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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...

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Hauptverfasser: Cai, Lie (VerfasserIn) , Golatta, Michael (VerfasserIn) , Sidey-Gibbons, Chris (VerfasserIn) , Barr, Richard G. (VerfasserIn) , Pfob, André (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 30 January 2025
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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Zusammenfassung: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.
Beschreibung:Gesehen am 17.09.2025
Beschreibung:Online Resource
ISSN:1432-0711
DOI:10.1007/s00404-024-07901-8

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