In European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PURPOSE : Postoperative complication prediction helps surgeons to inform and manage patient expectations. Deep learning, a model that finds patterns in large samples of data, outperform traditional statistical methods in making predictions. This study aimed to create a deep learning-based model (DLM) to predict postoperative complications in patients with cervical ossification of the posterior longitudinal ligament (OPLL).
METHODS : This prospective multicenter study was conducted by the 28 institutions, and 478 patients were included in the analysis. Deep learning was used to create two predictive models of the overall postoperative complications and neurological complications, one of the major complications. These models were constructed by learning the patient's preoperative background, clinical symptoms, surgical procedures, and imaging findings. These logistic regression models were also created, and these accuracies were compared with those of the DLM.
RESULTS : Overall complications were observed in 127 cases (26.6%). The accuracy of the DLM was 74.6 ± 3.7% for predicting the overall occurrence of complications, which was comparable to that of the logistic regression (74.1%). Neurological complications were observed in 48 cases (10.0%), and the accuracy of the DLM was 91.7 ± 3.5%, which was higher than that of the logistic regression (90.1%).
CONCLUSION : A new algorithm using deep learning was able to predict complications after cervical OPLL surgery. This model was well calibrated, with prediction accuracy comparable to that of regression models. The accuracy remained high even for predicting only neurological complications, for which the case number is limited compared to conventional statistical methods.
Ito Sadayuki, Nakashima Hiroaki, Yoshii Toshitaka, Egawa Satoru, Sakai Kenichiro, Kusano Kazuo, Tsutui Shinji, Hirai Takashi, Matsukura Yu, Wada Kanichiro, Katsumi Keiichi, Koda Masao, Kimura Atsushi, Furuya Takeo, Maki Satoshi, Nagoshi Narihito, Nishida Norihiro, Nagamoto Yukitaka, Oshima Yasushi, Ando Kei, Takahata Masahiko, Mori Kanji, Nakajima Hideaki, Murata Kazuma, Miyagi Masayuki, Kaito Takashi, Yamada Kei, Banno Tomohiro, Kato Satoshi, Ohba Tetsuro, Inami Satoshi, Fujibayashi Shunsuke, Katoh Hiroyuki, Kanno Haruo, Oda Masahiro, Mori Kensaku, Taneichi Hiroshi, Kawaguchi Yoshiharu, Takeshita Katsushi, Matsumoto Morio, Yamazaki Masashi, Okawa Atsushi, Imagama Shiro
2023-Feb-06
Cervical spine, Deep learning, Ossification of the posterior longitudinal ligament, Postoperative complications
