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Background: Bone age assessments (BAAs) is an important clinical modality to investigate endocrine, genetic and growth disorders in children. It is generally performed by radiological examination of the left hand by using either the Greulich-Pyle (GP) or the Tanner-Whitehouse (TW) method. However, both clinical procedures show several limitations, from significant intra- and inter-operator variability to examination effort of clinicians. To address these problems, several automated approaches have been proposed; nevertheless, some disparity still exists between automated BAAs and manual BAAs to be employed in clinical practice. To overcome this disparity, deep learning-based bone age assess software using GP and TW3 hybrid method has been developed. In this study, we evaluate the accuracy and efficiency of the new automated hybrid software system for bone age assessment and validate its feasibility in clinical practice. Materials and Methods: Greulich-Pyle (GP) and Tanner-Whitehouse (TW3) hybrid method-based deep-learning technique was used to develop the automated software system for bone age assessment. Total 102 radiographs from children with the chronological age of 4.9-17.0 years (mean age 10.9±2.3, 51 cases for females and 51 cases for males) were selected and bone age was estimated with this software. For validation of the automated software system, three human experts have manually performed BAAs at expert’s discretion based on GP method for accuracy estimation and one naïve radiologist performed BAAs with automated software system assist and BAAs reading time was recorded in each session for efficiency evaluation. The performance of automated software system was assessed by comparing mean absolute difference (MAD) between the system estimates and the experts manual BAAs.Results: The results of bone age assessment by human experts and automated software system showed no significant difference between the two groups. Each assessed average of bone age were 11.39 ± 2.74 and 11.35 ± 2.76, respectively. MAD was 0.39 years between automated software system BAAs and experts manual BAAs. The 95% confidence interval of the MAD was 0.33 years and 0.45 years. BAAs reading time was reduced from 56.81 sec (95% confidence interval 52.81 - 60.81 sec) in naïve manual BAAs to 31.72 sec (95% confidence interval 29.74 - 33.69 sec) in automated software system assisted BAAs and statistically significant (p < 0.001). MAD showed 0.42 years between naïve manual BAAs and the software-assisted BAAs (95% confidence interval 0.31-0.47 years).Conclusion: The newly developed GP and TW3 hybrid automated software system were reliable for bone age assessments with equivalent accuracy to human experts. Also, the automated system appeared to enhance efficiency by reducing reading times without compromising diagnostic accuracy.

MON-LB017 The Effectiveness of Computed Assessment Using GP and TW3 Hybrid System