Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament

Satoshi Maki*, Takeo Furuya, Keiichi Katsumi, Hideaki Nakajima, Kazuya Honjoh, Shuji Watanabe, Takashi Kaito, Shota Takenaka, Yuya Kanie, Motoki Iwasaki, Masayuki Furuya, Gen Inoue, Masayuki Miyagi, Shinsuke Ikeda, Shiro Imagama, Hiroaki Nakashima, Sadayuki Ito, Hiroshi Takahashi, Yoshiharu Kawaguchi, Hayato FutakawaKazuma Murata, Toshitaka Yoshii, Takashi Hirai, Masao Koda, Seiji Ohtori, Masashi Yamazaki

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Fingerprint

Dive into the research topics of 'Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament'. Together they form a unique fingerprint.

Keyphrases

Medicine and Dentistry

Nursing and Health Professions

Biochemistry, Genetics and Molecular Biology

Neuroscience