Aim of this prospective study was to evaluate mid-term clinical and radiographic outcomes in total hip arthroplasty using an acetabular cup made of an innovative biomaterial, Trabecular Titanium™, whose highly porous structure and mechanical properties have been designed to mimic those of the natural bone, thus promoting a more physiological load transfer and a more durable fixation. Between September 2007 and November 2009, 134 total hip replacements and eight revisions were carried out using DELTA-TT primary cups (Lima Corporate, Villanova di San Daniele del Friuli, Italy) in 133 consecutive patients. Mean age was 57.5 ± 14.7 SD (18–92) years. Diagnosis was primarily hip osteoarthritis in 85 (63 %) cases, developmental dysplasia of the hip (DDH) in 24 (18 %) and hip avascular necrosis (AVN) in 10 (7 %). All the revision procedures were due to aseptic loosening of the original implant. Approval of the Institutional Review Board of the IRCCS Policlinico San Matteo in Pavia was obtained for this study. Mean follow-up was 72.7 ± 7.9 SD (60–86) months. Average Harris Hip Score (HHS) significantly increased from 44.2 ± 5.4 SD (35–52) preoperatively to 95.9 ± 3.5 SD (88–100) at the last follow-up. No major post-operative complications were observed. 99.3 % of the acetabular components were radiographically stable at the last follow-up, without any radiolucent lines, sclerotic areas or periprosthetic osteolysis. Kaplan-Meier survival rate was 99.3 % at 5 years (95 % confidence interval). This first account on the mid-term clinical performance of the DELTA-TT cup shows primary and secondary stability, thus representing an optimal solution for patients with high demands or affected by severe hip conditions.

The remarkable progress in data aggregation and deep learning algorithms has positioned artificial intelligence (AI) and machine learning (ML) to revolutionize the field of medicine. AI is becoming more and more prevalent in the healthcare sector, and its impact on orthopedic surgery is already evident in several fields. This review aims to examine the literature that explores the comprehensive clinical relevance of AI-based tools utilized before, during, and after anterior cruciate ligament (ACL) reconstruction. The review focuses on current clinical applications and future prospects in preoperative management, encompassing risk prediction and diagnostics; intraoperative tools, specifically navigation, identifying complex anatomic landmarks during surgery; and postoperative applications in terms of postoperative care and rehabilitation. Additionally, AI tools in educational and training settings are presented. Orthopedic surgeons are showing a growing interest in AI, as evidenced by the applications discussed in this review, particularly those related to ACL injury. The exponential increase in studies on AI tools applicable to the management of ACL tears promises a significant future impact in its clinical application, with growing attention from orthopedic surgeons.