Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care (POC) diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (n = 6) and deep learning (DL) (n = 9) models across different background settings. Evaluation revealed that the random forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms. Among DL models suitable for mobile integration, DenseNet169 demonstrated superior performance, achieving 92.63% sensitivity, 92.22% specificity, and 92% AUC. Remarkably, DenseNet169 integration into a mobile POC system demonstrated exceptional accuracy (>0.84) in testing microfluidics at under challenging imaging settings. Our study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics. The integration of AI into healthcare systems holds promise for enhancing patient outcomes and streamlining healthcare delivery.

中文翻译：

---

人工智能在测试床旁微流体方面的性能


人工智能 （AI） 通过自动执行图像分割和模式识别等任务，正在彻底改变医学。这些 AI 方法支持与现有平台无缝集成，从而增强诊断、治疗和患者护理。虽然最近的进展表明 AI 在推进微流体技术用于护理点 （POC） 诊断方面具有优势，但在测试微流体方面对 AI 算法的比较评估仍然存在差距。我们对 AI 模型进行了比较评估，专门用于识别在各种成像条件下识别微流体通道中是否存在气泡的两类分类问题。使用具有单个通道的模型微流体系统，加载了 3D 透明对象（气泡），我们在不同的背景设置下对每个经过测试的机器学习 （ML） （n = 6） 和深度学习 （DL） （n = 9） 模型提出了挑战。评估显示，随机森林 ML 模型实现了 95.52% 的灵敏度、82.57% 的特异性和 97% 的 AUC，优于其他 ML 算法。在适合移动集成的 DL 模型中，DenseNet169 表现出优异的性能，实现了 92.63% 的灵敏度、92.22% 的特异性和 92% 的 AUC。值得注意的是，DenseNet169 集成到移动 POC 系统中，在具有挑战性的成像设置下测试微流体时表现出卓越的准确性 （>0.84）。我们的研究证实了人工智能在医疗保健领域的变革潜力，强调了它通过准确和可访问的诊断来彻底改变精准医疗的能力。将 AI 集成到医疗保健系统中有望提高患者治疗效果和简化医疗保健服务。