Abstract
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.
Highlights
Introduction published maps and institutional affilKnowing the approximate age of a bone fracture is a medically and forensically relevant issue, especially in the context of suspected non-accidental trauma in a child.Physicians often request radiologists to estimate the age of a specific fracture, which still might not clearly be answerable in many situations.Radiography can help estimating a fracture’s age due to specific changes in a fracture’s appearance or through the presence of reparative processes
We propose a standard, as well as guidelines for other researchers to follow; By utilizing the Monte-Carlo dropout method, which treats neural networks (NN) as a Gaussian sampling process, we are able to estimate the uncertainty of the proposed system decisions, unveiling prediction certainty to increase trustworthiness; We propose a novel system based on a convolutional neural networks (CNN) combining different features obtained from the medical reports/cases to estimate fracture age
The fusion of the input data in the system results in increased model accuracy, compared independently to any of its components; The amount of uncertainty of the proposed system is greater than the amount of uncertainty of its individual components; The uncertainty in the incorrect predictions of the proposed system is higher than the uncertainty in its correct prediction, which is the desirable system behavior; Uncertainty estimation can help with the output interpretability and can enhance the system usability, especially in cases where the data is poor and very skewed
Summary
Introduction published maps and institutional affilKnowing the approximate age of a bone fracture is a medically and forensically relevant issue, especially in the context of suspected non-accidental trauma in a child.Physicians often request radiologists to estimate the age of a specific fracture, which still might not clearly be answerable in many situations.Radiography can help estimating a fracture’s age due to specific changes in a fracture’s appearance or through the presence of reparative processes. Knowing the approximate age of a bone fracture is a medically and forensically relevant issue, especially in the context of suspected non-accidental trauma in a child. Physicians often request radiologists to estimate the age of a specific fracture, which still might not clearly be answerable in many situations. Radiography can help estimating a fracture’s age due to specific changes in a fracture’s appearance or through the presence of reparative processes. These processes include mechanisms like soft-tissue swelling in the early, and osteopenia or periosteal reaction in the later phases of healing. Systematic evaluation of the the published literature revealed that radiographic characteristics of bone healing differ substantially across individual investigations [1]. Digital radiography (DR) and computed tomography (CT) are used for fracture detection, and subsequently for estimating fracture iations
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