A neural cell automated analysis system based on pathological specimens in a gerbil brain ischemia model.

Acta cirurgica brasileira Pub Date : 2024-08-12 eCollection Date: 2024-01-01 DOI:10.1590/acb394224

Eri Katsumata, Abhishek Kumar Ranjan, Yoshihiko Tashima, Takayuki Takahata, Toshiyuki Sato, Motoaki Kobayashi, Masami Ishii, Toyomi Takahashi, Asahi Oda, Momoko Hirano, Yoji Hakamata, Kazuhisa Sugai, Eiji Kobayashi

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Abstract

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.

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基于沙鼠脑缺血模型病理标本的神经细胞自动分析系统。

目的：随着人们健康意识的提高，比药物作用更温和的天然产品越来越受欢迎。然而，只有少数系统能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来自动计数沙鼠模型中的细胞，旨在评估天然产品对缺血的疗效：方法：用深度学习模型（微调后的Detectron2）分析从包含沙鼠大脑的石蜡块上获取的图像：结果：当使用专家的视觉判断作为基本事实时，计数系统达到了79%的阳性预测值和85%的灵敏度：结论：我们的系统评估了氢水抗击缺血的潜力，发现它具有潜在的作用，这与专家的评估结果一致。由于天然产品的作用较为温和，评估需要大量数据集，因此人工测量耗费大量人力。因此，我们的系统为评估天然产品提供了一种前景广阔的新方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Acta cirurgica brasileira

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