Machine learning models for prognosis prediction in regenerative endodontic procedures.

Journal: BMC oral health
PMID: 39948515

Abstract

BACKGROUND: This study aimed to establish and validate machine learning (ML) models to predict the prognosis of regenerative endodontic procedures (REPs) clinically, assisting clinicians in decision-making and avoiding treatment failure.

Authors

  • Jing Lu
    Department of Nephrology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
  • Qianqian Cai
    Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control, and Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.
  • Kaizhi Chen
    School of Computer and Big Data, Fuzhou University, Fujian 350108, China.
  • Bill Kahler
    School of Dentistry, University of Sydney, Camperdown, Australia.
  • Jun Yao
    College of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610500, People's Republic of China; State Key Laboratory of Oil & Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, People's Republic of China. Electronic address: yjhwsgt@163.com.
  • Yanjun Zhang
    Tianjin State Key Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China tianjin_tcm001@sina.com.
  • Dali Zheng
    Fujian Key Laboratory of Oral Diseases, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China.
  • Youguang Lu
    Fujian Key Laboratory of Oral Diseases, School and Hospital of Stomatology, Fujian Medical University, Fuzhou, China. fjlyg63@fjmu.edu.cn.

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