Machine learning approach in predicting clinically significant improvements after surgery in patients with cervical ossification of the posterior longitudinal ligament

Satoshi Maki*, Takeo Furuya, Toshitaka Yoshii, Satoru Egawa, Kenichiro Sakai, Kazuo Kusano, Yukihiro Nakagawa, Takashi Hirai, Kanichiro Wada, Keiichi Katsumi, Kengo Fujii, Atsushi Kimura, Narihito Nagoshi, Tsukasa Kanchiku, Yukitaka Nagamoto, Yasushi Oshima, Kei Ando, Masahiko Takahata, Kanji Mori, Hideaki NakajimaKazuma Murata, Shunji Matsunaga, Takashi Kaito, Kei Yamada, Sho Kobayashi, Satoshi Kato, Tetsuro Ohba, Satoshi Inami, Shunsuke Fujibayashi, Hiroyuki Katoh, Haruo Kanno, Shiro Imagama, Masao Koda, Yoshiharu Kawaguchi, Katsushi Takeshita, Morio Matsumoto, Seiji Ohtori, Masashi Yamazaki, Atsushi Okawa

*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

Fingerprint

Dive into the research topics of 'Machine learning approach in predicting clinically significant improvements after surgery in patients with cervical ossification of the posterior longitudinal ligament'. Together they form a unique fingerprint.

Keyphrases

Medicine and Dentistry