Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip1Source: Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 007 ):;issue: 004::page 41003-1Author:Perry, Sheridan
,
Folkman, Matthew
,
O'Brien, Takara
,
Wilson, Lauren A.
,
Coyle, Eric
,
Liu, Raymond W.
,
Price, Charles T.
,
Huayamave, Victor A.
DOI: 10.1115/1.4064988Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head (FH). If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index (ACIN), center-edge angle, Sharp's angle (SA), and migration percentage (MP) metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction.
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contributor author | Perry, Sheridan | |
contributor author | Folkman, Matthew | |
contributor author | O'Brien, Takara | |
contributor author | Wilson, Lauren A. | |
contributor author | Coyle, Eric | |
contributor author | Liu, Raymond W. | |
contributor author | Price, Charles T. | |
contributor author | Huayamave, Victor A. | |
date accessioned | 2024-04-24T22:36:29Z | |
date available | 2024-04-24T22:36:29Z | |
date copyright | 4/2/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 2572-7958 | |
identifier other | jesmdt_007_04_041003.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4295526 | |
description abstract | Developmental dysplasia of the hip (DDH) is a condition in which the acetabular socket inadequately contains the femoral head (FH). If left untreated, DDH can result in degenerative changes in the hip joint. Several imaging techniques are used for DDH assessment. In radiographs, the acetabular index (ACIN), center-edge angle, Sharp's angle (SA), and migration percentage (MP) metrics are used to assess DDH. Determining these metrics is time-consuming and repetitive. This study uses a convolutional neural network (CNN) to identify radiographic measurements and improve traditional methods of identifying DDH. The dataset consisted of 60 subject radiographs rotated along the craniocaudal and mediolateral axes 25 times, generating 1500 images. A CNN detection algorithm was used to identify key radiographic metrics for the diagnosis of DDH. The algorithm was able to detect the metrics with reasonable accuracy in comparison to the manually computed metrics. The CNN performed well on images with high contrast margins between bone and soft tissues. In comparison, the CNN was not able to identify some critical points for metric calculation on a few images that had poor definition due to low contrast between bone and soft tissues. This study shows that CNNs can efficiently measure clinical parameters to assess DDH on radiographs with high contrast margins between bone and soft tissues with purposeful rotation away from an ideal image. Results from this study could help inform and broaden the existing bank of information on using CNNs for radiographic measurement and medical condition prediction. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip1 | |
type | Journal Paper | |
journal volume | 7 | |
journal issue | 4 | |
journal title | Journal of Engineering and Science in Medical Diagnostics and Therapy | |
identifier doi | 10.1115/1.4064988 | |
journal fristpage | 41003-1 | |
journal lastpage | 41003-12 | |
page | 12 | |
tree | Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 007 ):;issue: 004 | |
contenttype | Fulltext |