BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

920 related articles for article (PubMed ID: 33238691)

  • 1. [Establishment and test results of an artificial intelligence burn depth recognition model based on convolutional neural network].
    He ZY; Wang Y; Zhang PH; Zuo K; Liang PF; Zeng JZ; Zhou ST; Guo L; Huang MT; Cui X
    Zhonghua Shao Shang Za Zhi; 2020 Nov; 36(11):1070-1074. PubMed ID: 33238691
    [No Abstract]   [Full Text] [Related]  

  • 2. Improving burn depth assessment for pediatric scalds by AI based on semantic segmentation of polarized light photography images.
    Cirillo MD; Mirdell R; Sjöberg F; Pham TD
    Burns; 2021 Nov; 47(7):1586-1593. PubMed ID: 33947595
    [TBL] [Abstract][Full Text] [Related]  

  • 3. [Meta-analysis on the diagnostic value of laser Doppler imaging for burn depth].
    Huang Y; Qiu L; Mei AL; Li JX
    Zhonghua Shao Shang Za Zhi; 2017 May; 33(5):301-308. PubMed ID: 28651422
    [No Abstract]   [Full Text] [Related]  

  • 4. Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient.
    Jiao C; Su K; Xie W; Ye Z
    Burns Trauma; 2019; 7():6. PubMed ID: 30859107
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Time-Independent Prediction of Burn Depth Using Deep Convolutional Neural Networks.
    Cirillo MD; Mirdell R; Sjöberg F; Pham TD
    J Burn Care Res; 2019 Oct; 40(6):857-863. PubMed ID: 31187119
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Real-time burn depth assessment using artificial networks: a large-scale, multicentre study.
    Wang Y; Ke Z; He Z; Chen X; Zhang Y; Xie P; Li T; Zhou J; Li F; Yang C; Zhang P; Huang C; Kai L
    Burns; 2020 Dec; 46(8):1829-1838. PubMed ID: 32826097
    [TBL] [Abstract][Full Text] [Related]  

  • 7. [Advances in the research of artificial intelligence technology assisting the diagnosis of burn depth].
    Ben C; Li HH; Liu T; Wang ZJ; Cheng DS; Zhu SH
    Zhonghua Shao Shang Za Zhi; 2020 Mar; 36(3):244-246. PubMed ID: 32241051
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Segmentation and classification of burn images by color and texture information.
    Acha B; Serrano C; Acha JI; Roa LM
    J Biomed Opt; 2005; 10(3):034014. PubMed ID: 16229658
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Prediction of pulp exposure risk of carious pulpitis based on deep learning.
    Wang L; Wu F; Xiao M; Chen YX; Wu L
    Hua Xi Kou Qiang Yi Xue Za Zhi; 2023 Apr; 41(2):218-224. PubMed ID: 37056189
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [Antiseptic effect of compound lysostaphin disinfectant and its preventive effect on infection of artificial dermis after graft on full-thickness skin defect wound in rats].
    Jin J; Zhou H; Cui ZC; Wang L; Luo PF; Ji SZ; Hu XY; Ma B; Wang GY; Zhu SH; Xia ZF
    Zhonghua Shao Shang Za Zhi; 2018 Apr; 34(4):225-232. PubMed ID: 29690741
    [No Abstract]   [Full Text] [Related]  

  • 11. [Application of convolutional neural network to risk evaluation of positive circumferential resection margin of rectal cancer by magnetic resonance imaging].
    Xu JH; Zhou XM; Ma JL; Liu SS; Zhang MS; Zheng XF; Zhang XY; Liu GW; Zhang XX; Lu Y; Wang DS
    Zhonghua Wei Chang Wai Ke Za Zhi; 2020 Jun; 23(6):572-577. PubMed ID: 32521977
    [No Abstract]   [Full Text] [Related]  

  • 12. Influence of the Depth of the Convolutional Neural Networks on an Artificial Intelligence Model for Diagnosis of Orthognathic Surgery.
    Kim YH; Park JB; Chang MS; Ryu JJ; Lim WH; Jung SK
    J Pers Med; 2021 Apr; 11(5):. PubMed ID: 33946874
    [TBL] [Abstract][Full Text] [Related]  

  • 13. [Clinical characteristics and treatment of diabetic patients with superficial partial-thickness burn on feet].
    Ling XW; Zhang TT; Dai WT; Xia WD; Lin C
    Zhonghua Shao Shang Za Zhi; 2019 Jan; 35(1):25-30. PubMed ID: 30678398
    [No Abstract]   [Full Text] [Related]  

  • 14. GL-FusionNet: Fusing global and local features to classify deep and superficial partial thickness burn.
    Li Z; Huang J; Tong X; Zhang C; Lu J; Zhang W; Song A; Ji S
    Math Biosci Eng; 2023 Mar; 20(6):10153-10173. PubMed ID: 37322927
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A deep learning model for burn depth classification using ultrasound imaging.
    Lee S; Rahul ; Lukan J; Boyko T; Zelenova K; Makled B; Parsey C; Norfleet J; De S
    J Mech Behav Biomed Mater; 2022 Jan; 125():104930. PubMed ID: 34781225
    [TBL] [Abstract][Full Text] [Related]  

  • 16. [Influence of the depth of retained denatured dermis on the survival rate of grafted skin in burn swine with deep partial-thickness burn].
    Zhao YH; Yang HG; Deng HT; Yuan DL; Xu LH; Huang WQ; Shen YM
    Zhonghua Shao Shang Za Zhi; 2013 Aug; 29(4):365-70. PubMed ID: 24351536
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Dielectric measurement in experimental burns: a new tool for burn depth determination?
    Papp A; Lahtinen T; Härmä M; Nuutinen J; Uusaro A; Alhava E
    Plast Reconstr Surg; 2006 Mar; 117(3):889-98; discussion 899-901. PubMed ID: 16525281
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility.
    Zhou QQ; Wang J; Tang W; Hu ZC; Xia ZY; Li XS; Zhang R; Yin X; Zhang B; Zhang H
    Korean J Radiol; 2020 Jul; 21(7):869-879. PubMed ID: 32524787
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Clinical Investigation of a Rapid Non-invasive Multispectral Imaging Device Utilizing an Artificial Intelligence Algorithm for Improved Burn Assessment.
    Thatcher JE; Yi F; Nussbaum AE; DiMaio JM; Dwight J; Plant K; Carter JE; Holmes JH
    J Burn Care Res; 2023 Jul; 44(4):969-981. PubMed ID: 37082889
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Classification of burn injuries using near-infrared spectroscopy.
    Sowa MG; Leonardi L; Payette JR; Cross KM; Gomez M; Fish JS
    J Biomed Opt; 2006; 11(5):054002. PubMed ID: 17092151
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 46.