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

Gao, Chunfang; Qi, Qian; Li, Yangsheng; Xing, Xiaowei; He, X. T.; Lin, Min; Ding, Zhongxiang

doi:10.3389/fped.2022.976565

Cited by 12 publications

(15 citation statements)

References 24 publications

(36 reference statements)

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“…Data of carpal bone age and gonadal ultrasound were reviewed. Carpal bone age data were interpreted by YITU AI (YITU Medical, Shang Hai, CN) using three methods, including Tanner‐Whitehouse radius‐ulna‐short bones (TW3‐RUS), TW3‐Carpal bones, and CN05 with manual correction 25,26 . Manual corrections were performed by observers A (P.Z., 2 years) and B (L.Z., 2 years) under the supervision and guidance of two senior physicians mentioned above.…”

Section: Methodsmentioning

confidence: 99%

Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter‐Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls

Zou

Zhang

et al. 2023

Magnetic Resonance Imaging

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BackgroundIdiopathic central precocious puberty (ICPP) impairs child development, without early intervention. The current reference standard, the gonadotropin‐releasing hormone stimulation test, is invasive which may hinder diagnosis and intervention.PurposeTo develop a model for accurate diagnosis of ICPP, by integrating pituitary MRI, carpal bone age, gonadal ultrasound, and basic clinical data.Study TypeRetrospective.PopulationA total of 492 girls with PP (185 with ICPP and 307 peripheral precocious puberty [PPP]) were randomly divided by reference standard into training (75%) and internal validation (25%) data. Fifty‐one subjects (16 with ICPP, 35 with PPP) provided by another hospital as external validation.Field Strength/SequenceT1‐weighted (spin echo [SE], fast SE, cube) and T2‐weighted (fast SE‐fat suppression) imaging at 3.0 T or 1.5 T.AssessmentRadiomics features were extracted from pituitary MRI after manual segmentation. Carpal bone age, ovarian, follicle and uterine volumes and endometrium presence were assessed from radiographs and gonadal ultrasound. Four machine learning methods were developed: a pituitary MRI radiomics model, an integrated image model (with pituitary MRI, gonadal ultrasound and bone age), a basic clinical model (with age and sex hormone data), and an integrated multimodal model combining all features.Statistical TestsIntraclass correlation coefficients were used to assess consistency of segmentation. Receiver operating characteristic (ROC) curves and the Delong tests were used to assess and compare the diagnostic performance of models. P < 0.05 was considered statistically significant.ResultsThe area under of the ROC curve (AUC) of the pituitary MRI radiomics model, integrated image model, basic clinical model, and integrated multimodal model in the training data was 0.668, 0.809, 0.792, and 0.860. The integrated multimodal model had higher diagnostic efficacy (AUC of 0.862 and 0.866 for internal and external validation).ConclusionThe integrated multimodal model may have potential as an alternative clinical approach to diagnose ICPP.Evidence Level3.Technical EfficacyStage 2.

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Section: Methodsmentioning

confidence: 99%

Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter‐Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls

Zou

Zhang

et al. 2023

Magnetic Resonance Imaging

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“…Out of the 51 studies, 21 (41.18%) were conducted in different regions of Asia. Specifically, five studies were conducted in India [35][36][37][38][39], four in Turkey [40][41][42][43], three in Pakistan [30,44,45], two each in Saudi Arabia [46,47], China [48,49], and South Korea [50,51], and one each in Taiwan [52], Iran [53], and Israel [54].…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…Regarding study design, 25 (49.02%) were observational retrospective cohort studies [23,35,40,42,43,48,50,52,54,55,57,58,[61][62][63][64][65][66][67][68][69]76,79,83,84], followed by 19 (37.25%) crosssectional studies [30,[36][37][38][44][45][46]51,53,59,60,[72][73][74][75]77,78,81,82], and 7 (13.73%) observational prospective cohort studies [39,41,55,56,70,71,80].…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

“…Additionally, five studies (9.80%) were conducted exclusively in Africa, including two in South Africa [71,72], one in Zimbabwe [73] one in Botswana [74], and one in A total of 30 papers (58.8%) studied the accuracy [23,[35][36][37]40,41,43,45,48,49,[51][52][53]55,57,59,[64][65][66]69,70,[72][73][74][76][77][78][79][80]82], 1 of which also separately calculated the sensitivity and specificity [62]. A total of 27 (52.94%) studies analyzed precision [35,36,42,44,46,47,50,52,[56][57][58][60][61][62]…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

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Precision and Accuracy of Radiological Bone Age Assessment in Children among Different Ethnic Groups: A Systematic Review

Martín Pérez,

Vega González

et al. 2023

Diagnostics

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Aim: The aim was to identify, evaluate, and summarize the findings of relevant individual studies on the precision and accuracy of radiological BA assessment procedures among children from different ethnic groups. Materials and Methods: A qualitative systematic review was carried out following the MOOSE statement and previously registered in PROSPERO (CRD42023449512). A search was performed in MEDLINE (PubMed) (n = 561), the Cochrane Library (n = 261), CINAHL (n = 103), Web of Science (WOS) (n = 181), and institutional repositories (n = 37) using MeSH and free terms combined with the Booleans “AND” and “OR”. NOS and ROBINS-E were used to assess the methodological quality and the risk of bias of the included studies, respectively. Results: A total of 51 articles (n = 20,100) on radiological BA assessment procedures were precise in terms of intra-observer and inter-observer reliability for all ethnic groups. In Caucasian and Hispanic children, the Greulich–Pyle Atlas (GPA) was accurate at all ages, but in youths, Tanner–Whitehouse radius–ulna–short bones 3 (TW3-RUS) could be an alternative. In Asian and Arab subjects, GPA and Tanner–Whitehouse 3 (TW3) overestimated the BA in adolescents near adulthood. In African youths, GPA overestimated the BA while TW3 was more accurate. Conclusion: GPA and TW3 radiological BA assessment procedures are both precise but their accuracy in estimating CA among children of different ethnic groups can be altered by racial bias.

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“…TW3 computes scores for radius, ulna, and short bones (RUS), along with the carpal series bones, where each major bone in the hand contributes to the total score. However, this process can be time-consuming and susceptible to inter-observer variability, potentially resulting in inconsistencies in diagnosis and treatment planning ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

Validation of an established TW3 artificial intelligence bone age assessment system: a prospective, multicenter, confirmatory study

Liu,

Ouyang,

et al. 2024

Quant Imaging Med Surg

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Background In 2020, our center established a Tanner-Whitehouse 3 (TW3) artificial intelligence (AI) system using a convolutional neural network (CNN), which was built upon 9059 radiographs. However, the system, upon which our study is based, lacked a gold standard for comparison and had not undergone thorough evaluation in different working environments. Methods To further verify the applicability of the AI system in clinical bone age assessment (BAA) and to enhance the accuracy and homogeneity of BAA, a prospective multi-center validation was conducted. This study utilized 744 left-hand radiographs of patients, ranging from 1 to 20 years of age, with 378 boys and 366 girls. These radiographs were obtained from nine different children’s hospitals between August and December 2020. The BAAs were performed using the TW3 AI system and were also reviewed by experienced reviewers. Bone age accuracy within 1 year, root mean square error (RMSE), and mean absolute error (MAE) were statistically calculated to evaluate the accuracy. Kappa test and Bland-Altman (B-A) plot were conducted to measure the diagnostic consistency. Results The system exhibited a high level of performance, producing results that closely aligned with those of the reviewers. It achieved a RMSE of 0.52 years and an accuracy of 94.55% for the radius, ulna, and short bones series. When assessing the carpal series of bones, the system achieved a RMSE of 0.85 years and an accuracy of 80.38%. Overall, the system displayed satisfactory accuracy and RMSE, particularly in patients over 7 years old. The system excelled in evaluating the carpal bone age of patients aged 1–6. Both the Kappa test and B-A plot demonstrated substantial consistency between the system and the reviewers, although the model encountered challenges in consistently distinguishing specific bones, such as the capitate. Furthermore, the system’s performance proved acceptable across different genders and age groups, as well as radiography instruments. Conclusions In this multi-center validation, the system showcased its potential to enhance the efficiency and consistency of healthy delivery, ultimately resulting in improved patient outcomes and reduced healthcare costs.

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

Cited by 12 publications

References 24 publications

Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter‐Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls

Development and Validation of a Combined MRI Radiomics, Imaging and Clinical Parameter‐Based Machine Learning Model for Identifying Idiopathic Central Precocious Puberty in Girls

Precision and Accuracy of Radiological Bone Age Assessment in Children among Different Ethnic Groups: A Systematic Review

Validation of an established TW3 artificial intelligence bone age assessment system: a prospective, multicenter, confirmatory study

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