Interpretable Droplet Digital PCR Assay for Trustworthy Molecular Diagnostics
Journal:
arXiv
Published Date:
Jan 16, 2025
Abstract
Accurate molecular quantification is essential for advancing research and
diagnostics in fields such as infectious diseases, cancer biology, and genetic
disorders. Droplet digital PCR (ddPCR) has emerged as a gold standard for
achieving absolute quantification. While computational ddPCR technologies have
advanced significantly, achieving automatic interpretation and consistent
adaptability across diverse operational environments remains a challenge. To
address these limitations, we introduce the intelligent interpretable droplet
digital PCR (I2ddPCR) assay, a comprehensive framework integrating front-end
predictive models (for droplet segmentation and classification) with GPT-4o
multimodal large language model (MLLM, for context-aware explanations and
recommendations) to automate and enhance ddPCR image analysis. This approach
surpasses the state-of-the-art models, affording 99.05% accuracy in processing
complex ddPCR images containing over 300 droplets per image with varying
signal-to-noise ratios (SNRs). By combining specialized neural networks and
large language models, the I2ddPCR assay offers a robust and adaptable solution
for absolute molecular quantification, achieving a sensitivity capable of
detecting low-abundance targets as low as 90.32 copies/{\mu}L. Furthermore, it
improves model's transparency through detailed explanation and troubleshooting
guidance, empowering users to make informed decisions. This innovative
framework has the potential to benefit molecular diagnostics, disease research,
and clinical applications, especially in resource-constrained settings.
