Development of an adaptive machine learning platform for automated analysis of biomarkers in biomedical images

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ABSTRACT Manual analysis of biomedical images by researchers and pathologists is time intensive, requires intensive training, and prone to introduce bias and error. Optical analysis of targets within tissue samples, cultures, or specimens is fundamental to detecting biological properties, including protein interaction within the central nervous system, sperm counts, digestive-system parasites, and immune response to viral infections like COVID-19. 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 AITM, and the underlying artificial intelligence (AI) technology developed during our NIH SBIR Phase I award, have significantly increased inter- and intra-rater reliability of tissue sample analysis and decreased analysis time for multiplexed biomarkers. Pipsqueak AI is available now as an integration to ImageJ/FIJI (https://Pipsqueak.ai), and is capable of returning hundreds of accurate cellular target detections to the user within 300ms of image upload. During the last 6 months, Pipsqueak usership has exploded to over 1000 active monthly users, indicating high demand for computer vision technologies that improve the speed and accuracy of micrograph quantification. Our pre-trained ML models are capable of detecting multiple cellular morphologies and target types with precision and reproducibility that greatly exceed human analysis. Here, we propose to develop a pre-trained biomedical image analysis platform that rapidly and accurately identifies diverse cellular targets, and make this technology commercially available as a cloud computer vision service, called Sightologist.aiTM. Our computer vision AI-as-a-service (AIaaS) will be made available to research and clinical end-users through our Pipsqueak AI software and through 3rd party product integrations. To achieve these goals, we will build on our SBIR Phase I progress that developed ML models for biomarker detection, and implement cloud distribution methods to deliver our computer vision service to remote users and applications.