Satoshi Maki, Yuki Shiratani, Sumihisa Orita, Akinobu Suzuki, Koji Tamai, Takaki Shimizu, Kenichiro Kakutani, Yutaro Kanda, Hiroyuki Tominaga, Ichiro Kawamura, et al. Predicting Postoperative Neurological Outcomes in Metastatic Spinal Tumor Surgery Using Machine Learning. Spine. 2025
Ryohei Kasai, Kazuma Bando, Kazuhide Inage, Yawara Eguchi, Miyako Narita, Yasuhiro Shiga, Masahiro Inoue, Soichiro Tokeshi, Kohei Okuyama, Shuhei Ohyama, et al. Quantitative assessment of lumbar dural mater pulsations using granger causality testing for spinal dynamics. Scientific Reports. 2025. 15. 1
Satoshi Maki, Takeo Furuya, Keiichi Katsumi, Hideaki Nakajima, Kazuya Honjoh, Shuji Watanabe, Takashi Kaito, Shota Takenaka, Yuya Kanie, Motoki Iwasaki, et al. Response to "Letter to the editor regarding the article 'Multimodal Deep Learning-based Radiomics Approach for Predicting Surgical Outcomes in Patients with Cervical Ossification of the Posterior Longitudinal Ligament'". Spine. 2025
2020 - The International Spinal Cord Society 59th Annual Scientific Meeting 2020 Early Career Scholar Award A deep convolutional neural network with performance comparable to radiologists for differentiating between spinal schwannoma and meningioma
2019/08 - 第8回 JASA(Japan association of Spine Surgeons with Ambitious) Best presenter award 人工知能を用いた脊髄硬膜内髄外腫瘍(神経鞘腫と髄膜腫)の鑑別