A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative.

In osteoarthritis research, imaging plays an important role in clinical trials and epidemiological observational studies. In this narrative review article, we will describe recent developments in imaging of osteoarthritis in the research arena, mainly focusing on literature evidence published within the past 3 years (2014-2017). We will primarily focus on MRI including advanced imaging techniques that are not currently commonly used in routine clinical practice, although radiography, ultrasound and nuclear medicine (radiotracer) imaging will also be discussed. Research efforts to uncover the disease process of OA as well as to discover a disease modifying OA drug continue. MRI continues to play a large role in these endeavors, while compositional MRI techniques will increasingly become important due to their ability to assess "premorphologic" biochemical changes of articular cartilage and other tissues in and around joints. Radiography remain the primary imaging modality for defining inclusion/exclusion criteria as well as an outcome measure in OA clinical trials, despite known limitations for visualization of OA features. Compositional MRI techniques show promise for predicting structural and clinical outcomes in OA research. Ultrasound can be a useful adjunct to radiography and MRI particularly for evaluation of hand OA. Newer imaging techniques such as hybrid PET/MRI may have a potential but require further research and validation.

/pdf/imaging-of-osteoarthritis-recent-research-developments-and-4b50rttfyc.pdf

Imaging of osteoarthritis-recent research developments and future perspective.

Osteoarthritis (OA) is one of the most prevalent disorders in today's society, resulting in significant socio-economic costs and morbidity. MRI is widely used as a non-invasive imaging tool for OA of the knee. However, conventional knee MRI has limitations to detect subtle early cartilage degeneration before morphological changes are visually apparent. Novel MRI pulse sequences for cartilage assessment have recently received increased attention due to newly developed compositional MRI techniques, including: T2 mapping, T1rho mapping, delayed gadolinium-enhanced MRI of cartilage (dGEMRIC), sodium MRI, diffusion-weighted imaging (DWI)/ diffusion tensor imaging (DTI), ultrashort TE (uTE), and glycosaminoglycan specific chemical exchange saturation transfer (gagCEST) imaging. In this article, we will first review these quantitative assessments. Then, we will discuss the variations of quantitative values of knee articular cartilage with cartilage layer (depth)- and angle (regional)-dependent approaches. Multiple MRI sequence techniques can discern qualitative differences in knee cartilage. Normal articular hyaline cartilage has a zonal variation in T2 relaxation times with increasing T2 values from the subchondral bone to the articular surface. T1rho values were also higher in the superficial layer than in the deep layer in most locations in the medial and lateral femoral condyles, including the weight-bearing portion. Magic angle effect on T2 mapping is clearly observed in the both medial and lateral femoral condyles, especially within the deep layers. One of the limitations for clinical use of these compositional assessments is a long scan time. Recent new approaches with compressed sensing (CS) and MR fingerprinting (MRF) have potential to provide accurate and fast quantitative cartilage assessments.

https://www.jstage.jst.go.jp/article/mrms/advpub/0/advpub_rev.2021-0052/_pdf

Review of Quantitative Knee Articular Cartilage MR Imaging.

Detection and differentiation of early acute and following age stages of myocardial infarction with quantitative post-mortem cardiac 1.5T MR.

Here, we have characterized the spatial heterogeneity of the cereal grain's metabolism and demonstrated how, by integrating a distinct set of metabolic strategies, the grain has evolved to become an almost perfect entity for carbon storage. In vivo imaging revealed light-induced cycles in assimilate supply toward the ear/grain of barley (Hordeum vulgare) and wheat (Triticum aestivum). In silico modeling predicted that, in the two grain storage organs (the endosperm and embryo), the light-induced shift in solute influx does cause adjustment in metabolic flux without changes in pathway utilization patterns. The enveloping, leaf-like pericarp, in contrast, shows major shifts in flux distribution (starch metabolism, photosynthesis, remobilization, and tricarboxylic acid cycle activity) allow to refix 79% of the CO2 released by the endosperm and embryo, allowing the grain to achieve an extraordinary high carbon conversion efficiency of 95%. Shading experiments demonstrated that ears are autonomously able to raise the influx of solutes in response to light, but with little effect on the steady-state levels of metabolites or transcripts or on the pattern of sugar distribution within the grain. The finding suggests the presence of a mechanism(s) able to ensure metabolic homeostasis in the face of short-term environmental fluctuation. The proposed multicomponent modeling approach is informative for predicting the metabolic effects of either an altered level of incident light or a momentary change in the supply of sucrose. It is therefore of potential value for assessing the impact of either breeding and/or biotechnological interventions aimed at increasing grain yield.

http://www.plantphysiol.org/content/plantphysiol/169/3/1698.full.pdf

Metabolic architecture of the cereal grain and its relevance to maximize carbon use efficiency

This work was aimed at studying the method of computer-aided diagnosis of early knee OA (OA: osteoarthritis). Based on the technique of MRI (MRI: Magnetic Resonance Imaging) T2 Mapping, through computer image processing, feature extraction, calculation and analysis via constructing a classifier, an effective computer-aided diagnosis method for knee OA was created to assist doctors in their accurate, timely and convenient detection of potential risk of OA. In order to evaluate this method, a total of 1380 data from the MRI images of 46 samples of knee joints were collected. These data were then modeled through linear regression on an offline general platform by the use of the ImageJ software, and a map of the physical parameter T2 was reconstructed. After the image processing, the T2 values of ten regions in the WORMS (WORMS: Whole-organ Magnetic Resonance Imaging Score) areas of the articular cartilage were extracted to be used as the eigenvalues in data mining. Then,a RBF (RBF: Radical Basis Function) network classifier was built to classify and identify the collected data. The classifier exhibited a final identification accuracy of 75%, indicating a good result of assisting diagnosis. Since the knee OA classifier constituted by a weights-directly-determined RBF neural network didn't require any iteration, our results demonstrated that the optimal weights, appropriate center and variance could be yielded through simple procedures. Furthermore, the accuracy for both the training samples and the testing samples from the normal group could reach 100%. Finally, the classifier was superior both in time efficiency and classification performance to the frequently used classifiers based on iterative learning. Thus it was suitable to be used as an aid to computer-aided diagnosis of early knee OA.

Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

BACKGROUND Microvascular tissue reconstruction is a well-established, commonly used technique for a wide variety of the tissue defects. However, flap failure is associated with an additional hospital stay, medical cost burden, and mental stress. Therefore, understanding of the risk factors associated with this event is of utmost importance. AIM To develop machine learning-based predictive models for flap failure to identify the potential factors and screen out high-risk patients. METHODS Using the data set of 946 consecutive patients, who underwent microvascular tissue reconstruction of free flap reconstruction for head and neck, breast, back, and extremity, we established three machine learning models including random forest classifier, support vector machine, and gradient boosting. Model performances were evaluated by the indicators such as area under the curve of receiver operating characteristic curve, accuracy, precision, recall, and F1 score. A multivariable regression analysis was performed for the most critical variables in the random forest model. RESULTS Post-surgery, the flap failure event occurred in 34 patients (3.6%). The machine learning models based on various preoperative and intraoperative variables were successfully developed. Among them, the random forest classifier reached the best performance in receiver operating characteristic curve, with an area under the curve score of 0.770 in the test set. The top 10 variables in the random forest were age, body mass index, ischemia time, smoking, diabetes, experience, prior chemotherapy, hypertension, insulin, and obesity. Interestingly, only age, body mass index, and ischemic time were statistically associated with the outcomes. CONCLUSION Machine learning-based algorithms, especially the random forest classifier, were very important in categorizing patients at high risk of flap failure. The occurrence of flap failure was a multifactor-driven event and was identified with numerous factors that warrant further investigation. Importantly, the successful application of machine learning models may help the clinician in decision-making, understanding the underlying pathologic mechanisms of the disease, and improving the long-term outcome of patients.

https://doi.org/10.12998/wjcc.v10.i12.3729

Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms

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Computer-aided diagnosis of early knee osteoarthritis based on MRI T2 mapping.

Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms