Automated deep-learning quantification of nine patellofemoral instability parameters on multislice CT images : development and validation of the GU2Net model.

AIMS: Objective and precise measurement of patellar instability (PI) parameters on CT images is essential for accurate diagnosis and treatment planning. However, manual assessment is tedious, time-consuming, and prone to error. This study aimed to develop and validate a deep learning model that automatically quantifies PI parameters on axial knee CT images. METHODS: CT scans of 1,125 knees were randomly divided into training, validation, internal test, and hold-out test sets. A deep learning-based model was trained to localize anatomical landmarks and calculate nine PI parameters: lateral patellar tilt (LPT), bisect offset ratio (BSO), congruence angle (CA), sulcus angle (SA), trochlear groove depth (TGD), lateral trochlear inclination (LTI), trochlear groove-transepicondylar axis (TG-TEA) distance, tibial tubercle-trochlear groove (TT-TG) distance, and tibial tubercle-Roman arch (TT-RA) distance. Model performance was compared with manual measurements using the successful detection rate, mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient. RESULTS: The model accurately predicted landmark locations (MAE 0.84 to 2.72 mm) and PI parameters (ICC 0.826 to 0.997, r 0.705 to -0.994, p < 0.001) except for SA (ICC 0.701 to 0.862, r 0.542 to 0.744, p < 0.001). On the hold-out test set, the model outperformed inexperienced radiologists for LPT, CA, SA, LTI, and TGD (model: ICC 0.701 to 0.996, r 0.542 to 0.992, p < 0.001; radiologists: ICC 0.413 to 0.959, r 0.281 to 0.923, p < 0.05). CONCLUSION: The proposed deep learning model reliably automates PI measurement, reducing the time and variability associated with manual assessment and mitigating dependence on examiner experience.