首页> 外文期刊>JOR spine. >Development of a standardized histopathology scoring system using machine learning algorithms for intervertebral disc degeneration in the mouse model—An ORS spine section initiative
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Development of a standardized histopathology scoring system using machine learning algorithms for intervertebral disc degeneration in the mouse model—An ORS spine section initiative

机译:在小鼠模型中使用机器学习算法的标准化组织病理学评分系统的研制 - 鼠标模型 - 脊椎部分初探

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Mice have been increasingly used as preclinical model to elucidate mechanisms and test therapeutics for treating intervertebral disc degeneration (IDD). Several intervertebral disc (IVD) histological scoring systems have been proposed, but none exists that reliably quantitate mouse disc pathologies. Here, we report a new robust quantitative mouse IVD histopathological scoring system developed by building consensus from the spine community analyses of previous scoring systems and features noted on different mouse models of IDD. The new scoring system analyzes 14 key histopathological features from nucleus pulposus (NP), annulus fibrosus (AF), endplate (EP), and AF/NP/EP interface regions. Each feature is categorized and scored; hence, the weight for quantifying the disc histopathology is equally distributed and not driven by only a few features. We tested the new histopathological scoring criteria using images of lumbar and coccygeal discs from different IDD models of both sexes, including genetic, needle-punctured, static compressive models, and natural aging mice spanning neonatal to old age stages. Moreover, disc sections from common histological preparation techniques and stains including H&E, SafraninO/Fast green, and FAST were analyzed to enable better cross-study comparisons. Fleiss's multi-rater agreement test shows significant agreement by both experienced and novice multiple raters for all 14 features on several mouse models and sections prepared using various histological techniques. The sensitivity and specificity of the new scoring system was validated using artificial intelligence and supervised and unsupervised machine learning algorithms, including artificial neural networks, k -means clustering, and principal component analysis. Finally, we applied the new scoring system on established disc degeneration models and demonstrated high sensitivity and specificity of histopathological scoring changes. Overall, the new histopathological scoring system offers the ability to quantify histological changes in mouse models of disc degeneration and regeneration with high sensitivity and specificity.
机译:小鼠越来越多地用作临床前模型,以阐明治疗椎间盘变性(IDD)的机制和试验治疗剂。已经提出了几种椎间盘(IVD)组织学评分系统,但没有存在可靠地定量小鼠椎间盘病理学。在这里,我们报告了一种新的稳健定量小鼠IVD组织病理学分母评分系统,通过建立了从先前评分系统的脊柱社区分析和IDD不同鼠标模型上的特征的共识开发的。新的评分系统分析了来自核浆(NP),环纤维(AF),端板(EP)和AF / NP / EP接口区域的关键组织病理学特征。每个功能都被分类和评分;因此,用于量化盘组织病理学的重量同样分布,而不是仅由少数特征驱动。我们测试了使用来自两性不同IDD模型的腰椎和颈椎圆盘的图像进行了新的组织病理学评分标准,包括遗传,针刺,静态压缩模型,以及跨越新生儿的新生儿阶段的天然老龄化老鼠。此外,分析了包括H&E,Safranino / Fast Green,以及快速的常见组织学制剂技术和污渍的盘部分,以实现更好的交叉研究比较。 FALISS的多评价商协议测试显示了几种小鼠模型和使用各种组织技术制备的几种小鼠模型和部分的所有14种特征的经验丰富的和新手多重评估者的重大协议。使用人工智能和监督和无监督的机器学习算法验证了新评分系统的敏感性和特异性,包括人工神经网络,K-MEANS聚类和主成分分析。最后,我们在已建立的光盘变性模型上应用了新的评分系统,并表现出高敏感性和组织病理学评分变化的特异性。总体而言,新的组织病理学评分系统提供了量化椎间盘退变和再生的小鼠模型中的组织学变化的能力,具有高灵敏度和特异性。

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