arXiv 2501.092182025-01-16q-bio.QMcs.AI

用于可信分子诊断的可解释液滴数字PCR检测方法

Interpretable Droplet Digital PCR Assay for Trustworthy Molecular Diagnostics

The Chinese University of Hong Kong（香港中文大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）；Hainan University（海南大学）；The University of Hong Kong（香港大学）；Tsinghua University（清华大学）

Yuanyuan Wei, Yucheng Wu, Fuyang Qu, Yao Mu, Yi-Ping Ho, Ho-Pui Ho, Wu Yuan, Mingkun Xu

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AI总结：

针对现有计算式ddPCR技术自动解读与跨环境适配性不足的问题，提出整合前端预测模型与GPT-4o多模态大语言模型的I2ddPCR框架，提升定量准确率、灵敏度与可解释性，可应用于分子诊断等场景。

摘要：

精准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与诊断至关重要。液滴数字PCR（ddPCR）已成为实现绝对定量的金标准。尽管计算式ddPCR技术已取得显著进展，但在不同操作环境下实现自动解读和一致的适配性仍是一项挑战。为解决这些局限，我们提出了智能可解释液滴数字PCR（I2ddPCR）检测方法，这是一个整合前端预测模型（用于液滴分割与分类）与GPT-4o多模态大语言模型（MLLM，用于生成上下文感知的解释与建议）的综合框架，可实现ddPCR图像分析的自动化与性能提升。该方法性能超越现有最优模型，在处理每张包含300个以上液滴、信噪比（SNR）各异的复杂ddPCR图像时，准确率可达99.05%。通过结合专用神经网络与大语言模型，I2ddPCR检测方法为分子绝对定量提供了一种稳健且适配性强的解决方案，其灵敏度可检测低至90.32拷贝/微升的低丰度靶标。此外，它通过详细的解释和故障排查指导提升了模型的透明度，帮助用户做出知情决策。这一创新框架有望惠及分子诊断、疾病研究和临床应用，尤其是在资源有限的环境中。

英文摘要：

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/μL. 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.

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