How to Demonstrate the Glucose Specificity of a Non-Invasive CGM: A Case Study of the SKAMo-2 Clinical Trial and Neogly™

Abstract Background: Every non-invasive continuous glucose monitoring (NI-CGM) technology introduced into the landscape faces the same skeptical question, from regulators, clinicians, and competing developers alike: is the candidate signal actually specific to glucose, or does an apparently reasonable accuracy figure simply reflect a model fitting to motion, temperature, calibration offset, or trial-duration artifact? Existing evaluation practice does not answer this question directly. NI-CGM performance is instead reported almost exclusively with metrics inherited from minimally invasive, subcutaneous CGM, the Mean Absolute Relative Difference (MARD), Clarke/Parkes error grids, and ISO 15197-style agreement rates, which were designed for sensors whose glucose specificity is already chemically established and which therefore take specificity as a premise rather than treating it as a result to be demonstrated. Methods: We present a methodology for demonstrating NI-CGM technology glucose specificity during the algorithm-development phase, and illustrate it with a case study based on a quantum-cascade-laser (QCL) photoacoustic NI-CGM device (Neogly) evaluated in the SKAMo-2 free-living clinical trial (eight participants with type 1 diabetes). The methodology combines a white-noise control, a constant-glycemia control, a sensor-ablation control that removes the candidate physical signal while retaining auxiliary covariates, and explicit reporting of the train/test generalization level, so that a reported MARD can be read as evidence of specificity rather than taken on faith. Results: Removing the mid-infrared photoacoustic (PA) signal from the model while retaining all auxiliary sensors (accelerometer, skin temperature, hygrometry, PPG) degraded performance at every generalization level tested, inter-patient MARD rose from 35.0% with the PA signal to 43.1% without it, and intra-experimentation MARD rose from 22.5% to 23.9%, providing direct, internal evidence that the PA channel itself, and not merely the auxiliary covariates, carries glucose-specific information. At the same time, an algorithm trained on pure Gaussian noise produced a MARD of 25% over short test windows, and a trivial constant-glycemia predictor outperformed every machine-learning model tested when generalization was extended from a single recording to an unseen patient (MARD 55% for the naive constant model versus 37% for a deep neural network on inter-patient splits). Reported in isolation, any of these MARD values is uninterpretable; reported against one another, they jointly demonstrate that the signal is specific to glucose while also bounding how much of the headline accuracy figure that specificity currently explains. Conclusions: We propose a specificity-demonstration methodology for NI-CGM technology development, comprising (1) signal quality gating prior to any algorithm benchmarking, (2) a white-noise control to test for genuine information content, (3) a constant-glycemia control to expose trial-duration bias, (4) a sensor-ablation control that isolates the contribution of the candidate physical signal from auxiliary covariates, (5) explicit reporting of the data-splitting generalization level (intra-experimentation, intra-patient, inter-patient). This methodology answers a question that precedes clinical accuracy reporting and that recognized clinical frameworks such as the IFCC Working Group on CGM's Dynamic Glucose Regions guideline are not designed to answer: not how accurate is the device, but is the device measuring glucose at all. We argue that without these controls, MARD and error-grid values for NI-CGM are not comparable across studies and may either overstate clinical readiness or undermine promising technologies. We recommend that this specificity methodology be applied routinely once a candidate NI-CGM sensor reaches algorithm-development stage, alongside and as a deliberate complement to IFCC-style clinical accuracy reporting once the device is mature enough for that evaluation. Keywords: non-invasive continuous glucose monitoring; glucose specificity; algorithm validation; MARD; benchmarking; machine learning; photoacoustic spectroscopy; sensor ablation; Clarke error grid