基于沙鼠脑缺血模型病理标本的神经细胞自动分析系统。
A neural cell automated analysis system based on pathological specimens in a gerbil brain ischemia model.
机构信息
Sysmex Co. - Hyogo, Japan.
Sept.Sapie Co. - Tokyo, Japan.
出版信息
Acta Cir Bras. 2024 Aug 12;39:e394224. doi: 10.1590/acb394224. eCollection 2024.
PURPOSE
Amid rising health awareness, natural products which has milder effects than medical drugs are becoming popular. However, only few systems can quantitatively assess their impact on living organisms. Therefore, we developed a deep-learning system to automate the counting of cells in a gerbil model, aiming to assess a natural product's effectiveness against ischemia.
METHODS
The image acquired from paraffin blocks containing gerbil brains was analyzed by a deep-learning model (fine-tuned Detectron2).
RESULTS
The counting system achieved a 79%-positive predictive value and 85%-sensitivity when visual judgment by an expert was used as ground truth.
CONCLUSIONS
Our system evaluated hydrogen water's potential against ischemia and found it potentially useful, which is consistent with expert assessment. Due to natural product's milder effects, large data sets are needed for evaluation, making manual measurement labor-intensive. Hence, our system offers a promising new approach for evaluating natural products.
目的
随着健康意识的提高，比药物副作用更小的天然产品越来越受欢迎。然而，目前只有少数系统能够定量评估它们对生物体的影响。因此，我们开发了一种深度学习系统，以自动对沙鼠模型中的细胞进行计数，旨在评估天然产物对缺血的有效性。
方法
对包含沙鼠大脑的石蜡块获取的图像进行分析，使用深度学习模型（微调后的 Detectron2）。
结果
当以专家的目视判断作为基准时，计数系统的阳性预测值为 79%，灵敏度为 85%。
结论
我们的系统评估了氢水对缺血的潜在作用，发现其具有潜在的益处，这与专家评估结果一致。由于天然产物的副作用较小，因此需要大量数据进行评估，这使得手动测量变得非常耗时。因此，我们的系统为评估天然产物提供了一种有前途的新方法。
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