Point-of-Care No-Specimen Diagnostic Platform using Machine Learning and Raman Spectroscopy: Proof-of-Concept Studies for Both COVID-19 and Blood Glucose

Abstract Significance We describe a novel, specimen-free diagnostic platform that can immediately detect both a metabolite (glucose) or an infection (COVID-19), by non-invasively using Raman spectroscopy and machine learning. Aim Current diagnostic testing for infections and glucose monitoring requires specimens, disease specific reagents, processing, and increases environmental waste. We propose a new hardware-software paradigm by designing and constructing a finger-scanning, hardware device to acquire Raman spectroscopy readouts and, by varying a machine learning algorithm to interpret the data, allows for diverse diagnoses. Approach 455 patients were enrolled prospectively in the COVID-19 study. 148 tested positive and 307 tested negative on nasal PCR testing done concurrently with testing using our viral detector. The tests were performed on both outpatients (N=382) and inpatients (N= 73) at Holy Name Medical Center in Teaneck, NJ between June 2021 and August 2022. Patients’ fingers were scanned using an 830nm Raman System and then, using machine learning, processed to provide an immediate result. In a separate study between April 2023 and August 2023, measurements using the same device and scanning a finger were used to detect blood glucose levels. Using a Dexcom sensor and an Accu-Chek device as references, a cross- validation based regression of 205 observations of blood glucose was performed with a machine learning algorithm. Results In a five-fold cross-validation analysis (including asymptomatic patients), a machine learning classifier using the Raman spectra as input achieved a specificity for COVID-19 of 0.837 at a sensitivity of 0.80 and an area under receiver operating curve (AUROC) of 0.896). However, when the data were split by time, with training data consisting of observations before 1 July 2022 and test data consisting of observations after it, the model achieved an AUROC of 0.67, with 0.863 sensitivity at a specificity of 0.517. This decrease in AUROC may be due to substantial domain shift as the virus evolves. A similar five-fold cross validation analysis of Raman glucose detection produces an area under precision-recall curve (AUPR) of 0.58. Conclusion The combination of Raman spectroscopy, AI/ML, and our patient-interface admitting only a patient’s finger and using no specimen, offers unprecedented flexibility in introducing new diagnostic tests or adapting existing ones. As the ML algorithm can be iteratively retrained with new data and the software deployed to field devices remotely, it promises to be a valuable tool for detecting rapidly emerging infectious outbreaks, as well as disease specific biomarkers, such as glucose.