An Artificial Intelligence Framework for the Diagnosis of Prosthetic Joint Infection Based on 99Mtc-Mdp Dynamic Bone Scintigraphy

Dynamic bone scintigraphy (DBS) is the first widely reliable and simple imaging modality in nuclear medicine that can be used to diagnose prosthetic joint infection (PJI). We aimed to apply artificial intelligence to diagnose PJI in patients after total hip or knee arthroplasty (THA or TKA) based on 99mTc-methylene diphosphonate (99mTc-MDP) DBS. A total of 449 patients (255 THAs and 194 TKAs) with a final diagnosis were retrospectively enrolled and analyzed. The dataset was divided into a training and validation set and an independent test set. A customized framework composed of two data preprocessing algorithms and a diagnosis model (dynamic bone scintigraphy effective neural network, DBS-eNet) was compared with mainstream modified classification models and experienced nuclear medicine specialists on corresponding datasets. In the fivefold cross-validation test, diagnostic accuracies of 86.48