The impact of updated imaging software on the performance of machine learning models for breast cancer diagnosis: a multi-center, retrospective study
Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance of AI models is unclear. We aimed to develop machine learn...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
30 January 2025
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| In: |
Archives of gynecology and obstetrics
Year: 2025, Jahrgang: 312, Heft: 1, Pages: 139-147 |
| ISSN: | 1432-0711 |
| DOI: | 10.1007/s00404-024-07901-8 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00404-024-07901-8
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| Verfasserangaben: | Lie Cai, Michael Golatta, Chris Sidey-Gibbons, Richard G. Barr, André Pfob |
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| 520 | |a Artificial Intelligence models based on medical (imaging) data are increasingly developed. However, the imaging software on which the original data is generated is frequently updated. The impact of updated imaging software on the performance of AI models is unclear. We aimed to develop machine learning models using shear wave elastography (SWE) data to identify malignant breast lesions and to test the models’ generalizability by validating them on external data generated by both the original updated software versions. | ||
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