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Multicenter Study
. 2025 Sep;34(9):3714-3723.
doi: 10.1007/s00586-025-08708-0. Epub 2025 Feb 10.

A deep learning-based prediction model for prognosis of cervical spine injury: a Japanese multicenter survey

Sadayuki Ito  1 , Hiroaki Nakashima  2 , Naoki Segi  1 , Noriaki Yokogawa  3 , Takeshi Sasagawa  3   4 , Toru Funayama  5 , Fumihiko Eto  6 , Akihiro Yamaji  7 , Kota Watanabe  8 , Satoshi Nori  8 , Kazuki Takeda  8   9 , Takeo Furuya  10 , Atsushi Yunde  10 , Hideaki Nakajima  11 , Tomohiro Yamada  12   13 , Tomohiko Hasegawa  12 , Yoshinori Terashima  14   15 , Ryosuke Hirota  14 , Hidenori Suzuki  16 , Yasuaki Imajo  16 , Shota Ikegami  17 , Masashi Uehara  17 , Hitoshi Tonomura  18 , Munehiro Sakata  18   19 , Ko Hashimoto  20 , Yoshito Onoda  20 , Kenichi Kawaguchi  21 , Yohei Haruta  21 , Nobuyuki Suzuki  22 , Kenji Kato  22 , Hiroshi Uei  23   24 , Hirokatsu Sawada  24 , Kazuo Nakanishi  25 , Kosuke Misaki  25 , Hidetomi Terai  26 , Koji Tamai  26 , Akiyoshi Kuroda  27 , Gen Inoue  27 , Kenichiro Kakutani  28 , Yuji Kakiuchi  28 , Katsuhito Kiyasu  29 , Hiroyuki Tominaga  30 , Hiroto Tokumoto  30 , Yoichi Iizuka  31 , Eiji Takasawa  31 , Koji Akeda  32 , Norihiko Takegami  32 , Haruki Funao  33   34   35 , Yasushi Oshima  36 , Takashi Kaito  37 , Daisuke Sakai  38 , Toshitaka Yoshii  39 , Tetsuro Ohba  40 , Bungo Otsuki  41 , Shoji Seki  42 , Masashi Miyazaki  43 , Masayuki Ishihara  44 , Masahiro Oda  45 , Seiji Okada  37 , Shiro Imagama  1 , Satoshi Kato  3
Affiliations
Multicenter Study

A deep learning-based prediction model for prognosis of cervical spine injury: a Japanese multicenter survey

Sadayuki Ito et al. Eur Spine J. 2025 Sep.

Abstract

Purpose: Cervical spine injuries in the elderly (defined as individuals aged 65 years and older) are increasing, often resulting from falls and minor trauma. Prognosis varies widely, influenced by multiple factors. This study aimed to develop a deep-learning-based predictive model for post-injury outcomes.

Methods: This study analyzed a nationwide dataset from the Japan Association of Spine Surgeons with Ambition, comprising 1512 elderly patients (aged 65 years and older) with cervical spine injuries from 2010 to 2020. Deep learning predictive models were constructed for residence, mobility, and the American Spinal Injury Association Impairment Scale (AIS). The model's performance was compared with that of a traditional statistical analysis.

Results: The deep-learning model predicted the residence and AIS outcomes with varying accuracies. The highest accuracy was observed in predicting residence one year post-injury. The model also identified that the AIS score at discharge was significantly predicted by upper extremity trauma, mobility, and elbow extension strength. The deep learning model highlighted factors, such as upper extremity trauma, that were not considered significant in the traditional statistical analysis.

Conclusion: Our deep learning-based model offers a novel method for predicting outcomes following cervical spine injuries in the elderly population. The model is highly accurate and provides additional insights into potential prognostic factors. Such models can improve patient care and individualize future interventions.

Keywords: Cervical spine injury; Deep learning; Prediction; Prognosis.

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Conflict of interest statement

Declarations. Conflict of interest: The authors declare no conflict of interest.

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