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

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 and 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.