School of Environmental and Chemical Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, PR China.
Department of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai 200018, PR China.
ACS Sens. 2024 Oct 25;9(10):5342-5353. doi: 10.1021/acssensors.4c01588. Epub 2024 Oct 15.
Smartphone-based colorimetry has been widely applied in clinical analysis, although significant challenges remain in its practical implementation, including the need to consider biases introduced by the ambient imaging environment, which limit its potential within a clinical decision pathway. In addition, most commercial devices demonstrate variability introduced by manufacturer-to-manufacturer differences. Here, we undertake a systematic characterization of the potential imaging interferences that lead to this limited performance in conventional smartphones and, in doing so, provide a comprehensive new understanding of smartphone color imaging. Through derivation of a strongly correlated parameter for sample quantification, we enable real-time imaging, which for the first time, takes the first steps to turning the mobile phone camera into an analytical instrument - irrespective of model, software, and the operating systems used. We demonstrate clinical applicability through the imaging of patients' skin, enabling rapid and convenient diagnosis of cyanosis and measurement of local oxygen concentration to a level that unlocks clinical decision-making for monitoring cardiovascular disease and anemia. Importantly, we show that our solution also accounts for the differences in individuals' skin tones as measured across the Fitzpatrick scale, overcoming potential clinically significant errors in current optical oximetry.
基于智能手机的比色法已广泛应用于临床分析,但在实际应用中仍存在重大挑战,包括需要考虑环境成像环境带来的偏差,这限制了其在临床决策途径中的应用潜力。此外,大多数商业设备都表现出制造商之间差异带来的可变性。在这里,我们系统地描述了导致传统智能手机性能受限的潜在成像干扰,并在此过程中,全面深入地了解了智能手机的彩色成像。通过推导出一个用于样品定量的强相关参数,我们实现了实时成像,这首次将手机摄像头变成了分析仪器——无论使用的是哪种型号、软件或操作系统。我们通过对患者皮肤的成像展示了临床适用性,实现了对发绀的快速便捷诊断,并能够测量局部氧浓度,达到了可以进行心血管疾病和贫血监测的临床决策水平。重要的是,我们表明我们的解决方案还考虑了个体皮肤色调的差异,这是根据 Fitzpatrick 量表进行测量的,克服了当前光血氧计中潜在的临床显著误差。