Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
Department of Biomedical Engineering, Emory University, Atlanta, GA, USA.
Lab Chip. 2024 Oct 22;24(21):4998-5008. doi: 10.1039/d4lc00671b.
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) ( = 6) and deep learning (DL) ( = 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.
人工智能（AI）正在通过自动化图像分割和模式识别等任务来彻底改变医学。这些 AI 方法支持与现有平台的无缝集成，增强诊断、治疗和患者护理。虽然最近的进展已经证明 AI 在推进微流控用于即时诊断方面具有优势，但在比较 AI 算法在微流控测试中的性能方面仍存在差距。我们专门针对在各种成像条件下识别微流道中是否存在气泡的二分类问题，对 AI 模型进行了比较评估。使用带有单个通道的模型微流控系统，该通道中装有 3D 透明物体（气泡），我们对所有经过测试的机器学习（ML）（=6）和深度学习（DL）（=9）模型在不同的背景设置下进行了挑战。评估结果表明，随机森林 ML 模型的灵敏度为 95.52%，特异性为 82.57%，AUC 为 97%，优于其他 ML 算法。在适合移动集成的 DL 模型中，DenseNet169 表现出卓越的性能，灵敏度为 92.63%，特异性为 92.22%，AUC 为 92%。值得注意的是，DenseNet169 集成到移动即时诊断系统中，在具有挑战性的成像设置下测试微流控时，准确率>0.84。我们的研究证实了 AI 在医疗保健领域的变革潜力，强调了其通过准确和可及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改善患者的治疗效果和简化医疗保健服务的交付。
