  The postoperative range of motion is one of the crucial factors indicating
the outcome of Total Knee Arthroplasty (TKA). Although the correlation between
range of knee flexion and posterior condylar offset (PCO) is controversial in
the literature, PCO maintains its importance on evaluation of TKA. Due to
limitations on PCO measurement, two novel parameters, posterior condylar offset
ratio (PCOR) and anterior condylar offset ratio (ACOR), were introduced.
Nowadays, the calculation of PCOR and ACOR on plain lateral radiographs is done
manually by orthopedic surgeons. In this regard, we developed a software,
AutoCOR, to calculate PCOR and ACOR autonomously, utilizing unsupervised
machine learning algorithm (k-means clustering) and digital image processing
techniques. The software AutoCOR is capable of detecting the anterior/posterior
edge points and anterior/posterior cortex of the femoral shaft on true
postoperative lateral conventional radiographs. To test the algorithm, 50
postoperative true lateral radiographs from Istanbul Kosuyolu Medipol Hospital
Database were used (32 patients). The mean PCOR was 0.984 (SD 0.235) in
software results and 0.972 (SD 0.164) in ground truth values. It shows strong
and significant correlation between software and ground truth values (Pearson
r=0.845 p<0.0001). The mean ACOR was 0.107 (SD 0.092) in software results and
0.107 (SD 0.070) in ground truth values. It shows moderate and significant
correlation between software and ground truth values (Spearman's rs=0.519
p=0.0001412). We suggest that AutoCOR is a useful tool that can be used in
clinical practice.
