Correlación en la determinación de la edad ósea radiológica mediante el método de Greulich y Pyle versus la evaluación automatizada utilizando el software BoneXpert

Lepe, Georgette Pose; Villacrés, Fabián; Fuente-Alba, Claudio Silva; Guiloff, Stefan

doi:10.4067/s0370-41062018005000705

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The largest difference was observed in prepubertal and pubertal male, in the case of specialists and AI, respectively. Other studies comparing BA using another AI (BoneXpert, Hørsholm, Denmark) showed that both male and female tended to measure BA younger in prepubertal age (male, 0.001–0.61 years of age; female, 0.02–00.76 years of age), and older in pubertal age (male, 0.43–1.64 years of age; female, 0.03–1.24 years of age), and the study also showed the greatest difference in pubertal male [ 1 , 6 ].…”

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confidence: 71%

Accuracy of predicted adult height using the Greulich-Pyle method and artificial intelligence medical device

Cho

Choi

et al. 2023

Clin Exp Pediatr

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It is expected that AI medical devices would be introduced in medical images earlier than in other fields, and in fact, there are many cases where AI-based bone age medical devices are used in primary medical institutions. Besides, there are few studies comparing bone age and final adult height prediction of humans and AI. [1][2][3] Medical image reading by AI is by deep learning based on the GP and TW methods, and this study aimed to compare the accuracy of bone age and final adult height prediction of VUNO Med®-BoneAge™, the most commonly used AI program in Korea and specialist.Our study included 190 children and adolescents (73 male and 117 female) aged 8-12 years who visited the Growth Clinic of Pediatric Endocrinology in 2012 to evaluate their bone age (BA) and predicted adult height (PAH). The height, weight, BMI, and height of the father and mother were retrospectively reviewed, and their BA and PAH were predicted based on the GP method by pediatric endocrinologist and musculoskeletal specialist of radiology in 2012 and parents' and subject's height in 2021 were collected by a telephone survey. [4][5] Those who had chronic diseases, treatment to improve growth, height below 3 percentile or above 97 percentile in 2012, the difference in chronological age and BA is above 2 years, did not reach final adult height(FAH) by 2021 were excluded. Of the total 961 individuals (322 male and 639 female), 190 (73 male and 117 female) were studied.In this study, VUNO Med®-BoneAge™ was used as an AI medical device, and its principle was based on deep learning to find the atlas of the most statistically similar bone age and to provide the final value to the first decimal place through the matching rate

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mentioning

confidence: 71%

Accuracy of predicted adult height using the Greulich-Pyle method and artificial intelligence medical device

Cho

Choi

et al. 2023

Clin Exp Pediatr

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“…Revista Colombiana de Nefrología izquierda (carpograma) en niños >3 años en quienes se haya documentado alteración de la VdC 43,46,70,71 ; esta prueba no es recomendable en <2 años, en quienes la evaluación de la edad ósea es menos confiable. De igual forma se recomienda tener especial cuidado en niños con obesidad, en quienes la edad ósea es típicamente avanzada 54 .…”

Section: Pregunta 10 ¿En Qué Población Y Con Qué Frecuencia Está Indi...unclassified

Recomendaciones para el uso de la hormona de crecimiento humana recombinante en pacientes pediátricos de talla baja en Colombia

Serrano

Lopez²,

Toro-Ramos

et al. 2020

Rev. Colomb. Nefrol.

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Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

Offiah

2021

Pediatr Radiol

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Artificial intelligence (AI) is playing an ever-increasing role in radiology (more so in the adult world than in pediatrics), to the extent that there are unfounded fears it will completely take over the role of the radiologist. In relation to musculoskeletal applications of AI in pediatric radiology, we are far from the time when AI will replace radiologists; even for the commonest application (bone age assessment), AI is more often employed in an AI-assist mode rather than an AI-replace or AI-extend mode. AI for bone age assessment has been in clinical use for more than a decade and is the area in which most research has been conducted. Most other potential indications in children (such as appendicular and vertebral fracture detection) remain largely in the research domain. This article reviews the areas in which AI is most prominent in relation to the pediatric musculoskeletal system, briefly summarizing the current literature and highlighting areas for future research. Pediatric radiologists are encouraged to participate as members of the research teams conducting pediatric radiology artificial intelligence research.

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Correlación en la determinación de la edad ósea radiológica mediante el método de Greulich y Pyle versus la evaluación automatizada utilizando el software BoneXpert

Abstract: Objective: To determine

Cited by 7 publications

References 11 publications

Accuracy of predicted adult height using the Greulich-Pyle method and artificial intelligence medical device

Accuracy of predicted adult height using the Greulich-Pyle method and artificial intelligence medical device

Recomendaciones para el uso de la hormona de crecimiento humana recombinante en pacientes pediátricos de talla baja en Colombia

Current and emerging artificial intelligence applications for pediatric musculoskeletal radiology

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