Electronic Nose Sensor Drift Affects Diagnostic Reliability and Accuracy of Disease-Specific Algorithms

Overcoming methodological barriers in electronic nose clinical studies, a simulation data-based approach

Analysis of volatile organic compounds by electronic nose (e-nose) may address gaps in non-invasive screening for neoplasia. Machine learning impacts study design and sample size requirements, but guidance on clinical study design is limited. This study evaluates how neoplasia prevalence, augmented data, and the number of e-nose devices impact sample size requirements. Simulated e-nose breath test data were created using real-world study data. We examined the effect of varying neoplasia prevalence (50%-5%) and data augmentation on model performance, as well as the impact of using multiple devices. Prediction models were developed using single value decomposition and random forest, and convolutional neural networks. Model performance was displayed as area under the receiver operating characteristics curve and F1-score. Stable model performance was defined as the phase where additional data no longer increases model performance. We found that lower neoplasia prevalence significantly increased sample size requirements, with low-prevalence settings (5%) requiring up to five times more data than high-prevalence settings (50%) for stable model performance. Model performance varied between devices, and integrating data from multiple devices required larger sample sizes. Approximately 400 data points per device at 50% prevalence, and 2100 data points at 5% prevalence, were necessary to reach stable model performance. Concluding, sample size requirements for e-nose studies are heavily influenced by disease prevalence and the number of devices used. Limiting device variability and ensuring sufficient case and control samples per device are crucial for achieving reliable predictive performance. Specific requirements will vary based on sensor and disease characteristics.ClinicalTrials.gov Identifier:Clinicaltrials.gov Identifier NCT03346005 (model study) and NCT04357158 (validation study).

Feasibility of an electronic nose to aid biochemical assessment of adrenal lesions

OBJECTIVES

Analysis of volatile organic compounds (VOCs) in exhaled breath has emerged as a promising non-invasive diagnostic tool for various diseases. The aim of this study was to evaluate the potential of an electronic nose to differentiate between functional adrenal lesions - pheochromocytoma (PHEO), primary hyperaldosteronism (PHA) and hypercortisolism (CS) - and nonfunctional adrenal lesions.

METHODS

A pilot study was conducted at two tertiary hospitals within the Netherlands. Patients with PHEO, PHA, CS and nonfunctional adrenal lesions underwent breath testing with an electronic nose between May 2021-June 2024. Each center employed a distinct electronic nose (device A&B). Comparability of data between the devices was assessed in a t-SNE plot, and an artificial neural network was trained to classify breath patterns.

RESULTS

Data obtained from the two electronic noses were too heterogeneous for pooling and device B had an insufficient sample size for further analysis. Therefore, VOC patterns of 76 functional lesions (27 PHEO, 33 PHA, 16 CS) and 29 nonfunctional adrenal lesions measured exclusively with device A were analyzed. Moderate discriminative performance was observed in the training data: pooled functional lesions (area under the curve (AUC) 0.76), PHEO (AUC 0.76), PHA (AUC 0.72) and CS (AUC 0.58) versus nonfunctional lesions. However, model performance declined significantly when applying the model developed with training data on test data, with wide confidence intervals across all comparisons.

CONCLUSIONS

While slight differences in VOC patterns were detected between functional and nonfunctional adrenal lesions, the electronic nose demonstrated limited discriminative value for clinical practice.

ENose: a new frontier for non-invasive cancer detection and monitoring

Electronic Nose (ENose) technology has emerged as a transformative tool in medical diagnostics, leveraging sensor arrays that mimic the human olfactory system to detect odors and volatile organic compounds (VOCs) in various biological samples. ENose systems utilize a range of sensor types, such as metal oxide semiconductors and conducting polymers, to generate unique “smell fingerprints” through pattern recognition algorithms. These systems have shown promise in diagnosing various medical conditions, including respiratory diseases, infectious diseases, metabolic disorders, and neurological conditions. Notably, ENose technology holds significant promise in cancer diagnostics, offering a non-invasive, cost-effective, and rapid approach to early detection and monitoring. It has demonstrated impressive accuracy (85%-95%) in detecting cancers and monitoring complications. However, challenges remain, including issues with standardization, sensor sensitivity, and data interpretation. Despite these hurdles, ENose technology’s market growth is fueled by the increasing prevalence of chronic diseases. Recent developments in Artificial Intelligence (AI), particularly machine learning techniques like deep learning, have enhanced the diagnostic accuracy and robustness of ENose devices. This paper explores the evolution, core principles, applications, challenges, and future potential of ENose technology, with particular emphasis on integrating recent advancements in AI for enhanced detection and interpretation. Future research and collaboration across sectors are essential to overcome existing challenges and integrate ENose into mainstream healthcare.

Recent advances in signal processing algorithms for electronic noses

Electronic nose (e-nose) technology has emerged as a pivotal tool in various domains, which has been widely utilized for odor identification, concentration evaluation, and prediction tasks. This review provides a comprehensive survey on the most recent advances in the development of e-nose systems and their algorithmic applications, emphasizing the roles of various methodologies and deep learning technologies in odor classification and concentration forecasting. Additionally, we delve into model evaluation methods, including multidimensional performance assessment and cross-validation. Future trends encompass broader application domains, advanced drift correction techniques, comprehensive multifactorial analysis, and enhanced capabilities for dealing with unknown interferents. These trends are set to propel significant breakthroughs in e-nose technology within scientific research and practical applications, solidifying the e-nose system as a crucial tool in many areas such as environmental monitoring, biomedicine, and public safety.

Use of a Novel Passive E-Nose to Monitor Fermentable Prebiotic Fiber Consumption

We developed a home-based electronic nose (E-Nose) to passively monitor volatile organic compounds (VOCs) emitted following bowel movements and assessed its validity by correlating the output with prebiotic fiber intake. Healthy, non-overweight participants followed a three-week protocol which included the following: (1) installing the E-Nose in their bathroom; (2) activating the device following each bowel movement; (3) recording their dietary intake; (4) consuming a fiber bar (RiteCarbs) containing a blend of 10 g of prebiotic fiber daily during weeks two and three; and (5) submit stool specimens at the beginning and end of the study for 16S rRNA gene sequencing and analysis. Participants' fecal microbiome displayed significantly increased relative abundance of putative total SCFA-producing genera (p = 0.0323) [total acetate-producing genera (p = 0.0214), total butyrate-producing genera (p = 0.0131)] and decreased Gram-negative proinflammatory genera (p = 0.0468). Prebiotic intervention significantly increased the participants' fiber intake (p = 0.0152), E-Nose Min/Max (p = 0.0339), and area over the curve in VOC-to-fiber output (p = 0.0044). Increased fiber intake was negatively associated (R2 = 0.53, p = 0.026) with decreased relative abundance of putative Gram-negative proinflammatory genera. This proof-of-concept study demonstrates that a prototype E-Nose can noninvasively detect a direct connection between fiber intake and VOC outputs in a home-based environment.

A machine learning-based electronic nose for detecting neonatal sepsis: Analysis of volatile organic compound biomarkers in fecal samples

BACKGROUND

Neonatal sepsis is a global health threat, contributing to high morbidity and mortality rates among newborns. Recognizing the profound impact of neonatal sepsis on long-term health outcomes emphasizes the critical need for timely detection to mitigate its consequences and ensure optimal health for the affected newborns. Currently, various diagnostic approaches have been implemented, but they are limited by their invasiveness, high costs, centralized testing, frequent delays, inaccuracies in results, and the need for sophisticated laboratory equipment.

METHODS

We introduced a novel, non-invasive, cost-efficient, and easy-to-use technology that can provide rapid results at a point-of-care. The technology utilized a lab-built metal oxide semiconductor-based electronic nose (cNose) combined with volatile organic compound (VOC) biomarkers identified through gas chromatography-mass spectrometry (GC-MS) analysis. The system was evaluated using fecal profiling tests involving a total of 32 samples, including 17 positive and 15 negative sepsis, confirmed by blood culture. To assess the performance in discriminating patients from healthy controls, four machine learning algorithms were implemented.

RESULTS

Based on the cross-validation results, the MLPNN model provided the best results in distinguishing between neonates with positive and negative sepsis, achieving high-performance results of 90.63 % accuracy, 88.24 % sensitivity, and 93.33 % specificity at a 95 % confidence interval. Specific VOCs associated with neonatal sepsis, such as alcohols, acids, and esters, were successfully identified through GC-MS analysis, further validating the diagnostic capability of the cNose device.

CONCLUSION

The overall observations show the feasibility of using cNose system as a promising tool for real-time and bedside sepsis detection, potentially improving patient outcomes.

Application of a Multi-Gas Detector for Monitoring Gas Composition in Minced Beef During Storage

This study aims to assess the capability of using a specially designed device to monitor changes in gas concentration (CO2, NH3, H2S, and O2) in the atmosphere above the minced beef meat, during storage at refrigerated temperature. With its array of sensing channels, the multi-gas detector device facilitates the detection of precise gas concentrations in sensitive environments, enabling the monitoring of various processes occurring within stored meat. To delve into the connection between microbial activity and gas emissions during storage, fluctuations in microbial populations in the meat were observed, focusing on prevalent meat microbiota such as lactic acid bacteria (LAB) and Enterobacteriaceae. A significant reduction of O2 content in the stored samples was observed after seven days (p < 0.05), while a significant release of CO2 was detected on the fourth day of storage. Significant changes (p < 0.05) in the gas content were tracked until the 11th day of storage followed by intensive microbial growth. NH3 and H2S levels remained undetectable throughout the experiment. The results showed a correlation between an increase in gas content in the headspace and an increase in the number of LAB and Enterobacteriaceae in meat. Modern multi-gas detector devices can indirectly determine microbial contamination in closed meat packaging.

Developments of Recent Applications for Early Diagnosis of Diseases Using Electronic-Nose and Other VOC-Detection Devices

This Editorial provides summaries and an overview of research and review articles published in the Sensors journal, volumes 21 (2021), 22 (2022), and 23 (2023), within the biomedical Special Issue "Portable Electronic-Nose Devices for Noninvasive Early Disease Detection", which focused on recent sensors, biosensors, and clinical instruments developed for noninvasive early detection and diagnosis of human and animal diseases. The ten articles published in this Special Issue provide new information associated with recent electronic-nose (e-nose) and related volatile organic compound (VOC)-detection technologies developed to improve the effectiveness and efficiency of diagnostic methodologies for early disease detection prior to symptom development. For review purposes, the summarized articles were placed into three broad groupings or topic areas, including veterinary-wildlife pathology, human clinical pathology, and the detection of dietary effects on VOC emissions. These specified categories were used to define sectional headings devoted to related research studies with a commonality based on a particular disease being investigated or type of analytical instrument used in analyses.

Early Detection of Prostate Cancer: The Role of Scent

Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I2, 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I2, 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I2, 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I2, 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa.

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.