In human exhaled breath, more than 3000 volatile organic compounds (VOCs) are found, which are directly or indirectly related to internal biochemical processes in the body. Electronic noses (E-noses) could play a potential role in screening/analyzing various respiratory and systemic diseases by studying breath signatures. An E-nose integrates a sensor array and an artificial neural network that responds to specific patterns of VOCs, and thus can act as a non-invasive technology for disease monitoring. The gold standard blood glucose monitoring test for diabetes diagnostics is invasive and highly uncomfortable. This contributes to the massive need for technologies which are non-invasive and can be used as an alternative to blood measurements for glucose detection. While lung cancer is one of the deadliest cancers with the highest death rate and an extremely high yearly global burden, the conventional diagnosis means, such as sputum cytology, chest radiography, or computed tomography, do not support wide-range population screening. A few standard non-invasive techniques, such as mass spectrometry and gas chromatography, are expensive, non-portable, and require skilled personnel for operation and are again not suitable for large-scale screening. Breath contains markers for both diabetes and lung cancer along with markers for several diseases and thus, a non-invasive technique such as the E-nose would greatly improve analysis procedures over existing invasive methods. This review shows the state-of-the-art technologies for VOC detection and machine learning approaches for two clinical models: diabetes and lung cancer detection.

No abstract

There have been substantial advancements in optical spectroscopybased imaging techniques in recent years. These developments can potentially herald a transformational change in the diagnostic pathway for diseases such as cancer. In this paper, we review the clinical and engineering aspects of novel optical spectroscopy-based imaging tools. We provide a comprehensive analysis of optical and non-optical spectroscopy-based breast cancer diagnosis techniques vis-a-vis the current standard techniques such as X-Ray mammography, ultrasonography, and tissue biopsy. The recent advancements in optical spectroscopy-based imaging systems such as Transillumination Imaging (TI) and the various types of Diffuse Optical Imaging (DOI) systems (parallel-plate, bed-based, and handheld) are examined. The engineering aspects, including mechanical, electronics, optics, automatic interpretation using artificial intelligence (AI), and ergonomics are discussed. The abilities of these technologies for measuring several cancer biomarkers such as hemoglobin, water, lipid, collagen, oxygen saturation (SO2), and tissue oxygenation index (TOI) are investigated. This article critically assesses the diagnostic ability and practical deployment of these new technologies to differentiate between the normal and cancerous tissue.

No abstract

No abstract