Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches.

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Title: Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches.
Authors: Lee J; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.; ClariPi Research, ClariPi Inc., Seoul 03088, Republic of Korea., Park H; Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea., Yang Z; Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea., Woo OH; Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea., Kang WY; Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea., Kim JH; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.; ClariPi Research, ClariPi Inc., Seoul 03088, Republic of Korea.; Department of Radiology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.; Department of Radiology, Seoul National University Hospital, Seoul 03080, Republic of Korea.; Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon 16229, Republic of Korea.
Source: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2024 Nov 06; Vol. 14 (22). Date of Electronic Publication: 2024 Nov 06.
Publication Type: Journal Article
Journal Info: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101658402 Publication Model: Electronic Cited Medium: Print ISSN: 2075-4418 (Print) Linking ISSN: 20754418 NLM ISO Abbreviation: Diagnostics (Basel) Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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  Data: Improved Detection Accuracy of Chronic Vertebral Compression Fractures by Integrating Height Loss Ratio and Deep Learning Approaches.
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  Data: <searchLink fieldCode="AU" term="%22Lee+J%22">Lee J</searchLink>; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.; ClariPi Research, ClariPi Inc., Seoul 03088, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Park+H%22">Park H</searchLink>; Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Yang+Z%22">Yang Z</searchLink>; Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Woo+OH%22">Woo OH</searchLink>; Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kang+WY%22">Kang WY</searchLink>; Department of Radiology, Korea University Guro Hospital, Seoul 08308, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+JH%22">Kim JH</searchLink>; Department of Applied Bioengineering, Graduate School of Convergence Science and Technology, Seoul National University, Seoul 08826, Republic of Korea.; ClariPi Research, ClariPi Inc., Seoul 03088, Republic of Korea.; Department of Radiology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.; Department of Radiology, Seoul National University Hospital, Seoul 03080, Republic of Korea.; Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon 16229, Republic of Korea.
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  Data: <searchLink fieldCode="JN" term="%22101658402%22">Diagnostics (Basel, Switzerland)</searchLink> [Diagnostics (Basel)] 2024 Nov 06; Vol. 14 (22). <i>Date of Electronic Publication: </i>2024 Nov 06.
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        Value: 10.3390/diagnostics14222477
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              Text: 2024 Nov 06
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