Portable multimodal sensing system for screening of diabetes mellitus from human urine

Abstract A multimodal sensing approach enables the holistic collection of data, enabling a more comprehensive analysis and predictive capabilities for disease screening. Despite the advantages of fusing multiple sensing modalities, the approach is challenging due to the need for multiple sample types, which can lead to extensive sample preprocessing and potential cross‐interference. In this study, we developed a portable multimodal sensing system for diabetes screening that integrates an electrochemical sensor and a chemiresistive gas sensor for the simultaneous detection of glucose and acetone vapor from the same urine sample. We demonstrate that the use of commercial glucometer strips in urine does not compromise the performance of an accompanying metal oxide‐based gas sensor, and vice versa. The device provides readings comparable to benchtop equipment ( R 2 = .9662) for the electrochemical sensing and gas sensing ( R 2 = .9645). The combined information may help screen for diabetic patients without the need for invasive sample acquisition, which highlights the importance of advancing integrated systems to address complex screening challenges for multi‐parametric diseases. This can contribute to improved health outcomes for individuals at risk of diabetes and other metabolic disorders.

Authors

Institutions

Publication Details

Journal
Open Research (University of Surrey)
Published
2025-06-12
DOI
https://doi.org/10.1002/viw.20250010
Citations
1
Primary Topic
Electrochemical sensors and biosensors
Type
article
Field-Weighted Citation Impact
0.73

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Portable multimodal sensing system for screening of diabetes mellitus from human urine

Yuvaraj Sivalingam, Abdullah Bukhamsin, Khaled N. Salama, Yurii Tsyban et al.
1 citations
Open Research (University of Surrey)
Electrochemical sensors and biosensors
0.73
article

Portable multimodal sensing system for screening of diabetes mellitus from human urine

Yuvaraj Sivalingam, Abdullah Bukhamsin, Khaled N. Salama, Yurii Tsyban, Mario Soto Martinez, Alexander Przybysz
article en
1 citations

Abstract

Abstract A multimodal sensing approach enables the holistic collection of data, enabling a more comprehensive analysis and predictive capabilities for disease screening. Despite the advantages of fusing multiple sensing modalities, the approach is challenging due to the need for multiple sample types, which can lead to extensive sample preprocessing and potential cross‐interference. In this study, we developed a portable multimodal sensing system for diabetes screening that integrates an electrochemical sensor and a chemiresistive gas sensor for the simultaneous detection of glucose and acetone vapor from the same urine sample. We demonstrate that the use of commercial glucometer strips in urine does not compromise the performance of an accompanying metal oxide‐based gas sensor, and vice versa. The device provides readings comparable to benchtop equipment ( R 2 = .9662) for the electrochemical sensing and gas sensing ( R 2 = .9645). The combined information may help screen for diabetic patients without the need for invasive sample acquisition, which highlights the importance of advancing integrated systems to address complex screening challenges for multi‐parametric diseases. This can contribute to improved health outcomes for individuals at risk of diabetes and other metabolic disorders.

Open Research (University of Surrey)Vol. 6(5)
King Abdullah University of Science and Technology (SA)
King Abdullah University of Science and Technology
Openalex Percentile: Top 27%
Electrochemical sensors and biosensors
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.