Development of the machine learning model that is highly validated and easily applicable to predict radiographic knee osteoarthritis progression.

Many models using the aid of artificial intelligence have been recently proposed to predict the progression of knee osteoarthritis. However, previous models have not been properly validated with an external data set or have reported poor predictive performances. Therefore, the purpose of this study was to design a machine learning model for knee osteoarthritis progression, focusing on high validation quality and clinical applicability. A retrospective analysis was conducted on prospectively collected data, using the Osteoarthritis Initiative data set (5966 knees) for model development and the Multicenter Osteoarthritis Study data set (3392 knees) for validation. The analysis aimed to predict Kellgren-Lawrence grade (KLG) progression over 4-5 years in knees with initial KLG of 0, 1, or 2. Possible predictors included demographics, comorbidities, history of meniscectomy, gait speed, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, and radiological findings. The Random Forest algorithm was employed for the predictive model development. Baseline KLG, contralateral knee osteoarthritis, lateral joint space narrowing (JSN) grade, BMI, medial JSN grade, and total WOMAC score were six features selected for the model in descending order of importance. Odds ratios of baseline KLG, contralateral knee osteoarthritis, and lateral JSN grade were 1.76, 2.59, and 4.74, respectively (all p < 0.001). The area-under-the-curve of the ROC curve in the validation set was 0.76 with an accuracy of 0.68 and an F1-score of 0.56. The progression of knee osteoarthritis in 4 ~ 5 years could be well-predicted using easily available variables. This simple and validated model may aid surgeons in knee osteoarthritis patient management.

Fixed-Bearing and Higher Postoperative Knee Flexion Angle as Predictors of Satisfaction in Asian Patients Undergoing Posterior-Stabilized Total Knee Arthroplasty.

Background: Patient-reported satisfaction following total knee arthroplasty (TKA) can be affected by various factors. This study aimed to assess patient satisfaction rates and identify factors related to patients, surgery, and postoperative knee motion associated with satisfaction in posterior-stabilized TKA among Asian patients. Methods: A retrospective cross-sectional study was conducted in patients with primary osteoarthritis who underwent TKA and had a follow-up period of over 2 years. Patient satisfaction was measured using a 5-point Likert scale, and the patients were divided into satisfied and dissatisfied groups. The factors potentially affecting satisfaction were collected, including demographics, comorbidities, surgical options, and knee motion. Univariate and multivariate regression analyses were performed. Results: Of the 858 patients included, 784 (91.4%) were satisfied and 74 (8.6%) were dissatisfied. Fixed-bearing implants and higher postoperative knee flexion angles were associated with satisfaction (odds ratio [OR], 2.366; p = 0.001 and OR, 1.045; p < 0.001, respectively), whereas cerebrovascular disease was related to dissatisfaction (OR, 0.403; p = 0.005). The regression model demonstrated moderate predictability (R 2 = 0.112). Conclusions: Fixed-bearing implants and higher postoperative knee flexion angles were associated with patient satisfaction following TKA, whereas cerebrovascular disease was associated with dissatisfaction. The identification of these factors could help improve surgical outcomes and patient satisfaction following TKA.

Can individual functional improvements be predicted in osteoarthritic patients after total knee arthroplasty?

PURPOSE: Total knee arthroplasty (TKA) is an effective treatment for advanced osteoarthritis, and achieving optimal outcomes can be challenging due to various influencing factors. Previous research has focused on identifying factors that affect postoperative functional outcomes. However, there is a paucity of studies predicting individual postoperative improvement following TKA. Therefore, a quantitative prediction model for individual patient outcomes is necessary. MATERIALS AND METHODS: Demographic data, radiologic variables, intraoperative variables, and physical examination findings were collected from 976 patients undergoing TKA. Preoperative and 1-year postoperative Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were assessed, and multivariate regression analysis was conducted to identify significant factors influencing one-year WOMAC scores and changes in WOMAC scores. A predictive model was developed on the basis of the findings. RESULTS: The predictive accuracy of the model for 1-year WOMAC scores was poor (all adjusted R2 < 0.08), whereas the model for changes in WOMAC scores demonstrated strong predictability (all adjusted R2 > 0.75). Preoperative WOMAC scores, sex, and postoperative knee range of motion significantly affected all pain, stiffness, and physical function aspects of the WOMAC scores (all P < 0.05). Age, cerebrovascular disease, and patellar resurfacing were associated with changes in physical function (all P < 0.05). CONCLUSIONS: The developed quantitative model demonstrated high accuracy in predicting changes in WOMAC scores after TKA. The identified factors influencing postoperative improvement in WOMAC scores can assist in optimizing patient outcomes after TKA.

Medial Joint Opening in the Operated Knee After Unilateral High Tibial Osteotomy: Risk of Osteoarthritis and Future Surgery in the Operated and Nonoperated Knee.

BACKGROUND: High tibial osteotomy (HTO) modifies the mechanics of the affected knee but can also affect the nonoperated knee. However, no research has reported on the prognosis and risk factors related to the nonoperated knee after unilateral HTO. PURPOSE: To assess the radiological parameters associated with osteoarthritis (OA) progression and the need for surgery in the nonoperated knee after unilateral HTO, with concurrent assessment of the operated knee. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: The medical charts of 197 patients with knee OA who underwent unilateral HTO between March 2007 and December 2020 were retrospectively investigated. Radiological parameters such as the Kellgren-Lawrence grade, weightbearing line ratio, joint line convergence angle (JLCA), and joint line obliquity angle were assessed preoperatively and 1 year postoperatively. RESULTS: The mean follow-up length for the 197 patients was 5.9 ± 3.2 years for the operated knee and 5.5 ± 3.2 years for the nonoperated knee. A smaller postoperative JLCA in the operated knee was a significant risk factor for OA progression (P = .027) and undergoing surgery (P = .006) in the nonoperated knee. Conversely, a larger postoperative JLCA in the operated knee was a significant risk factor for OA progression (P = .014) and conversion to arthroplasty (P = .027) in the operated knee. A postoperative JLCA >1.5° (P < .001) and <3.9° (P < .001) was needed to reduce the risk of undergoing surgery in the nonoperated knee and OA progression in the operated knee, respectively. Additionally, a pre- to postoperative change in the JLCA (ΔJLCA) between -5.6° and -1.7° (P = .021 and P = .004, respectively) was needed to reduce the risk of OA progression in both knees. CONCLUSION: A large medial joint opening (a small postoperative JLCA) in the operated knee after unilateral HTO was associated with a higher risk of OA progression and surgery in the nonoperated knee. Conversely, a small medial joint opening (a large postoperative JLCA) was associated with a higher risk of OA progression and conversion to arthroplasty in the operated knee. For a balanced medial joint opening, if the postoperative JLCA was between 1.5° and 3.9° or the ΔJLCA was between -5.6° and -1.7°, a favorable prognosis in both knees could be anticipated.

Primary Total Knee Arthroplasty for Treating Osteoarthritic Knees with Neglected Patellar Dislocation.

Background and Objectives: Neglected patellar dislocation in the presence of end-stage osteoarthritis (OA) is a rare condition characterized by the patella remaining laterally dislocated without reduction. Due to the scarcity of reported cases, the optimal management approach is still uncertain. However, primary total knee arthroplasty (TKA) can serve as an effective treatment option. This study aimed to present the clinical and radiological outcomes achieved using our surgical technique. Materials and Methods: A retrospective review of 12 knees in 8 patients with neglected patellar dislocation and end-stage OA who underwent primary TKA was conducted. The surgical procedure involved conventional TKA techniques (e.g., medial parapatellar arthrotomy) and additional procedures specific to the individual pathologies of neglected patellar dislocation (e.g., lateral release, medial plication, and quadriceps lengthening). Clinical outcomes, including patient-reported outcome measures (PROMs) (Knee Society Scores and the Western Ontario and McMaster Universities Osteoarthritis Index) and knee range of motion (ROM), were assessed preoperatively and two years postoperatively. Radiological measures including mechanical femorotibial angle and patellar tilt angle were assessed preoperatively and until the last follow-up examinations. Any complications were also reviewed. Results: There were significant improvements in all PROMs, knee ROM, and radiological outcomes, including mechanical femorotibial angle and patellar tilt angle (all p < 0.05). At a mean follow-up of 68 months, no major complications requiring revision surgery, including patellar dislocation, were reported. Conclusions: Primary TKA is an effective procedure for correcting various pathologies associated with neglected patellar dislocation in end-stage OA without necessitating additional bony procedures. Satisfactory clinical and radiological outcomes can be expected using pathology-specific procedures.

Accurate, automated classification of radiographic knee osteoarthritis severity using a novel method of deep learning: Plug-in modules.

BACKGROUND: Fine-grained classification deals with data with a large degree of similarity, such as cat or bird species, and similarly, knee osteoarthritis severity classification [Kellgren-Lawrence (KL) grading] is one such fine-grained classification task. Recently, a plug-in module (PIM) that can be integrated into convolutional neural-network-based or transformer-based networks has been shown to provide strong discriminative regions for fine-grained classification, with results that outperformed the previous deep learning models. PIM utilizes each pixel of an image as an independent feature and can subsequently better classify images with minor differences. It was hypothesized that, as a fine-grained classification task, knee osteoarthritis severity may be classified well using PIMs. The aim of the study was to develop this automated knee osteoarthritis classification model. METHODS: A deep learning model that classifies knee osteoarthritis severity of a radiograph was developed utilizing PIMs. A retrospective analysis on prospectively collected data was performed. The model was trained and developed using the Osteoarthritis Initiative dataset and was subsequently tested on an independent dataset, the Multicenter Osteoarthritis Study (test set size: 17,040). The final deep learning model was designed through an ensemble of four different PIMs. RESULTS: The accuracy of the model was 84%, 43%, 70%, 81%, and 96% for KL grade 0, 1, 2, 3, and 4, respectively, with an overall accuracy of 75.7%. CONCLUSIONS: The ensemble of PIMs could classify knee osteoarthritis severity using simple radiographs with a fine accuracy. Although improvements will be needed in the future, the model has been proven to have the potential to be clinically useful.

Performance of ChatGPT on Solving Orthopedic Board-Style Questions: A Comparative Analysis of ChatGPT 3.5 and ChatGPT 4.

Background: The application of artificial intelligence and large language models in the medical field requires an evaluation of their accuracy in providing medical information. This study aimed to assess the performance of Chat Generative Pre-trained Transformer (ChatGPT) models 3.5 and 4 in solving orthopedic board-style questions. Methods: A total of 160 text-only questions from the Orthopedic Surgery Department at Seoul National University Hospital, conforming to the format of the Korean Orthopedic Association board certification examinations, were input into the ChatGPT 3.5 and ChatGPT 4 programs. The questions were divided into 11 subcategories. The accuracy rates of the initial answers provided by Chat GPT 3.5 and ChatGPT 4 were analyzed. In addition, inconsistency rates of answers were evaluated by regenerating the responses. Results: ChatGPT 3.5 answered 37.5% of the questions correctly, while ChatGPT 4 showed an accuracy rate of 60.0% (p < 0.001). ChatGPT 4 demonstrated superior performance across most subcategories, except for the tumor-related questions. The rates of inconsistency in answers were 47.5% for ChatGPT 3.5 and 9.4% for ChatGPT 4. Conclusions: ChatGPT 4 showed the ability to pass orthopedic board-style examinations, outperforming ChatGPT 3.5 in accuracy rate. However, inconsistencies in response generation and instances of incorrect answers with misleading explanations require caution when applying ChatGPT in clinical settings or for educational purposes.

Isolated Polyethylene Insert Exchange for Instability after Total Knee Arthroplasty: Comparable Survival Rates and Range of Motion and Improved Clinical Scores Regardless of Hyperextension.

Background: Isolated polyethylene insert exchange (IPIE) has not been established as a treatment option for hyperextension instability after primary total knee arthroplasty (TKA). The purpose of the study was to evaluate the survival rate and clinical outcomes of IPIE for the treatment of instability with or without hyperextension after TKA. Methods: This study retrospectively reviewed 46 patients who underwent IPIE for symptomatic prosthetic knee instability by dividing them into 2 groups based on the presence of hyperextension (without for group I and with for group IH). Patient demographics, clinical scores, radiographic data, range of motion (ROM), and surgical information were collected. Clinical failure was defined as a subsequent surgery following IPIE for any reason. The survival rate of IPIE and differences in demographics, clinical scores, and ROM were compared. Results: There were 46 patients (91% were women) with an average age of 70.1 years and a mean follow-up of 44.8 months. The average time between primary TKA and IPIE surgery was 6.5 ± 4.2 years, and during IPIE, 2 out of the 8 cruciate-retaining inserts were converted to "deep-dish" ultracongruent inserts while the insert thickness increased from 11.9 ± 1.8 mm to 17.1 ± 3.1 mm. After IPIE surgery, a significantly thicker tibial insert was used in the group with hyperextension (15.39 ± 2.4 mm for group I, 18.3 ± 2.9 mm for group IH; p < 0.001 by independent t-test), and no significant differences were observed in the ROM and clinical scores before and after IPIE between the 2 groups. The overall survival rate for IPIE was 83% at 5 years and 57% at 10 years, and there were no statistically significant differences between the groups using the Cox proportional hazards regression model. Conclusions: IPIE demonstrated an overall survival rate of 83% at 5 years with no difference in the recurrence of instability regardless of hyperextension. This study highlighted the effectiveness of using thicker inserts to resolve instability without significant differences in the ROM or clinical scores between the groups, suggesting its potential as a decision-making reference for surgeons.

Estimation of patient-reported outcome measures based on features of knee joint muscle co-activation in advanced knee osteoarthritis.

Electromyography (EMG) is considered a potential predictive tool for the severity of knee osteoarthritis (OA) symptoms and functional outcomes. Patient-reported outcome measures (PROMs), such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and visual analog scale (VAS), are used to determine the severity of knee OA. We aim to investigate muscle activation and co-contraction patterns through EMG from the lower extremity muscles of patients with advanced knee OA patients and evaluate the effectiveness of an interpretable machine-learning model to estimate the severity of knee OA according to the WOMAC (pain, stiffness, and physical function) and VAS using EMG gait features. To explore neuromuscular gait patterns with knee OA severity, EMG from rectus femoris, medial hamstring, tibialis anterior, and gastrocnemius muscles were recorded from 84 patients diagnosed with advanced knee OA during ground walking. Muscle activation patterns and co-activation indices were calculated over the gait cycle for pairs of medial and lateral muscles. We utilized machine-learning regression models to estimate the severity of knee OA symptoms according to the PROMs using muscle activity and co-contraction features. Additionally, we utilized the Shapley Additive Explanations (SHAP) to interpret the contribution of the EMG features to the regression model for estimation of knee OA severity according to WOMAC and VAS. Muscle activity and co-contraction patterns varied according to the functional limitations associated with knee OA severity according to VAS and WOMAC. The coefficient of determination of the cross-validated regression model is 0.85 for estimating WOMAC, 0.82 for pain, 0.85 for stiffness, and 0.85 for physical function, as well as VAS scores, utilizing the gait features. SHAP explanation revealed that greater co-contraction of lower extremity muscles during the weight acceptance and swing phases indicated more severe knee OA. The identified muscle co-activation patterns may be utilized as objective candidate outcomes to better understand the severity of knee OA.

Shallow trochlear groove and narrow medial trochlear width at the proximal trochlea in patients with trochlear dysplasia: A three-dimensional computed tomography analysis.

PURPOSE: Although the Dejour classification is the primary classification system for evaluating trochlear dysplasia, concerns have been raised about its reliability owing to its qualitative criteria and challenges associated with obtaining accurate radiographs. This study aimed to quantify trochlear dysplasia using three-dimensional (3D) computed tomography (CT) reconstruction with novel parameters related to the transepicondylar axis (TEA). METHODS: Sixty patients were enrolled, including 20 with trochlear dysplasia and 40 healthy controls. The 3D CT model was generated using the Materialise Interactive Medical Image Control System software. The following six parameters were measured in eight consecutive planes at 15° intervals (planes 0-105): the distance from the TEA to the most cortical point of the lateral condyle ('LP-TEA', where LP stands for lateral peak), medial condyle ('MP-TEA', MP for medial peak) and deepest point of the trochlea ('TG-TEA', TG for trochlear groove). The distances from the medial epicondyle (MEC) to the corresponding TEA points were measured ('LP-MEC', 'MP-MEC' and 'TG-MEC'). RESULTS: In the dysplasia group, TG-TEA (planes 0, 15 and 30) and MP-MEC (planes 0, 15 and 30) were significantly greater than those in the control group (all p < 0.05 for planes of TG-TEA and MP-MEC). For type A dysplasia, LP-MEC (plane 0) was greater than that in the control group. For type B dysplasia, the MP-MEC (planes 0 and 15) and TG-TEA (planes 0 and 15) were greater than those of the control group. For type D dysplasia, MP-MEC (planes 0, 15 and 30) and TG-TEA (planes 0 and 15) were elevated. CONCLUSION: The 3D CT reconstruction analysis established a reproducible method for quantifying osseous trochlear morphology. Patients with trochlear dysplasia had a shallow TG and narrow medial trochlear width at tracking angles of 0°-30°. This finding corroborates the clinical manifestations of recurrent patellar instability that occur during early flexion. LEVEL OF EVIDENCE: Level III.

Selective targeting of dipeptidyl-peptidase 4 (DPP-4) positive senescent chondrocyte ameliorates osteoarthritis progression.

Senescent cells increase in many tissues with age and induce age-related pathologies, including osteoarthritis (OA). Senescent chondrocytes (SnCs) are found in OA cartilage, and the clearance of those chondrocytes prevents OA progression. However, targeting SnCs is challenging due to the absence of a senescent chondrocyte-specific marker. Therefore, we used flow cytometry to screen and select senescent chondrocyte surface markers and cross-validated with published transcriptomic data. Chondrocytes expressing dipeptidyl peptidase-4 (DPP-4), the selected senescent chondrocyte-specific marker, had multiple senescence phenotypes, such as increased senescence-associated-galactosidase, p16, p21, and senescence-associated secretory phenotype expression, and showed OA chondrocyte phenotypes. To examine the effects of DPP-4 inhibition on DPP-4+ SnCs, sitagliptin, a DPP-4 inhibitor, was treated in vitro. As a result, DPP-4 inhibition selectively eliminates DPP-4+ SnCs without affecting DPP-4- chondrocytes. To assess in vivo therapeutic efficacy of targeting DPP-4+ SnCs, three known senolytics (ABT263, 17DMAG, and metformin) and sitagliptin were comparatively verified in a DMM-induced rat OA model. Sitagliptin treatment specifically and effectively eliminated DPP-4+ SnCs, compared to the other three senolytics. Furthermore, Intra-articular sitagliptin injection to the rat OA model increased collagen type II and proteoglycan expression and physical functions and decreased cartilage destruction, subchondral bone plate thickness and MMP13 expression, leading to the amelioration of OA phenotypes. Collectively, OARSI score was lowest in the sitagliptin treatment group. Taken together, we verified DPP-4 as a surface marker for SnCs and suggested that the selective targeting of DPP-4+ chondrocytes could be a promising strategy to prevent OA progression.

Comparative analysis of gait: Similar coronal but different sagittal effects between closing-wedge and opening-wedge high tibial osteotomy.

BACKGROUND: High tibial osteotomy is an established surgical option for medial compartment osteoarthritis of the knee with varus alignment. It can be divided into open wedge and closing wedge by operative technique. Although they have fundamental differences, little is known about the biomechanical consequences of the two surgical methods. METHODS: Thirty-eight patients with medial compartment osteoarthritis who underwent high tibial osteotomy (19 open-wedge and 19 closing-wedge) were retrospectively reviewed. Clinical scores and radiological measurements were assessed until postoperative two years. Gait analysis was performed preoperatively and again at postoperative one year. FINDINGS: Varus alignment was corrected in both groups without a significant difference between them (p = 0.543). However, posterior tibial slope was higher, and the Blackburne-Peel ratio was lower in the open wedge osteotomy group after surgery (both p < 0.001). Reduction of dynamic knee varus and knee adduction moment were observed in both groups without significant differences. However, after surgery, average knee range of motion (63.3° vs 57.3°, p < 0.001) and the magnitude of knee flexion moment was significantly lower (p = 0.005) in the closing wedge group. There were no significant differences in the Kujala Anterior Knee Pain Scale and the occurrence of patellofemoral arthritis between the groups postoperatively. INTERPRETATION: After osteotomy, a smaller average knee range of motion in the sagittal plane and a higher knee flexion moment were observed in the open wedge osteotomy group, suggesting quadriceps muscle avoidance. However, no differences in clinical scores or the short-term occurrence of patellofemoral arthritis were noted between the two surgical techniques.

Patella Cartilage Status Does Not Affect the Clinical Outcomes of Non-resurfaced Patella in Mobile-Bearing Total Knee Arthroplasty.

Background: During total knee arthroplasty (TKA), patellar retention is performed when the cartilage is fairly well preserved and the thickness of the patella is relatively thin. However, clinical outcomes of the non-resurfaced patella in TKA according to the cartilage status are lacking in the literature. The purpose of this study was to compare patient-reported outcome measures (PROMs) according to the grade and location of the patellar cartilage lesion in TKA patients. Methods: The outcomes of 165 osteoarthritis patients (186 knees) who underwent cemented mobile-bearing TKA without patellar resurfacing were assessed and classified according to the grade and location of the patellar cartilage lesion. PROMs using the Western Ontario and MacMaster Universities Osteoarthritis index, the Knee Society Score (Knee Society Function Score and Knee Society Knee Score), and the Hospital for Special Surgery score were evaluated preoperatively and at postoperative 2, 4, 6, and 8 years. The correlations between PROMs and the grade and location of the cartilage lesion were assessed. Additionally, radiologic outcomes including the patellar tilt angle and patellar height were assessed and their correlation with the grade of cartilage lesion was analyzed. Analysis of variance was used to determine statistical significance. Results: There was no significant difference between PROMs according to the grades and locations of cartilage lesions at any postoperative follow-up. Radiologic parameters also showed no significant differences according to the grades of patellar cartilage lesions. Conclusions: The grade and location of the patellar cartilage lesion had no influence on clinical outcomes in mobile-bearing TKA with patellar retention at short- and long-term follow-up.

Minimal impact of beam projection angle deviations in skyline (Laurin) view and the efficacy of the anterior border of proximal tibia as a guiding landmark.

OBJECTIVE: Obtaining an optimal knee skyline view is challenging due to inaccuracies in beam projection angles (BPAs) and soft tissue obscuring bony landmarks. This study aimed to assess the impact of BPA deviations on patellofemoral index measurements and assessed the anterior border of the proximal tibia as an anatomic landmark for guiding BPAs. MATERIALS AND METHODS: This retrospective study consisted of three parts. The first was a simulation study using 52 CT scans of knees with a 20° flexion contracture to replicate the skyline (Laurin) view. Digitally reconstructed radiographs simulated neutral, 5° downward, and 5° upward tilt BPAs. Five patellofemoral indices (sulcus angle, congruence angle, patellar tilt angle, lateral facet angle, and bisect ratio) were measured and compared. The second part was a proof of concept study on 162 knees to examine patellar indices differences across these BPAs. Lastly, the alignment of the anterior border of the proximal tibia with the BPA tangential to the patellar articular surface was tested from the CT scans. RESULTS: No significant differences in patellofemoral indices were found across various BPAs in both the simulation and proof of concept studies (all p > 0.05). The angle between the anterior border of the proximal tibia and the patellar articular surface was 1.5 ± 5.3°, a statistically significant (p = 0.037) yet clinically acceptable deviation. CONCLUSION: Patellofemoral indices in skyline view remained consistent regardless of BPA deviations. The anterior border of the proximal tibia proved to be an effective landmark for accurate beam projection.

Prediction of postoperative gait speed change after bilateral primary total knee arthroplasty in female patients using a machine learning algorithm.

BACKGROUND: An important aim of total knee arthroplasty is to achieve functional recovery, which includes post-operative increase in walking speed. Therefore, predicting whether a patient will walk faster or slower after surgery is important in TKA, which has not been studied in previous literatures. Who walks faster and who walks slower after TKA? Can we predict these kinds of patients before surgery? HYPOTHESIS: Whether or not a patient walk faster after total knee arthroplasty can be predicted with preoperative characteristics. PATIENTS AND METHODS: In this retrospective cohort study, 128 female patients who underwent staged bilateral total knee arthroplasty were analyzed with gait analysis preoperatively and at postoperative two years. These patients were divided into three different groups according to the percentage of gait speed change after total knee arthroplasty: 1) V(+), more than 10% gait speed increase; 2) V(-), more than 10% gait speed decrease; and 3) V(0), those in-between. Gait parameters, mechanical axis angles, WOMAC pain score and Knee Society scores of the two groups (V(+) and V(-)) were compared. Furthermore, a classification model predicting whether a patient walks faster after total knee arthroplasty was designed using a machine learning algorithm. RESULTS: After total knee arthroplasty, average gait speed increased by 0.07m/s from 0.87m/s to 0.94m/s (p<0.001) and gait speed increased in 43.8% of the patients (n=56). However, gait speed decreased in a significant number of patients (n=17, 13.3%). When V(+) and V(-) groups were compared, gait speed, cadence, sagittal/coronal knee range of motion, and Knee Society Function score were lower in the V(+) group before surgery, but became higher after surgery. Gait speed change could be predicted using three variables (preoperative gait speed, age, and the magnitude of mechanical axis angle). The area under the receiver operating characteristic curve of the machine learning model was 0.86. DISCUSSION: After total knee arthroplasty, gait speed was maintained or increased in most patients. However, gait speed decreased in a significant number of patients. The machine learning classification model showed a good predictive performance, which could aid in the decision-making and the timing of total knee arthroplasty. LEVEL OF EVIDENCE: III; retrospective cohort study.

Machine learning/artificial intelligence in sports medicine: state of the art and future directions.

Machine learning (ML) is changing the way health care is practiced and recent applications of these novel statistical techniques have started to impact orthopaedic sports medicine. Machine learning enables the analysis of large volumes of data to establish complex relationships between "input" and "output" variables. These relationships may be more complex than could be established through traditional statistical analysis and can lead to the ability to predict the "output" with high levels of accuracy. Supervised learning is the most common ML approach for healthcare data and recent studies have developed algorithms to predict patient-specific outcome after surgical procedures such as hip arthroscopy and anterior cruciate ligament reconstruction. Deep learning is a higher-level ML approach that facilitates the processing and interpretation of complex datasets through artificial neural networks that are inspired by the way the human brain processes information. In orthopaedic sports medicine, deep learning has primarily been used for automatic image (computer vision) and text (natural language processing) interpretation. While applications in orthopaedic sports medicine have been increasing exponentially, one significant barrier to widespread adoption of ML remains clinician unfamiliarity with the associated methods and concepts. The goal of this review is to introduce these concepts, review current machine learning models in orthopaedic sport medicine, and discuss future opportunities for innovation within the specialty.

Rectangular-Tunnel Anterior Cruciate Ligament Reconstruction Using Quadriceps Tendon-Patellar Bone Autograft Can Reduce Early Donor Site Morbidity While Maintaining Comparable Short-term Clinical Outcomes.

Background: Rectangular tunnel and graft have been recently designed to closely resemble the native anatomy in anterior cruciate ligament reconstruction (ACLR). This study was performed to compare the short-term clinical outcomes between rectangular and round femoral tunnels in ACLR using quadriceps tendon-patellar bone (QTPB) autografts. Methods: A total of 78 patients who underwent primary ACLR with QTPB autografts performed by three senior surgeons and had at least 1 year of postoperative follow-up were retrospectively reviewed. Patients who underwent rectangular tunnel ACLR (n = 40) were compared to those treated with the conventional round tunnel ACLR (n = 38). Outcomes including knee stability, clinical scores, quadriceps strength, associated complications, postoperative knee range of motion, and cross-sectional area of the graft were assessed. Results: Significant improvements in knee stability and clinical scores were observed after surgery in both groups (all p < 0.001). The postoperative measurements of knee stability and clinical scores were not significantly different between the two groups. Knee extension strength deficit at 60°/sec was significantly less in the rectangular tunnel group than in the round tunnel group at postoperative 6 months (41.7% vs. 48.9%, p = 0.032). The cross-sectional area of the partial-thickness QTPB graft was approximately 60% of the full-thickness QTPB graft. Conclusions: In the short-term, rectangular tunnel ACLR was comparable to round tunnel ACLR with QTPB autograft despite the smaller cross-sectional area. Additionally, the rectangular tunnel ACLR allowed partial-thickness grafting technique, which could subsequently reduce early donor site morbidity.

Predicting delirium and the effects of medications in hospitalized COVID-19 patients using machine learning: A retrospective study within the Korean Multidisciplinary Cohort for Delirium Prevention (KoMCoDe).

Objective: Delirium is commonly reported from the inpatients with Coronavirus disease 2019 (COVID-19) infection. As delirium is closely associated with adverse clinical outcomes, prediction and prevention of delirium is critical. We developed a machine learning (ML) model to predict delirium in hospitalized patients with COVID-19 and to identify modifiable factors to prevent delirium. Methods: The data set (n = 878) from four medical centers was constructed. Total of 78 predictors were included such as demographic characteristics, vital signs, laboratory results and medication, and the primary outcome was delirium occurrence during hospitalization. For analysis, the extreme gradient boosting (XGBoost) algorithm was applied, and the most influential factors were selected by recursive feature elimination. Among the indicators of performance for ML model, the area under the curve of the receiver operating characteristic (AUROC) curve was selected as the evaluation metric. Results: Regarding the performance of developed delirium prediction model, the accuracy, precision, recall, F1 score, and the AUROC were calculated (0.944, 0.581, 0.421, 0.485, 0.873, respectively). The influential factors of delirium in this model included were mechanical ventilation, medication (antipsychotics, sedatives, ambroxol, piperacillin/tazobactam, acetaminophen, ceftriaxone, and propacetamol), and sodium ion concentration (all p < 0.05). Conclusions: We developed and internally validated an ML model to predict delirium in COVID-19 inpatients. The model identified modifiable factors associated with the development of delirium and could be clinically useful for the prediction and prevention of delirium in COVID-19 inpatients.

Enhanced deep learning model enables accurate alignment measurement across diverse institutional imaging protocols.

BACKGROUND: Achieving consistent accuracy in radiographic measurements across different equipment and protocols is challenging. This study evaluates an advanced deep learning (DL) model, building upon a precursor, for its proficiency in generating uniform and precise alignment measurements in full-leg radiographs irrespective of institutional imaging differences. METHODS: The enhanced DL model was trained on over 10,000 radiographs. Utilizing a segmented approach, it separately identified and evaluated regions of interest (ROIs) for the hip, knee, and ankle, subsequently integrating these regions. For external validation, 300 datasets from three distinct institutes with varied imaging protocols and equipment were employed. The study measured seven radiologic parameters: hip-knee-ankle angle, lateral distal femoral angle, medial proximal tibial angle, joint line convergence angle, weight-bearing line ratio, joint line obliquity angle, and lateral distal tibial angle. Measurements by the model were compared with an orthopedic specialist's evaluations using inter-observer and intra-observer intraclass correlation coefficients (ICCs). Additionally, the absolute error percentage in alignment measurements was assessed, and the processing duration for radiograph evaluation was recorded. RESULTS: The DL model exhibited excellent performance, achieving an inter-observer ICC between 0.936 and 0.997, on par with an orthopedic specialist, and an intra-observer ICC of 1.000. The model's consistency was robust across different institutional imaging protocols. Its accuracy was particularly notable in measuring the hip-knee-ankle angle, with no instances of absolute error exceeding 1.5 degrees. The enhanced model significantly improved processing speed, reducing the time by 30-fold from an initial 10-11 s to 300 ms. CONCLUSIONS: The enhanced DL model demonstrated its ability for accurate, rapid alignment measurements in full-leg radiographs, regardless of protocol variations, signifying its potential for broad clinical and research applicability.