Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification.

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Title: Enhanced Detection Performance of Acute Vertebral Compression Fractures Using a Hybrid Deep Learning and Traditional Quantitative Measurement Approach: Beyond the Limitations of Genant Classification.
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., Kim M; 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: Bioengineering (Basel, Switzerland) [Bioengineering (Basel)] 2025 Jan 13; Vol. 12 (1). Date of Electronic Publication: 2025 Jan 13.
Publication Type: Journal Article
Journal Info: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101676056 Publication Model: Electronic Cited Medium: Print ISSN: 2306-5354 (Print) Linking ISSN: 23065354 NLM ISO Abbreviation: Bioengineering (Basel) Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
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ISSN:2306-5354
DOI:10.3390/bioengineering12010064