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Abstract Background Degeneration of both intervertebral discs (IVDs) and facet joints in the lumbar spine has been associated with low back pain, but whether and how IVD/joint degeneration contributes to pain remains an open question. Joint degeneration can be identified by pairing T1 and T2 magnetic resonance imaging (MRI) with analysis techniques such as Pfirrmann grades (IVD degeneration) and Fujiwara scores (facet degeneration). However, these grades are subjective, prompting the need to develop an automated technique to enhance inter‐rater reliability. This study introduces an automated convolutional neural network (CNN) technique trained on clinical MRI images of IVD and facet joints obtained from public‐access Lumbar Spine MRI Dataset. The primary goal of the automated system is to classify health of lumbar discs and facet joints according to Pfirrmann and Fujiwara grading systems and to enhance inter‐rater reliability associated with these grading systems. Methods Performance of the CNN on both the Pfirrmann and Fujiwara scales was measured by comparing the percent agreement, Pearson's correlation and Fleiss kappa value for results from the classifier to the grades assigned by an expert grader. Results The CNN demonstrates comparable performance to human graders for both Pfirrmann and Fujiwara grading systems, but with larger errors in Fujiwara grading. The CNN improves the reliability of the Pfirrmann system, aligning with previous findings for IVD assessment. Conclusion The study highlights the potential of using deep learning in classifying the IVD and facet joint health, and due to the high variability in the Fujiwara scoring system, highlights the need for improved imaging and scoring techniques to evaluate facet joint health. All codes required to use the automatic grading routines described herein are available in the Data Repository for University of Minnesota (DRUM).

Abstract Background Lumbar facet joint arthritis is characterized by degeneration of articular cartilage, loss of joint spacing, and increased boney spur formation. These signs of facet joint degeneration have been previously measured using destructive biochemical and mechanical analysis. Nondestructive clinical evaluation of the facet joint has also been performed using MRI scoring, which ranks the health of the facet joint using the Fujiwara scale. However, nondestructive clinical evaluation of facet joint arthritis using standard MRI scoring provides low resolution images which result in high interobserver variability. Therefore, to assess the accuracy of nondestructive MRI analysis with regard to the health of the facet joint, this study determined whether any correlations existed between lumbar facet joint articular cartilage mechanics, facet articular cartilage biochemical signatures, and Fujiwara scores. Materials and Method To accomplish this aim, human cadaveric lumbar spines were obtained and imaged using T1 MRI, then independently scored by three spine researchers. An osteochondral plug from each of the L2 thru L5 facet joints was obtained and loaded under unconfined compression. Results The experiments showed no trends between histological images and changes in the Fujiwara score. The mechanical properties of articular cartilage (thickness, Young's modulus, instantaneous modulus, and permeability) also had no correlations with the Fujiwara score. Conclusions These results show that the current Fujiwara score cannot accurately describe the biomechanics or biochemical composition of facet joint articular cartilage.

Abstract At the onset of COVID‐19, researchers quickly recognized the need for research on the consequences of the pandemic for agricultural and food systems, both in terms of immediate impacts on access to inputs and labor, disruptions in transportation and markets, and the longer‐term implications on crop productivity, income, and livelihoods. Vegetable production and supply chains are particularly vulnerable due to the perishable nature of the products and labor‐intensive production practices. The purpose of this study was to understand the impacts of COVID‐19 on vegetable production in Burkina Faso in terms of both the biophysical aspects such as yields and access to inputs and socioeconomic aspects such as access to labor, markets, and social services. A survey was developed to better understand smallholder farmer experiences regarding the impacts of COVID‐19 on their vegetable production systems and social well‐being. The survey was administered (between August and October 2020) with smallholder farmers (n = 605) in 13 administrative regions covering all agroecological zones of Burkina Faso. The survey results clearly show impacts of COVID‐19 on vegetable systems, including a reduction in access to inputs, a reduction in yields, a loss of income, reduced access to local and urban markets, reduced access to transportation, and an increase in post‐harvest loss. Market access, distribution, and disruptions were a major shock to the system. Results also showed an increase in women's labor in the household, and for youth, an increase in unemployment, job loss, and concerns of poverty. Finally, food security and social supports were highlighted as major issues for resilience and livelihoods. The results from this survey should be helpful to policymakers and researchers to develop policies and strategies to minimize the negative impacts of this ongoing pandemic on the agri‐food systems and support smallholder farmers to overcome stress caused by COVID‐19.