Knee Magnetic Resonance Imaging Research Articles

Fresh osteochondral allograft transplantation for knee full-thickness articular cartilage lesions using femoral head of living donors: short-term results.

Fresh osteochondral allograft transplantation is a good treatment option of cartilage defects. However, this treatment option is not available in all countries due to limited graft availability and tissue banks limitations. The purpose of this study is to assess the short term functional and imaging outcomes of fresh osteochondral allograft transplantation in the knee using the femoral head of living donors. Fresh osteochondral allografts from the femoral heads of living donors is a valid graft source for management of distal Femur cartilage defects. This technique can improve functional knee scores with good radiological outcomes. Prospective case series. Fifteen patients with full thickness cartilage defects of the distal femur underwent osteochondral allograft transplantation from the femoral heads of living donors. Grafts were transplanted by both shell and multiple dowels techniques. The average follow up duration was 18.3 months (range, 12-25 months). Patients were evaluated by Lysholm and International Knee Documentation Committee (IKDC) scores, radiography and MR imaging using Osteochondral Allograft MRI Scoring System (OCAMRISS). There was a statistically significant improvement (P < 0.001) in both Lysholm and IKDC average scores at 6 months and 12 months postoperative. Postoperative MRI was done at an average 6.8 months (range, 5-11 months) postoperative. The mean total OCAMRISS score was 3.4 (range, 1-7). A second look arthroscopy was done in four patients and showed intact articular cartilage in all three patients. Femoral head of living donors is a valid new source for fresh osteochondral allograft transplantation of knee osteochondral lesions. Short term results showed improvement in clinical assessment scores. Follow up imaging showed graft incorporation and good MRI scores.

Accelerated High-Resolution Deep Learning Reconstruction Turbo Spin Echo MRI of the Knee at 7 T.

The aim of this study was to compare the image quality of 7 T turbo spin echo (TSE) knee images acquired with varying factors of parallel-imaging acceleration reconstructed with deep learning (DL)-based and conventional algorithms. This was a prospective single-center study. Twenty-three healthy volunteers underwent 7 T knee magnetic resonance imaging. Two-, 3-, and 4-fold accelerated high-resolution fat-signal-suppressing proton density (PD-fs) and T1-weighted coronal 2D TSE acquisitions with an encoded voxel volume of 0.31 × 0.31 × 1.5 mm3 were acquired. Each set of raw data was reconstructed with a DL-based and a conventional Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) algorithm. Three readers rated image contrast, sharpness, artifacts, noise, and overall quality. Friedman analysis of variance and the Wilcoxon signed rank test were used for comparison of image quality criteria. The mean age of the participants was 32.0 ± 8.1 years (15 male, 8 female). Acquisition times at 4-fold acceleration were 4 minutes 15 seconds (PD-fs, Supplemental Video is available at http://links.lww.com/RLI/A938) and 3 minutes 9 seconds (T1, Supplemental Video available at http://links.lww.com/RLI/A939). At 4-fold acceleration, image contrast, sharpness, noise, and overall quality of images reconstructed with the DL-based algorithm were significantly better rated than the corresponding GRAPPA reconstructions (P < 0.001). Four-fold accelerated DL-reconstructed images scored significantly better than 2- to 3-fold GRAPPA-reconstructed images with regards to image contrast, sharpness, noise, and overall quality (P ≤ 0.031). Image contrast of PD-fs images at 2-fold acceleration (P = 0.087), image noise of T1-weighted images at 2-fold acceleration (P = 0.180), and image artifacts for both sequences at 2- and 3-fold acceleration (P ≥ 0.102) of GRAPPA reconstructions were not rated differently than those of 4-fold accelerated DL-reconstructed images. Furthermore, no significant difference was observed for all image quality measures among 2-fold, 3-fold, and 4-fold accelerated DL reconstructions (P ≥ 0.082). This study explored the technical potential of DL-based image reconstruction in accelerated 2D TSE acquisitions of the knee at 7 T. DL reconstruction significantly improved a variety of image quality measures of high-resolution TSE images acquired with a 4-fold parallel-imaging acceleration compared with a conventional reconstruction algorithm.

Exploring chat generated pre-trained transformer-3 ability to interpret MRI knee images and generate reports

Objectives: The study’s objective was to determine if Chat Generated Pre-Trained Transformer-3 (ChatGPT)-4V can interpret magnetic resonance imaging (MRI) knees and generate preliminary reports based on images and clinical history provided by the radiologist. Materials and Methods: This cross-sectional observational study involved selecting 10 MRI knees with representative imaging findings from the institution’s radiology reporting database. Key MRI images were then input into the ChatGPT-4V model, which was queried with four questions: (i) What does the image show?; (ii) What is the sequence?; (iii) What is the key finding?; and, (iv) Finally, the model generated a report based on the provided clinical history and key finding. Responses from ChatGPT-4 were documented and independently evaluated by two musculoskeletal radiologists through Likert scoring. Results: The mean scores for various questions in the assessment were as follows: 2 for “What does the image show?,” 2.10 for “What is the sequence?,” 1.15 for “What is the key finding?,” and the highest mean score of 4.10 for the command “Write a report of MRI of the…” Radiologists consistently gave mean scores ranging from 2.0 to 2.5 per case, with no significant differences observed between different cases (P &gt; 0.05). The interclass correlation coefficient between the two raters was 0.92 (95% Confidence interval: 0.85–0.96). Conclusion: ChatGPT-4V excelled in generating reports based on user-fed clinical information and key findings, with a mean score of 4.10 (good to excellent proficiency). However, its performance in interpreting medical images was subpar, scoring ≤2.10. ChatGPT-4V, as of now, cannot interpret medical images accurately and generate reports.

A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis.

This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs). This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears. This study's findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears-whether in the anterior or posterior horn-with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94. Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool.

#2698 Regression of amyloid deposits after treatment with tocilizumab

Abstract Background and Aims AA amyloidosis is a multisystem disease characterized by the deposition of serum amyloid A protein, which is secondary to chronic inflammation. Tocilizumab (an interleukin-6 inhibitor monoclonal antibody) is effective in suppressing inflammation, normalizing serum amyloid A protein levels and inducing remission in patients with amyloidosis. Recently, tocilizumab treatment has been associated with the disappearance of amyloid deposits. Results We report a case of AA amyloidosis secondary to chronic arthritis in a 61-year-old woman referred to our hospital in 2011 due to oligoarthritis of the knees and elevation of acute-phase reactants. Corticosteroids and methotrexate were prescribed, without clinical response. Two years later, the patient presented with foamy urine, nocturia, sweating and dizziness. An elevated C-reactive protein (CRP), erythrocyte sedimentation rate and nephrotic-range proteinuria were found. Rectal and oral mucosa biopsies were performed and amyloid AA deposits were detected in both (Fig. 1A, B). Magnetic resonance imaging (MRI) of the right knee showed arthropathy due to amyloid deposition (Fig. 2). Intravenous monthly tocilizumab was prescribed with rapid improvement of CRP, proteinuria (Fig. 3) and nephrotic syndrome symptoms. Arthritis also improved significantly. Two years later, a new biopsy of the rectal mucosa did not show amyloid deposits (Fig. 1C, D) and right knee MRI was normal, without evidence of amyloid synovitis (Fig. 4). Eight years later, the patient continues treatment with tocilizumab and remains clinically stable, with no evidence of recurrence. Literature review: The efficacy of tocilizumab has been described mainly in patients with rheumatoid arthritis, but also in AA amyloidosis secondary to systemic juvenile idiopathic arthritis, familial Mediterranean fever, psoriatic arthritis, ankylosing spondylitis, Still's disease, Muckle-Wells syndrome, Behçet syndrome, polyarteritis nodosa and Takayasu arteritis. Some of the studies report long-term effectiveness of up to 9 years. In 11 case reports of patients with AA amyloidosis treated with tocilizumab, a follow-up biopsy was performed and a reduction or disappearance of the deposits was also observed. Lane et al. analyzed pre- and on-treatment serial scintigraphy scans in 20 patients with AA amyloidosis, showing that AA amyloid deposits either regressed or did not increase whilst on tocilizumab. We have not identified any study demonstrating the disappearance of amyloid arthropathy in MR images. Conclusion Tocilizumab treatment controls chronic inflammatory disease and improves symptoms of AA amyloidosis. Long-term treatment with tocilizumab may remove amyloid deposits from tissues, leading to a definitive cure of this disease.

Two-Year Functional Outcomes of Nonsurgical Treatment in Concomitant Anterior Cruciate Ligament and Medial Collateral Ligament Injuries: A Case-Control Study.

The anterior cruciate ligament (ACL) is a common knee injury in high-intensity sports, which can cause early career loss in young athletes. Concomitant damage to other knee stabilizers may occur, such as the medial collateral ligament (MCL). Recent studies have shown that knee stability can increase without surgical intervention in patients with ACL and MCL injuries. Regarding the importance of functional tests in return to exercise prediction, this study aims to measure nonsurgical approach's long-term outcome for concomitant ACL and MCL injuries with a focus on functional tests. This is a case-control study with a 2-year follow-up. The case group consisted of patients who had provided written consent and completed their 2-year follow-up, and the control group was made up of healthy people who did not have any knee medical conditions and were matched by age, gender, and activity level. Physical examinations, Tegner and International Knee Documentation Committee questionnaires, and knee magnetic resonance imaging were conducted, and functional performance tests were performed after a 10-minute warm-up. Lody's index (the ratio of injured-to-uninjured knee results) was calculated. The data were analyzed using independent t-test, one-way analysis of variance, chi-squared test, and Fisher's exact test. The study involved 11 patients in each concomitant ACL and MCL injury cases and healthy control groups with a mean age of 32.4 and 28 years, respectively. None of the patients reported knee instability symptoms in the 2-year follow-up. More than half of the patients continued their sports field without reinjury, with no significant difference in activity levels between case and control groups. The 6-meter hop test and single-leg hop test showed no significant difference between case and control groups (p-value: 0.326, 0.859), and no significant difference was observed in the three Carioca, cocontraction, and Shuttle tests in the 2-year follow-up. Functional tests in ACL and MCL injuries revealed normal outcomes, implying a nonsurgical approach for patients with proximal ACL tears, better knee stability, and no significant differences between the injured and control groups.

One-stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation.

Anterior cruciate ligament (ACL) injuries are closely associated with knee osteoarthritis (OA). However, diagnosing ACL injuries based on knee magnetic resonance imaging (MRI) has been subjective and time-consuming for clinical doctors. Therefore, we aimed to devise a deep learning (DL) model leveraging MRI to enable a comprehensive and automated approach for the detection of ACL injuries. A retrospective study was performed extracting data from the Osteoarthritis Initiative (OAI). A total of 1,589 knees (comprising 1,443 intact, 90 with partial tears, and 56 with full tears) were enrolled to construct the classification model. This one-stop detection pipeline was developed using a tailored YOLOv5m architecture and a ResNet-18 convolutional neural network (CNN) to facilitate tasks based on sagittal 2-dimensional (2D) intermediate-weighted fast spin-echo sequence at 3.0T. To ensure the reliability and robustness of the classification system, it was subjected to external validation across 3 distinct datasets. The accuracy, sensitivity, specificity, and the mean average precision (mAP) were utilized as the evaluation metric for the model performance by employing a 5-fold cross-validation approach. The radiologist's interpretations were employed as the reference for conducting the evaluation. The localization model demonstrated an accuracy of 0.89 and a sensitivity of 0.93, achieving a mAP score of 0.96. The classification model demonstrated strong performance in detecting intact, partial tears, and full tears at the optimal threshold on the internal dataset, with sensitivities of 0.941, 0.833, and 0.929, specificities of 0.925, 0.947, and 0.991, and accuracies of 0.940, 0.941, and 0.989, respectively. In comparison, on a subset consisting of 171 randomly selected knees from the OAI, the radiologists demonstrated a sensitivity ranging between 0.660 and 1.000, specificity ranging between 0.691 and 1.000, and accuracy ranging between 0.689 and 1.000. On a subset consisting of 170 randomly selected knees from the Chinese dataset, the radiologists exhibited a sensitivity ranging between 0.711 and 0.948, specificity ranging between 0.768 and 0.977, and accuracy ranging between 0.683 and 0.917. After retraining, the model achieved sensitivities ranging between 0.630 and 0.961, specificities ranging between 0.860 and 0.961, and accuracies ranging between 0.832 and 0.951, respectively, on the external validation dataset. The proposed model utilizing knee MRI showcases robust performance in the domains of ACL localization and classification.

Correlating quadriceps patellar tendon angle and lateral patellar tilt angle in patients with irregular alignment: a cross-sectional study with retrospective data.

The newly defined angle, quadriceps-patella angle (QPA), reflects the combined force transmitted to the patella by the quadriceps muscles and patellar tendon. An increase in QPA may correlate with an increased force on the patella, which is significant in diagnosing patellofemoral instability and pain syndrome. In our study, we examined how various angles and pathologies vary depending on lateral patellar tilt angle (LPTA). QPA and patellar malalignment was investigated. Thus, the importance of understanding patellar malalignment and the research gap. Three hundred and fifty patients who underwent knee magnetic resonance imaging (MRI) examinations were included. The cross-sectional study conducted retrospectively between the years of 2018-2020 in a tertiary care outpatient clinic. Shapiro-Wilk normality, Chi-square, Mann-Whitney-U, Spearman correlation and receiver operating characteristic (ROC) curve analysis, statistical tests used for analysis. The patellar tendon length, patellar height, tibial tubercle-trochlear groove distance (TT-TG), patella angle, trochlear sulcus angle, trochlear groove depth (TGD), medial trochlea length (MT), lateral trochlea length (LT), medial trochlear/lateral trochlear length ratio (MT/LT), LPTA, patella-patellar tendon angle (PPTA), QPA, Insall-Salvati index (ISI), medial trochlear inclination (MTI), lateral trochlear inclination (LTI) were among these measurements. In addition, we aim to reveal whether there is a significant relationship between two important angles LPTA and QPA. Whether there is a significant increase in the development of chondromalacia for the patient group with LPTA >5°. We examined how the frequency of chondromalacia changes in the patient group with LPTA >5°. Two hundred and seventy seven patients included in the study and many measurements were performed on MRI. Fad-pad edema was found to be significantly higher in the group with LPTA <5° (P=0.046). TT-TG distance was significantly higher, TGD and MT were significantly lower in patients with higher LPTA (P=0.001, P=0.002 and P=0.017, respectively). A low level of significant positive correlation was found between QPA and patellar tendon length. There is no significant difference between QPA and PPTA angles between the groups with LPTA <5° and >5° (P=0.503, P=0.188). In the ROC analysis performed to determine the cut-off value, the LPTA value ≤14.2° which significantly predicted the presence of fad-pad edema, had the highest sensitivity and specificity [sensitivity: 76.71%, specificity: 39.90%, area under the curve (AUC): 0.588, P=0.024]. QPA is independent from many angles of the knee and does not change significantly. As the patellar tendon length increases, QPA angle also increases. In patients with abnormal LPTA, the frequency of TT-TG distance and chondromalacia increased, while TGD and MT decreased. Patients with a low LPTA can be more carefully examined for chondromalacia and fad-pad edema in clinical and MRI examination.

A comprehensive MRI investigation to identify potential biomarkers of Osgood Schlatter disease in adolescents: A cross sectional study comparing Osgood Schlatter disease with controls.

Osgood-Schlatter disease (OSD) is the most common knee pain complaintamong adolescents playing sports. Despite this, there remains controversy over the pathophysiology and whether specific anatomical characteristics are associated with OSD. This study aimed to systematically and comprehensively characterize adolescents with OSD using magnetic resonance imaging (MRI) compared to pain-free controls, including both tissue abnormalities that may be associated with OSD, as well as anatomical characteristics. A secondary objective was to identify potential imaging biomarkers associated with pain. Cross-sectional study. Adolescents with OSD and controls were recruited from 2020 to 2022. Following a clinical exam, demographics, pain, sports participation, and Tanner stage were collected. Knee MRI was conducted on the participants' most symptomatic knee (OSD) or the dominant leg (controls). Sixty-seven adolescents (46 with OSD and 30 controls) were included. 80% of participants with OSD had at least one tissue alteration compared to 54% of controls. Compared to controls, OSD had 36.3 (95%CI 4.5 to 289.7) higher odds of bony oedema at the tibial tuberosity, and 32.7 (95%CI 4.1 to 260.6) and 5.3 (95%CI 0.6 to 46.2) higher odds of bony oedema at the tibial epiphysis and metaphysis respectively. Participants with OSD also had higher odds of fluid/oedema at the patellar tendon (12.3 95%CI 3.3 to 46.6), and superficial infrapatellar bursitis (7.2). Participants with OSD had a more proximal tendon attachment (mean tibial attachment portion difference, -0.05, 95% CI: -0.1 to 0.0, p = 0.02), tendon thickness (proximal mean difference, -0.09, 95% CI: -0.4 to 0.2, p = 0.04; distal mean difference, -0.6, 95% CI: -0.9 to -0.2, p = 0.01). Those with bony/tendon oedema had 1.8 points (95% CI: 0.3 to 3.2) higher pain on palpation than those without (t = -2.5, df = 26.6, p = 0.019), but there was no difference between these groups in a functional single leg pain provocation. Adolescents with OSD present with tissue and structural abnormalities on MRI that differed from age-matched controls. The majority had findings in the patellar tendon and bone, which often co-occurred. However, a small proportion of OSD also presents without alterations. It appears these findings may be associated with clinical OSD-related pain on palpation of the tibial tuberosity. Our highlight the pathophysiology on imaging, which has implications for understanding the mechanism and treatment of OSD.

Improving inceptionV4 model based on fractional-order snow leopard optimization algorithm for diagnosing of ACL tears

In the current research study, a new method is presented to diagnose Anterior Cruciate Ligament (ACL) tears by introducing an optimized version of the InceptionV4 model. Our proposed methodology utilizes a custom-made variant of the Snow Leopard Optimization Algorithm, known as the Fractional-order Snow Leopard Optimization Algorithm (FO-LOA), to extract essential features from knee magnetic resonance imaging (MRI) images. This results in a substantial improvement in the accuracy of ACL tear detection. By effectively extracting critical features from knee MRI images, our proposed methodology significantly enhances diagnostic accuracy, potentially reducing false negatives and false positives. The enhanced model based on FO-LOA underwent thorough testing using the MRNet dataset, demonstrating exceptional performance metrics including an accuracy rate of 98.00%, sensitivity of 98.00%, precision of 97.00%, specificity of 98.00%, F1-score of 98.00%, and Matthews Correlation Coefficient (MCC) of 88.00%. These findings surpass current methodologies like Convolutional Neural Network (CNN), Inception-v3, Deep Belief Networks and Improved Honey Badger Algorithm (DBN/IHBA), integration of the CNN with an Amended Cooking Training-based Optimizer version (CNN/ACTO), Self-Supervised Representation Learning (SSRL), signifying a significant breakthrough in ACL injury diagnosis. Using FO-SLO to optimize the InceptionV4 framework shows promise in improving the accuracy of ACL tear identification, enabling prompt and efficient treatment interventions.

Tumor-Like Distal Femoral Cortical Irregularities of the Knee in Adolescent Competitive Alpine Skiers: Longitudinal Assessment Over 48 Months

Background: Tumor-like distal femoral cortical irregularities (DFCIs) are a frequent incidental finding on knee magnetic resonance imaging (MRI) and are common in young competitive athletes. Purpose: To assess and compare the morphology and prevalence of DFCIs in competitive alpine skiers over 48 months during adolescence. Study Design: Case series; Level of evidence, 4. Methods: Adolescent competitive alpine skiers were prospectively recruited in 2018 and received bilateral 3-T MRI of the knee at baseline and after 48 months. All MRIs were evaluated for the presence and location of DFCIs, which were marked at 1 of 3 anatomic positions: (1) the femoral attachment of the medial head of the gastrocnemius muscle, (2) the lateral head of the gastrocnemius muscle, or (3) the attachment of the adductor magnus aponeurosis. The size of the DFCI was measured by 2 radiologists independently. The measurements were compared using the Wilcoxon signed-rank test, the interclass correlation coefficient (ICC), and Cohen Kappa. Results: A total of 63 athletes (mean age at follow-up, 19.6 ± 1.2 years; n = 25 female) were included in the study. At baseline, DFCIs were detected in 84 out of 126 knees (67%). At the 48-month follow-up, DFCIs were found in 88 out of 126 knees (70%), with multiple DFCIs in 3 knees and no significant difference between male and female patients (n = 24 male, n = 19 female; P = .71). No significant increase was detected for the number (P = .21) and size of the DFCIs between the baseline and the 48-month follow-up (mean size: baseline, 3.7 ± 0.8 mm; 48-month follow-up: 3.6 ± 0.9 mm; P = .66). The interrater agreement for the mean size measurements of DFCIs was good to excellent (ICC 0.88). Conclusion: DFCIs remain a frequent finding on knee MRI in competitive alpine skiers after skeletal maturation and do not disappear during adolescence. The DFCI size was constant in athletes aged between 15 and 19 years. Moreover, DFCIs should not be mistaken for a pathologic finding.

Measuring synovial thickness on knee MRI in pediatric patients with arthritis: is contrast necessary?

The use of contrast-enhanced imaging has long been standard for magnetic resonance imaging (MRI) assessments of synovitis in juvenile idiopathic arthritis (JIA). However, advancements in MRI technology have allowed for reliable identification of synovium without contrast. To assess the equivalence of unenhanced MRI with contrast-enhanced MRI in evaluating synovial thickness. This is an institutional review board approved, retrospective study performed in a tertiary children's hospital. Pediatric JIA patients under 21 years old were included who underwent knee MRI scans (1.5 T or 3 T) without and with contrast between January 2012 and January 2022. Two radiologists independently measured synovial thickness at 6 knee sites on contrast-enhanced and unenhanced sequences. Numerical measurements and ordinal scores based on juvenile idiopathic arthritis magnetic resonance imaging scoring (JAMRIS) system were recorded, and tests of equivalence were conducted, as well as between-reader and within-reader reliability by concordance correlation coefficient (CCC). All tests were considered significant at the 5% level. A total of 38 studies from 35 patients (25 females, median age 14 years; interquartile range 7 to 15.7) were included. Equivalence was demonstrated at each of the 6 sites for both continuous measurements (P-values < 0.05) and ordinal scores (P-values < 0.05) based on the average over readers. Within-reader reliability was moderate to high (CCC 0.50-0.89), except for the cruciate ligaments site. Averaged over the 6 sites, reliability between readers was low for unenhanced (CCC 0.47, with 95% CI: [0.41, 0.53]) and moderate for contrast-enhanced (CCC 0.64, with 95% CI: [0.59, 0.69]) sequences. Unenhanced knee MRI is equivalent to contrast-enhanced MRI in assessment of synovial thickness using conventional MRI sequences. Contrast material helped improve inter-reader reliability.

Hybrid image encryption using advanced least significant bit algorithm, chaotic maps and DNA encoding for digital healthcare

Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted.

Visual interpretable MRI fine grading of meniscus injury for intelligent assisted diagnosis and treatment

Meniscal injury represents a common type of knee injury, accounting for over 50% of all knee injuries. The clinical diagnosis and treatment of meniscal injury heavily rely on magnetic resonance imaging (MRI). However, accurately diagnosing the meniscus from a comprehensive knee MRI is challenging due to its limited and weak signal, significantly impeding the precise grading of meniscal injuries. In this study, a visual interpretable fine grading (VIFG) diagnosis model has been developed to facilitate intelligent and quantified grading of meniscal injuries. Leveraging a multilevel transfer learning framework, it extracts comprehensive features and incorporates an attributional attention module to precisely locate the injured positions. Moreover, the attention-enhancing feedback module effectively concentrates on and distinguishes regions with similar grades of injury. The proposed method underwent validation on FastMRI_Knee and Xijing_Knee dataset, achieving mean grading accuracies of 0.8631 and 0.8502, surpassing the state-of-the-art grading methods notably in error-prone Grade 1 and Grade 2 cases. Additionally, the visually interpretable heatmaps generated by VIFG provide accurate depictions of actual or potential meniscus injury areas beyond human visual capability. Building upon this, a novel fine grading criterion was introduced for subtypes of meniscal injury, further classifying Grade 2 into 2a, 2b, and 2c, aligning with the anatomical knowledge of meniscal blood supply. It can provide enhanced injury-specific details, facilitating the development of more precise surgical strategies. The efficacy of this subtype classification was evidenced in 20 arthroscopic cases, underscoring the potential enhancement brought by intelligent-assisted diagnosis and treatment for meniscal injuries.

Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers.

To investigate the potential of combining compressed sensing (CS) and artificial intelligence (AI), in particular deep learning (DL), for accelerating three-dimensional (3D) magnetic resonance imaging (MRI) sequences of the knee. Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence with four different acceleration levels (10, 13, 15, and 17). All sequences were accelerated with CS and reconstructed using the conventional and a new DL-based algorithm (CS-AI). Subjective image quality was evaluated by two blinded readers using seven criteria on a 5-point-Likert-scale (overall impression, artifacts, delineation of the anterior cruciate ligament, posterior cruciate ligament, menisci, cartilage, and bone). Using mixed models, all CS-AI sequences were compared to the clinical standard (sense sequence with an acceleration factor of 2) and CS sequences with the same acceleration factor. 3D sequences reconstructed with CS-AI achieved significantly better values for subjective image quality compared to sequences reconstructed with CS with the same acceleration factor (p ≤ 0.001). The images reconstructed with CS-AI showed that tenfold acceleration may be feasible without significant loss of quality when compared to the reference sequence (p ≥ 0.999). For 3-T 3D-MRI of the knee, a DL-based algorithm allowed for additional acceleration of acquisition times compared to the conventional approach. This study, however, is limited by its small sample size and inclusion of only healthy volunteers, indicating the need for further research with a more diverse and larger sample. DRKS00024156. Using a DL-based algorithm, 54% faster image acquisition (178s versus 384s) for 3D-sequences may be possible for 3-T MRI of the knee. • Combination of compressed sensing and DL improved image quality and allows for significant acceleration of 3D knee MRI. •DL-based algorithm achieved better subjective image quality than conventional compressed sensing. •For 3D knee MRI at 3 T, 54% faster image acquisition may be possible.

Anatomical Safety Area for Periarticular Analgesic Infiltration through the Posterior Capsule in Total Knee Arthroplasty: Radiological Study in Magnetic Resonance.

Background: One of the main challenges of orthopedic surgery is adequate pain management after total knee arthroplasty. This work aimed to determine the anatomical safety area for infiltration through the posterior capsule of the knee in prosthetic surgery using Magnetic Resonance Imaging (MRI). Methods: A descriptive, observational, cross-sectional study was performed on 126 knee MRIs. The variables studied were age, sex, and distance between different neurovascular structures of the popliteal fossa (tibial nerve, common peroneal nerve, and vascular bundle). Data were analyzed for normality (Kolmogorov-Smirnov) and variance homogeneity (Levène). A value of p < 0.05 and a confidence interval of 9% were considered statistically significant for all comparisons. Student's t-test was used to compare the means between independent samples. Results: We observed statistically significant differences between the sexes regarding EP-EPS (external plateau-external popliteal sciatic nerve (common peroneal)), EP-IPS (external plateau-internal popliteal sciatic nerve (tibial)), and IP-PA (internal plateau-popliteal artery) measurements. The average distance between both nerves, EPS-IPS (external popliteal sciatic nerve and internal popliteal sciatic nerve), was 25.96 mm in females, while the value obtained in males was 29.93 mm, but this difference was not statistically significant. Conclusions: The average distance from the posterior capsule to the EPS and IPS nerves is greater in males than in females, despite no statistical differences. The presence of a lateralized arteriovenous bundle reduces the infiltration area of the external compartment. Regarding the safety area, infiltration of the internal compartment is safe since the volume diffuses into the muscle mass of the internal gastrocnemius upon injection. To infiltrate the external compartment, the needle must move at least 2 cm from the midline toward the external side (to exceed the maximum displacement of the neurovascular bundle established at 1.82 cm), and not advance beyond 0.76 cm (minimum distance at which we located the common peroneal nerve in the external compartment).

Intra-Articular Mineralization on Computerized Tomography of the Knee and Risk of Cartilage Damage: The Multicenter Osteoarthritis Study.

Intra-articular (IA) mineralization may contribute to osteoarthritis (OA) structural progression. We studied the association of IA mineralization on knee computed tomography (CT) with cartilage damage worsening on knee magnetic resonance imaging (MRI), with a focus on location- and tissue-specific effects. Participants from the Multicenter Osteoarthritis Study with knee CT and MRI scans were included. Presence of IA mineralization on CT was defined as a Boston University Calcium Knee Score >0 anywhere in the knee. Cartilage worsening on MRI was defined as any increase in the MRI OA Knee Score, including incident damage. We evaluated the association of whole-knee, compartment-specific (ie, medial or lateral), and subregion-specific (ie, location-matched) IA mineralization at baseline with cartilage worsening at two years' follow-up in the corresponding locations using binomial regression with generalized estimating equations, adjusting for age, sex, and body mass index (BMI). We included 1,673 participants (mean age 60 years, 56% female, mean BMI 29). Nine percent had any IA mineralization in the knee, and 47.4% had any cartilage worsening on follow-up. Mineralization of any tissue in the knee, regardless of location, was not associated with MRI cartilage worsening. However, cartilage mineralization was associated with 1.39 (95% confidence interval 1.04-1.88) times higher risk of cartilage worsening in the same compartment, with similar results in subregion-specific analysis. CT-detected IA mineralization in the cartilage was associated with higher risk of MRI cartilage worsening in the same compartment and subregion over two years. These findings suggest potential localized, tissue-specific effects of IA mineralization on cartilage pathology in knee OA.

Adiposity and cartilage lesions following ACL reconstruction

ObjectiveTo determine if global, central, or peripheral adiposity is associated with prevalent and worsening cartilage lesions following anterior cruciate ligament reconstruction (ACLR). MethodsIn 107 individuals one-year post-ACLR, adiposity was assessed globally (body mass index, BMI), centrally (waist circumference), and peripherally (knee subcutaneous adipose tissue thickness) from magnetic resonance imaging (MRI). Tibiofemoral and patellofemoral cartilage lesions were assessed from knee MRIs at 1- and 5-years post-ACLR. Poisson regression evaluated the relation of adiposity with prevalent and worsening tibiofemoral and patellofemoral cartilage lesions adjusting for age, sex, and activity level. ResultsThe prevalence ratios of adiposity with tibiofemoral (presence in 49%) and patellofemoral (44%) cartilage lesions ranged from 0.99 to 1.03. Adiposity was more strongly associated with longitudinal changes in tibiofemoral (worsening in 21%) and patellofemoral (44%) cartilage lesions. One-unit increase in global (kg/m2), central (cm), and peripheral (mm) adiposity was associated with a higher risk of worsening tibiofemoral cartilage lesions by 17% (risk ratios [95% CI]: 1.17 [1.09 to 1.23]), 5% (1.05 [1.02 to 1.08]), and 9% (1.09 [1.03 to 1.16]), and patellofemoral cartilage lesions by 5% (1.05 [1.00 to 1.12]) 2% (1.02 [1.00 to 1.04]) and 2% (1.02 [1.00 to 1.04]), respectively. ConclusionGreater adiposity was a risk factor for worsening cartilage lesions up to 5 years post-ACLR. Clinical interventions aimed at mitigating excess adiposity may be beneficial in preventive approaches for early post-traumatic OA.

Reliability and modality analysis of patellar height measurement in pediatric knee.

Various measurement methods and imaging technique are in use to measure patellar height in pediatric patients. However, there is no gold standard as to which measurement method and modality are the most reliable for pediatric patients. Therefore, the aim of this study was to determine the inter-observer reliability, intra-observer reliability, and applicability of various patellar height measurement methods in pediatric knee. Additionaly, we analyzed the reliability across different imaging modalities. Total 450 pediatric patients (age: 5-18 years) were evaluated using lateral knee radiographs and magnetic resonance imaging (MRI). The patellar height ratios were measured using five methods. Five methods were Insall-Salvati (IS), Koshino-Sugimoto (KS), Blackburne-Peel (BP), modified Insall-Salvati (MIS), and Caton-Deschamps (CD). The patients were categorized into two age groups: P (ages 5-13) and Q (ages 14-18). Each measurement was conducted twice by two raters. The intra-observer reliability, inter-observer reliability and inter-modality reliability were calculated. In addition, applicability was defined as the possibility to apply each measurement method to each age group. The KS method showed the highest inter-observer reliability and intra-observer reliability when using MRI for both age groups. The inter-observer reliability and intra-observer reliability of the IS for lateral knee radiographs was highest among all observers for group Q. The CD method showed the highest inter-observer reliability in group P, while the KS showed the highest intra-observer reliability in group P using lateral radiographs. The KS method showed the highest inter-modality reliability in group P, while the IS showed the highest inter-modality reliability in group Q. The KS method was applicable to all patients when using lateral knee radiography, and the IS method was applicable to all patients when using MRI. Our results show that the reliability of various measurement method and imaging technique differed based on pediatric knee age group when measuring patellar height. Therefore, in the case of pediatric patients, reliability measurement methods and imaging techniques according to the patient's age should be applied.

The Dejour classification for trochlear dysplasia shows slight interobserver and substantial intraobserver reliability.

Trochlear dysplasia is one of the main risk factors for recurrent patellar dislocation. The Dejour classification identifies four categories that can be used to classify trochlear dysplasia. The purpose of this study is to evaluate the inter- and intraobserver reliability of the Dejour classification for trochlear dysplasia. The hypothesis was that both intra- and interobserver reliability would be at least moderate. This is a cross-sectional, reliability study. Twenty-eight examiners from the International Patellofemoral Study Group 2022 meeting evaluated lateral radiographs of the knee and axial magnetic resonance images from 15 cases of patellofemoral instability with trochlear dysplasia. They classified each case according to Dejour's classification for trochlear dysplasia (A-D). There were three rounds: one with only computed radiograph (CR), one with only magnetic resonance imaging (MRI) and one with both. Inter- and intraobserver reliability were calculated using κ coefficient (0-1). The mean age of patients was: 14.6 years; 60% were female and 53% had open physis. The interobserver reliability κ probabilities were 0.2 (CR), 0.13 (MRI) and 0.12 (CR and MRI). The intraobserver reliability κ probabilities were 0.45 (CR), 0.44 (MRI) and 0.65 (CR and MRI). The Dejour classification for trochlear dysplasia has slight interobserver reliability and substantial intraobserver reliability. Level I.