Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Gudiño-Ochoa, Alberto; García-Rodríguez, Julio Alberto; Ochoa-Ornelas, Raquel; Cuevas-Chávez, Jorge Ivan; Sánchez-Arias, Daniel Alejandro

doi:10.3390/s24041294

Cited by 4 publications

(1 citation statement)

References 67 publications

(278 reference statements)

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“…Finally, real-time samples of exhaled gases were collected by the electronic nose system, and the integrated TinyML model was used to determine if the subjects had diabetes. Among these, the XGBoost machine learning algorithm achieved a detection accuracy of 95%, DNN achieved 94.44%, and 1D-CNN achieved 94.4% [108].…”

Section: Health Monitoringmentioning

confidence: 99%

AI-Driven Sensing Technology: Review

Chen,

Xia,

Zhao

et al. 2024

Sensors

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Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements.

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Section: Health Monitoringmentioning

confidence: 99%

AI-Driven Sensing Technology: Review

Chen,

Xia,

Zhao

et al. 2024

Sensors

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Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Bhaiyya,

Panigrahi,

Rewatkar

et al. 2024

ACS Sens.

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Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.

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Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study

Lee,

Kao,

Hsieh

et al. 2024

Respir Res

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Background Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. Methods Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. Results In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). Conclusion Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. Clinical trial registration This study is not a clinical trial and was therefore not registered.

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Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose

Cited by 4 publications

References 67 publications

AI-Driven Sensing Technology: Review

AI-Driven Sensing Technology: Review

Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study

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