Oeding, Jacob F

Artificial intelligence (AI) has emerged as a transformative force in orthopedic surgery. Potentially encompassing pre-, intra-, and postoperative processes, it can process complex medical imaging, provide real-time surgical guidance, and analyze large datasets for outcome prediction and optimization. AI has shown improvements in surgical precision, efficiency, and patient outcomes across orthopedic subspecialties, and large language models and agentic AI systems are expanding AI utility beyond surgical applications into areas such as clinical documentation, patient education, and autonomous decision support. The successful implementation of AI in orthopedic surgery requires careful attention to validation, regulatory compliance, and healthcare system integration. As these technologies continue to advance, maintaining the balance between innovation and patient safety remains crucial, with the ultimate goal of achieving more personalized, efficient, and equitable healthcare delivery while preserving the essential role of human clinical judgment. This review examines the current landscape and future trajectory of AI applications in orthopedic surgery, highlighting both technological advances and their clinical impact. Studies have suggested that AI-assisted procedures achieve higher accuracy and better functional outcomes compared to conventional methods, while reducing operative times and complications. However, these technologies are designed to augment rather than replace clinical expertise, serving as sophisticated tools to enhance surgeons’ capabilities and improve patient care.

Background: When injuring the anterior cruciate ligament (ACL), simultaneous meniscal tears often occur. However, little data describe ACL reconstruction (ACLR) and the subsequent risk of revision ACLR, as well as cartilaginous and meniscal damage after primary ACLR with different treatments for injury of the lateral meniscus (LM). Purpose: To investigate (1) how different LM treatments during primary ACLR affect the odds of revision ACLR and the cartilaginous and meniscal status at revision in a long-term follow-up and (2) whether the increased risk of subsequent meniscal and cartilaginous injuries seen with meniscal resection in the short term would continue in the long term, and (3) the increased risk with subsequent meniscal injuries with meniscal repair. Study Design: Cohort study; Level of evidence, 3. Methods: Data from the Swedish National Knee Ligament Registry were used. The study compared 2 groups: patients with isolated ACLR and patients with ACLR and concomitant LM injury, further subdividing the latter into 4 treatment types: repair, resection, both, and left in situ. Patients with medial meniscal injuries were excluded. The main outcomes studied were the odds for revision ACLR and the cartilaginous and meniscal status at revision 5 and 10 years after primary surgery. Results: Data for 5 years were available for 22,208 patients and data for 10 years were available for 11,058 patients. Compared with isolated ACLR, patients with concomitant LM injury left in situ had higher odds of revision ACLR at both 5 years (odds ratio [OR], 1.49; 95% CI, 1.14-1.95; P = .004) and 10 years (OR, 1.55; 95% CI, 1.09-2.19; P = .01), while those with LM repair had higher odds at 5 years (OR, 1.73; 95% CI, 1.23-2.43; P = .002). LM repair also increased the odds of subsequent meniscal injuries at 5 years (OR, 3.52; 95% CI, 1.76-7.03; P = .0004) and 10 years (OR, 7.26; 95% CI, 1.67-31.52; P = .008). Partial meniscectomy did not show an increased risk of cartilage injury, subsequent meniscal injury, or revision ACLR at long-term follow-up compared with the control group. Conclusion: This demonstrates that over 5 and 10 years, LM repair during primary ACLR is associated with an increased risk of revision ACLR and subsequent meniscal injuries. The higher risk of revision ACLR when the LM is left in situ emphasizes the need for careful consideration of treatment methods. Conversely, partial meniscectomy did not show an associated increased risk of cartilaginous injury in the long term and is associated with a lower risk of subsequent meniscal injury and revision ACLR than other treatment options, raising questions about whether preserving the meniscus at all costs is always the best approach.

Background: Randomized controlled trials (RCTs) evaluating the efficacy of platelet-rich plasma (PRP) for the management of lateral epicondylitis (LE) have been characterized by substantial variability in reported outcomes. The source of this heterogeneity is uncertain. Purpose: To determine the effect of estimated platelet concentration on the efficacy of PRP for the management of LE. Study Design: Systematic review and meta-analysis; Level of evidence, 2. Methods: All RCTs evaluating the efficacy of PRP in managing LE were identified. RCTs were classified according to whether the study documented a platelet concentration factor of PRP representing a greater than 3-fold increase over whole blood or a supraphysiological platelet dose (high-dose vs low-dose PRP). The primary outcome was the mean difference (MD) in the visual analog scale (VAS) score at latest follow-up. Random-effects and mixed-effects meta-analyses were performed, and meta-regression was used to evaluate whether differences in outcomes after treatment with PRP could be explained by differences in the concentration of PRP used. Results: Overall, 13 RCTs with a total of 791 patients were included in this analysis, with 5 that utilized low-dose PRP and 8 that used high-dose PRP. Meta-analysis of VAS scores reported by studies that used high-dose PRP resulted in an MD of −1.31 (95% CI, −1.87 to −0.75) in favor of PRP over all alternative treatment strategies ( P < .001). Meta-analysis of VAS scores reported by studies that used low-dose PRP resulted in an MD of 0.08 (95% CI, −0.51 to 0.68), suggesting no difference in the effect between PRP and all alternative treatment strategies ( P = .79). The platelet concentration factor of PRP used in each RCT was found to be strongly predictive of the VAS score at final follow-up in meta-regression ( P < .001), with 58.5% of the heterogeneity in the outcomes of PRP between studies explained by the platelet concentration factor alone. Conclusion: The platelet concentration of PRP may play a significant role in the outcomes of patients with LE. A direct linear relationship was observed between the platelet concentration factor of PRP used and the magnitude of patient-reported symptom relief after the management of LE with PRP. Clinicians should ensure a supraphysiological platelet concentration when preparing PRP for the management of LE.

Abstract Deep learning is revolutionizing medical imaging analysis by enabling the classification of various pathoanatomical conditions at scale. Unfortunately, there have been a limited number of accurate and efficient machine learning (ML) algorithms that have been developed for the diagnostic workup of morphological hip pathologies, including developmental dysplasia of the hip and femoroacetabular impingement. The current study reports on the performance of a novel ML model with YOLOv5 and ConvNeXt-Tiny architecture in predicting the morphological features of these conditions, including cam deformity, ischial spine sign, dysplastic appearance, and other abnormalities. The model achieved 78.0% accuracy for detecting cam deformity, 87.2% for ischial spine sign, 76.6% for dysplasia, and 71.6% for all abnormalities combined. The model achieved an Area under the Receiver Operating Curve of 0.89 for ischial spine sign, 0.80 for cam deformity, 0.80 for dysplasia, and 0.81 for all abnormalities combined. Inter-rater agreement among surgeons, assessed using Gwet’s AC1, was substantial for dysplasia (0.83) and all abnormalities (0.88), and moderate for ischial spine sign (0.75) and cam deformity (0.61).

Background: A retear after anterior cruciate ligament (ACL) reconstruction remains a common and devastating complication. Knee bone morphology is associated with the risk of ACL injuries, ACL retears, and osteoarthritis, and a combination of tools that derive bone shape from clinical imaging, such as magnetic resonance imaging (MRI) and statistical shape modeling, could identify patients at risk of developing these joint conditions. Purpose: To identify bone shape features before primary ACL reconstruction in patients with an eventual retear compared to those with a known intact ACL graft. Study Design: Case-control study; Level of evidence, 3. Methods: Bone was automatically segmented on 2-dimensional proton density–weighted MRI of the knee in patients at the time of the initial ACL injury using deep convolutional neural networks. Patients with a subsequent retear after reconstruction within 3 years (22 femurs, 19 tibias) were compared with those with an intact ACL graft at 3 years (20 femurs, 22 tibias) using statistical shape modeling to identify preoperative bone shape features predictive of a retear after ACL reconstruction. Results: Statistical shape modeling revealed 2 specific bone shape features (modes) in the femur and 1 mode in the tibia that demonstrated significant differences at the time of the initial injury in patients with subsequent retears. In the femur, a narrower intercondylar notch width, a widened medial condylar width, an increased femoral condylar offset ratio, increased surface area along the lateral femoral condyle relative to the medial condyle, and a more prominent trochlear sulcus at the time of the initial injury were associated with retears after ACL reconstruction. In the tibia, a diminished ACL facet prominence, a squared lateral and medial tibial plateaus, and a broader and flattened tibial spine at the time of the initial injury were associated with retears after ACL reconstruction. Conclusion: Using the automatic bone segmentation pipeline on preoperative MRI, the authors identified bone shape features associated with a retear after ACL reconstruction. The use of this pipeline enables large-scale studies of bone shape on MRI and could predict patients at risk of ACL retears to alter treatment decisions.

Abstract Purpose The purpose of this article is to explore the potential of digital twin technologies in orthopaedics and to evaluate how their integration with artificial intelligence (AI) and deep learning (DL) can improve orthopaedic evaluation and treatment. This review addresses key applications of digital twins, including surgical planning, patient‐specific outcome prediction, augmented reality‐assisted surgery and simulation‐based surgical training. Methods Existing studies on digital twins in various domains, including engineering, biomedical and orthopaedics are reviewed. We also reviewed advancements in AI and DL relevant to digital twins. We focused on identifying key benefits, challenges and future directions for the implementation of digital twins in orthopaedic practice. Results The review highlights that digital twins offer significant potential to revolutionise orthopaedic care by enabling precise surgical planning, real‐time outcome prediction and enhanced training. Digital twins can model patient‐specific anatomy using advanced imaging techniques and dynamically update with real‐time data, providing valuable insights during surgery and postoperative care. However, challenges such as the need for large‐scale data sets, technological limitations and integration issues must be addressed to fully realise these benefits. Conclusion Digital twins represent a promising frontier in orthopaedic research and practice, with the potential to improve patient outcomes and enhance surgical precision. To enable widespread adoption, future research must focus on overcoming current challenges and further refining the integration of digital twins with AI and DL technologies. Level of Evidence Level V.

Purpose To evaluate the available literature on the relationship between knee bony morphology and medial meniscus posterior root tears (MMPRTs) to determine which tibiofemoral morphologic risk factors may predispose the development of MMPRTs. Methods Embase, MEDLINE, and PubMed databases were searched to identify all relevant human clinical studies investigating knee morphologic features and MMPRTs. Shape features were compared between control groups and patients with MMPRTs. The methodological index for nonrandomized studies (MINORS) instrument was utilized to assess the methodological quality of included studies. Results Thirteen level III evidence studies and 1 level IV evidence study were included in this review (n = 2,181), with 895 patients in the MMPRT group and 1,286 in the control group. Tibial morphology features associated with an increased risk for MMPRTs included an increased medial tibial slope (6 studies), increased tibial torsion (1 study), increased medial meniscal slope (1 study), and shallower medial tibial plateau concavity (1 study). Varus mechanical alignment was found to increase the risk for MMPRTs (4 studies). Femoral morphology features associated with an increased risk for MMPRTs included an A‐type intercondylar notch (1 study), increased medial femoral condyle (MFC) angle (1 study), narrower intercondylar distance (1 study), narrower intercondylar notch width (1 study), shorter MFC distal offset distance (1 study), increased MFC width (1 study), increased MFC to medial tibial condyle width ratio (1 study), greater distance between the medial tibial eminence and MFC (1 study), and smaller femoral offset ratios of both the medial and lateral condyles (1 study). Conclusions Multiple tibiofemoral shape features, including a steeper slope of the medial tibial plateau, greater varus alignment, and a narrower intercondylar distance and notch width, were found to be predictive factors for MMPRTs. Level of Evidence Level IV, systematic review of Level III and IV studies.

Background: Value-based decision-making regarding nonoperative management versus early surgical stabilization for first-time anterior shoulder instability (ASI) events remains understudied. Purpose: To perform (1) a systematic review of the current literature and (2) a Markov model–based cost-effectiveness analysis comparing an initial trial of nonoperative management to arthroscopic Bankart repair (ABR) for first-time ASI. Study Design: Economic and decision analysis; Level of evidence, 3. Methods: A Markov chain Monte Carlo probabilistic model was developed to evaluate the outcomes and costs of 1000 simulated patients (mean age, 20 years; range, 12-26 years) with first-time ASI undergoing nonoperative management versus ABR. Utility values, recurrence rates, and transition probabilities were derived from the published literature. Costs were determined based on the typical patient undergoing each treatment strategy at the authors’ institution. Outcome measures included costs, quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio (ICER). Results: The Markov model with Monte Carlo microsimulation demonstrated mean (± standard deviation) 10-year costs for nonoperative management and ABR of $38,649 ± $10,521 and $43,052 ± $9352, respectively. Total QALYs acquired over the 10-year time horizon were 7.67 ± 0.43 and 8.44 ± 0.46 for nonoperative management and ABR, respectively. The ICER comparing ABR with nonoperative management was found to be just $5725/QALY, which falls substantially below the $50,000 willingness-to-pay (WTP) threshold. The mean numbers of recurrences were 2.55 ± 0.31 and 1.17 ± 0.18 for patients initially assigned to the nonoperative and ABR treatment groups, respectively. Of 1000 samples run over 1000 trials, ABR was the optimal strategy in 98.7% of cases, with nonoperative management the optimal strategy in 1.3% of cases. Conclusion: ABR reduces the risk for recurrent dislocations and is more cost-effective despite higher upfront costs when compared with nonoperative management for first-time ASI in the young patient. While all these factors are important to consider in surgical decision-making, ultimate treatment decisions should be made on an individual basis and occur through a shared decision-making process.

Background: Understanding interactions between multiple risk factors for shoulder and elbow injuries in Major League Baseball (MLB) pitchers is important to identify potential avenues by which risk can be reduced while minimizing impact on player performance. Purpose: To apply a novel game theory–based approach to develop a machine-learning model predictive of next-season shoulder and elbow injuries in MLB pitchers and use this model to understand interdependencies and interaction effects between the most important risk factors. Study Design: Case-control study; Level of evidence, 3. Methods: Pitcher demographics, workload measures, pitch-tracking metrics, and injury data between 2017 and 2022 were used to construct a database of MLB pitcher-years, where each item in the database corresponded to a pitcher's information and metrics for that year. An extreme gradient boosting machine-learning model was trained to predict next-season shoulder and elbow injuries utilizing Shapley additive explanation values to quantify feature importance as well as interdependencies and interaction effects between predictive variables. Results: A total of 3808 pitcher-years were included in this analysis; 606 (15.9%) of these involved a shoulder or elbow injury resulting in placement on the MLB injured list. Of the >65 candidate features (including workload, demographic, and pitch-tracking metrics), the most important contributors to predicting shoulder/elbow injury were increased: pitch velocity (all pitch types), utilization of sliders (SLs), fastball (FB) spin rate, FB horizontal movement, and player age. The strongest game theory interaction effects were that higher FB velocity did not alter a younger pitcher's predicted risk of shoulder/elbow injury versus older pitchers, risk of shoulder/elbow injury increased with the number of high-velocity pitches thrown (regardless of pitch type and in an additive fashion), and FB velocity <95 mph (<152.9 kph) demonstrated strong negative interaction effects with higher SL percentage, suggesting that the overall predicted risk of injury for pitchers throwing a high number of SLs could be attenuated by lower FB velocity. Conclusion: Pitch-tracking metrics were substantially more predictive of future injury than player demographics and workload metrics. There were many significant game theory interdependencies of injury risk. Notably, the increased risk of injury that was conferred by throwing with a high velocity was even further magnified if the pitchers were also older, threw a high percentage of SLs, and/or threw a greater number of pitches.