Please use this identifier to cite or link to this item: doi:10.22028/D291-35964
Title: Classification of Distal Growth Plate Ossification States of the Radius Bone Using a Dedicated Ultrasound Device and Machine Learning Techniques for Bone Age Assessments
Author(s): Brausch, Lukas
Dirksen, Ruth
Risser, Christoph
Schwab, Martin
Stolz, Carole
Tretbar, Steffen
Rohrer, Tilman
Hewener, Holger
Language: English
Title: Applied Sciences
Volume: 12
Issue: 7
Publisher/Platform: MDPI
Year of Publication: 2022
Free key words: bone age
growth plate fusion
mobile ultrasound
machine learning
DDC notations: 610 Medicine and health
Publikation type: Journal Article
Abstract: X-ray imaging, based on ionizing radiation, can be used to determine bone age by examining distal growth plate fusion in the ulna and radius bones. Legal age determination approaches based on ultrasound signals exist but are unsuitable to reliably determine bone age. We present a low-cost, mobile system that uses one-dimensional ultrasound radio frequency signals to obtain a robust binary classifier enabling the determination of bone age from data of girls and women aged 9 to 24 years. These data were acquired as part of a clinical study conducted with 148 subjects. Our system detects the presence or absence of the epiphyseal plate by moving ultrasound array transducers along the forearm, measuring reflection and transmission signals. Even though classical digital signal processing methods did not achieve a robust classifier, we achieved an F1 score of approximately 87% for binary classification of completed bone growth with machine learning approaches, such as the gradient boosting machine method CatBoost. We demonstrate that our ultrasound system can classify the fusion of the distal growth plate of the radius bone and the completion of bone growth with high accuracy. We propose a non-ionizing alternative to established X-ray imaging methods for this purpose.
DOI of the first publication: 10.3390/app12073361
Link to this record: urn:nbn:de:bsz:291--ds-359640
ISSN: 2076-3417
Date of registration: 12-Apr-2022
Faculty: M - Medizinische Fakultät
Department: M - Pädiatrie
Professorship: M - Keiner Professur zugeordnet
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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