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机器学习引导的毒素诱导皮肤缩分析 tooL: VIDAL

William Laprade¹, Keirah E Bartlett², Charlotte R Christensen³

¹Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark.

Scientific reports

|December 8, 2023

概括

毒蛇咬伤会导致严重的组织损伤. 一个新的AI工具VIDAL准确地测量了小鼠的亡,改善了对这种被忽视的热带疾病的临床前抗毒试验.

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科学领域:

毒理学毒理学毒理学
生物医学工程生物医学工程
机器学习机器学习

背景情况:

毒蛇咬伤是全球主要的健康问题,导致严重的死亡率和发病率,特别是在低收入国家.
临床影响包括,出血和,需要有效的抗毒疗法.
在小鼠模型中对抗皮肤腐蚀的抗毒物的临床前评估至关重要,但目前的方法是劳动密集型和易出错的.

研究的目的:

开发和验证一种基于机器学习的自动化工具,用于在小鼠模型中量化皮肤缩.
引入一种新型指标 - - 皮肤瘤单位 (DnU),以更全面地评估瘤严重程度.
提供高通量,准确和可重复的方法来评估抗毒药对细胞毒毒蛇咬伤效应的疗效.

主要方法:

开发一种基于图像的机器学习解决方案 - - 毒素诱导皮肤缩分析 tooL (VIDAL).
视达尔自动识别皮肤瘤病变,纠正照明变化,缩放图像,提取病变区域和变色数据.
使用新定义的皮肤病单位 (DnU) 计算皮肤病的严重程度.

主要成果:

在小鼠模型中,VIDAL准确量化了皮肤瘤病变.
该工具的性能与传统的体病理学分析相提并论.
皮肤瘤单位 (DnU) 提供了一个更细致的衡量瘤严重程度的指标.

结论:

在临床前的蛇研究中,VIDAL提供了一种可访问,准确和可重复的方法来评估皮肤.
这种高通量技术对于加速开发和验证蛇治疗方法至关重要.
像VIDAL这样的自动化分析工具对于解决被忽视的热带疾病蛇中毒至关重要.