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Case Reports
. 2023 Jul 10;13(14):2320.
doi: 10.3390/diagnostics13142320.

Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications

Michele Di Dio  1 , Simona Barbuto  2 , Claudio Bisegna  1 , Andrea Bellin  2 , Mario Boccia  2 , Daniele Amparore  2 , Paolo Verri  2 , Giovanni Busacca  2 , Michele Sica  2 , Sabrina De Cillis  2 , Federico Piramide  2 , Vincenzo Zaccone  1 , Alberto Piana  2   3 , Stefano Alba  3 , Gabriele Volpi  4 , Cristian Fiori  2 , Francesco Porpiglia  2 , Enrico Checcucci  2
Affiliations
Case Reports

Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3D®) Models in Surgical Planning of Challenging Robotic Nephron-Sparing Surgery: A Case Report and Snapshot of the State-of-the-Art with Possible Future Implications

Michele Di Dio et al. Diagnostics (Basel). .

Abstract

Recently, 3D models (3DM) gained popularity in urology, especially in nephron-sparing interventions (NSI). Up to now, the application of artificial intelligence (AI) techniques alone does not allow us to obtain a 3DM adequate to plan a robot-assisted partial nephrectomy (RAPN). Integration of AI with computer vision algorithms seems promising as it allows to speed up the process. Herein, we present a 3DM realized with the integration of AI and a computer vision approach (CVA), displaying the utility of AI-based Hyper Accuracy Three-dimensional (HA3D®) models in preoperative planning and intraoperative decision-making process of challenging robotic NSI. A 54-year-old Caucasian female with no past medical history was referred to the urologist for incidental detection of the right renal mass. Preoperative contrast-enhanced abdominal CT confirmed a 35 × 25 mm lesion on the anterior surface of the upper pole (PADUA 7), with no signs of distant metastasis. CT images in DICOM format were processed to obtain a HA3D® model. RAPN was performed using Da Vinci Xi surgical system in a three-arm configuration. The enucleation strategy was achieved after selective clamping of the tumor-feeding artery. Overall operative time was 85 min (14 min of warm ischemia time). No intra-, peri- and post-operative complications were recorded. Histopathological examination revealed a ccRCC (stage pT1aNxMx). AI is breaking new ground in medical image analysis panorama, with enormous potential in organ/tissue classification and segmentation, thus obtaining 3DM automatically and repetitively. Realized with the integration of AI and CVA, the results of our 3DM were accurate as demonstrated during NSI, proving the potentialities of this approach for HA3D® models' reconstruction.

Keywords: 3D models; artificial intelligence; kidney cancer; robotics; three-dimensional.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Axial slice of CT scan.
Figure 2
Figure 2
HA3D® model workflow.
Figure 3
Figure 3
HA3D® virtual model. (A) Standard 3D virtual model. (B) Standard 3D virtual model with transparent parenchyma. (C) A 3D virtual perfusion model.
Figure 4
Figure 4
Result of automatic segmentation.
Figure 5
Figure 5
Pre-, peri- and post-operative patient characteristics.
Figure 6
Figure 6
Results of 3D reconstruction: (A) deep learning only; (B) deep learning combined with computer vision.
See this image and copyright information in PMC

References

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