Diagnosis Of Meniscus Injuries Research Articles

Development of artificial intelligence-based clinical decision support system for diagnosis of meniscal injury using magnetic resonance images

BackgroundMagnetic resonance imaging (MRI) examinations are often necessary for the diagnosis of meniscal injuries. Due to the quantity and detail of the images in an MRI examination, a careful reading of MR images is time-consuming even for experienced physicians. The use of artificial intelligence (AI) models for interpreting MR images may address this problem. ObjectiveThis study aimed to utilize MR images and arthroscopic findings as a dataset to develop an AI model that can help detect meniscal lesions, and thus aid in the diagnosis of meniscus injuries. MethodOur dataset comprised data from 811 knee MRI studies. MR images were labeled and annotated by two orthopedic surgeons. The training pipeline had two parts. First, Scaled-YOLOv4 was used to detect the position of the meniscus. Second, the EfficientNet-B7 model structure was used to detect meniscal tears. A brief report demonstrating the probability and the position of a normal or torn meniscus was then generated by the AI-based clinical decision support system. ResultThe Scaled-YOLOv4 model had areas under the curve (AUCs) of 0.948 and 0.963 in the sagittal and coronal views, respectively. The EfficientNet-B7 model yielded AUCs of 0.984 and 0.972 in the sagittal and coronal views, respectively. High AUCs were achieved both in meniscus localization and meniscal tear detection. DiscussionIn this study, we utilized arthroscopic findings, obtained using the optimal meniscal tear diagnosis method, as our ground truth label. Nevertheless, this approach did not provide better model performance than that based on MRI clinical reports. ConclusionsThe contributions of this paper include the following points. First, an AI-based clinical decision support system was established to detect meniscal injury based on a deep learning model algorithm. High sensitivity and specificity were achieved by the model system. The model may have substantially facilitated the diagnosis of meniscal injuries from MR images. Second, through the AI detection system, clinicians can obtain a structured report of meniscus rupture, which enables physicians to interpret images more quickly and saves time for physicians to read images.

A Balanced Arthroscopic Debridement of the Inner Layer of the Knee Retinaculum Increases the Tibiofemoral Joint Space Width.

Introduction Traditional techniques can enlarge the medial tibiofemoral joint space width (JSW) for meniscal repairs, but a remnant ligament laxity may be developed. Alternatively, the debridement of the inner retinaculum layer may result in a balanced JSW without causing extra-ligament damage (retinaculum layers II and collateral ligament). Purpose The purpose of this study was to determine whether a concentric arthroscopic debridement of the inner retinaculum layer increases the tibiofemoral JSW in patients with meniscal injuries. Secondarily, we determine whether the increase in JSW is symmetrical between compartments and describe the rate of complications and patient satisfaction. Method Twenty middle-aged (15 male and five female) patients diagnosed with acute meniscal injury aged 36 ± 12 years were enrolled. The patients were submitted to an arthroscopic debridement of the inner layer of the knee retinaculum for both the medial and lateral compartments. The tibiofemoral JSW was measured intra-articularly using a custom instrument. A two-way ANOVA for repeated measures was used to compare the JSW. A Bland–Altman analysis and test-retest analysis were performed. Results The JSW increased following the debridement of the inner retinaculum layer, for both the medial and lateral compartments (p < 0.001). No complications were identified, and the patients were satisfied with the intervention. The minimal detectable change and bias of the custom instrument were 0.06 mm and 0.02 mm, respectively. Conclusion The debridement allows a clinically important (>1 mm) symmetric tibiofemoral JSW enlargement. The technique suggests favoring the diagnosis of meniscus injuries and manipulating arthroscopic instruments without secondary complications after one year.

Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.

Racial, Socioeconomic, and Demographic Disparities in the Management of Meniscal Tears (179)

Objectives:Meniscal surgery, repair or debridement, makes up about a third of all arthroscopic related surgery. In the adult population, there is much debate about which patients should undergo surgical intervention and which should be treated nonoperatively. There seems to be a paucity of literature related to how a patient’s demographics, insurance status, socioeconomic status, and race impact the likelihood of undergoing surgical intervention. In the present study, we sought to determine how these factors influence whether a patient undergoes surgical intervention following a meniscal injury.Methods:From 2009-2018, claims for adult (≥18 years of age) patients who were diagnosed with a primary meniscus injury were identified in the New York Statewide Planning and Research Cooperative System (SPARCS) database. SPARCS is a comprehensive all-payer database collecting all inpatient and outpatient pre-adjudicated claims in New York. ICD 9/10 CM codes were used to identify the initial diagnosis for each patient. ICD9/10 PCS and Current Procedural Terminology (CPT) codes were used to identify subsequent meniscus surgery. The procedures identified were linked with the initial diagnosis and patients were noted as either having meniscus surgery, or not having meniscus surgery. After missing data was removed, logistic regression analysis was performed to determine the effect of patient factors on the likelihood of having surgery after a diagnosis of meniscus injury.Results:Of 394,007 adult meniscus injury diagnoses, 84.7% proceeded to undergo surgical intervention. The median age for no surgery was 51 (mean: 49.4) and the median age for surgery was 52 (mean: 50.2) (p<.0001) (Table 1). Females relative to males (OR=0.757, p<.0001), increased social deprivation index (SDI) (OR=0.994, p<.0001), African American compared to white race (OR=0.822, p<.0001), hispanic relative to non-hispanic ethnicity (OR=0.682, p<.0001), federal relative to private insurance (OR=0.459, p<.0001), and self-pay relative to private insurance (OR=0.437, p<.0001) were all associated with decreased odds of undergoing meniscus surgery. Older patients (OR=1.009, p<.0001), worker’s compensation relative to private insurance (OR=1.284, p<.0001) and patients with a charlson comorbidity index (CCI) score ≥1 had increased odds of undergoing surgery (OR=1.129, p<.0001) (Table 2).Conclusions:Multiple demographic, insurance status, socioeconomic status, and race factors influence the decision to manage meniscal tears surgically. Females, those with increased social deprivation index, African-Americans, Hispanics, or those with federal insurance or self-pay (when compared to private insurance) were less likely to undergo surgical intervention for meniscal tears. Those with workers’ compensation, who were of older age and higher CCI were more likely to undergo surgical intervention. Further research is ultimately needed to elicit causes for disparities between demographic groups.

Application of Magnetic Nano-MR Molecular Imaging Technology in the Relationship Between Meniscus Injury and Knee Joint Movement

Knee osteoarthritis (KOA) can lead to joint malformations and dysfunction. The meniscus, as the fibrous cartilage of the medial and lateral articular surfaces of the tibia, functions in load transfer, shock absorption, and knee joint stabilization. Injury of the meniscus reduces its protective effect on the knee joint and increases the risk of KOA. Early detection of meniscus injury and accurate assessment of its severity could help to clinically formulate corresponding treatment plans, delay disease progression, and reduce the risk of KOA. Magnetic resonance imaging (MRI) can clearly characterize the tissue structure of the meniscus in all directions, which is convenient for observing the damage to it. However, the sensitivity of traditional MRI to cartilage tissue is still limited. Superparamagnetic iron oxide nanoparticles (SPION), as a nano-biomaterial with superparamagnetic properties, can enhance MR signal intensity and imaging sensitivity. The use of SPION as a contrast agent for MRI diagnosis of meniscus injury may help to improve the diagnostic accuracy. In this study, the results of arthroscopy were used as a gold standard to compare the diagnostic value of traditional MRI and MRI+SPION in meniscus injury. We found that SPION could significantly improve the sensitivity, specificity, and accuracy of MRI in diagnosing meniscus injury. According to the classification of the severity of meniscus injury by MRI+SPION, six degrees of freedom of knee joints were compared in each group, and major differences were found in each degree of freedom of knee joints in the patients with different severity grades. The more severe the injury, the smaller the range of degrees of freedom of the knee. The above results show that the knee joint activity of patients with meniscus injury can be evaluated according to magnetic nano-magnetic resonance molecular imaging technology, and the corresponding clinical treatment plan can be made according to the diagnosis and evaluation results.

Clinical Diagnosis of Meniscus Injury of Knee Joint

Clinical Diagnosis of Meniscus Injury of Knee Joint