Customizable Artificial Intelligence for the Biomedical Masses: Development of a User-Friendly Automated Machine Learning Platform for Biology Image Analysis.

面向生物医学大众的可定制人工智能：开发用于生物图像分析的用户友好的自动化机器学习平台。

• 批准号: 10699828
• 负责人: John H Harkness
• 金额: $ 27.58万
• 依托单位: REWIRE NEUROSCIENCE, LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10699828
• 关键词: Abbreviations; Algorithms; Artificial Intelligence; Attention; Automation; Bioinformatics; Biologic Characteristic; Biological; Biological Markers; Biology; Biomedical Research; Cells; Cellular Morphology; Clinical; Code; Communities; Computer Vision Systems; Computer software; Consumption; Custom; Data; Data Set; Detection; Development; Environment; Euclidean Space; Evidence Based Medicine; FOS gene; Fiji; Generations; Goals; Healthcare; Human; Image; Image Analysis; Imaging Techniques; Intuition; Label; Legal patent; Libraries; Machine Learning; Manuals; Measurement; Medical; Methods; Microscopy; Minority; Modeling; Morphology; Neurons; Neurosciences; Optics; Pathologist; Performance; Phase; Plug-in; Process; Property; Recommendation; Reproducibility; Reproducibility of Results; Research; Research Personnel; Resources; Sampling; Scientific Inquiry; Services; Small Business Innovation Research Grant; Specificity; Specimen; Speed; Standardization; System; Technical Expertise; Techniques; Technology; Testing; Time; Tissue Sample; Tissues; Training; Variant; Visual; Work; biomedical imaging; cellular targeting; clinical application; clinical diagnostics; computer generated; computer science; data submission; deep learning model; improved; innovation; interest; machine learning model; microscopic imaging; novel; novel marker; prevent; tool; transfer learning; user-friendly

ABSTRACT Manual analysis of biomedical images by researchers and pathologists is time consuming, requires intensive training, and is prone to introduce bias and error. Optical analysis of targets within tissue samples, cultures, or specimens is fundamental to detecting biological properties. Unintentional bias and attentional limitations during analysis of biomarkers can underlie poor reproducibility of findings in biomedical research and potentially introduce errors to clinical diagnostics. These problems are significant barriers to delivering the most beneficial evidence-based medicine, developing effective medical treatments, and promoting public confidence in scientific inquiry. Application of computer vision for cellular target detection is a promising approach to reducing human bias, subjectivity, and errors that limit the reproducibility of research and slow the development of effective medical treatments. Our image analysis software, called Pipsqueak ProTM, and our underlying artificial intelligence (AI) technology, have significantly increased inter- and intra-rater reliability of tissue sample analysis and decreased analysis time for multiplexed biomarkers. Our pre-trained cell detection models identify multiple cellular morphologies and target types and enable fast, accurate image analysis that greatly exceed human analysis. While the use of pre-trained deep learning models reduces computational and expertise requirements, detection accuracy and precision are significantly reduced when analyzing images that deviate from training parameters. Here, we propose to develop methods that will dramatically increase accessibility of machine learning for biomedical image analysis across diverse fields and applications. Our computer vision service will be made available to research and clinical end-users through our Pipsqueak Pro software and through 3rd party product integrations. To achieve these goals, we will build on our previous SBIR Phase I & II progress that developed pre-trained ML models for biomarker detection. We propose to develop a machine learning platform that is capable of reducing human bias, subjectivity, and errors in biomedical research and healthcare through a highly-innovative, adaptable “AutoML” system. This patented system will allow users to easily generate custom computer vision capabilities in a “no-code” environment. The specific innovations proposed here will improve the accessibility of powerful computer vision techniques for biomedical image analysis, by democratizing access to machine learning by users who lack expertise in bioinformatics, deep machine learning, or computer science. The software and tools resulting from the work proposed here will benefit the development of novel evidence-based medicines and development of effective medical treatments.

抽象的 研究人员和病理学家对生物医学图像的手动分析很耗时，需要密集 训练，很容易引入偏见和错误。组织样品，培养物或 标本对于检测生物学特性至关重要。无意的偏见和注意力限制 在分析生物标志物的过程中，生物医学研究中发现的可重复性不佳，并且 可能会引入临床诊断的错误。这些问题是交付最大的障碍 有益循证医学，开发有效的医疗治疗以及促进公众信心 在科学探究中。 将计算机视觉应用于细胞目标检测是减少人类偏见的一种有前途的方法 主观性以及限制研究可重复性并减缓有效医学发展的错误 治疗。我们的图像分析软件，称为Pipsqueak Protm和我们的基本人工智能（AI） 技术，已显着提高组织样本分析的评估者间和内部可靠性 多路复用生物标志物的分析时间减少。我们的预训练的细胞检测模型确定了多个 细胞形态和靶类型，并实现快速，准确的图像分析，大大超过了人类 分析。虽然使用预训练的深度学习模型会降低计算和专业知识 当分析偏差的图像时，要求，检测准确性和精度会大大降低 来自培训参数。 在这里，我们建议开发将大大提高机器学习的可访问性的方法 用于潜水场和应用的生物医学图像分析。我们的计算机视觉服务将是 通过我们的Pipsqueak Pro软件和第三方可用于研究和临床最终用户 产品集成。为了实现这些目标，我们将以我们以前的SBIR第一阶段和II的进步来建立 开发了用于生物标志物检测的预训练的ML模型。我们建议开发机器学习平台 能够通过生物医学研究和医疗保健中的人类偏见，主观性和错误 一种高度创新的，适应性的“汽车”系统。该专利系统将允许用户轻松生成 在“无代码”环境中定制计算机视觉功能。这里提出的具体创新将 通过 通过缺乏生物信息学专业知识的用户对机器学习的民主化，深度机器 学习或计算机科学。这里提出的工作产生的软件和工具将使 开发新颖的循证药物并开发有效的医疗治疗。