Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm

Guo, Longjun; Wang, Juan; Teng, Jiaqi; Chen, Yukun

doi:10.3389/fenrg.2021.813650

Cited by 9 publications

(10 citation statements)

References 25 publications

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“…Например, хорошо известная программа BoneXpert© [12] может быть недоступна для отечественных врачей вследствие ее высокой стоимости. Тем не менее, есть надежда, что разработки из союзных государств и отечественные разработки станут более доступными в будущем, что положительно повлияет на использование этих методов [20][21][22][23][24][25][26][27][28][29][30] СПИСОК ИСТОЧНИКОВ…”

Section: клинические исследованияunclassified

Comparison of bone age assessment methods using a hand radiography in patients with active growth plate and anteromedial knee instability

Ivanov,

Mininkov,

Gushchina

et al. 2024

jour

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Background Bone age is essential for pediatric patients with active growth zones and anteromedial instability to facilitate optimal treatment strategy and minimize postoperative complications. However, many people are unaware of various tools for determining bone age, including classical methods and modern machine learning techniques.The objective was to show and compare different methods for calculating bone age and determining surgical strategy for patients with anteromedial instability of the knee joint.Material and methods All-Inside anterior cruciate ligament reconstruction was performed for 20 patients. Wrist radiographs were performed for bone age assessment using the "point scoring system" of Tanner and Whitehouse and the "atlas matching" method of Greulich and Pyle. Machine learning programs were used in addition to standard bone age assessments.Results The findings showed an average difference of 21 months (80 %) in a group of 20 individuals with bone age ahead of the passport age and an average difference of 18 months (20 %) in patients with retarded bone age.Discussion The findings showed the difference between chronological and bone age and could be encountered in scientific articles on endocrinology and pediatrics. No scientific studies on the use of the methods could be found in the specialty “trauma and orthopaedics”.Conclusion Bone age assessment, prediction of children's target height are essential for surgical treatment of patients with open growth plates.

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Section: клинические исследованияunclassified

Comparison of bone age assessment methods using a hand radiography in patients with active growth plate and anteromedial knee instability

Ivanov,

Mininkov,

Gushchina

et al. 2024

jour

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show abstract

“…In the medical field today, computerised approaches are used in place of hand-held procedures to the extent that this produces superior evaluation to address these limitations. The research is to minimize issues with algorithms and high diagnostic accuracy when dividing up current systems [7]. An innovative Tanner-Whitehouse bone age assessment method is proposed by this work.…”

Section: Literature Reviewmentioning

confidence: 99%

Age and gender classification with bone images using deep learning algorithms

Sundarasamy,

Kuttuva Rajendran

2023

IJEECS

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In paediatrics, bone age is a crucial indicator of how a child's skeleton is developing. They have had great success ever since the creation of deep learning (DL)-based bone age prediction tools. Deep features learning, however, has a significant computing overhead problem. Deep convolution layers are used in this technique to learn representative features in the small yet useful regions that are extracted for feature learning. This work suggests using an extreme learning machine algorithm as the fundamental architecture in the final bone age assessment study to realise the rapid computation speed and feature interaction. The viability and efficacy of the suggested strategy have been verified by experiments using data that is openly accessible. The suggested model is explicitly trained using a cutting-edge end-to-end learning architecture employing bone scans to extract the most discriminative patches from the original high-resolution image. The bone picture is the foundation of the procedure. Our main objective is to categorize individuals by age using convolution neural network (CNN) classification models, such as the Xception and Mobile Net models. As a result, we have achieved results that are 90% and 94% accurate in classifying people by age using CNN models.

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“…This hybrid model was tested using public and private datasets, with better results than models based on single architecture and non-modified optimization process. To decrease the computational overhead, a TW hybrid system based on DL and extreme machine learning (ELM) was proposed in ( 45 ). ELM is a single hidden layer FFNN, introduced in ( 58 ).…”

Section: Application Of Deep Learning In Endocrinologymentioning

confidence: 99%

An overview of deep learning applications in precocious puberty and thyroid dysfunction

2022

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In the last decade, deep learning methods have garnered a great deal of attention in endocrinology research. In this article, we provide a summary of current deep learning applications in endocrine disorders caused by either precocious onset of adult hormone or abnormal amount of hormone production. To give access to the broader audience, we start with a gentle introduction to deep learning and its most commonly used architectures, and then we focus on the research trends of deep learning applications in thyroid dysfunction classification and precocious puberty diagnosis. We highlight the strengths and weaknesses of various approaches and discuss potential solutions to different challenges. We also go through the practical considerations useful for choosing (and building) the deep learning model, as well as for understanding the thought process behind different decisions made by these models. Finally, we give concluding remarks and future directions.

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Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm

Cited by 9 publications

References 25 publications

Comparison of bone age assessment methods using a hand radiography in patients with active growth plate and anteromedial knee instability

Comparison of bone age assessment methods using a hand radiography in patients with active growth plate and anteromedial knee instability

Age and gender classification with bone images using deep learning algorithms

An overview of deep learning applications in precocious puberty and thyroid dysfunction

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