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非常耗时，需要大量的工作 训练，并且容易引入组织样本、培养物或目标内的光学分析偏差和错误。 样本是检测生物学特性的基础。 在生物标志物分析过程中，可能会导致生物医​​学研究结果的重复性差， 这些问题可能会给临床诊断带来很大的障碍。 有益的循证医学，开发有效的医疗方法，增强公众信心 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， 主观性和错误限制了研究的可重复性并减缓了有效医学的发展 我们的图像分析软件 Pipsqueak ProTM 以及我们的底层人工智能 (AI)。 技术，显着提高了组织样本分析的评估者间和评估者内部的可靠性 减少了多重生物标志物的分析时间。我们的预训练细胞检测模型可识别多种生物标志物。 细胞形态和目标类型，并实现快速、准确的图像分析，远远超过人类 而使用预先训练的深度学习模型减少了计算量和专业知识。 分析偏差的图像时，检测准确度和精确度会显着降低 来自训练参数。 在这里，我们建议开发能够显着提高机器学习可访问性的方法 我们的计算机视觉服务将用于跨领域和应用的生物医学图像分析。 通过我们的 Pipsqueak Pro 软件和第三方向研究和临床最终用户提供 为了实现这些目标，我们将在之前的 SBIR 第一阶段和第二阶段进展的基础上 开发了用于生物标志物检测的预训练机器学习模型。我们建议开发一个机器学习平台。 能够通过以下方式减少生物医学研究和医疗保健中的人类偏见、主观性和错误 高度创新、适应性强的“AutoML”系统该专利系统将允许用户轻松生成。 这里提出的具体创新将在“无代码”环境中实现自定义计算机视觉功能。 提高用于生物医学图像分析的强大计算机视觉技术的可访问性，通过 使缺乏生物信息学、深度机器专业知识的用户对机器学习的访问民主化 这里提出的工作所产生的软件和工具将使人们受益。 开发新型循证药物和开发有效的医疗方法。