Assessment of the accuracy of the Greulich and Pyle hand-wrist atlas for age estimation in a contemporary Australian population

Maggio, Ariane; Flavel, Ambika; Hart, R. C.; Franklin, Daniel

doi:10.1080/00450618.2016.1251970

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…The current study has demonstrated that both methods are reproducible with the TW3 being more reproducible than the GP method which is consistent with other studies [21,[25][26][27][28], potentially explained by individual maturity indicator scoring, rather than the GP global grading approach. GP BA units are whole numbersless precise than the TW3 approach with decimals.…”

Section: Discussionsupporting

confidence: 91%

Evaluation of Two Methods of Bone Age Assessment in Peripubertal Children in Zimbabwe

et al. 2022

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Section: Discussionsupporting

confidence: 91%

Evaluation of Two Methods of Bone Age Assessment in Peripubertal Children in Zimbabwe

et al. 2022

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“…However, it should be noted that this method is established on Caucasian ethnicity and is highly dependent on the experience of radiologists. It's prone to cause bias when GP method was applied to different generations, races or specific age groups for bone age assessment (21)(22)(23)(24)(25)(26). Similarly, due to this study was a retrospective design, all x-ray images were from the China Medical University Hospital and Asia University Hospital.…”

Section: Discussionmentioning

confidence: 99%

A Study to Evaluate Accuracy and Validity of the EFAI Computer-Aided Bone Age Diagnosis System Compared With Qualified Physicians

et al. 2022

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Study ObjectivesIn previous research, we built a deep neural network model based on Inception-Resnet-v2 to predict bone age (EFAI-BAA). The primary objective of the study was to determine if the EFAI-BAA was substantially concordant with the qualified physicians in assessing bone ages. The secondary objective of the study was to determine if the EFAI-BAA was no different in the clinical rating (advanced, normal, or delayed) with the qualified physicians.MethodThis was a retrospective study. The left-hand X-ray images of male subjects aged 3–16 years old and female subjects aged 2–15 years old were collected from China Medical University Hospital (CMUH) and Asia University Hospital (AUH) retrospectively since the trial began until the included image amount reached 368. This was a blinded study. The qualified physicians who ran, read, and interpreted the tests were blinded to the values assessed by the other qualified physicians and the EFAI-BAA.ResultsThe concordance correlation coefficient (CCC) between the EFAI-BAA (EFAI-BAA), the evaluation of bone age by physician in Kaohsiung Veterans General Hospital (KVGH), Taichung Veterans General Hospital (TVGH2), and in Taipei Tzu Chi Hospital (TZUCHI-TP) was 0.9828 (95% CI: 0.9790–0.9859, p-value = 0.6782), 0.9739 (95% CI: 0.9681–0.9786, p-value = 0.0202), and 0.9592 (95% CI: 0.9501–0.9666, p-value = 0.4855), respectively.ConclusionThere was a consistency of bone age assessment between the EFAI-BAA and each one of the three qualified physicians (CCC = 0.9). As the significant difference in the clinical rating was only found between the EFAI-BAA and the qualified physician in TVGH2, the performance of the EFAI-BAA was considered similar to the qualified physicians.

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“…In addition, the Greulich-Pyle method is non-population-specific and contemporaneous given that the atlas is based on radiographs from a Caucasian population [1,3]. In this context, numerous studies have shown improved accuracy of BA assessment by implementation of temporally and geographically specific standards [21][22][23][24][25]. Therefore, a contemporaneous and objective approach, which represents the investigated novel AI software version in this study, may substantially increase the accuracy of BA assessment compared to conventional methods.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method

et al. 2020

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Background: Bone age (BA) assessment performed by artificial intelligence (AI) is of growing interest due to improved accuracy, precision and time efficiency in daily routine. The aim of this study was to investigate the accuracy and efficiency of a novel AI software version for automated BA assessment in comparison to the Greulich-Pyle method. Methods: Radiographs of 514 patients were analysed in this retrospective study. Total BA was assessed independently by three blinded radiologists applying the GP method and by the AI software. Overall and genderspecific BA assessment results, as well as reading times of both approaches, were compared, while the reference BA was defined by two blinded experienced paediatric radiologists in consensus by application of the Greulich-Pyle method. Results: Mean absolute deviation (MAD) and root mean square deviation (RSMD) were significantly lower between AI-derived BA and reference BA (MAD 0.34 years, RSMD 0.38 years) than between reader-calculated BA and reference BA (MAD 0.79 years, RSMD 0.89 years; p < 0.001). The correlation between AI-derived BA and reference BA (r = 0.99) was significantly higher than between reader-calculated BA and reference BA (r = 0.90; p < 0.001). No statistical difference was found in reader agreement and correlation analyses regarding gender (p = 0.241). Mean reading times were reduced by 87% using the AI system. Conclusions: A novel AI software enabled highly accurate automated BA assessment. It may improve efficiency in clinical routine by reducing reading times without compromising the accuracy compared with the Greulich-Pyle method.

show abstract

Assessment of the accuracy of the Greulich and Pyle hand-wrist atlas for age estimation in a contemporary Australian population

Cited by 9 publications

References 30 publications

Evaluation of Two Methods of Bone Age Assessment in Peripubertal Children in Zimbabwe

Evaluation of Two Methods of Bone Age Assessment in Peripubertal Children in Zimbabwe

A Study to Evaluate Accuracy and Validity of the EFAI Computer-Aided Bone Age Diagnosis System Compared With Qualified Physicians

Artificial intelligence in bone age assessment: accuracy and efficiency of a novel fully automated algorithm compared to the Greulich-Pyle method

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