Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

Janczyk, Kamil; Rumiński, Jacek; Neumann, Tomasz; Kowalczyk, Natalia; Wiśniewski, Piotr

doi:10.3390/app12136608

Cited by 7 publications

(3 citation statements)

References 24 publications

(30 reference statements)

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“…For knees, an atlas was prepared by Pyle and Hoerr 26 . Janczyk et al considered low-dose, whole-body dual-energy x-ray scans of patients up to 19 years and obtained an MAE of 1.3 years, which is comparable to our results 27 .…”

Section: Discussionsupporting

confidence: 91%

Pediatric age estimation from thoracic and abdominal CT scout views using deep learning

Demircioğlu

Naßenstein

Umutlu

2023

Sci Rep

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Age assessment is regularly used in clinical routine by pediatric endocrinologists to determine the physical development or maturity of children and adolescents. Our study investigates whether age assessment can be performed using CT scout views from thoracic and abdominal CT scans using a deep neural network. Hence, we retrospectively collected 1949 CT scout views from pediatric patients (acquired between January 2013 and December 2018) to train a deep neural network to predict the chronological age from CT scout views. The network was then evaluated on an independent test set of 502 CT scout views (acquired between January 2019 and July 2020). The trained model showed a mean absolute error of 1.18 ± 1.14 years on the test data set. A one-sided t-test to determine whether the difference between the predicted and actual chronological age was less than 2.0 years was statistically highly significant (p < 0.001). In addition, the correlation coefficient was very high (R = 0.97). In conclusion, the chronological age of pediatric patients can be assessed with high accuracy from CT scout views using a deep neural network.

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Section: Discussionsupporting

confidence: 91%

Pediatric age estimation from thoracic and abdominal CT scout views using deep learning

Demircioğlu

Naßenstein

Umutlu

2023

Sci Rep

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“…The authors concluded that the two categories-low density followed by low volume have large association with major osteoporotic fractures. 2022) did experiments on low resolution full body DXA images to find the ages of persons [15]. The authors suggested pre-processing procedures and used partially pre-trained convolutional neural network algorithms.…”

Section: Whittier Et Al (2022) Applied Fuzzy C Means Clustering On Hi...mentioning

confidence: 99%

Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

Amiya,

Murugan,

Ramaraj

et al. 2023

Preprint

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Osteoporosis (OP) is an osteometabolic disorder characterized by a lesser bone mineral density (BMD) and the disruption of bone tissue micro - architecture, resulting in a greater bone fragility and higher likelihoods of fractures. OP emerges once the bone mass declines faster than the body's capacity to replenish it, leading to a significant reduction in the strength of bone. OP impacts each and every bone throughout the body and provides no clinical signs until a fracture happens. Aging leads to the reduction in BMD, and the rate of percentage of fractures rises over time, usually causing mortality and morbidity. Numerous BMD evaluation methods are available, and they are used in a variety of settings by considering the location of the fracture. Dual-energy x-ray absorptiometry (DEXA/DXA) is recognized as the gold standard for predicting the fracture, since it is the most advanced, technologically affirmed, and also has excellent performance. According to the findings, most researchers do not endeavour the identification and the segmentation of low bone masses from DEXA images. Medical image segmentation supports in analyzing and visualizing the bone's low bone mass. The envisaged hybrid approach, that integrates GLCM for feature extraction and AlexNet for a low bone mass variation classification, provides segmented images that assist in categorizing bone health as normal, osteopenia, or osteoporosis. The developed algorithm's performance metrics, including Dice Co-efficient, Sensitivity, and Specificity, were 92.35%, 90.26%, and 92.42%, respectively. The Orthopedicians ascertained the efficacy of the outcomes rendered by the proposed algorithm.

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“…Since a bone age assessment is of great importance, this topic has been addressed in order to support physicians with an automated analysis of the data, making this task less labor intensive. In the literature, there are many approaches to address this problem, when analyzing X-ray images of hands [39][40][41][42][43][44][45], the chest [40,46], or whole-body images [47,48]. In the case of a fully automated deep learning approach, first the hand region was determined in the image using the U-Net network for semantic segmentation of the hand region, then the image registration was applied to allow for an easy determination of hand regions corresponding to each other between various images.…”

Section: Introductionmentioning

confidence: 99%

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

Obuchowicz

Nurzynska

Pierzchała³

et al. 2023

JCM

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Currently, bone age is assessed by X-rays. It enables the evaluation of the child’s development and is an important diagnostic factor. However, it is not sufficient to diagnose a specific disease because the diagnoses and prognoses may arise depending on how much the given case differs from the norms of bone age. Background: The use of magnetic resonance images (MRI) to assess the age of the patient would extend diagnostic possibilities. The bone age test could then become a routine screening test. Changing the method of determining the bone age would also prevent the patient from taking a dose of ionizing radiation, making the test less invasive. Methods: The regions of interest containing the wrist area and the epiphyses of the radius are marked on the magnetic resonance imaging of the non-dominant hand of boys aged 9 to 17 years. Textural features are computed for these regions, as it is assumed that the texture of the wrist image contains information about bone age. Results: The regression analysis revealed that there is a high correlation between the bone age of a patient and the MRI-derived textural features derived from MRI. For DICOM T1-weighted data, the best scores reached 0.94 R2, 0.46 RMSE, 0.21 MSE, and 0.33 MAE. Conclusions: The experiments performed have shown that using the MRI images gives reliable results in the assessment of bone age while not exposing the patient to ionizing radiation.

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Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

Cited by 7 publications

References 24 publications

Pediatric age estimation from thoracic and abdominal CT scout views using deep learning

Pediatric age estimation from thoracic and abdominal CT scout views using deep learning

Expeditious detection and segmentation of bone mass variation in DEXA images using the hybrid GLCM-AlexNet approach

Texture Analysis for the Bone Age Assessment from MRI Images of Adolescent Wrists in Boys

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