The painful knee: choosing the right imaging test

Artificial intelligence for detection of effusion and lipo-hemarthrosis in X-rays and CT of the knee

BACKGROUND

Traumatic knee injuries are challenging to diagnose accurately through radiography and to a lesser extent, through CT, with fractures sometimes overlooked. Ancillary signs like joint effusion or lipo-hemarthrosis are indicative of fractures, suggesting the need for further imaging. Artificial Intelligence (AI) can automate image analysis, improving diagnostic accuracy and help prioritizing clinically important X-ray or CT studies.

OBJECTIVE

To develop and evaluate an AI algorithm for detecting effusion of any kind in knee X-rays and selected CT images and distinguishing between simple effusion and lipo-hemarthrosis indicative of intra-articular fractures.

METHODS

This retrospective study analyzed post traumatic knee imaging from January 2016 to February 2023, categorizing images into lipo-hemarthrosis, simple effusion, or normal. It utilized the FishNet-150 algorithm for image classification, with class activation maps highlighting decision-influential regions. The AI's diagnostic accuracy was validated against a gold standard, based on the evaluations made by a radiologist with at least four years of experience.

RESULTS

Analysis included CT images from 515 patients and X-rays from 637 post traumatic patients, identifying lipo-hemarthrosis, simple effusion, and normal findings. The AI showed an AUC of 0.81 for detecting any effusion, 0.78 for simple effusion, and 0.83 for lipo-hemarthrosis in X-rays; and 0.89, 0.89, and 0.91, respectively, in CTs.

CONCLUSION

The AI algorithm effectively detects knee effusion and differentiates between simple effusion and lipo-hemarthrosis in post-traumatic patients for both X-rays and selected CT images further studies are needed to validate these results.

Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach

Large numbers of radiographic images are available in musculoskeletal radiology practices which could be used for training of deep learning models for diagnosis of knee abnormalities. However, those images do not typically contain readily available labels due to limitations of human annotations. The purpose of our study was to develop an automated labeling approach that improves the image classification model to distinguish normal knee images from those with abnormalities or prior arthroplasty. The automated labeler was trained on a small set of labeled data to automatically label a much larger set of unlabeled data, further improving the image classification performance for knee radiographic diagnosis. We used BioBERT and EfficientNet as the feature extraction backbone of the labeler and imaging model, respectively. We developed our approach using 7382 patients and validated it on a separate set of 637 patients. The final image classification model, trained using both manually labeled and pseudo-labeled data, had the higher weighted average AUC (WA-AUC 0.903) value and higher AUC values among all classes (normal AUC 0.894; abnormal AUC 0.896, arthroplasty AUC 0.990) compared to the baseline model (WA-AUC = 0.857; normal AUC 0.842; abnormal AUC 0.848, arthroplasty AUC 0.987), trained using only manually labeled data. Statistical tests show that the improvement is significant on normal (p value < 0.002), abnormal (p value < 0.001), and WA-AUC (p value = 0.001). Our findings demonstrated that the proposed automated labeling approach significantly improves the performance of image classification for radiographic knee diagnosis, allowing for facilitating patient care and curation of large knee datasets.

Diagnostic Accuracy of Magnetic Resonance Images and Weight-Bearing Radiographs in Patients With Arthroscopic-Proven Medial Osteoarthritis of the Knee

Aims:

The aim of this study is to analyze the diagnostic value of weight-bearing radiographs, magnetic resonance images (MRI), and the combination of both in osteoarthritic knees when using arthroscopic findings as the “gold standard” to compare with.

Methods:

A total of 59 patients were studied because of chronic pain in 1 of their knees. Radiographs were classified according to Kellgren-Lawrence scale. Magnetic resonance images were classified according to Vallotton, and arthroscopic findings according to Outerbridge criteria.

Results:

Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were, respectively, 75.0%, 60.0%, 56.2%, 77.8%, and 66.1% for weight-bearing radiographs, and 70.8%, 88.6%, 81.0%, 81.6%, and 81.4% for MRI. Logistic regression analysis showed that a weight-bearing radiograph added to MRI offered no additional diagnostic value compared with MRI alone (P < .001).

Conclusions:

Magnetic resonance images presented higher specificity, positive and negative predictive values, and accuracy than weight-bearing radiographs for knee osteoarthritis. The combination of radiographs and MRI did not improve the diagnostic accuracy, compared with MRI alone.

A new method to diagnose discoid lateral menisci on radiographs

PURPOSE

The aim of this study was to prove that it is feasible to diagnose discoid lateral meniscus in radiographs. Plain radiographic findings of discoid lateral menisci with matched controls were analysed and compared in a quantitative method.

METHODS

Sixty consecutive patients (60 knees) who were diagnosed with discoid lateral meniscus (discoid group) by magnetic resonance imaging (MRI) were included. Another 60 age- and sex-matched controls with normal medial and lateral menisci on the basis of MRI findings were included as the control group. Each plain radiograph was evaluated from the anteroposterior view for the following variables: height of the fibular head (HFH), lateral joint space distance (LJSD), height of the lateral tibial spine (HLTS), obliquity of the lateral tibial plateau, obliquity of the lateral femoral condyle, distance from the lateral tibial spine to the lateral femoral condyle, height of the medial tibial spine, chordal distance of the femoral condyle (CDLF, CDMF), the HFH/LJSD, LJSD/HLTS and the CDLF/CDMF.

RESULTS

A significant difference was found in the HFH, LJSD, HLTS, DLC, CDLF, HFH/LJSD and LJSD/HLTS between the two groups. The cut-off values of the HFH, LJSD, HLTS, DLC, CDLF, HFH/LJSD and LJSD/HLTS were 12.9 mm, 6.6 mm, 7.8 mm, 3.0 mm, 2.7 mm, 2.0 and 0.9, respectively. Among the cut-off values in diagnosing discoid lateral meniscus, the sensitivity, specificity and ROC curve area of LJSD/HLTS were as high as 73.6 %, 83.0 % and 0.8, respectively. The corresponding values of the HFH/LJSD were as high as 66.0 %, 86.8 % and 0.8. For the first two indicators, the results of the HFH/LJSD and LJSD/HLTS were higher than that of most other parameters. At the same time, the ROC curve area of the HFH/LJSD and LJSD/HLTS ranked highest among all the results.

CONCLUSION

There were significant differences in the HFH, LJSD, HLTS, DLC, CDLF, HFH/LJSD and LJSD/HLTS, especially the HFH/LJSD and the LJSD/HLTS, between plain radiographic findings of discoid lateral meniscus patients and normal controls. The results of the HFH/LJSD and the LJSD/HLTS showed a positive impact on the diagnosis of discoid lateral meniscus in this research. These findings enable radiographs to screen for discoid lateral meniscus.

LEVEL OF EVIDENCE

II.

An Unusual MRI Appearance of Osseous Metastases

Bone metastases can present in a wide variety of appearances across all imaging modalities. We present a unique appearance of a distal femoral metastasis in a patient who initially complained of knee pain. The radiographic and CT findings were initially suspicious for calcium pyrophosphate deposition (CPPD) arthropathy; however, an MRI demonstrated multiple lesions with a lamellated appearance confirmed on biopsy to be metastatic disease. This unusual lamellated appearance has not been previously described. We present this case to help distinguish this entity radiographically and better classify this finding as a manifestation of metastatic disease.

Ultrasound for knee effusion: lipohaemarthrosis and tibial plateau fracture

Knee trauma and effusion are common Emergency Department presentations. This article outlines the clinician's sonographic approach to knee effusion detection and localisation. A case where lipohaemarthrosis was detected with ultrasound is demonstrated, and the characteristic appearance of this pathology discussed. The sonographic detection of the associated tibial fracture is also demonstrated.

Evidence-based practice: best imaging practice in musculoskeletal disorders

As musculoskeletal disorders are a common cause of emergency department visits in the United States, it is vital for nurses and nurse practitioners to understand the decision rules for ordering imaging tests when triaging patients with musculoskeletal complaints. Proper knowledge and command of selecting the most appropriate imaging for these frequent emergency department presentations will help reduce costs, decrease ionizing radiation exposure, and increase patient throughput. This article reviews the current evidence-based literature for musculoskeletal imaging in the emergency department and discusses the epidemiology, etiology, management, and prevention of the most common musculoskeletal disorders.

Sonographic evaluation of knee cartilage defects implanted with preconditioned scaffolds

OBJECTIVES

The purpose of this study was to develop a novel method for creating an acellular bioactive scaffold, to prove its efficacy in vivo and in vitro for the augmentation of biological repair, and to confirm that sonographic microscopy is a viable modality for monitoring the healing process of osteochondral defects implanted with preconditioned bioactive scaffolds.

METHODS

Rabbit marrow stromal cells were retrovirally transduced with either bone morphogenetic protein 7 (BMP-7) or insulinlike growth factor 1 (IGF-1) genes, cultured for 9 weeks in nonwoven poly-L-lactic acid scaffolds, and then frozen and lyophilized. The knees were evaluated at 3, 6, and 12 weeks after surgery using 20-MHz ultrasound and then prepared for routine histologic analysis. B-scans of the extracellular matrix defects were compared to histologic results.

RESULTS

Control defects showed a void or a mixture of fibrocartilage tissue. Both types of scaffolds resulted in a higher percentage (both P< .001) of primarily hyaline cartilage tissue with intact articular surfaces. The osteochondral defects were clearly observed in each sonographic signature. There were no differences between images of scaffolds treated with IGF-1 or BMP-7. Extracellular matrix regrowth was found to closely parallel (R(2) = 0.968; P < .003) the histologic images. A 3-mm defect depth and a 2.5-mm scaffold thickness were measured on the sonograms, comparing well to actual dimensions.

CONCLUSIONS

There was a gradual increase in healing bordering the defects for the 3-, 6-, and 12-week samples. Also, we have shown that sonography can aid in monitoring implantation of preconditioned scaffolds in osteochondral defects and thus assessing the healing process and cartilage/bone quality.

MR imaging of anterior knee pain: a pictorial essay

Anterior knee pathology is a frequent cause of joint pain and limitation of function and mobility among patients presenting to an orthopaedic department. Proper recognition and treatment of pathologic conditions depend on the knowledge of normal anatomy and of the various abnormalities, which affect this area of the knee and may present with anterior knee pain. A broad array of benign and malignant processes may be manifested as anterior knee discomfort, and this common clinical entity is among the most frequent indications for MR imaging of the lower extremities. Clinical history and physical examination are also of paramount importance. The disorders can be categorized and differentiated primarily according to their location. Traumatic or non-traumatic disorders of the patella, patellar retinacula, quadriceps and patellar tendons and supra or infrapatellar fat pad can be the source of symptoms. This article includes a comprehensive pictorial essay of the characteristic MR features of common and uncommon disorders causing anterior knee pain. For accurate assessment of the aforementioned clinical problem, a radiologist should be able to identify typical MR imaging patterns that contribute in establishing the correct diagnosis and thus tailoring the appropriate therapy. Level of evidence IV.