Citations

Background: Treatment of ulnar collateral ligament (UCL) tears with suture tape augmentation has gained interest given preliminary reports of favorable biomechanical characteristics. No study to date has quantitatively assessed the biomechanical effects of multiple augmentation techniques relative to the native UCL. Purpose: To perform a systematic review and meta-analysis of controlled laboratory studies to assess and comparatively rank biomechanical effects of UCL repair or reconstruction with or without augmentation. Study Design: Systematic review and meta-analysis; Level of evidence, 4. Methods: PubMed, OVID/Medline, and Cochrane databases were queried in January 2023. A frequentist network meta-analytic approach was used to perform mixed-treatment comparisons of UCL repair and reconstruction techniques with and without augmentation, with the native UCL as the reference condition. Pooled treatment estimates were quantified under the random-effects assumption. Competing treatments were ranked in the network meta-analysis by using point estimates and standard errors to calculate P scores (greater P score indicates superiority of treatment for given outcome). Results: Ten studies involving 206 elbow specimens in which a distal UCL tear was simulated were included. UCL reconstruction with suture tape augmentation (AugRecon) restored load to failure to a statistically noninferior magnitude (mean difference [MD], –1.99 N·m; 95% CI, –10.2 to 6.2 N·m; P = .63) compared with the native UCL. UCL reconstruction (Recon) (MD, –12.7 N·m; P < .001) and UCL repair with suture tape augmentation (AugRepair) (MD, –14.8 N·m; P < .001) were both statistically inferior to the native UCL. The AugRecon condition conferred greater load to failure compared with Recon ( P < .001) and AugRepair ( P = .002) conditions. AugRecon conferred greater torsional stiffness relative to all other conditions and was not statistically different from the native UCL (MD, 0.32 N·m/deg; 95% CI, –0.30 to 0.95 N·m/deg; P = .31). Medial ulnohumeral gapping was not statistically different for the AugRepair (MD, 0.30 mm; 95% CI, –1.22 to 1.82 mm; P = .70), AugRecon (MD, 0.57 mm; 95% CI, –0.70 to 1.84 mm; P = .38), or Recon (MD, 1.02 mm; 95% CI, –0.02 to 2.05 mm; P = .055) conditions compared with the native UCL. P-score analysis indicated that AugRecon was the most effective treatment for increasing ultimate load to failure and torsional stiffness, whereas AugRepair was the most effective for minimizing medial gapping. Conclusion: AugRecon restored load to failure and torsional stiffness most similar to the parameters of the native UCL, whereas Recon and AugRepair did not restore the same advantageous properties at time zero. Medial ulnohumeral gapping during a valgus load was minimized by all 3 treatments. Based on network interactions, AugRecon was the superior treatment approach for restoring important biomechanical features of the UCL at time zero that are jeopardized during a complete distal tear.

Background: An increased posterior tibial slope (PTS) corresponds with an increased risk of graft failure after anterior cruciate ligament (ACL) reconstruction (ACLR). Validated methods of manual PTS measurements are subject to potential interobserver variability and can be inefficient on large datasets. Purpose/Hypothesis: To develop a deep learning artificial intelligence technique for automated PTS measurement from standard lateral knee radiographs. It was hypothesized that this deep learning tool would be able to measure the PTS on a high volume of radiographs expeditiously and that these measurements would be similar to previously validated manual measurements. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: A deep learning U-Net model was developed on a cohort of 300 postoperative short-leg lateral radiographs from patients who underwent ACLR to segment the tibial shaft, tibial joint surface, and tibial tuberosity. The model was trained via a random split after an 80 to 20 train-validation scheme. Masks for training images were manually segmented, and the model was trained for 400 epochs. An image processing pipeline was then deployed to annotate and measure the PTS using the predicted segmentation masks. Finally, the performance of this combined pipeline was compared with human measurements performed by 2 study personnel using a previously validated manual technique for measuring the PTS on short-leg lateral radiographs on an independent test set consisting of both pre- and postoperative images. Results: The U-Net semantic segmentation model achieved a mean Dice similarity coefficient of 0.885 on the validation cohort. The mean difference between the human-made and computer-vision measurements was 1.92° (σ = 2.81° [ P = .24]). Extreme disagreements between the human and machine measurements, as defined by ≥5° differences, occurred <5% of the time. The model was incorporated into a web-based digital application front-end for demonstration purposes, which can measure a single uploaded image in Portable Network Graphics format in a mean time of 5 seconds. Conclusion: We developed an efficient and reliable deep learning computer vision algorithm to automate the PTS measurement on short-leg lateral knee radiographs. This tool, which demonstrated good agreement with human annotations, represents an effective clinical adjunct for measuring the PTS as part of the preoperative assessment of patients with ACL injuries.

Purpose To perform a systematic review of the literature to evaluate (1) activity level and knee function, (2) reoperation and failure rates, and (3) risk factors for reoperation and failure of autologous osteochondral transfer (AOT) at long‐term follow‐up. Methods A comprehensive review of the long‐term outcomes of AOT was performed. Studies reported on activity‐based outcomes (Tegner Activity Scale) and clinical outcomes (Lysholm score and International Knee Documentation Committee score). Reoperation and failure rates as defined by the publishing authors were recorded for each study. Modified Coleman Methodology Scores were calculated to assess study methodological quality. Results Twelve studies with a total of 495 patients and an average age of 32.5 years at the time of surgery and a mean follow‐up of 15.1 years (range, 10.4‐18.0 years) were included. The mean defect size was 3.2 cm 2 (range, 1.9‐6.9 cm 2 ). The mean duration of symptoms before surgery was 5.1 years. Return to sport rates ranged from 86% to 100%. Conversion to arthroplasty rates ranged from 0% to 16%. The average preoperative International Knee Documentation Committee scores ranged from 32.9 to 36.8, and the average postoperative International Knee Documentation Committee scores at final follow‐up ranged from 66.3 to 77.3. The average preoperative Lysholm scores ranged from 44.5 to 56.0 and the average postoperative Lysholm scores ranged from 70.0 to 96.5. The average preoperative Tegner scores ranged from 2.5 to 3.0, and the average postoperative scores ranged from 4.1 to 7.0. Conclusions AOT of the knee resulted in high rates of return to sport with correspondingly low rates of conversion to arthroplasty at long‐term follow‐up. In addition, AOT demonstrated significant improvements in long‐term patient‐reported outcomes from baseline. Level of Evidence Level IV, systematic review of Level I‐IV studies.

Purpose To develop a machine learning model capable of identifying subscapularis tears before surgery based on imaging and physical examination findings. Methods Between 2010 and 2020, 202 consecutive shoulders underwent arthroscopic rotator cuff repair by a single surgeon. Patient demographics, physical examination findings (including range of motion, weakness with internal rotation, lift/push‐off test, belly press test, and bear hug test), and imaging (including direct and indirect signs of tearing, biceps status, fatty atrophy, cystic changes, and other similar findings) were included for model creation. Results Sixty percent of the shoulders had partial or full thickness tears of the subscapularis verified during surgery (83% of these were upper third). Using only preoperative imaging‐related parameters, the XGBoost model demonstrated excellent performance at predicting subscapularis tears (c‐statistic, 0.84; accuracy, 0.85; F1 score, 0.87). The top 5 features included direct signs related to the presence of tearing as evidenced on magnetic resonance imaging (MRI) (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Conclusions In this study, machine learning was successful in predicting subscapularis tears by MRI alone in 85% of patients, and this accuracy did not decrease by isolating the model to the top features. The top five features included direct signs related to the presence of tearing as evidenced on MRI (changes in tendon morphology and signal), as well as the quality of the MRI and biceps pathology. Last, in advanced modeling, the addition of physical examination or patient characteristics did not make a significant difference in the predictive ability of this model. Level of Evidence Level III, diagnostic case‐control study.

Objective Subchondral insufficiency fracture of the knee (SIFK) is associated with high rates of osteoarthritis (OA) and arthroplasty. The implantable shock absorber (ISA) is an extra-capsular implant that unloads the medial knee compartment. This study compared the 2-year freedom from arthroplasty rates in subjects with medial knee OA and SIFK when treated with an ISA versus a matched cohort of patients treated non-surgically. Design This retrospective case-control study compared 2-year conversion rates to arthroplasty in SIFK score-, age-, and body mass index (BMI)–matched control subjects without prior surgical history with ISA-implanted subjects from an ongoing prospective study. Baseline and final radiographs, and MRIs were reviewed for evaluation of meniscus or ligament injuries, insufficiency fractures, and subchondral edema. Kaplan-Meier analysis assessed survival. Results Forty-two patients (21 Control: 21 ISA), mean age = 52.3 ± 8.7 years, BMI = 29.5 ± 3.9 kg/m 2 , 40% female were evaluated. Both ISA and Control arms had the same numbers of low ( n = 4), medium ( n = 11), and high-risk ( n = 6) SIFK scores. One- and 2-year freedom-from-arthroplasty rates were both 100% for ISA subjects, and 76% and 55%, respectively, for Controls ( P = 0.001 for cross-group comparison). Control knees with low, medium, and high-risk SIFK scores had respective 1- and 2-year survival rates of 100% and 100%, 90% and 68% ( P = 0.07 vs. ISA), and 33% and 0% ( P = 0.002 vs. ISA). Conclusions ISA intervention was strongly associated with avoidance of arthroplasty at a minimum 2 years, especially in patients with high-risk SIFK scores. SIFK severity scoring predicted relative risk of conversion to arthroplasty through at least 2 years in non-surgically treated subjects.

AbstractDeep learning has the potential to be one of the most transformative technologies to impact orthopedic surgery. Substantial innovation in this area has occurred over the past 5 years, but clinically meaningful advancements remain limited by a disconnect between clinical and technical experts. That is, it is likely that few orthopedic surgeons possess both the clinical knowledge necessary to identify orthopedic problems, and the technical knowledge needed to implement deep learning‐based solutions. To maximize the utilization of rapidly advancing technologies derived from deep learning models, orthopedic surgeons should understand the steps needed to design, organize, implement, and evaluate a deep learning project and its workflow. Equipping surgeons with this knowledge is the objective of this three‐part editorial review. Part I described the processes involved in defining the problem, team building, data acquisition, curation, labeling, and establishing the ground truth. Building on that, this review (Part II) provides guidance on pre‐processing and augmenting the data, making use of open‐source libraries/toolkits, and selecting the required hardware to implement the pipeline. Special considerations regarding model training and evaluation unique to deep learning models relative to “shallow” machine learning models are also reviewed. Finally, guidance pertaining to the clinical deployment of deep learning models in the real world is provided. As in Part I, the focus is on applications of deep learning for computer vision and imaging.

Background: Routine hip magnetic resonance imaging (MRI) before arthroscopy for patients with femoroacetabular impingement syndrome (FAIS) offers questionable clinical benefit, delays surgery, and wastes resources. Purpose: To assess the clinical utility of preoperative hip MRI for patients aged ≤40 years who were undergoing primary hip arthroscopy and who had a history, physical examination findings, and radiographs concordant with FAIS. Study Design: Cohort study; Level of evidence, 3. Methods: Included were 1391 patients (mean age, 25.8 years; 63% female; mean body mass index, 25.6) who underwent hip arthroscopy between August 2015 and December 2021 by 1 of 4 fellowship-trained hip surgeons from 4 referral centers. Inclusion criteria were FAIS, primary surgery, and age ≤40 years. Exclusion criteria were MRI contraindication, reattempt of nonoperative management, and concomitant periacetabular osteotomy. Patients were stratified into those who were evaluated with preoperative MRI versus those without MRI. Those without MRI received an MRI before surgery without deviation from the established surgical plan. All preoperative MRI scans were compared with the office evaluation and intraoperative findings to assess agreement. Time from office to arthroscopy and/or MRI was recorded. MRI costs were calculated. Results: Of the study patients, 322 were not evaluated with MRI and 1069 were. MRI did not alter surgical or interoperative plans. Both groups had MRI findings demonstrating anterosuperior labral tears treated intraoperatively (99.8% repair, 0.2% debridement, and 0% reconstruction). Compared with patients who were evaluated with MRI and waited 63.0 ± 34.6 days, patients who were not evaluated with MRI underwent surgery 6.5 ± 18.7 days after preoperative MRI. MRI delayed surgery by 24.0 ± 5.3 days and cost a mean $2262 per patient. Conclusion: Preoperative MRI did not alter indications for primary hip arthroscopy in patients aged ≤40 years with a history, physical examination findings, and radiographs concordant with FAIS. Rather, MRI delayed surgery and wasted resources. Routine hip MRI acquisition for the younger population with primary FAIS with a typical presentation should be challenged.

Background: Biomechanical studies support the use of suture tape reinforcement for limiting graft elongation and increasing strength in knee ligament reconstructions. Purpose: To compare posterior cruciate ligament (PCL) laxity, complication and reoperation rates, and patient-reported outcomes (PROs) after all-inside single-bundle PCL reconstruction (PCLR) with versus without independent suture tape reinforcement. Study Design: Cohort study; Level of evidence, 3. Methods: A retrospective cohort study of consecutive patients who underwent primary, all-inside allograft single-bundle PCLR with and without independent suture tape reinforcement at a single academic institution from 2012 to 2019. Medical records were reviewed for patient characteristics, additional injuries, and concomitant procedures. PRO scores (including the International Knee Documentation Committee [IKDC], Tegner activity scale, and Lysholm scores), bilateral comparison kneeling radiographs, and physical examination findings were collected at a minimum of 2 years postoperatively. Results: Included were 50 patients: 19 with suture tape reinforcement (mean age 30.6 ± 2.9 years) and 31 without suture tape reinforcement (control group; mean age 26.2 ± 1.6 years). One PCLR graft in the suture tape group failed. Posterior drawer examination revealed grade 1+ laxity in 4 of 19 (21%) of the suture tape cohort versus 6 of 31 (19%) of the control cohort ( P > .999). Bilateral kneeling radiographs showed similar side-to-side differences in laxity between the groups (suture tape vs control: mean, 1.9 ± 0.4 vs 2.6 ± 0.6 mm; P = .361). There were no statistically significant differences between the groups in postoperative IKDC (suture tape vs control: 79.3 vs 79.6; P = .779), Lysholm (87.5 vs 84.3; P = .828), or Tegner activity (5.6 vs 5.7; P = .562) scores. Conclusion: All-inside single-bundle PCLR with and without independent suture tape reinforcement demonstrated low rates of graft failure, complications, and reoperations, with satisfactory PROs at a minimum 2-year follow-up. Radiographic posterior tibial translation was comparable between the 2 groups.

AbstractDeep learning has a profound impact on daily life. As Orthopedics makes use of this rapid escalation in technology, Orthopedic surgeons will need to take leadership roles on deep learning projects. Moreover, surgeons must possess an understanding of what is necessary to design and implement deep learning‐based project pipelines. This review provides a practical guide for the Orthopedic surgeon to understand the steps needed to design, develop, and deploy a deep learning pipeline for clinical applications. A detailed description of the processes involved in defining the problem, building the team, acquiring and curating the data, labeling the data, establishing the ground truth, pre‐processing and augmenting the data, and selecting the required hardware is provided. In addition, an overview of unique considerations involved in the training and evaluation of deep learning models is provided. This review strives to provide surgeons with the groundwork needed to identify gaps in the clinical landscape that deep learning models may be able to fill and equips them with the knowledge needed to lead an interdisciplinary team through the process of creating novel deep‐learning‐based solutions to fill those gaps.

AbstractSubchondral insufficiency fractures of the knee (SIFK) can result in high rates of osteoarthritis and arthroplasty. The implantable shock absorber (ISA) implant is a titanium and polycarbonate urethane device which reduces the load on the medial compartment of the knee by acting as an extra-articular load absorber while preserving the joint itself. The purpose of this study was to evaluate whether partially unloading the knee with the ISA altered the likelihood of progression to arthroplasty utilizing a validated predictive risk model (SIFK score). A retrospective case-control (2:1) study was performed on patients with SIFK without any previous surgery and on those implanted with the ISA with the primary outcome being progression to arthroplasty compared with nonoperative treatment at 2 years. Baseline and final radiographs, as well as magnetic resonance imagings, were reviewed for the evaluation of meniscus or ligament injuries, insufficiency fractures, and subchondral edema. Patients from a prospective study were matched using the exact SIFK Score, a validated predictive score for progression to arthroplasty in patients with SIFK, to those who received the ISA implant. Kaplan–Meier analysis was conducted to assess survival. A total of 57 patients (38 controls:19 ISA) with a mean age of 60.6 years and 54% female were included. The SIFK score was matched exactly between cases and controls for all patients. The 2-year survival rate of 100% for the ISA group was significantly higher than the corresponding rate of 61% for the control group (p < 0.01). In ISA, 0% of the patients converted to arthroplasty at 2 years, and 5% (one patient) had hardware removal at 1 year. When stratified by risk, the ISA group did not have a significantly higher survival compared with low-risk (p = 0.3) or medium-risk (p = 0.2) controls, though it had a significantly higher survival for high-risk groups at 2 years (100 vs. 15%, p < 0.01). SIFK of the medial knee can lead to significant functional limitation and high rates of conversion to arthroplasty. Implants such as the ISA have the potential to alter the progression to arthroplasty in these patients, especially those at high risk.