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    Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip1

    Source: Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 007 ):;issue: 004::page 41003-1
    Author:
    Perry, Sheridan
    ,
    Folkman, Matthew
    ,
    O'Brien, Takara
    ,
    Wilson, Lauren A.
    ,
    Coyle, Eric
    ,
    Liu, Raymond W.
    ,
    Price, Charles T.
    ,
    Huayamave, Victor A.
    DOI: 10.1115/1.4064988
    Publisher: 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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      Unaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip1

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4295526
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    • Journal of Engineering and Science in Medical Diagnostics and Therapy

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    contributor authorPerry, Sheridan
    contributor authorFolkman, Matthew
    contributor authorO'Brien, Takara
    contributor authorWilson, Lauren A.
    contributor authorCoyle, Eric
    contributor authorLiu, Raymond W.
    contributor authorPrice, Charles T.
    contributor authorHuayamave, Victor A.
    date accessioned2024-04-24T22:36:29Z
    date available2024-04-24T22:36:29Z
    date copyright4/2/2024 12:00:00 AM
    date issued2024
    identifier issn2572-7958
    identifier otherjesmdt_007_04_041003.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295526
    description abstractDevelopmental 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.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleUnaligned Hip Radiograph Assessment Utilizing Convolutional Neural Networks for the Assessment of Developmental Dysplasia of the Hip1
    typeJournal Paper
    journal volume7
    journal issue4
    journal titleJournal of Engineering and Science in Medical Diagnostics and Therapy
    identifier doi10.1115/1.4064988
    journal fristpage41003-1
    journal lastpage41003-12
    page12
    treeJournal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 007 ):;issue: 004
    contenttypeFulltext
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