An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs

Dominik Mairhöfer; Manuel Laufer; Paul Martin Simon; Malte Sieren; Arpad Bischof; Thomas Käster; Erhardt Barth; Jörg Barkhausen; Thomas Martinetz

An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs

Dominik Mairhöfer, Manuel Laufer, Paul Martin Simon, Malte Sieren, Arpad Bischof, Thomas Käster, Erhardt Barth, Jörg Barkhausen, Thomas Martinetz

9 Zitate (Scopus)

Abstract

The quality of radiographs is of major importance for diagnosis and treatment planning. While most research regarding automated radiograph quality assessment uses technical features such as noise or contrast, we propose to use anatomical structures as more appropriate features. We show that based on such anatomical features, a modular deep-learning framework can serve as a quality control mechanism for the diagnostic quality of ankle radiographs. For evaluation, a dataset consisting of 950 ankle radiographs was collected and their quality was labeled by radiologists. We obtain an average accuracy of 94.1%, which is better than the expert radiologists are on average.

Originalsprache	Englisch
Titel	Proceedings of the Fourth Conference on Medical Imaging with Deep Learning
Redakteure/-innen	Mattias Heinrich, Qi Dou, Marleen de Bruijne, Jan Lellmann, Alexander Schläfer, Floris Ernst
Seitenumfang	13
Band	143
Herausgeber (Verlag)	PMLR
Erscheinungsdatum	01.07.2021
Seiten	484-496
Publikationsstatus	Veröffentlicht - 01.07.2021

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4.43-05 Bild- und Sprachverarbeitung, Computergraphik und Visualisierung, Human Computer Interaction, Ubiquitous und Wearable Computing
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https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/MaLaSiSiBiKaBaBaMa21.pdf

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Mairhöfer, D., Laufer, M., Simon, P. M., Sieren, M., Bischof, A., Käster, T., Barth, E., Barkhausen, J., & Martinetz, T. (2021). An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. in M. Heinrich, Q. Dou, M. de Bruijne, J. Lellmann, A. Schläfer, & F. Ernst (Hrsg.), Proceedings of the Fourth Conference on Medical Imaging with Deep Learning (Band 143, S. 484-496). (Proceedings of Machine Learning Research). PMLR. https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/MaLaSiSiBiKaBaBaMa21.pdf

Mairhöfer, Dominik ; Laufer, Manuel ; Simon, Paul Martin et al. / An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. Proceedings of the Fourth Conference on Medical Imaging with Deep Learning. Hrsg. / Mattias Heinrich ; Qi Dou ; Marleen de Bruijne ; Jan Lellmann ; Alexander Schläfer ; Floris Ernst. Band 143 PMLR, 2021. S. 484-496 (Proceedings of Machine Learning Research).

@inproceedings{798da295869d4f5e95df96d9507fe2e6,

title = "An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs",

abstract = "The quality of radiographs is of major importance for diagnosis and treatment planning. While most research regarding automated radiograph quality assessment uses technical features such as noise or contrast, we propose to use anatomical structures as more appropriate features. We show that based on such anatomical features, a modular deep-learning framework can serve as a quality control mechanism for the diagnostic quality of ankle radiographs. For evaluation, a dataset consisting of 950 ankle radiographs was collected and their quality was labeled by radiologists. We obtain an average accuracy of 94.1\%, which is better than the expert radiologists are on average.",

author = "Dominik Mairh{\"o}fer and Manuel Laufer and Simon, \{Paul Martin\} and Malte Sieren and Arpad Bischof and Thomas K{\"a}ster and Erhardt Barth and J{\"o}rg Barkhausen and Thomas Martinetz",

year = "2021",

month = jul,

day = "1",

language = "English",

volume = "143",

series = "Proceedings of Machine Learning Research",

publisher = "PMLR",

pages = "484--496",

editor = "Mattias Heinrich and Qi Dou and \{de Bruijne\}, Marleen and Jan Lellmann and Alexander Schl{\"a}fer and Floris Ernst",

booktitle = "Proceedings of the Fourth Conference on Medical Imaging with Deep Learning",

}

Mairhöfer, D, Laufer, M, Simon, PM, Sieren, M, Bischof, A, Käster, T , Barth, E , Barkhausen, J & Martinetz, T 2021, An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. in M Heinrich, Q Dou, M de Bruijne, J Lellmann, A Schläfer & F Ernst (Hrsg.), Proceedings of the Fourth Conference on Medical Imaging with Deep Learning. Bd. 143, Proceedings of Machine Learning Research, PMLR, S. 484-496. <https://webmail.inb.uni-luebeck.de/inb-publications/pdfs/MaLaSiSiBiKaBaBaMa21.pdf>

An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. / Mairhöfer, Dominik; Laufer, Manuel; Simon, Paul Martin et al.
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning. Hrsg. / Mattias Heinrich; Qi Dou; Marleen de Bruijne; Jan Lellmann; Alexander Schläfer; Floris Ernst. Band 143 PMLR, 2021. S. 484-496 (Proceedings of Machine Learning Research).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs

AU - Mairhöfer, Dominik

AU - Laufer, Manuel

AU - Simon, Paul Martin

AU - Sieren, Malte

AU - Bischof, Arpad

AU - Käster, Thomas

AU - Barth, Erhardt

AU - Barkhausen, Jörg

AU - Martinetz, Thomas

PY - 2021/7/1

Y1 - 2021/7/1

N2 - The quality of radiographs is of major importance for diagnosis and treatment planning. While most research regarding automated radiograph quality assessment uses technical features such as noise or contrast, we propose to use anatomical structures as more appropriate features. We show that based on such anatomical features, a modular deep-learning framework can serve as a quality control mechanism for the diagnostic quality of ankle radiographs. For evaluation, a dataset consisting of 950 ankle radiographs was collected and their quality was labeled by radiologists. We obtain an average accuracy of 94.1%, which is better than the expert radiologists are on average.

AB - The quality of radiographs is of major importance for diagnosis and treatment planning. While most research regarding automated radiograph quality assessment uses technical features such as noise or contrast, we propose to use anatomical structures as more appropriate features. We show that based on such anatomical features, a modular deep-learning framework can serve as a quality control mechanism for the diagnostic quality of ankle radiographs. For evaluation, a dataset consisting of 950 ankle radiographs was collected and their quality was labeled by radiologists. We obtain an average accuracy of 94.1%, which is better than the expert radiologists are on average.

M3 - Conference contribution

VL - 143

T3 - Proceedings of Machine Learning Research

SP - 484

EP - 496

BT - Proceedings of the Fourth Conference on Medical Imaging with Deep Learning

A2 - Heinrich, Mattias

A2 - Dou, Qi

A2 - de Bruijne, Marleen

A2 - Lellmann, Jan

A2 - Schläfer, Alexander

A2 - Ernst, Floris

PB - PMLR

ER -

Mairhöfer D, Laufer M, Simon PM, Sieren M, Bischof A, Käster T et al. An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs. in Heinrich M, Dou Q, de Bruijne M, Lellmann J, Schläfer A, Ernst F, Hrsg., Proceedings of the Fourth Conference on Medical Imaging with Deep Learning. Band 143. PMLR. 2021. S. 484-496. (Proceedings of Machine Learning Research).

An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs

Abstract

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