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Article

Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs

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Department of Computer Engineering, Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka 51000, Croatia
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Division of General Radiology, Department of Radiology, Medical University of Graz, 8036 Graz, Austria
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Center for Artificial Intelligence and Cybersecurity, University of Rijeka, Radmile Matejčić 2, Rijeka 51000, Croatia
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Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, 8036 Graz, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Konstantin Kozlov
Mathematics 2021, 9(24), 3227; https://doi.org/10.3390/math9243227
Received: 16 November 2021 / Revised: 11 December 2021 / Accepted: 12 December 2021 / Published: 14 December 2021
(This article belongs to the Special Issue New Trends in Graph and Complexity Based Data Analysis and Processing)
In clinical practice, fracture age estimation is commonly required, particularly in children with suspected non-accidental injuries. It is usually done by radiologically examining the injured body part and analyzing several indicators of fracture healing such as osteopenia, periosteal reaction, and fracture gap width. However, age-related changes in healing timeframes, inter-individual variabilities in bone density, and significant intra- and inter-operator subjectivity all limit the validity of these radiological clues. To address these issues, for the first time, we suggest an automated neural network-based system for determining the age of a pediatric wrist fracture. In this study, we propose and evaluate a deep learning approach for automatically estimating fracture age. Our dataset included 3570 medical cases with a skewed distribution toward initial consultations. Each medical case includes a lateral and anteroposterior projection of a wrist fracture, as well as patients’ age, and gender. We propose a neural network-based system with Monte-Carlo dropout-based uncertainty estimation to address dataset skewness. Furthermore, this research examines how each component of the system contributes to the final forecast and provides an interpretation of different scenarios in system predictions in terms of their uncertainty. The examination of the proposed systems’ components showed that the feature-fusion of all available data is necessary to obtain good results. Also, proposing uncertainty estimation in the system increased accuracy and F1-score to a final 0.906±0.011 on a given task. View Full-Text
Keywords: fracture age; forensic; deep learning; uncertainty estimation; Gaussian process; X-ray fracture age; forensic; deep learning; uncertainty estimation; Gaussian process; X-ray
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MDPI and ACS Style

Hržić, F.; Janisch, M.; Štajduhar, I.; Lerga, J.; Sorantin, E.; Tschauner, S. Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs. Mathematics 2021, 9, 3227. https://doi.org/10.3390/math9243227

AMA Style

Hržić F, Janisch M, Štajduhar I, Lerga J, Sorantin E, Tschauner S. Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs. Mathematics. 2021; 9(24):3227. https://doi.org/10.3390/math9243227

Chicago/Turabian Style

Hržić, Franko, Michael Janisch, Ivan Štajduhar, Jonatan Lerga, Erich Sorantin, and Sebastian Tschauner. 2021. "Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs" Mathematics 9, no. 24: 3227. https://doi.org/10.3390/math9243227

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