Semiautomated Three-Dimensional Landmark Placement on Knee Models Is a Reliable Method to Describe Bone Shape and...

To assess the inter- and intrarater reliability of 21 anatomical landmarks initially placed with an artificial intelligence algorithm and then manually verified with human input.

Thirty computed tomography scans of the knees of participants from the Multicenter Osteoarthritis Study (MOST) ages 45 to 55 years were included. Approximately one-half experienced progression of patellofemoral osteoarthritis, defined as an increased cartilage score in the patellofemoral compartment on magnetic resonance imaging over 2 years. The algorithm automatically placed 19 anatomic landmarks on the femur, tibia, and patella. An additional 2 landmarks were added manually. Two landmark reviewers separately reviewed all 30 scans and verified all landmarks. After 2 weeks, one reviewer repeated the process for the same dataset. The mean Euclidean distance of manual landmark displacement, mean absolute disagreement between and within raters, and intraclass correlation coefficients for inter- and intrarater reliability were calculated.

All landmarks had excellent inter-rater reliability. The tibial and femoral shaft centers had intraclass correlation coefficients (ICCs) of 1, indicating their positions did not differ. Seventeen landmarks had ICCs between 0.90 and 0.99 and the tibial tuberosity had an ICC of 0.87. Intrarater reliability scores were 1 for 16 landmarks and between 0.90 and 0.99 for the remaining 5.

There was excellent agreement on the locations of all 21 landmarks evaluated in this study.

The potential role of artificial intelligence in medical imaging and orthopaedic research is a growing area of interest. The excellent reliability demonstrated across multiple landmarks in our study reveals the potential for semiautomated 3-dimensional methods to enhance precision of anatomical measurements of the knee over 2-dimensional methods.