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Purpose <p>Lumbosacral nerve root anomalies are relatively rare but can be a risk factor for intraoperative nerve injury. However, it is often difficult to evaluate them with preoperative imaging. We developed a software that automatically generates three-dimensional (3D) nerve root images from magnetic resonance (MR) imaging using artificial intelligence (AI). This study aims to evaluate the accuracy and utility of this modality in clinical practice by conducting an epidemiological study of nerve root anomalies.</p> Methods <p>The incidence and morphology of nerve root anomalies were evaluated in the 3D images of 1,500 patients. The accuracy of the images was evaluated by comparing the images generated automatically using this AI software with those created manually by conventional methods.</p> Results <p>Of 1,500 cases, 53 (3.5%) had nerve root anomalies with total of 58 nerve root anomalies. With respect to the spinal level, 35 nerve root anomalies were found in the L5-S1 level, the most common (60.3%). As for morphology, 47 nerve roots (81.0%) were of the Neidre–MacNab classification Type 1. The images matched in 1,493 out of 1,500 cases (99.5%) between the two methods, and the remaining 7 cases all had nerve root abnormalities, which were detected as abnormal by the AI software.</p> Conclusion <p>The MR nerve root 3D imaging provided a 3D visualization and understanding of nerve root morphology, including nerve root anomalies. The AI software enables easy and precise 3D nerve root imaging, which greatly aids in the preoperative evaluation for spinal surgery.</p>

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Artificial intelligence-driven 3D MRI of lumbosacral nerve root anomalies: accuracy, incidence, and clinical utility

  • 期刊论文

  • 发表日期：

    2025年3月1日

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  • Daisuke Ukeba,
  • Ken Nagahama,
  • Katsuhisa Yamada,
  • Yuichiro Abe,
  • Yoshinori Hyugaji,
  • Tsutomu Endo,
  • Takashi Ohnishi,
  • Hiroyuki Tachi,
  • Yuichi Hasegawa,
  • Hideki Sudo,
  • Norimasa Iwasaki

摘要

Purpose

Lumbosacral nerve root anomalies are relatively rare but can be a risk factor for intraoperative nerve injury. However, it is often difficult to evaluate them with preoperative imaging. We developed a software that automatically generates three-dimensional (3D) nerve root images from magnetic resonance (MR) imaging using artificial intelligence (AI). This study aims to evaluate the accuracy and utility of this modality in clinical practice by conducting an epidemiological study of nerve root anomalies.

Methods

The incidence and morphology of nerve root anomalies were evaluated in the 3D images of 1,500 patients. The accuracy of the images was evaluated by comparing the images generated automatically using this AI software with those created manually by conventional methods.

Results

Of 1,500 cases, 53 (3.5%) had nerve root anomalies with total of 58 nerve root anomalies. With respect to the spinal level, 35 nerve root anomalies were found in the L5-S1 level, the most common (60.3%). As for morphology, 47 nerve roots (81.0%) were of the Neidre–MacNab classification Type 1. The images matched in 1,493 out of 1,500 cases (99.5%) between the two methods, and the remaining 7 cases all had nerve root abnormalities, which were detected as abnormal by the AI software.

Conclusion

The MR nerve root 3D imaging provided a 3D visualization and understanding of nerve root morphology, including nerve root anomalies. The AI software enables easy and precise 3D nerve root imaging, which greatly aids in the preoperative evaluation for spinal surgery.