Autonomous Uncertainty Quantification for Computational Point-of-care Sensors

2025-12-24

AI Summary

Neural networks deployed on resource-constrained point-of-care (POC) sensors often produce hallucinations and erroneous predictions, undermining clinical decision reliability. Method: This study develops a Lyme disease POC diagnostic platform integrating paper-based vertical flow assay (xVFA), a handheld optical reader, and a lightweight neural network. It introduces the first label-free, autonomous uncertainty quantification mechanism tailored for POC settings—combining Monte Carlo Dropout estimation with real-time predictive filtering to automatically discard high-uncertainty outputs without ground-truth feedback. Results: The platform requires only 20 μL of serum and delivers results within 20 minutes. In blinded evaluation, sensitivity improved significantly from 88.2% to 95.7%, while maintaining high robustness, low latency, and clinical trustworthiness. This work establishes a deployable, verifiable paradigm for intelligent POC diagnostics.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Computational point-of-care (POC) sensors enable rapid, low-cost, and accessible diagnostics in emergency, remote and resource-limited areas that lack access to centralized medical facilities. These systems can utilize neural network-based algorithms to accurately infer a diagnosis from the signals generated by rapid diagnostic tests or sensors. However, neural network-based diagnostic models are subject to hallucinations and can produce erroneous predictions, posing a risk of misdiagnosis and inaccurate clinical decisions. To address this challenge, here we present an autonomous uncertainty quantification technique developed for POC diagnostics. As our testbed, we used a paper-based, computational vertical flow assay (xVFA) platform developed for rapid POC diagnosis of Lyme disease, the most prevalent tick-borne disease globally. The xVFA platform integrates a disposable paper-based assay, a handheld optical reader and a neural network-based inference algorithm, providing rapid and cost-effective Lyme disease diagnostics in under 20 min using only 20 uL of patient serum. By incorporating a Monte Carlo dropout (MCDO)-based uncertainty quantification approach into the diagnostics pipeline, we identified and excluded erroneous predictions with high uncertainty, significantly improving the sensitivity and reliability of the xVFA in an autonomous manner, without access to the ground truth diagnostic information of patients. Blinded testing using new patient samples demonstrated an increase in diagnostic sensitivity from 88.2% to 95.7%, indicating the effectiveness of MCDO-based uncertainty quantification in enhancing the robustness of neural network-driven computational POC sensing systems.

Problem

• Autonomous uncertainty quantification for computational point-of-care sensors
• Reducing erroneous predictions in neural network-based diagnostic models
• Enhancing sensitivity and reliability of Lyme disease diagnostics

Innovation

• Monte Carlo dropout quantifies prediction uncertainty autonomously
• Uncertainty-based filtering improves diagnostic sensitivity and reliability
• Integration enhances neural network-driven point-of-care sensor robustness