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000281434 1001_ $$0P:(DE-2719)9001988$$aRassmann, Sebastian$$b0$$eFirst author
000281434 245__ $$aPopulation-specific calibration and validation of an open-source bone age AI.
000281434 260__ $$a[London]$$bSpringer Nature$$c2025
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000281434 520__ $$aAssessing skeletal maturity through bone age (BA) evaluation is crucial for monitoring children's growth and guiding treatments, such as hormonal therapy and orthopedic interventions. In recent years, artificial intelligence (AI) methods have been developed to automate BA assessment. However, bone growth patterns may vary by ancestry, and many AI models are trained on limited population datasets, raising concerns about their applicability to populations not included in the training process. To address this shortcoming for the case of the Georgian population, we retrospectively collected 381 pediatric hand X-rays and established a manual BA reference rating from seven local pediatric radiologists and endocrinologists. We then used a subset of 121 images to perform a sex-specific linear calibration of the open-source AI, Deeplasia, creating Deeplasia-GE. On the held-out test set (n = 260), the default version of Deeplasia achieved a mean absolute difference (MAD) of 6.57 months, which improved to 5.69 months after calibration. We observed that the default Deeplasia overestimates the BA in the Georgian cohort with a signed mean difference (SMD) of + 2.85 and + 5.35 months for females and males respectively, which after calibration is significantly reduced to -0.03 and + 0.58 months for females and males, respectively. We find that Deeplasia-GE has a smaller error than all the raters and, by design, Deeplasia-GE inherits the high test-retest reliability from Deeplasia. These findings suggest that Deeplasia-GE is a reliable AI-based BA assessment method for Georgian children.
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000281434 650_7 $$2Other$$aArtificial intelligence
000281434 650_7 $$2Other$$aGlobal health equity
000281434 650_7 $$2Other$$aHand x-rays
000281434 650_7 $$2Other$$aModel calibration
000281434 650_7 $$2Other$$aOpen-Source
000281434 650_7 $$2Other$$aPediatric bone age
000281434 650_2 $$2MeSH$$aHumans
000281434 650_2 $$2MeSH$$aMale
000281434 650_2 $$2MeSH$$aFemale
000281434 650_2 $$2MeSH$$aAge Determination by Skeleton: methods
000281434 650_2 $$2MeSH$$aChild
000281434 650_2 $$2MeSH$$aArtificial Intelligence
000281434 650_2 $$2MeSH$$aCalibration
000281434 650_2 $$2MeSH$$aRetrospective Studies
000281434 650_2 $$2MeSH$$aChild, Preschool
000281434 650_2 $$2MeSH$$aAdolescent
000281434 650_2 $$2MeSH$$aInfant
000281434 650_2 $$2MeSH$$aReproducibility of Results
000281434 7001_ $$aAbashishvili, Luka$$b1
000281434 7001_ $$aMelikidze, Elene$$b2
000281434 7001_ $$aSukhiashvili, Anastasia$$b3
000281434 7001_ $$aLartsuliani, Megi$$b4
000281434 7001_ $$aChkhaidze, Ivane$$b5
000281434 7001_ $$aTskhvediani, Nino$$b6
000281434 7001_ $$aGordeziani, Tinatin$$b7
000281434 7001_ $$aKvaratskhelia, Ekaterine$$b8
000281434 7001_ $$aKheladze, Nino$$b9
000281434 7001_ $$aRekhviashvili, Maia$$b10
000281434 7001_ $$aRodonaia, Salome$$b11
000281434 7001_ $$aSukhitashvili, Natia$$b12
000281434 7001_ $$aUrushadze, Nata$$b13
000281434 7001_ $$aKrawitz, Peter$$b14
000281434 7001_ $$aTkemaladze, Tinatin$$b15
000281434 7001_ $$aJavanmardi, Behnam$$b16
000281434 773__ $$0PERI:(DE-600)2615211-3$$a10.1038/s41598-025-20148-w$$gVol. 15, no. 1, p. 32673$$n1$$p32673$$tScientific reports$$v15$$x2045-2322$$y2025
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