Chinese Journal of Tissue Engineering Research ›› 2026, Vol. 30 ›› Issue (21): 5369-5375.doi: 10.12307/2026.776

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Construction and validation of a deep learning prediction model for cervical instability

Lu Guangqi, Sun Xinyue, Han Xue, Liu Yakun, Ma Mingming, Mao Hanze, Zhou Shuaiqi, Liang Long, Li Jing, Hu Jiaming, Zhu Liguo, Yu Jie, Zhuang Minghui   

  1. Wangjing Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing 100102, China  
  • Accepted:2025-08-26 Online:2026-07-28 Published:2026-03-03
  • Contact: Yu Jie, MD, Chief physician, Wangjing Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing 100102, China Zhuang Minghui, MD, Attending physician, Wangjing Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing 100102, Chin
  • About author:Lu Guangqi, MD candidate, Physician, Wangjing Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing 100102, China
  • Supported by:
    Special Project on Clinical Evidence-Based Research on Traditional Chinese Medicine in the High-Level Traditional Chinese Medicine Hospital Construction Project of Wangjing Hospital of Chinese Academy of Traditional Chinese Medicine, No. WJYY-XZKT-2023-01 (to YJ); National Natural Science Foundation of China (General Program), No. 82274560 (to YJ); National Natural Science Foundation of China (Youth Program), No. 82405438 (to ZMH); Special Project on Independently Selected Topics of Wangjing Hospital of Chinese Academy of Traditional Chinese Medicine, No. WJYY-ZZXT-2023-24 (to ZMH)

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Abstract: BACKGROUND: Early prediction of cervical instability is crucial for the prevention and treatment of cervical spondylosis, and deep learning technology can provide robust support for intelligent prediction of cervical instability. 
OBJECTIVE: To develop a deep learning model of cervical instability based on cervical magnetic resonance imaging for early intelligent prediction of cervical instability. 
METHODS: This study recruited young and middle-aged participants (18-45 years), including both cervical instability patients and healthy controls, through the Spine Department Outpatient Clinic of Wangjing Hospital, China Academy of Chinese Medical Sciences, as well as community-based recruitment. All participants underwent cervical magnetic resonance imaging examinations. On the axial magnetic resonance imaging images, five key anatomical structures were manually annotated: intervertebral disc, facet, prevertebral muscle, deep muscle group in the back of the neck, and superficial muscle group in the back of the neck. A deep learning algorithm was then employed to develop a predictive model for cervical instability, utilizing both the original images and the delineated regions of interest. Finally, the model's predictive performance was systematically evaluated and validated. 
RESULTS AND CONCLUSION: (1) The study included a total of 308 young and middle-aged participants, comprising 196 individuals with cervical instability and 112 healthy controls. Based on enrollment time, the subjects' data were allocated to either the model training set or the test set. (2) The model demonstrated high predictive performance, with an area under the curve values of 0.97, an F1-score of 0.98, a precision of 0.98, and a recall of 0.97 in the training set. In the test set, these metrics were 0.97, 0.95, 1.00, and 0.90, respectively. (3) The results indicate that the deep learning model based on cervical magnetic resonance imaging images can effectively enable early intelligent prediction of cervical instability, exhibiting strong diagnostic accuracy.

Key words: young and middle-aged adults, cervical instability, cervical magnetic resonance imaging, deep learning, prediction model, the area under the receiver operating characteristic curve

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