The orthopedic field is on the brink of a significant transformation—a shift from retrospective analysis to real-time decision-making fueled by data. The dependence on historical trends or long-term studies is yielding to an era where data flows dynamically, allowing medical professionals to adjust protocols instantly. This isn’t just an evolution; it’s a revolution. Data is no longer a passive observer of outcomes—it’s an active participant in shaping them. Imagine a future where wearable devices, artificial intelligence (AI) algorithms, and predictive analytics come together to guide surgeons in real time. For example, wearables monitor vital signs during surgery and oversee rehabilitation while AI analyzes data to predict complications. Postoperative protocols adapt to individual recovery journeys, not averages. Complication risks are flagged preemptively, and treatment plans evolve with patient progress. This shift empowers orthopedic professionals to respond and anticipate, creating a level of care precision that was once unimaginable. What if we viewed data not merely as a tool but as collaborators? With AI and machine learning, the surgical suite of tomorrow transforms into ecosystems where data communicates directly providing insights, suggesting strategies, and enhancing outcomes. This collaborative approach encourages our conventional medical mindset to prioritize adaptability and individualization. The provocative truth is that the game-changer in orthopedics isn’t a new implant design or surgical technique—it’s the mindset shift to trust real-time data as the foundation of every decision. Orthopedics is no longer about perfecting procedures but refining protocols for every patient consistently. As we race toward the future, equitable access becomes crucial. As William Gibson noted, “The future is already here — it’s just not very evenly distributed.” We must ensure these breakthroughs reach everyone, bridging the gap between potential and practice. The future of orthopedics isn’t just a vision — it’s a promise, and it’s time to deliver.

Manual anatomical landmarking for morphometric knee bone characterization in orthopedics is highly time-consuming and shows high operator variability. Therefore, automation could be a substantial improvement for diagnostics and personalized treatments relying on landmark-based methods. Applications include implant sizing and planning, meniscal allograft sizing, and morphological risk factor assessment. For twenty MRI-based 3D bone and cartilage models, anatomical landmarks were manually applied by three experts, and morphometric measurements for 3D characterization of the distal femur and proximal tibia were calculated from all observations. One expert performed the landmark annotations three times. Intra- and inter-observer variations were assessed for landmark position and measurements. The mean of the three expert annotations served as the ground truth. Next, automated landmark annotation was performed by elastic deformation of a template shape, followed by landmark optimization at extreme positions (highest/lowest/most medial/lateral point). The results of our automated annotation method were compared with ground truth, and percentages of landmarks and measurements adhering to different tolerances were calculated. Reliability was evaluated by the intraclass correlation coefficient (ICC). For the manual annotations, the inter-observer absolute difference was 1.53 ± 1.22 mm (mean ± SD) for the landmark positions and 0.56 ± 0.55 mm (mean ± SD) for the morphometric measurements. Automated versus manual landmark extraction differed by an average of 2.05 mm. The automated measurements demonstrated an absolute difference of 0.78 ± 0.60 mm (mean ± SD) from their manual counterparts. Overall, 92% of the automated landmarks were within 4 mm of the expert mean position, and 95% of all morphometric measurements were within 2 mm of the expert mean measurements. The ICC (manual versus automated) for automated morphometric measurements was between 0.926 and 1. Manual annotations required on average 18 min of operator interaction time, while automated annotations only needed 7 min of operator-independent computing time. Considering the time consumption and variability among observers, there is a clear need for a more efficient, standardized, and operator-independent algorithm. Our automated method demonstrated excellent accuracy and reliability for landmark positioning and morphometric measurements. Above all, this automated method will lead to a faster, scalable, and operator-independent morphometric analysis of the knee.

Purpose: This study aimed to compare the return to sports, return to competition, Tegner score and anterior cruciate ligament‐return to sports injury (ACL‐RSI) scores between patients who underwent ACL reconstruction (ACLR) combined with anterolateral ligament reconstruction (ALLR) and those who underwent ACLR alone.Methods: Two independent reviewers conducted a literature search in PubMed (MEDLINE), EMBASE, Google Scholar and the Cochrane Library in July 2024, followed by data extraction and quality assessment. This study followed the Preferred Reporting Items for Systematic Reviews and meta‐analysis guidelines. The return to sports rate, return to competition rate, Tegner score and ACL‐RSI score were compared between patients who underwent primary ACLR with ALLR and those who underwent isolated primary or revision ACLR. The methodological quality of the included studies was assessed via the Cochrane risk‐of‐bias tool and methodological items for nonrandomized studies.Results: In total, 12,139 studies were screened, and 14 (four randomized controlled trials and 10 nonrandomized studies) studies were ultimately evaluated. Compared with isolated ACLR, ACLR combined with ALLR resulted in a higher rate of return to sports and competition. Nevertheless, no significant differences were found in the Tegner score or ACL‐RSI score between the two groups.Conclusion: Patients who underwent ACLR in combination with ALLR had higher rates of return to sports and competition, but their Tegner activity and ACL‐RSI scores were similar to those of patients who underwent ACLR alone. This finding may assist surgeons in making decisions when treating patients undergoing ACLR, especially athletes.Level of Evidence: Level III.

Anterior cruciate ligament injuries are common seen in active and young patients. The ACL reconstruction technique in skeletally immature patients tries to minimize the growth disturbance. All physeal sparing technique is technically demanding. In this paper, we described physeal sparing technique of ACL reconstruction in skeletally immature patient. This is an all-epiphyseal all-inside ACL reconstruction with retrodrill of the femoral and the tibial sockets. Both sockets are within epiphysis. This technique used free loop system. This free loop system allows us to make a socket with length 15 mm, that is safe from the physis. This socket also allows us to tension the graft adequately with minimum sockets length. We present a case of 16 years old male who underwent all-epiphyseal all inside reconstruction with our rehabilitation protocol.