标题： 可解释的液滴数字PCR检测方法用于可信的分子诊断 显示英文标题

标题： Interpretable Droplet Digital PCR Assay for Trustworthy Molecular Diagnostics

摘要： 准确的分子定量对于推动传染病、癌症生物学和遗传疾病等领域的研究和诊断至关重要。液滴数字PCR（ddPCR）已成为实现绝对定量的金标准。虽然计算ddPCR技术已取得显著进展，但在不同操作环境中实现自动解释和一致适应性仍是一个挑战。为解决这些限制，我们引入了智能可解释液滴数字PCR（I2ddPCR）检测方法，这是一个综合框架，将前端预测模型（用于液滴分割和分类）与GPT-4o多模态大语言模型（MLLM，用于上下文感知的解释和建议）相结合，以自动化和增强ddPCR图像分析。这种方法超越了最先进的模型，在处理每张图像包含超过300个液滴且信噪比（SNRs）各异的复杂ddPCR图像时，准确率达到了99.05%。通过结合专用神经网络和大语言模型，I2ddPCR检测方法为绝对分子定量提供了一个强大且适应性强的解决方案，其灵敏度能够检测到低丰度目标，低至90.32 copies/{\mu }L。此外，它通过详细的解释和故障排除指导提高了模型的透明度，使用户能够做出明智的决策。这一创新框架有望在分子诊断、疾病研究和临床应用中发挥作用，特别是在资源匮乏的环境中。 显示英文摘要

摘要： 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.