Predicting strength of femora with metastatic lesions from single 2D radiographic projections using convolutional neural networks

CNNs, trained on a large dataset generated via FE models, predicted experimentally measured strength of femora with artificial metastatic lesions with accuracy comparable to 3D FE models. By eliminating the need for 3D imaging and reducing computational demands, this novel approach demonstrates potential for application in a clinical setting.

Authors

Dieter H. Pahr (ORCID: https://orcid.org/0000-0002-5822-2082)
Lena Hirtler (ORCID: https://orcid.org/0000-0001-5194-9118)
Emir Benca (ORCID: https://orcid.org/0000-0002-9029-7172)
Roxane Licandro
Alexander Synek (ORCID: https://orcid.org/0000-0002-5253-7403)

Institutions

TU Wien (AT)
Medical University of Vienna (AT)

Publication Details

Journal: Computer Methods and Programs in Biomedicine
Published: 2025-03-15
DOI: https://doi.org/10.1016/j.cmpb.2025.108724
Citations: 1
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 1.36

Funders

Technische Universität Wien Bibliothek

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article

Predicting strength of femora with metastatic lesions from single 2D radiographic projections using convolutional neural networks

Dieter H. Pahr, Lena Hirtler, Emir Benca, Roxane Licandro et al.

Mar 15, 2025

1 citations

Computer Methods and Programs in Biomedicine

Radiomics and Machine Learning in Medical Imaging

1.36

article

Predicting strength of femora with metastatic lesions from single 2D radiographic projections using convolutional neural networks

Dieter H. Pahr, Lena Hirtler, Emir Benca, Roxane Licandro, Alexander Synek

March 15, 2025article en

1 citations

Abstract

CNNs, trained on a large dataset generated via FE models, predicted experimentally measured strength of femora with artificial metastatic lesions with accuracy comparable to 3D FE models. By eliminating the need for 3D imaging and reducing computational demands, this novel approach demonstrates potential for application in a clinical setting.

Computer Methods and Programs in BiomedicineVol. 265

TU Wien (AT), Medical University of Vienna (AT)

Technische Universität Wien Bibliothek

Openalex Percentile: Top 23%

Radiomics and Machine Learning in Medical Imaging

1.36

DOI Open Access PDF OpenAlex

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