Predicting Attainment of Clinically Important Difference in the Japanese Orthopaedic Association Back Pain Evaluation Questionnaire
All three AI models reliably predicted postoperative improvements. Such AI-based prediction models may enhance clinical decision-making and patient counseling in lumbar spine surgery.
Authors
- Takahito Fujimori (ORCID: https://orcid.org/0000-0002-2758-7919)
- Takashi Kaito (ORCID: https://orcid.org/0000-0003-4882-2997)
- Yukitaka Nagamoto (ORCID: https://orcid.org/0000-0002-7993-3959)
- Koki Kishimoto
- Yuki Suzuki (ORCID: https://orcid.org/0000-0002-1572-5771)
- Yukikazu Ukon
- Koki Hosozawa
- Shota Takenaka (ORCID: https://orcid.org/0000-0001-9956-5914)
- Masatoshi Hori (ORCID: https://orcid.org/0000-0002-2523-3789)
- Kosuke Kita (ORCID: https://orcid.org/0000-0002-5825-3348)
- Masayuki Furuya (ORCID: https://orcid.org/0000-0002-1652-7216)
- Seiji Okada (ORCID: https://orcid.org/0000-0002-5107-8209)
- Yuya Kanie (ORCID: https://orcid.org/0000-0001-5977-1959)
- Nozomu Nakajima
- Kei Shinyashiki
Institutions
- Sakai Municipal Hospital (JP)
- Osaka Rosai Hospital (JP)
- Osaka Hospital (JP)
- The University of Osaka (JP)
Publication Details
- Journal
- Spine
- Published
- 2025-10-09
- DOI
- https://doi.org/10.1097/brs.0000000000005534
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00