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Landmark−based screening: femoral head coverage and Graf Classification in infant developmental dysplasia of the hip

Abstract:

Infant Development Dysplasia of the Hip (DDH) is a disorder where the hip joint does not form properly. The Graf method and The Femoral Head Coverage (FHC) method are ultrasound-based screening techniques which use anatomical landmarks to classify disease severity and guide treatment.

Deep learning has been used for either Graf Classification or for FHC, yet there has been only minimal investigation into combining the two. No work to-date has detected FHC using landmarks alone.

This paper develops a model which predicts both Graf Classification and FHC from landmarks only. In this method, Recall (Precision) improved when combining methods compared to FHC and Graf methods alone. Two external datasets were used to evaluate model performance under domain shift. Improvements are needed to generalise the model to new datasets. Since the model encompasses both techniques, it gains a clinical understanding of automated methods for DDH screening and improves clinical use.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-031-92089-9_1

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000−0003−0339−3674
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000−0002−7329−6792
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
ORCID:
0000-0002-9104-8012


Publisher:
Springer
Host title:
Computer Vision – ECCV 2024 Workshops. ECCV 2024
Pages:
1–14
Series:
Lecture Notes in Computer Science
Series number:
15644
Publication date:
2025-05-12
Acceptance date:
2024-08-09
Event title:
Women in Computer Vision workshop in conjunction with ECCV 2024
Event location:
Milan, Italy
Event website:
https://sites.google.com/view/wicveccv2024/home
Event start date:
2024-09-30
Event end date:
2024-09-30
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
EISBN:
9783031920899
ISBN:
9783031920882


Language:
English
Keywords:
Pubs id:
2026901
Local pid:
pubs:2026901
Deposit date:
2024-09-11

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