Journal of Clinical Ultrasound / 2022

Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications

T. D’angelo, Danilo Caudo, A. Blandino, M. Albrecht, T. Vogl, Leon D Gruenewald, M. Gaeta, I. Yel, V. Koch, Simon S. Martin, L. Lenga, G. Muscogiuri

Computer VisionFoundation ModelsReinforcement Learning

Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.

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Title: Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications
Authors: T. D’angelo, Danilo Caudo, A. Blandino, M. Albrecht, T. Vogl, Leon D Gruenewald, M. Gaeta, I. Yel, V. Koch, Simon S. Martin, L. Lenga, G. Muscogiuri
Year: 2022
Venue: Journal of Clinical Ultrasound
Categories: Computer Vision, Foundation Models, Reinforcement Learning
Citations: 48
Paper URL: https://www.semanticscholar.org/paper/3121df3f5d2991b6ac2afadc7c008b9f7338022e
Open PDF: Not available

Abstract:
Artificial intelligence is rapidly expanding in all technological fields. The medical field, and especially diagnostic imaging, has been showing the highest developmental potential. Artificial intelligence aims at human intelligence simulation through the management of complex problems. This review describes the technical background of artificial intelligence, machine learning, and deep learning. The first section illustrates the general potential of artificial intelligence applications in the context of request management, data acquisition, image reconstruction, archiving, and communication systems. In the second section, the prospective of dedicated tools for segmentation, lesion detection, automatic diagnosis, and classification of musculoskeletal disorders is discussed.

Learning resources:
- Google Scholar: Google Scholar references (https://scholar.google.com/scholar?q=Artificial%20intelligence%2C%20machine%20learning%20and%20deep%20learning%20in%20musculoskeletal%20imaging%3A%20Current%20applications)
- Papers with Code: Papers with Code search (https://paperswithcode.com/search?q=Artificial%20intelligence%2C%20machine%20learning%20and%20deep%20learning%20in%20musculoskeletal%20imaging%3A%20Current%20applications)
- Semantic Scholar: Semantic Scholar paper page (https://www.semanticscholar.org/paper/3121df3f5d2991b6ac2afadc7c008b9f7338022e)
- YouTube: YouTube explanations (https://www.youtube.com/results?search_query=Artificial%20intelligence%2C%20machine%20learning%20and%20deep%20learning%20in%20musculoskeletal%20imaging%3A%20Current%20applications+paper+explained)