jats:p> </jats:sec>
1000 Sacherschließung
lokal Adolescent [MeSH]
lokal Female [MeSH]
lokal Growth
lokal Imaging Informatics and Artificial Intelligence
lokal Hand
lokal Humans [MeSH]
lokal Software [MeSH]
lokal Artificial intelligence
lokal Retrospective Studies [MeSH]
lokal Artificial Intelligence [MeSH]
lokal Infant [MeSH]
lokal Male [MeSH]
lokal Reproducibility of Results [MeSH]
lokal X-rays
lokal Germany [MeSH]
lokal Age Determination by Skeleton/methods [MeSH]
lokal Bone age measurements
lokal Child [MeSH]
lokal Child, Preschool [MeSH]
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-7680-0990|https://frl.publisso.de/adhoc/uri/QmVlc2tvdywgQW5uZSBCZXR0aW5h|https://frl.publisso.de/adhoc/uri/UGbDpGZmbGUsIFJvbGFuZA==|https://frl.publisso.de/adhoc/uri/Um9zb2xvd3NraSwgTWFjaWVq|https://frl.publisso.de/adhoc/uri/Q2h1bmcsIFRlayBTaW4=|https://frl.publisso.de/adhoc/uri/RGlGcmFuY28sIE1hdHRoZXcgRGF2aWQ=
1000 Hinweis
  • DeepGreen-ID: 2d74a0c912014cb29998c531f06b8f21 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. Universitätsklinikum Leipzig |
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Universitätsklinikum Leipzig |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6519909.rdf
1000 Erstellt am 2025-07-05T21:19:16.301+0200
1000 Erstellt von 322
1000 beschreibt frl:6519909
1000 Zuletzt bearbeitet 2025-08-08T09:22:07.017+0200
1000 Objekt bearb. Fri Aug 08 09:22:07 CEST 2025
1000 Vgl. frl:6519909
1000 Oai Id
  1. oai:frl.publisso.de:frl:6519909 |
1000 Sichtbarkeit Metadaten public
1000 Sichtbarkeit Daten public
1000 Gegenstand von

View source