Department of Molecular Pathobiology, Division of Biomaterials, Bioengineering Institute, New York University College of Dentistry, 433 First Ave. Rm 822, New York, NY 10010, USA.
Department of Pathology, Vilcek Institute, New York University School of Medicine, 160 E 34th St, New York, NY 10016, USA.
Sensors (Basel). 2022 Aug 24;22(17):6355. doi: 10.3390/s22176355.
We are beginning a new era of -integrated biosensors powered by recent innovations in embedded electronics, cloud computing, and artificial intelligence (AI). Universal and AI-based in vitro diagnostics (IVDs) have the potential to exponentially improve healthcare decision making in the coming years. This perspective covers current trends and challenges in translating Smart Diagnostics. We identify essential elements of Smart Diagnostics platforms through the lens of a clinically validated platform for digitizing biology and its ability to learn disease signatures. This platform for biochemical analyses uses a compact instrument to perform multiclass and multiplex measurements using fully integrated microfluidic cartridges compatible with the point of care. Image analysis by transforming fluorescence signals into inputs for learning disease/health signatures. The result is an intuitive reported to the patients and/or providers. This AI-linked universal diagnostic system has been validated through a series of large clinical studies and used to identify signatures for early disease detection and disease severity in several applications, including cardiovascular diseases, COVID-19, and oral cancer. The utility of this Smart Diagnostics platform may extend to multiple cell-based oncology tests via cross-reactive biomarkers spanning oral, colorectal, lung, bladder, esophageal, and cervical cancers, and is well-positioned to improve patient care, management, and outcomes through deployment of this resilient and scalable technology. Lastly, we provide a future perspective on the direction and trajectory of Smart Diagnostics and the transformative effects they will have on health care.
我们正迈入一个新的时代——嵌入式电子、云计算和人工智能 (AI) 的最新创新为集成生物传感器提供了动力。通用型和基于 AI 的体外诊断 (IVD) 有可能在未来几年极大地改善医疗保健决策。本观点涵盖了将智能诊断转化为现实所面临的当前趋势和挑战。我们通过用于数字化生物学及其学习疾病特征的能力的临床验证平台，从临床验证平台的角度来确定智能诊断平台的基本要素。该生物化学分析平台使用紧凑型仪器，通过与即时护理兼容的完全集成微流控盒来执行多类别和多重测量。通过将荧光信号转换为用于学习疾病/健康特征的输入来进行图像分析。结果是直观的报告给患者和/或提供者。该与 AI 相关的通用诊断系统已通过一系列大型临床研究进行了验证，并用于识别心血管疾病、COVID-19 和口腔癌等几种应用中早期疾病检测和疾病严重程度的特征。该智能诊断平台的实用性可通过跨反应性生物标志物扩展到多个基于细胞的肿瘤学测试，这些生物标志物涵盖口腔癌、结直肠癌、肺癌、膀胱癌、食道癌和宫颈癌，并且通过部署这种有弹性和可扩展的技术，有望改善患者的护理、管理和结果。最后，我们对智能诊断的方向和轨迹以及它们对医疗保健的变革性影响提供了未来展望。
