Comparison of tibial alignment parameters based on clinically relevant anatomical landmarks

Abstract

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 0.34o (SD 2.4o) 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 0.65o (SD 0.55o) 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.

Artificial intelligence in orthopaedic surgery

“…AI image recognition may soon be a highly sought-after application in orthopaedics, corroborated in a study by Jang et al 47 where CNNs were reported to identify bone and soft-tissue landmarks as objects on radiographs. Additionally, more accurate calculations using the DL model for knee alignment may provide the potential for preoperative planning in total knee arthroplasty (TKA).…”

“…Additionally, more accurate calculations using the DL model for knee alignment may provide the potential for preoperative planning in total knee arthroplasty (TKA). 47 However, several limitations such as the established ground truths, radiograph quality, alignment, or rotation indicate the variability and, as such, these methods are not yet employed in preoperative planning for TKA. 47 …”

“… 47 However, several limitations such as the established ground truths, radiograph quality, alignment, or rotation indicate the variability and, as such, these methods are not yet employed in preoperative planning for TKA. 47 …”

A role for artificial intelligence applications inside and outside of the operating theatre: a review of contemporary use associated with total knee arthroplasty

“…Early work has suggested disproportionate accuracy in prediction of final femoral component sizing although algorithm refinements are likely to show improved utility for tibial component sizing also [ 67 ]. Coupled with high-resolution preoperative imaging, AI applications have shown value in identifying anatomical landmarks to guide construct alignment [ 69 ], which may benefit the precision obtained intraoperatively.…”

Acceptance and understanding of artificial intelligence in medical research among orthopaedic surgeons

“…3,4 This field is growing rapidly as the techniques are applied widely across orthopaedics. [5][6][7][8][9][10] Interpretation of AI data requires that some of the core principles of EBM are re-framed. Currently, AI research predominantly consists of retrospective analysis of datasets with an exploratory approach, which may be seen as a lower level of evidence.…”