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

> **NIH NIH R44** · REWIRE NEUROSCIENCE, LLC · 2021 · $948,520

## Abstract

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.

## Key facts

- **NIH application ID:** 10259501
- **Project number:** 2R44GM134789-02A1
- **Recipient organization:** REWIRE NEUROSCIENCE, LLC
- **Principal Investigator:** John H Harkness
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $948,520
- **Award type:** 2
- **Project period:** 2019-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10259501

## Citation

> US National Institutes of Health, RePORTER application 10259501, Development of an adaptive machine learning platform for automated analysis of biomarkers in biomedical images (2R44GM134789-02A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10259501. Licensed CC0.

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