Explainable Deep-Learning-Assisted Sweat Assessment via a Programmable Colorimetric Chip
Abstract
Multianalytes and individual differences of biofluids (such as blood, urine, or sweat) pose enormous complexity and challenges to rapid, facile, high-throughput, and accurate clinical analysis or health assessment. Deep-learning (DL)-assisted image analysis has been demonstrated to be an efficient big data process which shows accurate individual identification. However, the data-driven "black boxes" of current DL algorithms are suffering from the nontransparent inner working mechanism. In this work, we designed a programmable colorimetric chip with explainable DL to approach accurate classification and quantification analysis of sweat samples. Gel (sodium alginate) capsules with different indicators were adopted to combinate as designed programmable colorimetric chips. We collected 4600 colorimetric response images as the data set and assessed two DL algorithms and seven machine learning (ML) algorithms. Glucose, pH, and lactate in human sweat could be facilely and 100% accurately classified and quantified by the convolutional neural network (CNN) DL algorithm, and the testing results of actual sweat via the DL-assisted colorimetric approach match 91.0-99.7% with the laboratory measurements. Class activation mapping (CAM) was processed to visualize the inner working mechanism of CNN operation, which could help to verify and explicate the design rationality of colorimetric chips. The explainable DL-assisted programmable colorimetric chip provided an "end-to-end" strategy to ascertain the black box of the DL algorithm, promoted software design or principium optimization, and contributed facile indicators for clinical monitoring, disease prevention, and even new scientific discoveries.
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Glucose, pH, and lactate in human sweat could be easily and accurately classified and quantified by the CNN and DL algorithm, and the testing results of actual sweat via the DL-assisted colorimetric approach matched 91.0−99.7% with the laboratory measurements.
“Figure 7a shows glucose, pH, and lactate in human sweat could be easily and accurately classified and quantified by the CNN and DL algorithm and the testing results of actual sweat via the DL-assisted colorimetric approach matched 91.0−99.7% with the laboratory measurements [233].”
From The emergence of AI-based wearable sensors for digital health technology: a review (2023) · cites this paper as [233]
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- Authors
- Liu, Z., Li, J., Li, J., Yang, T., Zhang, Z., Wu, H., Xu, H., Meng, J., & Li, F.
- Affiliations
- Jinan University (CN)
- Corresponding author
- Xu, Huihua
- Meng, Jianxin
- Li, Fengyu
- ORCID
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2022-11-01
- Volume
- 94
- Issue
- 45
- Pages
- 15864-15872
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- OpenAlex
- W4307827399
- Cited by
- 59
- References
- 30
- Citations by year
- 2026: 18 · 2025: 18 · 2024: 17 · 2023: 6
- FWCI
- 2.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Analytical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Ministry of Science and Technology of the People's Republic of China (2016YFC1100502)
- National Natural Science Foundation of China (21874056, 52003103)
- Key Projects in Basic and Applied Research of Jiangmen ([2019]256)
- Guangdong Province (2018GKTSCX004)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Biosensors and Analytical Detection
- Physical Sciences › Engineering › Biomedical Engineering › Advanced Chemical Sensor Technologies
- Physical Sciences › Chemistry › Organic Chemistry › Polydiacetylene-based materials and applications
- Keywords
- sweat analysis, explainable deep learning, convolutional neural networks, class activation mapping, sweat glucose, sweat pH, sweat lactate, patient monitoring
- MeSH terms
- Deep Learning, Algorithms, Colorimetry, Humans, Image Processing, Computer-Assisted, Sweat, Neural Networks, Computer
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