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Schaufelberger M, Kühle RP, Wachter A, Weichel F, Hagen N, Ringwald F, Eisenmann U, Hoffmann J, Engel M, Freudlsperger C, Nahm W. Impact of data synthesis strategies for the classification of craniosynostosis. Front Med Technol 2023; 5:1254690. [PMID: 38192519 PMCID: PMC10773901 DOI: 10.3389/fmedt.2023.1254690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 11/23/2023] [Indexed: 01/10/2024] Open
Abstract
Introduction Photogrammetric surface scans provide a radiation-free option to assess and classify craniosynostosis. Due to the low prevalence of craniosynostosis and high patient restrictions, clinical data are rare. Synthetic data could support or even replace clinical data for the classification of craniosynostosis, but this has never been studied systematically. Methods We tested the combinations of three different synthetic data sources: a statistical shape model (SSM), a generative adversarial network (GAN), and image-based principal component analysis for a convolutional neural network (CNN)-based classification of craniosynostosis. The CNN is trained only on synthetic data but is validated and tested on clinical data. Results The combination of an SSM and a GAN achieved an accuracy of 0.960 and an F1 score of 0.928 on the unseen test set. The difference to training on clinical data was smaller than 0.01. Including a second image modality improved classification performance for all data sources. Conclusions Without a single clinical training sample, a CNN was able to classify head deformities with similar accuracy as if it was trained on clinical data. Using multiple data sources was key for a good classification based on synthetic data alone. Synthetic data might play an important future role in the assessment of craniosynostosis.
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Affiliation(s)
- Matthias Schaufelberger
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Reinald Peter Kühle
- Department of Oral, Dental and Maxillofacial Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Andreas Wachter
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
| | - Frederic Weichel
- Department of Oral, Dental and Maxillofacial Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Niclas Hagen
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Friedemann Ringwald
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Urs Eisenmann
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Hoffmann
- Department of Oral, Dental and Maxillofacial Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Michael Engel
- Department of Oral, Dental and Maxillofacial Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Christian Freudlsperger
- Department of Oral, Dental and Maxillofacial Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Werner Nahm
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
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Kaiser C, Schaufelberger M, Kühle RP, Wachter A, Weichel F, Hagen N, Ringwald F, Eisenmann U, Engel M, Freudlsperger C, Nahm W. Generative-Adversarial-Network-Based Data Augmentation for the Classification of Craniosynostosis. Current Directions in Biomedical Engineering 2022. [DOI: 10.1515/cdbme-2022-1005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
Craniosynostosis is a congenital disease characterized by the premature closure of one or multiple sutures of the infant’s skull. For diagnosis, 3D photogrammetric scans are a radiation-free alternative to computed tomography. However, data is only sparsely available and the role of data augmentation for the classification of craniosynostosis has not yet been analyzed. In this work, we use a 2D distance map representation of the infants’ heads with a convolutional-neural-network-based classifier and employ a generative adversarial network (GAN) for data augmentation. We simulate two data scarcity scenarios with 15% and 10% training data and test the influence of different degrees of added synthetic data and balancing underrepresented classes. We used total accuracy and F1-score as a metric to evaluate the final classifiers. For 15% training data, the GAN-augmented dataset showed an increased F1-score up to 0.1 and classification accuracy up to 3 %. For 10% training data, both metrics decreased. We present a deep convolutional GAN capable of creating synthetic data for the classification of craniosynostosis. Using a moderate amount of synthetic data using a GAN showed slightly better performance, but had little effect overall. The simulated scarcity scenario of 10% training data may have limited the model’s ability to learn the underlying data distribution.
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Affiliation(s)
- Christian Kaiser
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe , Germany
| | - Matthias Schaufelberger
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe , Germany
| | - Reinald Peter Kühle
- Department of Oral and Maxillofacial Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg , Germany
| | - Andreas Wachter
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe , Germany
| | - Frederic Weichel
- Department of Oral and Maxillofacial Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg , Germany
| | - Niclas Hagen
- Institute of Medical Informatics, Heidelberg University Hospital, Im Neuenheimer Feld 130.3, Heidelberg , Germany
| | - Friedemann Ringwald
- Institute of Medical Informatics, Heidelberg University Hospital, Im Neuenheimer Feld 130.3, Heidelberg , Germany
| | - Urs Eisenmann
- Institute of Medical Informatics, Heidelberg University Hospital, Im Neuenheimer Feld 130.3, Heidelberg , Germany
| | - Michael Engel
- Department of Oral and Maxillofacial Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg , Germany
| | - Christian Freudlsperger
- Department of Oral and Maxillofacial Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 400, Heidelberg , Germany
| | - Werner Nahm
- Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe , Germany
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