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. 2023 Oct;18(10):1783-1793.
doi: 10.1007/s11548-023-02848-8. Epub 2023 Mar 1.

Robot-assisted implantation of additively manufactured patient-specific orthopaedic implants: evaluation in a sheep model

Affiliations

Robot-assisted implantation of additively manufactured patient-specific orthopaedic implants: evaluation in a sheep model

Tom Williamson et al. Int J Comput Assist Radiol Surg. 2023 Oct.

Abstract

Purpose: Bone tumours must be surgically excised in one piece with a margin of healthy tissue. The unique nature of each bone tumour case is well suited to the use of patient-specific implants, with additive manufacturing allowing production of highly complex geometries. This work represents the first assessment of the combination of surgical robotics and patient-specific additively manufactured implants.

Methods: The development and evaluation of a robotic system for bone tumour excision, capable of milling complex osteotomy paths, is described. The developed system was evaluated as part of an animal trial on 24 adult male sheep, in which robotic bone excision of the distal femur was followed by placement of patient-specific implants with operative time evaluated. Assessment of implant placement accuracy was completed based on post-operative CT scans.

Results: A mean overall implant position error of 1.05 ± 0.53 mm was achieved, in combination with a mean orientation error of 2.38 ± 0.98°. A mean procedure time (from access to implantation, excluding opening and closing) of 89.3 ± 25.25 min was observed, with recorded surgical time between 58 and 133 min, with this approximately evenly divided between robotic (43.9 ± 15.32) and implant-based (45.4 ± 18.97) tasks.

Conclusions: This work demonstrates the ability for robotics to achieve repeatable and precise removal of complex bone volumes of the type that would allow en bloc removal of a bone tumour. These robotically created volumes can be precisely filled with additively manufactured patient-specific implants, with minimal gap between cut surface and implant interface.

Keywords: Bone tumours; Orthopaedic oncology; Patient-specific implants; Robotic surgery; Surgical robotics.

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Conflict of interest statement

Stryker is a partner in the IMCRC initiative. Dave Hill, Tom Williamson, Peter Choong, Martin Leary, Darpan Shidid and Milan Brandt are co-authors on patents or patent applications related to the technology described in this work. Peter Choong provides consulting work with Stryker. Zac Sweeney and Ulrich Buehner were employed by Stryker during the period during which this work was completed. Stewart Ryan, Bill Lozanovski, Endri Kastrati, Arman Namvar, Thierry Beths, Romane Blanchard and Kate Fox declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Sheep 4 femur (green) registered to reference bone (dark red) and reference cutting geometry (pale blue). Approximate locations of registration initialization points (yellow spheres) and overall analysis coordinate system are also shown. Examples of the two implant types (lattice, upper in yellow and solid with ingrowth layer, lower in red) are shown in the centre. An intraoperative craniocaudal view of the performed cut is shown on the right
Fig. 2
Fig. 2
Left: developed system in the operating room. Right: surgical site during supervised autonomous bone cutting and user interface with overview of current robot and procedure state
Fig. 3
Fig. 3
The overall intraoperative surgical workflow. The robotic screw drilling approach (third column) was used only for cases 3 & 4
Fig. 4
Fig. 4
The multistep process for determining the implant placement error shown here for Sheep 9. Post-operative segmented data are shown in red; pre-operative plan data are shown in green. Post- and pre-operative image data are initially unaligned (a). A combination of manual manipulation and ICP of the segmented bone models is used for initial alignment (b). The error in implant placement (c) is the difference between the planned implant position (green) and segmented implant position after initial alignment (red). ICP is used to align the planned and segmented post-operative implant (d) with the resulting transformation representing the placement error (d)
Fig. 5
Fig. 5
Time required for each case (left) and distributions of time required for each workflow step (right). The order of cases on the left represents the order in which surgeries were performed. Colours refer to equivalent steps in Fig. 3. Workflow challenges resulting in increased surgical time are indicated in text
Fig. 6
Fig. 6
Distribution of observed implant positioning errors in translation and rotation (left) and implant positioning errors for each case (right)
Fig. 7
Fig. 7
Minimum, median and maximum observed errors in the axial plane (top) and coronal plane (bottom). The segmented implant position is outlined in red, the planned implant position in green. The red text refers to the specific case ID
Fig. 8
Fig. 8
Intraoperative photographs of implant fit for all cases

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