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<p>To evaluate the accuracy of a high-precision navigation system that projects a three-dimensional (3D) kidney model onto surgical images by limiting its application to moments of minimal organ deformation during partial nephrectomy (PN). We analyzed 29 patients who underwent PN at Kyoto University Hospital and Kobe City Medical Center General Hospital in Japan. 3D models of the kidney and tumor were generated using DICOM data, whereas 3D point clouds of the surgical field were obtained using stereo camera recordings. Noise reduction processing was applied to the camera-derived point clouds. Registration between the computed tomography-derived models and camera-derived point clouds was performed using the closest iterative point, and the accuracy was assessed using the root mean squared error. We evaluated the effects of the point-cloud surface area and camera-to-target distance on the registration accuracy. Without noise reduction, the median registration error was 2.33 mm, whereas noise reduction improved the accuracy by 1.83 mm. The accuracy was significantly higher when the camera-to-target distance was shorter, with and without noise reduction. The surface area was inversely correlated with the accuracy without noise reduction, but no significant correlation was observed with noise reduction. Focusing on moments with minimal organ deformation, we demonstrated that high-precision surgical navigation is achievable in PN using actual surgical recordings. This may contribute to improved tumor localization and the preservation of renal function.</p>

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Evaluation of our new three-dimensional navigation system in robot-assisted partial nephrectomy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Toshihiro Magaribuchi,
  • Masanao Koeda,
  • Shinya Somiya,
  • Akihiro Hamada,
  • Atsushi Sengiku,
  • Kimihiko Masui,
  • Takashi Kobayashi,
  • Atsuro Sawada

摘要

To evaluate the accuracy of a high-precision navigation system that projects a three-dimensional (3D) kidney model onto surgical images by limiting its application to moments of minimal organ deformation during partial nephrectomy (PN). We analyzed 29 patients who underwent PN at Kyoto University Hospital and Kobe City Medical Center General Hospital in Japan. 3D models of the kidney and tumor were generated using DICOM data, whereas 3D point clouds of the surgical field were obtained using stereo camera recordings. Noise reduction processing was applied to the camera-derived point clouds. Registration between the computed tomography-derived models and camera-derived point clouds was performed using the closest iterative point, and the accuracy was assessed using the root mean squared error. We evaluated the effects of the point-cloud surface area and camera-to-target distance on the registration accuracy. Without noise reduction, the median registration error was 2.33 mm, whereas noise reduction improved the accuracy by 1.83 mm. The accuracy was significantly higher when the camera-to-target distance was shorter, with and without noise reduction. The surface area was inversely correlated with the accuracy without noise reduction, but no significant correlation was observed with noise reduction. Focusing on moments with minimal organ deformation, we demonstrated that high-precision surgical navigation is achievable in PN using actual surgical recordings. This may contribute to improved tumor localization and the preservation of renal function.