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Artificial intelligence in musculoskeletal oncological radiology

Matjaz Vogrin

Matjaz Vogrin

  • Department of Orthopaedic Surgery, University Medical CenterMaribor, Slovenia
  • Faculty of Medicine, University of MariborMaribor, Slovenia
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Vogrin, Matjaz
, 
Teodor Trojner

Teodor Trojner

Department of Orthopaedic Surgery, University Medical CenterMaribor, Slovenia
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Trojner, Teodor
 und 
Robi Kelc

Robi Kelc

  • Department of Orthopaedic Surgery, University Medical CenterMaribor, Slovenia
  • Faculty of Medicine, University of MariborMaribor, Slovenia
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Kelc, Robi
  
10. Nov. 2020
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Artikel-Kategorie: Review
Online veröffentlicht: 10. Nov. 2020
Seitenbereich: 1 - 6
Eingereicht: 01. Juni 2020
Akzeptiert: 29. Sept. 2020
DOI: https://doi.org/10.2478/raon-2020-0068
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© 2021 Matjaz Vogrin, Teodor Trojner, Robi Kelc, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Figure 1

Schematic illustration of the hierarchy of artificial intelligence and its machine learning and deep learning subfields.
Schematic illustration of the hierarchy of artificial intelligence and its machine learning and deep learning subfields.

Figure 2

Schematic presentation of a neural network. Regions of interests (ROI) are defined, either by user or by an automated computer process. These present the input cells (in pink) a neural network. For each ROI the neural network extracts and compute features within the hidden layers (in grey) by using pre-trained data sets. Finally, the output cell offers the final results in different possible forms (yes/no, final diagnosis, probability of malignancy etc.).
Schematic presentation of a neural network. Regions of interests (ROI) are defined, either by user or by an automated computer process. These present the input cells (in pink) a neural network. For each ROI the neural network extracts and compute features within the hidden layers (in grey) by using pre-trained data sets. Finally, the output cell offers the final results in different possible forms (yes/no, final diagnosis, probability of malignancy etc.).
Sprache:
Englisch
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4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Medizin, Klinische Medizin, Allgemeinmedizin, Innere Medizin, Hämatologie, Onkologie, Radiologie
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