Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review
3
Zitationen
8
Autoren
2025
Jahr
Abstract
= 4). Throughout the article, we analyzed the concept of DL in ACL tears and provided examples of how these tools can impact clinical practice and patient care. DL models for MRI detection of ACL injury reported high values of accuracy, especially helpful for less experienced clinicians. Time efficiency was also demonstrated. Overall, the deep learning models have proven to be a valid resource, although still requiring technological developments for implementation in daily practice.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.561 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.452 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.948 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.797 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.