International orthopaedics · 2026
Patient-Specific Instrumentation Improves Technical Execution Accuracy After Artificial Intelligence-Assisted Preoperative Planning in Medial Unicompartmental Knee Arthroplasty.
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Liu Dehua, Zhao Yaheng, Gao Zirun, Niu Jinlong, Zhang Yafang, Liu Xingyu, Ji Gang, Liu Guobin
Аннотация
BACKGROUND: Artificial intelligence (AI)-assisted preoperative planning may improve anatomic characterization and implant-size prediction in unicompartmental knee arthroplasty (UKA). However, whether patient-specific instrumentation (PSI) provides additional technical benefit when added to AI-assisted planning remains unclear. We developed and validated an AI-assisted preoperative planning workflow combined with PSI for medial UKA and evaluated its effect on implant positioning accuracy.
METHODS: A hybrid architecture combining a convolutional neural network-based U-Net with a Transformer-based deep learning module (C-T Module) was developed to automate CT processing for AI-assisted preoperative planning and PSI design in UKA. Segmentation performance of the C-T Module was compared with that of a conventional 3D U-Net. PSI feasibility was validated using synthetic bone models. In a prospective randomized clinical study, 24 patients underwent AI-assisted planning plus PSI-assisted medial UKA (PSI group) and 24 patients underwent the same AI-assisted planning workflow followed by conventionally instrumented medial UKA (control group). Surgical accuracy, perioperative outcomes, short-term outcomes and implant-size prediction accuracy were compared.
RESULTS: The C-T Module demonstrated superior image segmentation accuracy compared to the conventional 3D U-Net. Compared with the control group, the PSI group significantly improved surgical accuracy, including more accurate tibial component positioning, greater tibial coverage, and less deviation in proximal tibial resection (all P < 0.001). Except for the significantly longer skin incision in the PSI group (P < 0.001), no other perioperative parameters differed significantly between groups. Case-sequence analysis showed no consistent changes in operative efficiency or most accuracy parameters across sequential PSI cases. The AI-based planning system demonstrated significantly higher accuracy in prosthesis size prediction than conventional templating (P < 0.001). Short-term follow-up showed no significant between-group differences in OKS, VAS pain score, or patient satisfaction.
CONCLUSION: The AI-assisted planning system accurately predicted implant size, and PSI improved technical execution accuracy in medial UKA. However, short-term exploratory clinical outcomes did not differ between groups, and whether these technical gains translate into durable clinical benefit remains uncertain.
METHODS: A hybrid architecture combining a convolutional neural network-based U-Net with a Transformer-based deep learning module (C-T Module) was developed to automate CT processing for AI-assisted preoperative planning and PSI design in UKA. Segmentation performance of the C-T Module was compared with that of a conventional 3D U-Net. PSI feasibility was validated using synthetic bone models. In a prospective randomized clinical study, 24 patients underwent AI-assisted planning plus PSI-assisted medial UKA (PSI group) and 24 patients underwent the same AI-assisted planning workflow followed by conventionally instrumented medial UKA (control group). Surgical accuracy, perioperative outcomes, short-term outcomes and implant-size prediction accuracy were compared.
RESULTS: The C-T Module demonstrated superior image segmentation accuracy compared to the conventional 3D U-Net. Compared with the control group, the PSI group significantly improved surgical accuracy, including more accurate tibial component positioning, greater tibial coverage, and less deviation in proximal tibial resection (all P < 0.001). Except for the significantly longer skin incision in the PSI group (P < 0.001), no other perioperative parameters differed significantly between groups. Case-sequence analysis showed no consistent changes in operative efficiency or most accuracy parameters across sequential PSI cases. The AI-based planning system demonstrated significantly higher accuracy in prosthesis size prediction than conventional templating (P < 0.001). Short-term follow-up showed no significant between-group differences in OKS, VAS pain score, or patient satisfaction.
CONCLUSION: The AI-assisted planning system accurately predicted implant size, and PSI improved technical execution accuracy in medial UKA. However, short-term exploratory clinical outcomes did not differ between groups, and whether these technical gains translate into durable clinical benefit remains uncertain.