2020
DOI: 10.2478/raon-2020-0068
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Artificial intelligence in musculoskeletal oncological radiology

Abstract: BackgroundDue to the rarity of primary bone tumors, precise radiologic diagnosis often requires an experienced musculoskeletal radiologist. In order to make the diagnosis more precise and to prevent the overlooking of potentially dangerous conditions, artificial intelligence has been continuously incorporated into medical practice in recent decades. This paper reviews some of the most promising systems developed, including those for diagnosis of primary and secondary bone tumors, breast, lung and colon neoplas… Show more

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Cited by 17 publications
(14 citation statements)
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References 51 publications
(80 reference statements)
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“…The focus of AI-related research for bone tumor diagnosis is mainly on the radiographic analysis for the moment (27,28). Bao et al (29) have incorporated various features from radiographic observations and demographic information to build a naïve Bayesian-based model for ranking and classifying a wide range of bone tumor diagnoses.…”
Section: Discussionmentioning
confidence: 99%
“…The focus of AI-related research for bone tumor diagnosis is mainly on the radiographic analysis for the moment (27,28). Bao et al (29) have incorporated various features from radiographic observations and demographic information to build a naïve Bayesian-based model for ranking and classifying a wide range of bone tumor diagnoses.…”
Section: Discussionmentioning
confidence: 99%
“…In recent years, the advent of artificial intelligence (AI) applications in medical imaging has also impacted musculoskeletal oncological imaging [ 19 ]. AI decision support systems promise to reduce interobserver variability in reporting.…”
Section: Discussionmentioning
confidence: 99%
“…One duplicate was discarded and 38 articles subsequently met the eligibility criteria (Fig. 1) [8,10,. 2 and 3.…”
Section: Selection and Methodological Characteristicsmentioning
confidence: 99%