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
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR 143:484-496, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v143-mairhofer21a, title = {An {AI}-based Framework for Diagnostic Quality Assessment of Ankle Radiographs}, author = {Mairh{\"o}fer, Dominik and Laufer, Manuel and Simon, Paul Martin and Sieren, Malte and Bischof, Arpad and K{\"a}ster, Thomas and Barth, Erhardt and Barkhausen, J{\"o}rg and Martinetz, Thomas}, booktitle = {Proceedings of the Fourth Conference on Medical Imaging with Deep Learning}, pages = {484--496}, year = {2021}, editor = {Heinrich, Mattias and Dou, Qi and de Bruijne, Marleen and Lellmann, Jan and Schläfer, Alexander and Ernst, Floris}, volume = {143}, series = {Proceedings of Machine Learning Research}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v143/mairhofer21a/mairhofer21a.pdf}, url = {https://proceedings.mlr.press/v143/mairhofer21a.html}, 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.} }
Endnote
%0 Conference Paper %T An AI-based Framework for Diagnostic Quality Assessment of Ankle Radiographs %A Dominik Mairhöfer %A Manuel Laufer %A Paul Martin Simon %A Malte Sieren %A Arpad Bischof %A Thomas Käster %A Erhardt Barth %A Jörg Barkhausen %A Thomas Martinetz %B Proceedings of the Fourth Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2021 %E Mattias Heinrich %E Qi Dou %E Marleen de Bruijne %E Jan Lellmann %E Alexander Schläfer %E Floris Ernst %F pmlr-v143-mairhofer21a %I PMLR %P 484--496 %U https://proceedings.mlr.press/v143/mairhofer21a.html %V 143 %X 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.
APA
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. Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 143:484-496 Available from https://proceedings.mlr.press/v143/mairhofer21a.html.

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