In The Journal of arthroplasty ; h5-index 65.0
BACKGROUND : Achieving adequate implant fixation is critical to optimize survivorship and postoperative outcomes after revision total knee arthroplasty (rTKA). Three anatomical zones (i.e., epiphysis, metaphysis, and diaphysis) have been proposed to assess fixation, but are not well-defined. The purpose of the study was to develop a deep learning workflow capable of automatically delineating rTKA zones and cone placements in a standardized way on postoperative radiographs.
METHODS : A total of 235 patients who underwent rTKA were randomly partitioned (6:2:2 training, validation, and testing split), and a U-Net segmentation workflow was developed to delineate rTKA fixation zones and assess revision cone placement on antero-posterior radiographs. Algorithm performance for zone delineation and cone placement were compared against ground truths from a fellowship-trained arthroplasty surgeon using the dice segmentation coefficient (DSC) and accuracy metrics.
RESULTS : On the testing cohort, the algorithm defined zones in 98% of images (8 seconds/image) using anatomical landmarks. The DSC between the model and surgeon was 0.89±0.08 (interquartile range[IQR]:0.88-0.94) for femoral zones, 0.91±0.08 (IQR:0.91-0.95) for tibial zones, and 0.90±0.05 (IQR:0.88-0.94) for all zones. Cone identification and zonal cone placement accuracy were 98% and 96%, respectively, for the femur and 96% and 89%, respectively, for the tibia.
CONCLUSION : A deep learning algorithm was developed to automatically delineate revision zones and cone placements on postoperative rTKA radiographs in an objective, standardized manner. The performance of the algorithm was validated against a trained surgeon, suggesting that the algorithm demonstrated excellent predictive capabilities in accordance with relevant anatomical landmarks used by arthroplasty surgeons in practice.
Jang Seong J, Flevas Dimitrios A, Kunze Kyle, Anderson Christopher, Fontana Mark A, Boettner Friedrich, Sculco Thomas P, Baldini Andrea, Sculco Peter K
2023-Feb-13
Artificial Intelligence, Deep Learning, Knee, Revision Total Knee Arthroplasty, Zonal Fixation
