A Deep Learning–Based Framework for Automatic Determination of Developmental Dysplasia of the Hip from Graf Angles.
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| Title: | A Deep Learning–Based Framework for Automatic Determination of Developmental Dysplasia of the Hip from Graf Angles. |
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| Authors: | Taşolar, Sevgi1 (AUTHOR) drsevgidemiroz@gmail.com, Günen, Mehmet Akif2 (AUTHOR) akif@gumushane.edu.tr, Sığırcı, Ahmet3 (AUTHOR) asigirci@gmail.com, Taşolar, Hakan4 (AUTHOR) hakantasolar@gmail.com |
| Source: | Journal of Imaging Informatics in Medicine. Feb2026, Vol. 39 Issue 1, p250-264. 15p. |
| Subjects: | Hip joint dislocation, Computer-assisted image analysis (Medicine), Human services programs, Research funding, Diagnostic imaging, Dysplasia, Hip joint, Deep learning, Conceptual structures, Artificial neural networks, Automation, Comparative studies, Transducers |
| Abstract: | Developmental dysplasia of the hip (DDH) is a common neonatal condition that necessitates early diagnosis to ensure effective treatment. The traditional Graf method, while widely used for evaluating infant hips via ultrasound, is limited by operator dependency and measurement variability. This research has proposed a framework using deep learning network, morphological operation and local maxima method to diagnose DDH in newborns using ultrasound images. The method utilizes DeepLabv3 + for image segmentation, evaluating multiple backbone architectures (ResNet50, InceptionResNetV2, MobilenetV2, and Xception) to identify the region of interest accurately. Local maxima method was used to determine the extremum points of the line defining the Graf angles. Denoising filters, including mean, median, and Wiener, are applied to determine local maxima points accurately. The evaluation comprises two stages: first, assessing the performance of DeepLabv3 + backbones in producing masks for Graf angles determination, and second, comparing the angles obtained through proposed framework with those determined by expert radiologists. Comparative analysis demonstrates that MobileNetV2 (94.64 accuracy, 86.99 Cohen's kappa, 94.31 F-score) surpasses other models in segmentation accuracy and measurement reliability. This conclusion is backed by key performance metrics such as accuracy, IoU, PSNR, F-score, SSIM, Cohen's kappa, as well as by the intraclass correlation coefficient and Bland–Altman analyses. The proposed framework shows considerable promise in automating hip ultrasound analysis for DDH diagnosis, minimizing operator dependency while enhancing measurement consistency. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Imaging Informatics in Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191694190 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Deep Learning–Based Framework for Automatic Determination of Developmental Dysplasia of the Hip from Graf Angles. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Taşolar%2C+Sevgi%22">Taşolar, Sevgi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> drsevgidemiroz@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Günen%2C+Mehmet+Akif%22">Günen, Mehmet Akif</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> akif@gumushane.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Sığırcı%2C+Ahmet%22">Sığırcı, Ahmet</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> asigirci@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Taşolar%2C+Hakan%22">Taşolar, Hakan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> hakantasolar@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Imaging+Informatics+in+Medicine%22">Journal of Imaging Informatics in Medicine</searchLink>. Feb2026, Vol. 39 Issue 1, p250-264. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hip+joint+dislocation%22">Hip joint dislocation</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Human+services+programs%22">Human services programs</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Dysplasia%22">Dysplasia</searchLink><br /><searchLink fieldCode="DE" term="%22Hip+joint%22">Hip joint</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Conceptual+structures%22">Conceptual structures</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Transducers%22">Transducers</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Developmental dysplasia of the hip (DDH) is a common neonatal condition that necessitates early diagnosis to ensure effective treatment. The traditional Graf method, while widely used for evaluating infant hips via ultrasound, is limited by operator dependency and measurement variability. This research has proposed a framework using deep learning network, morphological operation and local maxima method to diagnose DDH in newborns using ultrasound images. The method utilizes DeepLabv3 + for image segmentation, evaluating multiple backbone architectures (ResNet50, InceptionResNetV2, MobilenetV2, and Xception) to identify the region of interest accurately. Local maxima method was used to determine the extremum points of the line defining the Graf angles. Denoising filters, including mean, median, and Wiener, are applied to determine local maxima points accurately. The evaluation comprises two stages: first, assessing the performance of DeepLabv3 + backbones in producing masks for Graf angles determination, and second, comparing the angles obtained through proposed framework with those determined by expert radiologists. Comparative analysis demonstrates that MobileNetV2 (94.64 accuracy, 86.99 Cohen's kappa, 94.31 F-score) surpasses other models in segmentation accuracy and measurement reliability. This conclusion is backed by key performance metrics such as accuracy, IoU, PSNR, F-score, SSIM, Cohen's kappa, as well as by the intraclass correlation coefficient and Bland–Altman analyses. The proposed framework shows considerable promise in automating hip ultrasound analysis for DDH diagnosis, minimizing operator dependency while enhancing measurement consistency. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Imaging Informatics in Medicine is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10278-025-01518-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 250 Subjects: – SubjectFull: Hip joint dislocation Type: general – SubjectFull: Computer-assisted image analysis (Medicine) Type: general – SubjectFull: Human services programs Type: general – SubjectFull: Research funding Type: general – SubjectFull: Diagnostic imaging Type: general – SubjectFull: Dysplasia Type: general – SubjectFull: Hip joint Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Conceptual structures Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Automation Type: general – SubjectFull: Comparative studies Type: general – SubjectFull: Transducers Type: general Titles: – TitleFull: A Deep Learning–Based Framework for Automatic Determination of Developmental Dysplasia of the Hip from Graf Angles. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Taşolar, Sevgi – PersonEntity: Name: NameFull: Günen, Mehmet Akif – PersonEntity: Name: NameFull: Sığırcı, Ahmet – PersonEntity: Name: NameFull: Taşolar, Hakan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 29482925 Numbering: – Type: volume Value: 39 – Type: issue Value: 1 Titles: – TitleFull: Journal of Imaging Informatics in Medicine Type: main |
| ResultId | 1 |
