Accuracy, Reliability, and Repeatability of a Novel Artificial Intelligence Algorithm Converting Two-Dimensional Radiographs to Three-Dimensional Bone Models for Total Knee Arthroplasty

Fernandes, Levi Reina; Arce, Carlos H.; Martinho, Gonçalo; Campos, João Pedro; Meneghini, R. Michael

doi:10.1016/j.arth.2022.12.007

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…In addition, these technologies are often validated on cadaveric data, which is not reflective of real-world TKA patient anatomical characteristics. For example, Fernandes et al [ 9 ] supported further investigation into the real-world clinical value of an AI algorithm that demonstrated high accuracy in converting 2D X-rays to 3D bone models for TKA planning. When comparing the AI bone reconstructions to CT scans, four out of six anatomical measurement parameters showed mean absolute errors below 2 mm.…”

Section: Discussionmentioning

confidence: 99%

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Validating a Novel 2D to 3D Knee Reconstruction Method on Preoperative Total Knee Arthroplasty Patient Anatomies

Factor,

Gurel,

Dan

et al. 2024

JCM

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Background: As advanced technology continues to evolve, incorporating robotics into surgical procedures has become imperative for precision and accuracy in preoperative planning. Nevertheless, the integration of three-dimensional (3D) imaging into these processes presents both financial considerations and potential patient safety concerns. This study aims to assess the accuracy of a novel 2D-to-3D knee reconstruction solution, RSIP XPlan.ai™ (RSIP Vision, Jerusalem, Israel), on preoperative total knee arthroplasty (TKA) patient anatomies. Methods: Accuracy was calculated by measuring the Root Mean Square Error (RMSE) between X-ray-based 3D bone models generated by the algorithm and corresponding CT bone segmentations (distances of each mesh vertex to the closest vertex in the second mesh). The RMSE was computed globally for each bone, locally for eight clinically relevant bony landmark regions, and along simulated bone cut contours. In addition, the accuracies of three anatomical axes were assessed by comparing angular deviations to inter- and intra-observer baseline values. Results: The global RMSE was 0.93 ± 0.25 mm for the femur and 0.88 ± 0.14 mm for the tibia. Local RMSE values for bony landmark regions were 0.51 ± 0.33 mm for the five femoral landmarks and 0.47 ± 0.17 mm for the three tibial landmarks. The RMSE along simulated cut contours was 0.75 ± 0.35 mm for the distal femur cut and 0.63 ± 0.27 mm for the proximal tibial cut. Anatomical axial average angular deviations were 1.89° for the trans epicondylar axis (with an inter- and intra-observer baseline of 1.43°), 1.78° for the posterior condylar axis (with a baseline of 1.71°), and 2.82° (with a baseline of 2.56°) for the medial–lateral transverse axis. Conclusions: The study findings demonstrate promising results regarding the accuracy of XPlan.ai™ in reconstructing 3D bone models from plain-film X-rays. The observed accuracy on real-world TKA patient anatomies in anatomically relevant regions, including bony landmarks, cut contours, and axes, suggests the potential utility of this method in various clinical scenarios. Further validation studies on larger cohorts are warranted to fully assess the reliability and generalizability of our results. Nonetheless, our findings lay the groundwork for potential advancements in future robotic arthroplasty technologies, with XPlan.ai™ offering a promising alternative to conventional CT scans in certain clinical contexts.

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Section: Discussionmentioning

confidence: 99%

“…Still, adoption has been slow due to concerns about accuracy in real-world pathological TKA cases. Most validation studies have been limited to healthy knee cadaver specimens, raising doubts about their applicability in clinical practice [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Validating a Novel 2D to 3D Knee Reconstruction Method on Preoperative Total Knee Arthroplasty Patient Anatomies

Factor,

Gurel,

Dan

et al. 2024

JCM

View full text Add to dashboard Cite

show abstract

“…AI can assist orthopaedic surgeons in surgical planning by providing valuable insights and recommendations. 4 By analyzing patient data, such as medical images and patient records, AI algorithms could assist in preoperative planning, optimizing implant selection, and predicting surgical outcomes. During surgery, AI-powered robotic systems can provide real-time guidance, enhancing the precision and accuracy of surgical procedures.…”

Section: Surgical Planning and Assistancementioning

confidence: 99%