Search papers, labs, and topics across Lattice.
This study introduces THA-Flow, a conditional flow-matching model that predicts three-dimensional prosthesis geometry from preoperative CT scans for total hip arthroplasty (THA). By employing separate AutoencoderKL models for bone and prosthesis geometry alongside a three-dimensional UNet, the model generates a range of clinically viable prosthesis configurations, addressing the inherent one-to-many nature of surgical planning. The results show high fidelity in generated geometries, achieving a peak signal-to-noise ratio of 47.11 dB and a structural similarity index of 0.9964, indicating significant potential for enhancing preoperative planning in THA.
Generative AI can now redefine surgical planning by producing multiple viable prosthesis configurations from a single CT scan.
Preoperative planning for total hip arthroplasty (THA) is commonly framed as selecting a single prosthesis configuration and placement for a patient's osseous anatomy. In practice, however, the same anatomy may admit several clinically reasonable solutions, making planning inherently a one-to-many problem that is better represented by a conditional probability distribution. We present THA-Flow, a conditional flow-matching model that generates three-dimensional prosthesis geometry directly from preoperative CT. Separate AutoencoderKL models compress preoperative bone anatomy and prosthesis geometry, while a three-dimensional UNet learns a rectified flow from Gaussian noise to the prosthesis latent space under spatial bone conditioning and optional structured prosthesis parameters. The retrospective cohort comprised 1,355 hips from 1,149 patients undergoing primary THA. Following rigid registration of postoperative CT to preoperative CT, the actual postoperative prostheses were transformed independently according to the pelvic and femoral registrations and represented as a dual-channel truncated signed distance field. The prosthesis autoencoder achieved a peak signal-to-noise ratio of 47.11 dB and a structural similarity index of 0.9964 on the validation set. Complete acetabular and femoral geometries were generated across seven major stem models representing 93.4% of the cohort. Repeated bone-conditioned sampling preserved component position, alignment, and the principal bone-prosthesis interfaces while allowing limited local geometric variation. To our knowledge, THA-Flow represents the first application of generative AI to three-dimensional surgical planning for THA.
