Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study.
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| Title: | Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study. |
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| Authors: | Lin ET; Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan., Lu SC; Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan., Liu AS; Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan., Ko CH; Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan., Huang CH; Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan., Tsai CL; Department of Emergency Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, 7 Zhongshan S. Rd, Taipei, 100, Taiwan. chulintsai@ntu.edu.tw., Fu LC; Department of Computer Science and Information Engineering, National Taiwan University, CSIE Der Tian Hall No. 1, Sec. 4, Roosevelt Road, Taipei, 106319, Taiwan. lichen@ntu.edu.tw. |
| Source: | Journal of imaging informatics in medicine [J Imaging Inform Med] 2025 Apr; Vol. 38 (2), pp. 775-792. Date of Electronic Publication: 2024 Aug 19. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 2948-2933 |
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| DOI: | 10.1007/s10278-024-01209-4 |
