Influence of Robotic Versus Manual Technology for Achieving Sagittal Targets in Total Knee Arthroplasty Using a Cruciate-Retaining and Medial-Stabilized Implant.

BACKGROUND

Most of the focus regarding total knee arthroplasty (TKA) implant positioning and alignment has been centered on the coronal plane. Posterior condylar offset (PCO) and tibial slope (TS) are sagittal parameters that are measured on radiographs, managed intraoperatively, and are crucial to a stable TKA. We sought to compare whether robotic-assisted TKA (raTKA) versus manual TKA (mTKA) are different with regard to achieving a surgeon's preoperative sagittal targets.

METHODS

We trained a deep learning model based on a U-Net architecture that calculates PCO and TS on lateral knee radiographs. We deployed this model on a consecutive cohort of 280 patients who underwent either mTKA (n = 132) or raTKA (n = 148), with the same medial stabilized knee implant at a tertiary referral center. Measured resection was the technique for mTKA and either calipered kinematic alignment or gap balancing for raTKA.

RESULTS

Mean absolute error between the algorithm and human measurements was 1.3 ± 1.6° for TS and 1.7 ± 1.4 mm for PCO, which was less than the difference between the human annotators (2.0 ± 1.9° and 2.2 ± 2.6 mm, respectively). Mean difference between goal and postoperative TS was less in raTKA than mTKA (0.3 versus 1.3°; P = 0.03). However, the opposite was observed regarding restoration of native PCO, favoring mTKA (-1.7 versus 3.3 mm; P < 0.001). Overall, despite increased diversity in alignment philosophies and proportion of cementless fixation, there was less variability in raTKA postoperative data, suggesting increased precision.

CONCLUSIONS

We developed a deep learning algorithm to calculate PCO and TS on lateral knee radiographs. We observed significant differences between raTKA and mTKA in achieving sagittal plane targets, with raTKA being more precise than mTKA. Future studies are warranted to determine whether these differences are clinically relevant.