Efficacy Analysis of Selection of Distal Reference Point for Tibial Coronal Plane Osteotomy during Total Knee Arthroplasty: A Literature Review

Improved angle accuracy of tibial plateau osteotomy for total knee arthroplasty using tibial mechanical axis skin-mapping

Background

The tibial crest is often used as an anatomic landmark for tibial plateau osteotomy (TPO) in total knee arthroplasty (TKA), but it is not very accurate. This study aimed to investigate errors in using the tibial crest as a marker and present a simple approach to improve the angle accuracy of TPO by mapping the tibial mechanical axis (TMA), determined preoperatively, according to the tibial crest on the skin overlying the tibia.

Methods

We evaluated 50 healthy young volunteers and 100 pre-TKA osteoarthritic knees. The middle tibial crest lines (MTCLs) were marked on the shank tibial skin and covered with Kirschner wires. All participants underwent two sets of anteroposterior (AP) standing radiographs of the lower extremity, with the feet in neutral and external rotation positions. The MTCL-TMA angles were measured and compared. The TMA was mapped onto the tibial skin according to the MTCL-TMA angle prior to TKA and used for TPO. Postoperative outcomes were determined by the angle between the vertical tibial component axis (TCA) and the TMA.

Results

The MTCL had no evident relationship with the TMA. A few MTCLs were parallel to the TMA. External rotation of the foot significantly changed the MTCL-TMA relationship. The angle accuracy of the TPO as guided by TMA skin-mapping was 0.83 ± 0.76°. No postoperative errors exceeded 3°.

Conclusion

The MTCL was not equivalent to the TMA. The TPO error can be reduced by preoperatively marking the TMA on the tibial skin according to the MTCL.

Comparison of tibial alignment parameters based on clinically relevant anatomical landmarks : a deep learning radiological analysis

AIMS

Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre.

METHODS

Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli.

RESULTS

A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34^o (SD 2.4^o) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65^o (SD 0.55^o) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre.

CONCLUSION

The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning.Cite this article: Bone Jt Open 2022;3(10):767-776.