Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

Lee, Byoung Dai; Lee, Mu Sook

doi:10.3348/kjr.2020.0941

Cited by 38 publications

(30 citation statements)

References 46 publications

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“…The MAD and RMSE of our model were 0.39 and 0.50, respectively. This value shows that the accuracy of our hybrid model is similar to that of previous studies [ 14 15 18 29 ]. The MAD and RMSE of the GP method-based AI model MAD and RMSE were 0.50 and 0.63 [ 14 ].…”

Section: Discussionsupporting

confidence: 90%

“…The MAD and RMSE of the GP method-based AI model MAD and RMSE were 0.50 and 0.63 [ 14 ]. Another GP method-based model called “HH-boneage” has a MAD of 0.46 and an RMSE of 0.62 [ 15 ]. The 9-stage TW method showed the MAD and RMSE for 7 ROIs of 0.59 and 0.76 [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…Third, we only compared the accuracy of our model and the GP method. A recent article suggested that the question of whether AI bone age assessment should be compared with other bone age assessment methods, including the TW method or using other imaging modalities such as MRI or ultrasonography instead of the left hand and wrist radiography persists [ 15 ]. Therefore, further comparative studies are required to confirm this.…”

Section: Discussionmentioning

confidence: 99%

“…However, the TW method also has certain limitations. First, it is a more complex method that takes a longer time than the GP method [ 8 15 ]. Second, it classifies individual bones based on nine maturity grades (A to I); thus, the classification is sometimes ambiguous and has inherent deviation because one particular bone shape can have two different pre-defined labels of the same feature [ 16 17 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment

Lee

Kang

et al. 2021

Korean J Radiol

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Objective To evaluate the accuracy and clinical efficacy of a hybrid Greulich-Pyle (GP) and modified Tanner-Whitehouse (TW) artificial intelligence (AI) model for bone age assessment. Materials and Methods A deep learning-based model was trained on an open dataset of multiple ethnicities. A total of 102 hand radiographs (51 male and 51 female; mean age ± standard deviation = 10.95 ± 2.37 years) from a single institution were selected for external validation. Three human experts performed bone age assessments based on the GP atlas to develop a reference standard. Two study radiologists performed bone age assessments with and without AI model assistance in two separate sessions, for which the reading time was recorded. The performance of the AI software was assessed by comparing the mean absolute difference between the AI-calculated bone age and the reference standard. The reading time was compared between reading with and without AI using a paired t test. Furthermore, the reliability between the two study radiologists' bone age assessments was assessed using intraclass correlation coefficients (ICCs), and the results were compared between reading with and without AI. Results The bone ages assessed by the experts and the AI model were not significantly different (11.39 ± 2.74 years and 11.35 ± 2.76 years, respectively, p = 0.31). The mean absolute difference was 0.39 years (95% confidence interval, 0.33–0.45 years) between the automated AI assessment and the reference standard. The mean reading time of the two study radiologists was reduced from 54.29 to 35.37 seconds with AI model assistance ( p < 0.001). The ICC of the two study radiologists slightly increased with AI model assistance (from 0.945 to 0.990). Conclusion The proposed AI model was accurate for assessing bone age. Furthermore, this model appeared to enhance the clinical efficacy by reducing the reading time and improving the inter-observer reliability.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment

Lee

Kang

et al. 2021

Korean J Radiol

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“…We are now at a point where AI bone age analysis is so clearly superior to manual reading that it should become the tool of choice for bone age analysis. (27) For the present, BX cannot replace radiological review of bone age X-rays as it is not capable of identifying morphological abnormalities such as rickets or features of skeletal dysplasia, but it has replaced the onerous and burdensome task of manual bone age evaluation.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence in the endocrine clinic: automated bone age analysis in arabic children from UAE

et al. 2021

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Objective: Artificial intelligence (AI) is playing an increasing role in patient assessment. AI bone age analysis is such a tool, but its value in Arabic children presenting to an endocrine clinic has not been explored. We compared results from an experienced pediatric radiologist and the AI bone age system, BoneXpert (BX), (Visiana, Denmark) to assess its utility in a cohort of children presenting to the Al Jalila Children’s Specialty Hospital endocrine service. Materials and Methods: We conducted a retrospective chart review of 47 children with growth disorders, initially assessed by a single experienced radiologist and subsequently by BX, to confirm the usefulness of the BX system in our population. The results of the analyses were analysed using a Bland-Altman plot constructed to compare differences between the radiologist’s interpretation and BX across the available range of bone age. Results: Forty-four of the patient x-ray images were analysed by BX. Three X-ray images were rejected by BX due to post-processing artifacts, which prevented computer interpretation. For the remaining 44 X-rays, there was a close correlation between radiologist and BX results (r=0.93; p <0.00001). Two radiographs were identified with a large discrepancy in the reported bone ages. Blinded, independent re-evaluation of the radiographs showed the original manually interpreted bone age to have been erroneous, with the BX results corresponding closely to the amended bone age. A small positive bias was noted in bone age (+0.39 years) in the BX analyses, relative to manual interpretation. Conclusions: AI bone age analysis was of high utility in Arabic children from UAE presenting to an endocrine clinic, with results highly comparable to an experienced radiologist. In the two cases where a large discrepancy was found, independent re-evaluation showed AI analysis was correct.

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Evaluation of Maturation Among Adolescent Athletes

Ramos-García

López-Plaza

Herrera-Amante

et al. 2023

Human Growth and Nutrition in Latin American and Caribbean Countries

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Automated Bone Age Assessment Using Artificial Intelligence: The Future of Bone Age Assessment

Cited by 38 publications

References 46 publications

Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment

Clinical Validation of a Deep Learning-Based Hybrid (Greulich-Pyle and Modified Tanner-Whitehouse) Method for Bone Age Assessment

Artificial intelligence in the endocrine clinic: automated bone age analysis in arabic children from UAE

Evaluation of Maturation Among Adolescent Athletes

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