The relevance of prosthetic component orientation to prevent dislocation and impingement following total hip arthroplasty (THA) has been widely accepted. We investigated the use of a non-computer-based surgery to address the reciprocal orientation of the acetabular and femoral components. In the femur first technique, the cup is positioned relative to the stem. When the definitive antetorsion of femoral component is fixed, the cup is positioned in a compliant anteversion to the stem. Clinical and radiographic assessments were performed before and 3 months after THA. Radiographic assessment was performed in standing position with the EOS 2D/3D radiography system. 3D images were used to preoperative anterior pelvic plane (APP) angle, postoperative acetabular inclination (AI) and anteversion (AA), and postoperative stem antetorsion. Clinical assessment was performed with Harris Hip Score (HHS). Forty patients (40 hips) underwent primary THA with an average age of 61 years (range, 36–84). Average HHS increased from 43 ± 5 (range, 37–52) preoperatively to 97 ± 6 (range, 86–100) at the last follow-up (P < 0.0001). Average combined anteversion value of cup with liner and stem was 38° ± 9° (range, 12°-55°). Average AI value of cup with liner was 39° ± 6° (range, 30°-55°) in the group with standard stem and 45° ± 7° (range, 39°-58°) in the group with varized stem (P = 0.007). Relationship analysis showed no correlation between the combined anteversion values of the cup with liner and stem with APP angle values (r = 0.26, P = 0.87). Femur first technique allows the surgeon to achieve a combined anteversion ranging from 25° to 50° with a cup inclination ranging from 30° to 50°. The cup is positioned according to the functional plane of the patient regardless the preoperative pelvic tilt.

Background: Periprosthetic joint infection (PJI) remains a significant and complex complication following total hip and knee arthroplasty. This study aims to design, validate, and assess a machine learning (ML) model for predicting the likelihood of PJI in individuals undergoing revision arthroplasty procedures. Methods: A retrospective analysis was conducted on patients who underwent hip or knee revision arthroplasty between 1 January 2015 and 31 March 2021. Data were collected from preoperative clinical histories, laboratory results, and patient demographics. The final dataset was used to train multiple classification models for the preoperative prediction of PJI. Results: A total of 1360 patients were included, comprising 1141 cases in the aseptic group and 219 in the infected group. The best-performing model, a Linear Support Vector Machine (SVM), demonstrated reasonable predictive capability for PJI, achieving an area under the curve (AUC) of 0.770 ± 0.008 in the training set and 0.730 ± 0.078 in the testing set. Additionally, three key predictors of PJI were identified. Conclusions: The Linear SVM model, developed using preoperative clinical information, exhibited reasonable performance in predicting PJI. While further refinement and validation are necessary, integrating ML tools into the preoperative evaluation process has the potential to enhance personalized risk assessment, support informed decision-making, and optimize surgical preparation for patients undergoing prosthetic revision surgery.

Background: Single-stage hardware removal and total hip arthroplasty is a complex surgical procedure, comparable to revision surgery. The purpose of the current study is to evaluate single-stage hardware removal and THA outcomes, compare this technique with a matched control group that has undergone primary THA and assess the risk of periprosthetic joint infection with a 24-month minimum follow-up. Methods: This study included all those cases treated with THA and concomitant hardware removal from 2008 to 2018. The control group was selected on a 1:1 ratio among patients who underwent THA for primary OA. The Harris Hip (HHS) and University of California at Los Angeles Activity (UCLA) scores, infection rate and early and delayed surgical complications were recorded. Results: One hundred and twenty-three consecutive patients (127 hips) were included, and the same number of patients was assigned to the control group. The final functional scores were comparable between the two groups; a longer operative time and transfusion rate were recorded in the study group. Finally, an increased incidence of overall complications was reported (13.8% versus 2.4%), but no cases of early or delayed infection were found. Conclusions: Single-stage hardware removal and THA is a safe and effective but technically demanding technique, with a higher incidence of overall complications, making it more similar to revision THA than to primary THA.

The burden of osteoarthritis (OA) is around 300 million people affected worldwide, with the hip representing a commonly affected joint. Total hip arthroplasty (THA) has been used with notable success as a definitive treatment to improve pain and function in hip OA patients. The recent advent of new technologies, such as 3D printing, has pushed the application of these new concepts toward applications for the well-known THA. Currently, the evidence on the use of 3D printing to aid complex primary THA cases is still scarce. Methods: An extensive literature review was conducted to retrieve all articles centered on the use of 3D printing in the setting of primary THA. Results: A total of seven studies were included in the present systematic review. Four studies investigated the use of 3D-printed surgical guides to be used during surgery. The remaining three studies investigated the benefit of the use of 3D-printed templates of the pelvis to simulate the surgery. Conclusions: The use of 3D printing could be a promising aid to solve difficult primary total hip arthroplasty cases. However, the general enthusiasm in the field is not supported by high-quality studies, hence preventing us from currently recommending its application in everyday practice.

Background and objectives: The increasing volume of total hip and knee arthroplasty created a significant postoperative surveillance burden. While plain radiographs are standard, the detection of aseptic loosening is subjective. This review evaluates the state of the art regarding AI in radiographic analysis for identifying aseptic loosening and mechanical failure in primary hip and knee prostheses. Methods: A systematic search in PubMed, Scopus, Web of Science, and Cochrane was conducted up to November 2025, following PRISMA guidelines. Peer-reviewed studies describing AI tools applied to radiographs for detecting aseptic loosening or implant failure were included. Studies focusing on infection or acute complications were excluded. Results: Ten studies published between 2020 and 2025 met the inclusion criteria. In internal testing, AI models demonstrated high diagnostic capability, with accuracies ranging from 83.9% to 97.5% and AUC values between 0.86 and 0.99. A performance drop was observed during external validation. Emerging trends include the integration of clinical variables and the use of sequential imaging. Conclusions: AI models show robust potential to match or outperform standard radiographic interpretation for detecting failure. Clinical deployment is limited by variable performance on external datasets. Future research must prioritize robust multi-institutional validation, explainability, and integration of longitudinal data.

Background: The growth of arthroplasty procedures requires innovative strategies to reduce inpatients’ hospital length of stay (LOS). This study aims to develop a machine learning prediction model that may aid in predicting LOS after hip or knee arthroplasties. Methods: A collection of all the clinical notes of patients who underwent elective primary or revision arthroplasty from 1 January 2019 to 31 December 2019 was performed. The hospitalization was classified as “short LOS” if it was less than or equal to 6 days and “long LOS” if it was greater than 7 days. Clinical data from pre-operative laboratory analysis, vital parameters, and demographic characteristics of patients were screened. Final data were used to train a logistic regression model with the aim of predicting short or long LOS. Results: The final dataset was composed of 1517 patients (795 “long LOS”, 722 “short LOS”, p = 0.3196) with a total of 1541 hospital admissions (729 “long LOS”, 812 “short LOS”, p < 0.001). The complete model had a prediction efficacy of 78.99% (AUC 0.7899). Conclusions: Machine learning may facilitate day-by-day clinical practice determination of which patients are suitable for a shorter LOS and which for a longer LOS, in which a cautious approach could be recommended.