Estimation of Pelvic Sagittal Inclination from Anteroposterior Radiograph Using Convolutional Neural Networks: Proof-of-Concept Study
This addresses a specific need in total hip arthroplasty surgical planning for a less invasive and more widely applicable method to estimate PSI, though it appears incremental as it builds on prior CT-based approaches.
The study tackled the problem of estimating pelvic sagittal inclination (PSI) angle from a single anteroposterior radiograph without needing patient-specific CT scans, using two convolutional neural networks (CNNs) to reduce radiation exposure and increase accessibility in hospitals lacking CT equipment.
Alignment of the bones in standing position provides useful information in surgical planning. In total hip arthroplasty (THA), pelvic sagittal inclination (PSI) angle in the standing position is an important factor in planning of cup alignment and has been estimated mainly from radiographs. Previous methods for PSI estimation used a patient-specific CT to create digitally reconstructed radiographs (DRRs) and compare them with the radiograph to estimate relative position between the pelvis and the x-ray detector. In this study, we developed a method that estimates PSI angle from a single anteroposterior radiograph using two convolutional neural networks (CNNs) without requiring the patient-specific CT, which reduces radiation exposure of the patient and opens up the possibility of application in a larger number of hospitals where CT is not acquired in a routine protocol.