Current Applications of Machine Learning in Spine : From Clinical View
STUDY DESIGN: Narrative review.
OBJECTIVES: This review aims to present current applications of machine learning (ML) in spine domain to clinicians.
METHODS: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text.
RESULTS: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost.
CONCLUSIONS: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Global spine journal - 12(2022), 8 vom: 10. Okt., Seite 1827-1840 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ren, GuanRui [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Revised 29.10.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1177/21925682211035363 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM331699117 |
|---|
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| 245 | 1 | 0 | |a Current Applications of Machine Learning in Spine |b From Clinical View |
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| 500 | |a Date Revised 29.10.2022 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a STUDY DESIGN: Narrative review | ||
| 520 | |a OBJECTIVES: This review aims to present current applications of machine learning (ML) in spine domain to clinicians | ||
| 520 | |a METHODS: We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text | ||
| 520 | |a RESULTS: Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost | ||
| 520 | |a CONCLUSIONS: ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a current applications | |
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| 650 | 4 | |a spine | |
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| 700 | 1 | |a Xie, ZhiYang |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, PeiYang |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, YunTao |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, XiaoTao |e verfasserin |4 aut | |
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