KL Grading Research Articles

A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays

A Review for automated classification of knee osteoarthritis using KL grading scheme for X-rays

Intra-articular autologous conditioned plasma reduces pain in early osteoarthritis and improves stiffness in advanced osteoarthritis knee: a prospective observation

Background: In this study, we aimed to investigate the effects of single-dose intra-articular autologous conditioned plasma injection as the treatment for early and advanced osteoarthritis knee. Methods: A single centre-based prospective observational analysis was conducted among patients who opt for conservative management by intra-articular autologous conditioned plasma Injection between July 2022 to June 2023. Total 46 patients were included and analysed in study on 1, 3, and 6 months’ follow-up after ACP injection. The WOMAC score and its sub scores were analysed and compared pre-procedure and on subsequent follow ups. Results: A total of 46 patients were analysed in the study, with male predominance of 56.5% with mean BMI of 27.37±5.35. On KL grading, 34.8% patients had KL grade II Osteoarthritis, 52.2% had grade III and 13.0% had grade IV. On comparison of WOMAC score and its sub-scales, all the values were found to be statically significant while comparing before injection (p value <0.0001) and one month follow up and before injection and six months’ follow-up (p value <0.0001). Conclusions: Six months following intra-articular autologous conditioned plasma injection, there was a noteworthy decrease in pain in early stages of osteoarthritis and improvement in knee stiffness in advanced stages of osteoarthritis as compared to the pre-treatment state.

Importance of clinical and radiological assessment of patient of osteoarthritis knee foroptimum outcome in total knee replacement

Background: Total knee replacement (TKR) provides symptomatic relief in patients with severe arthritis by removing pathologic joint tissue and restoration of the joint anatomy and function by the replacing with metal or plastic components leading to more stable biomechanics. Inspite of all advances in the surgical techniques 1 out of 5 people who undergo TKA remain unsatisfied. Clinical and radiological grading of osteoarthritis of knee may be an important prognostic marker to assess the outcome following TKR. Methods: Study conducted was retrospective study. Pre-op routine and clinical assessment data noted from departmental records. Correlation between ‘initial knee society score’ (KSSi) and ‘radiological severity’ in terms of (Kellgren-Lawrence) grade and varus angle with ‘change in knee society score’ (ΔKSS) assessed to reach at conclusion. Results: Lower KSS values, higher KL grading, higher femorotibial angle and were associated significantly with higher change in KSS values, i.e. better outcome. Conclusions: Severe OA knee with high FTA and higher KL grade has shown better outcome following TKA. In early stages it is better to manage the patient conservatively with medications and physiotherapy.

Comparison of Ultrasonographic Findings with Plain Radiography in Patients with Knee Osteoarthritis

Background Knee osteoarthritis is one of the most commonly encountered joint disease with prevalence ranging upto 7.9%. There are various radiologic instruments to measure the disease severity of knee osteoarthritis. Kellgren Lawrence (KL grading) classification is commonly used radiographic tool to assess severity. High frequency musculoskeletal ultrasound is being used now-a-days as a reliable tool for assessment of osteoarthritis Objective To compare musculoskeletal findings with X-ray findings in knee osteoarthritis Method A prospective crosssectional study was conducted in patients with knee osteoarthritis in national centre for rheumatologic disease. An AP view standing X-ray was done for the involved knee and K-L grading was done. High frequency musculoskeletal ultrasound was done for the same knee. Synovial hypertrophy, Doppler activity, cartilage thickness (medial, lateral, intercondylar), osteophytes were noted. Pearson’s coefficient of correlation was calculated to compare the correlation between musculoskeletal ultrasound and radiographic findings Result A total of 138 patients with knee osteoarthritis were enrolled in the study with female predominance of 83% and mean age of 56.87± 10.61 years. A negative correlation was observed between cartilage thickness (medial) with joint space narrowing (KL grading) Conclusion Musculoskeletal ultrasound may be used in clinical practice to diagnose and monitor cases of knee osteoarthritis. It is potential imaging technique which might help in therapeutic interventions as well as disease monitoring.

Automated KL grading of knee X-ray images using convolutional neural network

Automated KL grading of knee X-ray images using convolutional neural network

Functional ability in knee osteoarthritis: role of neuropathic pain and central sensitization

BackgroundPain in osteoarthritis (OA) has been attributed traditionally to local tissue injury causing ‘nociceptive pain’. However, recent studies suggest that neuropathic and central sensitization mechanisms may contribute to the pain experience. However, the relationship between these pain mechanisms and physical function has not been thoroughly addressed. This study aimed to assess the association of central sensitization and neuropathic pain with physical function in knee OA.ResultsParticipants with a positive central sensitization inventory score (CSI) (≥ 40) had a decreased total Knee injury and Osteoarthritis Outcome Score (KOOS) and its subscales (p < 0.001), a longer timed up and go test time (p = 0.002) and a higher PainDETECT questionnaire (PD-Q) and visual analogue scale (p < 0.001, p = 0.026 respectively). The severity of Kellgren-Lawrence grading (KL) (p < 0.001), depressive and anxiety symptoms (p < 0.001) increased with neuropathic pain severity. In addition, participants with a high PD-Q score (≥ 19) had a longer timed up and go test time (p < 0.001) and a decreased total KOOS score (p < 0.001). Moreover, we found that CSI score, KOOS score, and KL grading were significantly predicted the PD-Q score (p = 0.046, p < 0.001, p = 0.007, respectively). Regarding the physical function predictors, multivariate linear regression analysis revealed that pressure pain threshold at right elbow and right knee (p = 0.005, p < 0.001) in addition to PD-Q (P < 0.001) were significantly associated with KOOS score, while CSI and Hospital Anxiety Depression Scale were not.ConclusionKnee OA patients with significant central sensitization and neuropathic pain reported increased pain, more functional impairment, more anxiety and depressive symptoms than OA patients without central sensitization and neuropathic pain. Additionally, neuropathic pain and presence of central sensitization were significant predictors for functional ability.

A Robust Framework for Severity Detection of Knee Osteoarthritis Using an Efficient Deep Learning Model

With the changing lifestyle, a large population suffers from a bone disease known as an osteoarthritis affecting the knee, spine, and hip. Therefore, timely detection and classification of the disease are necessary to minimize the loss, however, it is a time-consuming task and requires various tests and physicians’ in-depth analysis. Thus, an accurate automated technique, timely detection and classification are needed to cope with the aforementioned challenges. This study proposes a technique based on an efficient DenseNet that uses the knee image’ features to identify the Knee Osteoarthritis (KOA) and determine its severity level according to the KL grading system such as Grade-I, Grade-II, Grade-III, and Grade-IV. We introduced the reweighted cross-entropy loss function which makes our proposed algorithm more robust as the training data is imbalanced. The dense connections of efficient DenseNet with regularization power help to reduce the overfitting during the training of small knee sample training sets. The proposed algorithm is an efficient approach that can identify the early symptoms of KOA and classify the severity level of the disease for better decision making by orthopedics. The algorithm is a pre-trained network that does not require a huge training set, therefore, the existing dataset i.e. Mendeley VI has been utilized for the training and testing. Additionally, cross-validation has been employed using the OAI dataset to assess the performance of the proposed model. The algorithm achieved 98.22% accuracy over the testing set and 98.08% accuracy over cross-validation. Various experiments have been performed to confirm that our proposed algorithm is more consistent and capable of detecting and classifying the KOA disease than existing state of the art.

Patellofemoral arthroplasty conversion to total knee arthroplasty: An updated retrieval analysis and clinical outcomes

BackgroundA prior retrieval analysis found high rates of infection after conversion of PFA to TKA, but was limited by a small sample size. The purpose of this study is to perform a retrieval analysis with clinical correlation on an expanded group of patients to better understand conversion of PFA to TKA. MethodA retrospective review of an implant retrieval registry identified 62 conversions of PFA to TKA between 2004–2021. Implants were analyzed for wear pattern and cement fixation. Patient charts were reviewed for demographic information, perioperative data, prior and subsequent surgical procedures, complications and outcomes. Radiographs performed prior to index PFA and conversion procedures were assessed for KL grading. ResultsCement fixation was present on 86% of retrieved components and wear was more pronounced on the lateral side. The most common indication for conversion to TKA was progression of OA in 46.8% of patients, followed by unexplained pain in the absence of radiographic or clinical changes (37.1%), loosening (8.1%), mechanical symptoms (4.8%), traumatic (3.2%). Thirteen patient experienced complications requiring additional procedures including arthrofibrosis (n = 4, 7.3%), PJI (n = 3, 5.5%), instability (n = 3, 5.5%), hematoma (n = 2, 3.6%) and loosening (n = 1, 1.8%). Revision components were used in 1.8% of cases and average post-conversion arc of motion was 119 degrees. ConclusionsThe most common reason for PFA conversion to TKA was progression of osteoarthritis. PFA conversion to TKA is technically similar to primary TKA, but complication rates are more consistent with revision TKA in this study.

Artificial-Intelligence-Aided Radiographic Diagnostic of Knee Osteoarthritis Leads to a Higher Association of Clinical Findings with Diagnostic Ratings.

Radiographic knee osteoarthritis (OA) severity and clinical severity are often dissociated. Artificial intelligence (AI) aid was shown to increase inter-rater reliability in radiographic OA diagnosis. Thus, AI-aided radiographic diagnoses were compared against AI-unaided diagnoses with regard to their correlations with clinical severity. Seventy-one DICOMs (m/f = 27:42, mean age: 27.86 ± 6.5) (X-ray format) were used for AI analysis (KOALA software, IB Lab GmbH). Subjects were recruited from a physiotherapy trial (MLKOA). At baseline, each subject received (i) a knee X-ray and (ii) an assessment of five main scores (Tegner Scale (TAS); Knee Injury and Osteoarthritis Outcome Score (KOOS); International Physical Activity Questionnaire; Star Excursion Balance Test; Six-Minute Walk Test). Clinical assessments were repeated three times (weeks 6, 12 and 24). Three physicians analyzed the presented X-rays both with and without AI via KL grading. Analyses of the (i) inter-rater reliability (IRR) and (ii) Spearman's Correlation Test for the overall KL score for each individual rater with clinical score were performed. We found that AI-aided diagnostic ratings had a higher association with the overall KL score and the KOOS. The amount of improvement due to AI depended on the individual rater. AI-guided systems can improve the ratings of knee radiographs and show a stronger association with clinical severity. These results were shown to be influenced by individual readers. Thus, AI training amongst physicians might need to be increased. KL might be insufficient as a single tool for knee OA diagnosis.

Knee Osteoarthritis Detection and Classification Using X-Rays

Knee osteoarthritis is a common form of arthritis, a chronic and progressive disease recognized by joint space narrowing, osteophyte formation, sclerosis, and bone deformity that can be observed using radiographs. Radiography is regarded as the gold standard and is the cheapest and most readily available modality. X-ray images are graded using Kellgren and Lawrence’s (KL) grading scheme according to the order of severity of osteoarthritis from normal to severe. Early detection can help early treatment and hence slows down knee osteoarthritis degeneration. Unfortunately, most of the existing approaches either merge or exclude perplexing grades to improve the performance of their models. This study aims to automatically detect and classify knee osteoarthritis according to the KL grading system for radiographs. We have proposed an automated deep learning-based ordinal classification approach for early diagnosis and grading knee osteoarthritis using a single posteroanterior standing knee x-ray image. An Osteoarthritis Initiative(OAI) based dataset of knee joint X-ray images is chosen for this study. The dataset was split into the training, testing, and validation set with a 7: 2: 1 ratio. We took advantage of transfer learning and fine-tuned ResNet-34, VGG-19, DenseNet 121, and DenseNet 161 and joined them in an ensemble to improve the model’s overall performance. Our method has shown promising results by obtaining 98% overall accuracy and 0.99 Quadratic Weighted Kappa with a 95% confidence interval. Also, accuracy per KL grade is significantly improved. Furthermore, our methods outperform state-of-the-art automated methods.

Influence of Self-efficacy on Strength and Physical Function among Individuals with Knee Osteoarthritis: A Cross-sectional Study

Introduction: Knee osteoarthritis affects the elderly, potentially leading to disability, with varying symptoms and fatigue. The hip and knee joints experience discomfort, stiffness, reduced mobility, and depression. Muscle weakness, linked to lower limb atrophy, heightens the risk of falls. Self-efficacy, which is confidence in performing tasks despite discomfort, impacts knee strength and function. Factors such as age, education, depression, and physical decline shape self-efficacy. However, the impact of self-efficacy on osteoarthritis health outcomes is unclear due to reduced activity levels. The present study aims to explore the influence of self-efficacy on knee osteoarthritis function and strength. Aim: To identify the influence of self-efficacy on physical function and muscle strength in individuals with knee osteoarthritis. Materials and Methods: A cross-sectional study was conducted in Department of Physiotheraphy with 73 participants who met the inclusion criteria. The study took place between October 2022 and April 2023, spanning a period of six months, at KS Hegde Charitable Hospital in Mangaluru, Karnataka, India. Hip-knee muscle strength was measured using a push-pull dynamometer, physical function was assessed using the Timed Up and Go (TUG) test, and self-efficacy was evaluated using the Arthritis Self-efficacy Scale-8. All tests were performed once on the same day, with a five-minute rest between each test. Withingroup differences were identified using an Independent T-test, and between-group analysis was conducted using Analysis of Variance (ANOVA). Results: The mean age of the participants was 60.2±6.5 years, and the Basal Metabolic Index (BMI) was 25.2±2.0 kg/m2 . A statistically significant difference with a p-value of 0.001 was observed in hip abductor, knee flexor, knee extensor, and TUG tests between the high and low self-efficacy grades. The time taken for the TUG test also showed significance with a p-value of 0.001. Individuals with high levels of self-efficacy had better scores in physical function as well as muscle strength. No association was found between self-efficacy and KL grading. Conclusion: Participants with higher self-efficacy demonstrated good muscle strength in the hip muscles and good physical function. The study concluded that individuals with high selfefficacy grades had better muscle strength and mobility compared to those with low scores in both strength and mobility.

Knee Osteoarthritis Detection Using an Improved CenterNet With Pixel-Wise Voting Scheme

To detect knee disease, radiologists have been utilizing multi-view images such as computer tomography (CT) scans, MRIs, and X-rays. The cheapest method is X-ray to attain the images that is used widely. There exist various image processing techniques to detect knee disease at the initial stages; however, there is still room for improved accuracy and precision of the existing algorithms. Furthermore, in machine learning-based techniques, hand-crafted feature extraction mechanism is a tedious task. Therefore, in this paper, we suggest a technique based on customized CenterNet with a pixel-wise voting scheme to extract the features automatically. Our model uses the most representative features due to the best localization results and a weighted pixel-wise voting scheme which takes input from a predicted bounding box using modified CenterNet. It gives a more accurate bounding box based on the voting score from each pixel inside the former box. Moreover, we employed the distillation knowledge concept to make our model simple without increasing its computational cost, and transfer knowledge from a complex network to a simple network. Therefore, our proposed model detects the KOA in knee images precisely and determines its severity level according to the KL grading system such as Grade-I, Grade-II, Grade-III, and Grade-IV. Our proposed model is a robust and improved architecture based on CenterNet utilizing a simple DenseNet-201 as a base network for feature extraction. Due to the dense blocks employed in a base network, most representative features are extracted from the knee samples. We employed two benchmarks i.e. Mendeley VI for the training, and testing, and the OAI dataset for cross-validation. We evaluated the performance of the proposed technique using various experiments and it is estimated that our proposed model outperforms the existing techniques with an accuracy of 99.14% over testing and 98.97% over cross-validation.

Automated KL Grading of Knee X-ray Images using Convolutional Neural Network

Automated KL Grading of Knee X-ray Images using Convolutional Neural Network

DOES PRP IMPROVE SHORT TERM FUNCTIONAL OUTCOME IN PATIENTS WITH DEGENERATIVE OSTEOARTHRITIS - A PROSPECTIVE STUDY

Introduction: Osteoarthritis is one of the leading causes of disability having high prevalence in elderly population. There is a large debate regarding usage of biologicals in treatment of OA knee. The purpose of this study is to evaluate the efcacy and safety of intra articular PRP injection in treatment of OA knee. Materials and methods: 50 patients were enrolled in this prospective non-randomized study.10 patients were lost to follow up.40 patients and 55 knees were studied .All of them received 2 doses of 5ml intraarticular PRP injection 3 weeks apart. The WOMAC scores and VAS scores at baseline, 1month, 3months and 6 months follow up are compared. Results: Out of 40 patients 25 had unilateral 15 had bilateral symptomatic osteoarthritis knee. A total of 55 knees were studied. 9 patients were males and 31 were females with mean age of 55.65yrs and mean BMI of 29.2. According to KL grading 40 knees were grade 3 and 15 were grade 4.There is statistically signicant (p&lt;0.001) decrease in VAS scores and improvement in WOMAC functional scores after 2 doses of PRP injections. No signicant adverse effects were observed. Conclusion: Intraarticular PRP injection is safe, well tolerated and effective in treatment of primary osteoarthritis. There is decrease in pain and improvement in functional outcome after 2 doses of PRP. More well designed RCT'S with large sample and long term follow up are required to establish long term effectiveness of PRP injections.

Role of serum adropin as a novel biomarker in primary knee osteoarthritis patients: Relation to the functional status, radiographic grading and ultrasonographic findings

BackgroundKnee osteoarthritis (KOA) may lead to disabilities and affect work and daily activities. There is no specific blood test for its early diagnosis. Serum adropin is however a promising biomarker. Aim of the workTo determine the levels of serum adropin in primary KOA andassess its relation with the different radiographic grades of the disease. Patients and methodsA total of 40 primary KOA patients (64 affected knees) and 20 healthy control participated in this study. KOA grades was assessed using ultrasonography (US) and X-ray according to KL grading system. Serum adropin levels were measured. ResultsKOA was bilateral in 24 (60%) patients and knee effusion was present in 46 knees (71.9%). Serum adropin level in KOA patients (46.3 ± 27.9 pg/ml) was significantly lower than in controls (76.5 ± 27.2 pg/ml) (p < 0.00001). Serum adropin was lower in females than in males in patients (40.2 ± 17.7 pg/ml vs 75.2 ± 47.2 pg/ml; p = 0.0016). Serum adropin has significant negative correlation with the KL radiographic grading (r = -0.54, p = 0.0003).At cut off value of 54 and an AUC = 0.85, serum adropin could significantly distinguish between patients and controls (p < 0.05) at a sensitivity of 80%, specificity 90%. ConclusionsSerum adropin levels were significantly low in KOA patients with specificity of 90% and sensitivity of 80%, with a negative significant correlations with the degree of radiographic severity evaluated by KL grading scale and ultrasound. These findings suggest that low serum adropin levels could be a sensitive and specific test for the detection of KOA.

Role of global femoral cartilage in assessing severity of primary knee osteoarthritis

Background/objectiveOsteoarthritis is a degenerative joint disease marked by structural changes in the joint. Radiological evaluation can be used to assess structural changes. Pain, inflammation, and stiffness are common clinical symptoms, leading to limitations in daily activities. Ultrasound, unlike traditional radiography, allows for a direct examination of changes in soft tissues. In addition, it is sensitive in detecting osteophytes as well as identifying early OA changes in femoral cartilage associated with clinical manifestations and function.ResultsA cross- sectional study of 40 patients with primary KOA diagnosed according to the American College of Rheumatology (ACR) criteria. After radiographic evaluation using Kellgren-Lawrence (K-L) scale and US examination assessing global femoral hyaline cartilage (GFC), osteophytes, meniscal extrusion, effusion, and Baker’s cyst of the most symptomatic knee, there was significant correlation between (K-L) grading and (GFC) ultrasonographic grading (p = < 0.001). After assessment of pain and functional disability using Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scale, there was significant correlation between KL and GFC grading with age (p = < 0.001 for both), disease duration (p = < 0.001 for both) as well as WOMAC total scores (p = < 0.001 for both). GFC grading was the only independent predictor relative to other ultrasonographic variables for WOMAC total score (p = < 0.001).ConclusionsUS is a valid tool to evaluate knee joint space and is well correlated with radiographic images. KOA severity assessed by KL grading and GFC ultrasonographic grading showed good correlation with age, duration of the disease, pain intensity, and functional disability.

Comparison of Disease Progression in Patients with Osteoarthritis of Knee Treated with Versus without Intraarticular Injection of Corticosteroids

Objectives: To compare the frequency of disease progression in patients with osteoarthritis of knee treated with versus without intra-articular injection of corticosteroids. Design: The present study was a randomized single-blind controlled trial. Study Settings: The current study was conducted at Department of Orthopedic Surgery at Sir Ganga Ram Hospital Lahore over 1 year from May 2020 to April 2021. Study Procedure: The present study was conducted over 186 patients of both genders aged between 40-70 years presenting in outpatient department of orthopedic surgery with osteoarthritis of knee joint. The sampled patients were randomly allotted into two treatment arms. Patients from Group-A received intraarticular injection of steroids in addition to conservative management while those in Group-B were taken as controls and were managed conservatively without intraarticular injection of steroids. Outcome variable was frequency of disease progression over KL grading which was noted and compared between the groups. An informed on paper consent was obtained from every participant. Results: The mean age of the patients was 52.5±8.7 years. There was a female predominance with a male to female ratio of 1:1.7. The mean BMI of these patients was 28.3±3.6 Kg/m2 and 60 (32.3%) patients were obese. 103 (55.4%) patients had grade 2 while 83 (44.6%) patients had grade 3 osteoarthritis. Disease progression was noted in 30 (16.1%) patients after 6 months follow-up. The frequency of disease progression was significantly higher in patients receiving intra-articular injection of corticosteroids as compared to controls (26.9% vs. 5.4%; p-value&lt;0.001). When stratified, comparable difference was observed across various subgroups of patients based on patient’s age, gender, BMI and baseline Kellgren-Lawrence Grade. Conclusion: In the present study intraarticular injection of steroids was found to be associated with articular cartilage damage evident from increased frequency of disease progression following intraarticular injection as compared to controls which advocates that intraarticular injection of steroids should be avoided in the management of osteoarthritis and should be reserved only for patients with advanced disease and who are already planned for joint replacement. Keywords: Osteoarthritis, Intraarticular Steroids, Chondrotoxicity

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Automating classification of osteoarthritis according to Kellgren-Lawrence in the knee using deep learning in an unfiltered adult population

BackgroundPrevalence for knee osteoarthritis is rising in both Sweden and globally due to increased age and obesity in the population. This has subsequently led to an increasing demand for knee arthroplasties. Correct diagnosis and classification of a knee osteoarthritis (OA) are therefore of a great interest in following-up and planning for either conservative or operative management. Most orthopedic surgeons rely on standard weight bearing radiographs of the knee. Improving the reliability and reproducibility of these interpretations could thus be hugely beneficial. Recently, deep learning which is a form of artificial intelligence (AI), has been showing promising results in interpreting radiographic images. In this study, we aim to evaluate how well an AI can classify the severity of knee OA, using entire image series and not excluding common visual disturbances such as an implant, cast and non-degenerative pathologies.MethodsWe selected 6103 radiographic exams of the knee taken at Danderyd University Hospital between the years 2002-2016 and manually categorized them according to the Kellgren & Lawrence grading scale (KL). We then trained a convolutional neural network (CNN) of ResNet architecture using PyTorch. We evaluated the results against a test set of 300 exams that had been reviewed independently by two senior orthopedic surgeons who settled eventual interobserver disagreements through consensus sessions.ResultsThe CNN yielded an overall AUC of more than 0.87 for all KL grades except KL grade 2, which yielded an AUC of 0.8 and a mean AUC of 0.92. When merging adjacent KL grades, all but one group showed near perfect results with AUC > 0.95 indicating excellent performance.ConclusionWe have found that we could teach a CNN to correctly diagnose and classify the severity of knee OA using the KL grading system without cleaning the input data from major visual disturbances such as implants and other pathologies.

Comparison of Bone-Patellar Tendon-Bone Graft, Semitendinosus-Gracilis Graft and Semitendinosus-Gracilis with Preserved Tibial Insertion Graft in Anterior Cruciate Ligament Reconstruction in Sports Persons.

Introduction:Bone-patellar tendon-bone (BPTB) and semitendinosus–gracilis (STG) are the commonest grafts used for ACL reconstruction. However even after having been debated for years, there is no consensus about the ideal graft. Moreover, the literature is deficient about STG graft with preserved tibial insertion (STGPI) which preserves the proprioception. Our aim is to compare the outcome of BPTB, free STG and STGPI grafts after ACL reconstruction in professional sports persons. We compared the outcome in terms of mechanical stability, functional outcome, return to sports activity and degenerative changes.Material and Methods:Professional sports persons aged between 16-50 years operated for ACL tear using BPTB, free STG and STGPI grafts with minimum follow-up of two years were identified from hospital records. Patients with associated knee injuries were excluded. Patients, divided in three groups according to graft used, were compared in terms of mechanical stability (arthrometric examination KT-1000 score), functional outcome (Lysholm Score), return to sports activity (Tegner score and difference in thigh circumference) and degenerative changes (KL grading).Results:BPTB graft group was found to be better than free STG and STGPI graft groups in terms of KT-1000 score. There was no statistically significant difference among the groups in terms of Lysholm score, Tegner score, difference in thigh circumference and KL grading.Conclusion:BPTB graft is better than free STG and STGPI grafts in terms of knee stability. When compared for patient reported outcome, return to sports activity, osteoarthritic changes and graft failure there is no significant difference among the three types of grafts.