Search

Abstract Background The failure of disease-modifying osteoarthritis drugs (DMOADs) trials lies mainly in the heterogeneity of the disease, which calls for a more precise population with specific progression and outcomes. This study aimed to determine whether and which MRI-based structural phenotype of knee osteoarthritis (KOA) is associated with short-term structural progression and subsequent total knee replacement (TKR). Methods A longitudinal study was conducted among participants with baseline Kellgren-Lawrence grade (KLG) ≥ 2 from the Osteoarthritis Initiative (OAI). The structural phenotypes at baseline were defined as subchondral bone, meniscus/cartilage and inflammatory phenotypes according to the MRI Osteoarthritis Knee Score (MOAKS). The primary outcome was the progression of structural abnormalities within 24 months and multivariable logistic regressions were applied to evaluate the associations. The secondary outcome was the incidence of TKR during 108 months. Cox regressions and Kaplan–Meier survival curves were used for the analysis. Results A total of 733 participants with KOA were finally included in our study, with 493 (67.3%) having the three main structural phenotypes. For the primary outcome, the subchondral bone phenotype (OR [95% CI]:1.71 [1.02, 2.83], 1.52 [1.06, 2.18], 1.65 [1.11, 2.42], respectively) and the inflammatory phenotype (OR [95% CI]: 1.69 [1.05, 2.74], 1.82 [1.31, 2.52], 2.15 [1.48, 3.14], respectively) were both associated with the short-term progression of joint space narrowing, osteophytes and sclerosis in 24 months, whereas the meniscus/cartilage phenotype was only associated with the progression of osteophytes and sclerosis. For the secondary outcome, the subchondral bone phenotype (HR [95% CI]: 1.71 [1.06–2.78]) and inflammatory phenotype (HR [95%CI]: 2.00 [1.02–2.67]) were associated with shorter time to subsequent TKR, but not the meniscus/cartilage phenotype. Besides, the cumulative effect when the structural phenotype overlapped was confirmed in both outcomes. Conclusions The subchondral bone phenotype and inflammatory phenotype were associated with the progression of joint space narrowing, osteophytes and sclerosis in 24 months, along with subsequent TKR in 108 months. Besides, additive effects of overlapped phenotypes were further determined. These phenotypes could serve as valuable screening tools for future clinical trials and provide guidance for risk evaluation.

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field.

The complex interaction between a genotype and its environment controls the biophysical properties of a plant, manifested in observable traits, i.e., plant's phenome, which influences resources acquisition, performance, and yield. High-throughput automated image-based plant phenotyping refers to the sensing and quantifying plant traits non-destructively by analyzing images captured at regular intervals and with precision. While phenomic research has drawn significant attention in the last decade, extracting meaningful and reliable numerical phenotypes from plant images especially by considering its individual components, e.g., leaves, stem, fruit, and flower, remains a critical bottleneck to the translation of advances of phenotyping technology into genetic insights due to various challenges including lighting variations, plant rotations, and self-occlusions. The paper provides (1) a framework for plant phenotyping in a multimodal, multi-view, time-lapsed, high-throughput imaging system; (2) a taxonomy of phenotypes that may be derived by image analysis for better understanding of morphological structure and functional processes in plants; (3) a brief discussion on publicly available datasets to encourage algorithm development and uniform comparison with the state-of-the-art methods; (4) an overview of the state-of-the-art image-based high-throughput plant phenotyping methods; and (5) open problems for the advancement of this research field. [ABSTRACT FROM AUTHOR]