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@ARTICLE{Rassmann:281434,
      author       = {Rassmann, Sebastian and Abashishvili, Luka and Melikidze,
                      Elene and Sukhiashvili, Anastasia and Lartsuliani, Megi and
                      Chkhaidze, Ivane and Tskhvediani, Nino and Gordeziani,
                      Tinatin and Kvaratskhelia, Ekaterine and Kheladze, Nino and
                      Rekhviashvili, Maia and Rodonaia, Salome and Sukhitashvili,
                      Natia and Urushadze, Nata and Krawitz, Peter and Tkemaladze,
                      Tinatin and Javanmardi, Behnam},
      title        = {{P}opulation-specific calibration and validation of an
                      open-source bone age {AI}.},
      journal      = {Scientific reports},
      volume       = {15},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Springer Nature},
      reportid     = {DZNE-2025-01119},
      pages        = {32673},
      year         = {2025},
      abstract     = {Assessing skeletal maturity through bone age (BA)
                      evaluation is crucial for monitoring children's growth and
                      guiding treatments, such as hormonal therapy and orthopedic
                      interventions. In recent years, artificial intelligence (AI)
                      methods have been developed to automate BA assessment.
                      However, bone growth patterns may vary by ancestry, and many
                      AI models are trained on limited population datasets,
                      raising concerns about their applicability to populations
                      not included in the training process. To address this
                      shortcoming for the case of the Georgian population, we
                      retrospectively collected 381 pediatric hand X-rays and
                      established a manual BA reference rating from seven local
                      pediatric radiologists and endocrinologists. We then used a
                      subset of 121 images to perform a sex-specific linear
                      calibration of the open-source AI, Deeplasia, creating
                      Deeplasia-GE. On the held-out test set (n = 260), the
                      default version of Deeplasia achieved a mean absolute
                      difference (MAD) of 6.57 months, which improved to 5.69
                      months after calibration. We observed that the default
                      Deeplasia overestimates the BA in the Georgian cohort with a
                      signed mean difference (SMD) of + 2.85 and + 5.35 months for
                      females and males respectively, which after calibration is
                      significantly reduced to -0.03 and + 0.58 months for females
                      and males, respectively. We find that Deeplasia-GE has a
                      smaller error than all the raters and, by design,
                      Deeplasia-GE inherits the high test-retest reliability from
                      Deeplasia. These findings suggest that Deeplasia-GE is a
                      reliable AI-based BA assessment method for Georgian
                      children.},
      keywords     = {Humans / Male / Female / Age Determination by Skeleton:
                      methods / Child / Artificial Intelligence / Calibration /
                      Retrospective Studies / Child, Preschool / Adolescent /
                      Infant / Reproducibility of Results / Artificial
                      intelligence (Other) / Global health equity (Other) / Hand
                      x-rays (Other) / Model calibration (Other) / Open-Source
                      (Other) / Pediatric bone age (Other)},
      cin          = {AG Reuter},
      ddc          = {600},
      cid          = {I:(DE-2719)1040310},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40987805},
      doi          = {10.1038/s41598-025-20148-w},
      url          = {https://pub.dzne.de/record/281434},
}