Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development

Wang, Fengdan; Gu, Xiao; Chen, Shi; Liu, Yongliang; Shen, Qing; Pan, Hui; Shi, Lei; Jin, Zhengyu

doi:10.7717/peerj.8854

Cited by 24 publications

(23 citation statements)

References 30 publications

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“…Thus the China 05 RUS-CHN method is complicated and time-consuming, the evaluation process requires extensive experience, and the assessment results also differ with physicians' seniority of experience ( 7 ). Therefore, there is an urgent need to establish a rapid, reliable automated BAA system ( 8 , 9 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence–Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience

Zhou

Gong³

et al. 2022

Front. Pediatr.

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BackgroundThe accuracy and consistency of bone age assessments (BAA) using standard methods can vary with physicians' level of experience.MethodsTo assess the impact of information from an artificial intelligence (AI) deep learning convolutional neural network (CNN) model on BAA, specialists with different levels of experience (junior, mid-level, and senior) assessed radiographs from 316 children aged 4–18 years that had been randomly divided into two equal sets-group A and group B. Bone age (BA) was assessed independently by each specialist without additional information (group A) and with information from the model (group B). With the mean assessment of four experts as the reference standard, mean absolute error (MAE), and intraclass correlation coefficient (ICC) were calculated to evaluate accuracy and consistency. Individual assessments of 13 bones (radius, ulna, and short bones) were also compared between group A and group B with the rank-sum test.ResultsThe accuracies of senior, mid-level, and junior physicians were significantly better (all P < 0.001) with AI assistance (MAEs 0.325, 0.344, and 0.370, respectively) than without AI assistance (MAEs 0.403, 0.469, and 0.755, respectively). Moreover, for senior, mid-level, and junior physicians, consistency was significantly higher (all P < 0.001) with AI assistance (ICCs 0.996, 0.996, and 0.992, respectively) than without AI assistance (ICCs 0.987, 0.989, and 0.941, respectively). For all levels of experience, accuracy with AI assistance was significantly better than accuracy without AI assistance for assessments of the first and fifth proximal phalanges.ConclusionsInformation from an AI model improves both the accuracy and the consistency of bone age assessments for physicians of all levels of experience. The first and fifth proximal phalanges are difficult to assess, and they should be paid more attention.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence–Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience

Zhou

Gong³

et al. 2022

Front. Pediatr.

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“…The results of this study agreed with that of previous research where an automatic BAA system was used to rate the BA of Iranian children, which yielded an accuracy within a 1-year range of 95.32% for radiographs of female patients, and 96.51% for radiographs of male patients, respectively (19). Another study demonstrated an accuracy of 84.6% by applying an AI system based on the GP method among Chinese children with abnormal growth and development (11). It is worth mentioning that the accuracy of a rater with AI assistance was higher than that of AI alone (91.72% vs. 91.03%).…”

Section: Discussionmentioning

confidence: 99%

Clinical application of artificial intelligence in longitudinal image analysis of bone age among GHD patients

et al. 2022

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ObjectiveThis study aims to explore the clinical value of artificial intelligence (AI)-assisted bone age assessment (BAA) among children with growth hormone deficiency (GHD).MethodsA total of 290 bone age (BA) radiographs were collected from 52 children who participated in the study at Sun Yat-sen Memorial Hospital between January 2016 and August 2017. Senior pediatric endocrinologists independently evaluated BA according to the China 05 (CH05) method, and their consistent results were regarded as the gold standard (GS). Meanwhile, two junior pediatric endocrinologists were asked to assessed BA both with and without assistance from the AI-based BA evaluation system. Six months later, around 20% of the images assessed by the junior pediatric endocrinologists were randomly selected to be re-evaluated with the same procedure half a year later. Root mean square error (RMSE), mean absolute error (MAE), accuracy, and Bland-Altman plots were used to compare differences in BA. The intra-class correlation coefficient (ICC) and one-way repeated ANOVA were used to assess inter- and intra-observer variabilities in BAA. A boxplot of BA evaluated by different raters during the course of treatment and a mixed linear model were used to illustrate inter-rater effect over time.ResultsA total of 52 children with GHD were included, with mean chronological age and BA by GS of 6.64 ± 2.49 and 5.85 ± 2.30 years at baseline, respectively. After incorporating AI assistance, the performance of the junior pediatric endocrinologists improved (P < 0.001), with MAE and RMSE both decreased by more than 1.65 years (Rater 1: ΔMAE = 1.780, ΔRMSE = 1.655; Rater 2: ΔMAE = 1.794, ΔRMSE = 1.719), and accuracy increasing from approximately 10% to over 91%. The ICC also increased from 0.951 to 0.990. During GHD treatment (at baseline, 6-, 12-, 18-, and 24-months), the difference decreased sharply when AI was applied. Furthermore, a significant inter-rater effect (P = 0.002) also vanished upon AI involvement.ConclusionAI-assisted interpretation of BA can improve accuracy and decrease variability in results among junior pediatric endocrinologists in longitudinal cohort studies, which shows potential for further clinical application.

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“…In China, there are currently a large number of preschool children in the stages of rapid development (ages 3–7). Thus, BAA is of high clinical utility in this population ( 1 , 5 , 13 , 16 – 18 ).…”

Section: Discussionmentioning

confidence: 99%

A comparative study of three bone age assessment methods on Chinese preschool-aged children

Gao

et al. 2022

Front. Pediatr.

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BackgroundBone age assessment (BAA) is an essential tool utilized in outpatient pediatric clinics. Three major BAA methods, Greulich–Pyle (GP), Tanner–Whitehouse 3 (TW3), and China 05 RUS–CHN (RUS–CHN), were applied to comprehensively compare bone age (BA) and chronological age (CA) in a Chinese sample of preschool children. This study was designed to determine the most reliable method.MethodsThe BAA sample consisted of 207 females and 183 males aged 3–6 years from the Zhejiang Province in China. The radiographs were estimated according to the GP, TW3, and RUS–CHN methods by two pediatric radiologists. The data was analyzed statistically using boxplots, the Wilcoxon rank test, and Student’s t-test to explore the difference (D) between BA and CA.ResultsAccording to the distributions of D, the boxplots showed that the median D of the TW3 method was close to zero for both male and female subjects. The TW3 and RUS–CHN methods overestimated the age of both genders. The TW3 method had the highest correct classification rate for males but a similar rate for females. The GP method did not show any significant difference between the BA and CA when applied to 3-year-old males and 4-year-old females while the TW3 method showed similar results when applied to 6-year-old females. The RUS–CHN method showed the least consistent results among the three methods.ConclusionThe TW3 method was superior to the GP and RUS–CHN methods but not reliable on its own. It should be noted that a precise age diagnosis for preschool children cannot be easily made if only one of the methods is utilized. Therefore, it is advantageous to combine multiple methods when assessing bone age.

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Artificial intelligence system can achieve comparable results to experts for bone age assessment of Chinese children with abnormal growth and development

Cited by 24 publications

References 30 publications

Artificial Intelligence–Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience

Artificial Intelligence–Assisted Bone Age Assessment to Improve the Accuracy and Consistency of Physicians With Different Levels of Experience

Clinical application of artificial intelligence in longitudinal image analysis of bone age among GHD patients

A comparative study of three bone age assessment methods on Chinese preschool-aged children

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