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Thyroid nodules are one of the most common abnormalities in the thyroid gland, which are often harmless in nature (benign), but in a few unfortunate instances, they may be fatal (malignant). This review explores recent advancements in artificial intelligence (AI) applied to thyroid cancer detection and classification, with a focus on machine learning, deep learning, and image processing techniques. We provide a comprehensive evaluation of AI applications across key imaging modalities--Ultrasonography (USG), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET/CT)--as well as cytopathological analysis using Fine Needle Aspiration Biopsy (FNAB). By critically examining studies on AI-driven preoperative assessments, we highlight improvements in diagnostic accuracy, sensitivity, specificity and efficiency. This review also identifies current limitations in AI applications, including technical challenges and unresolved issues that hinder widespread clinical adoption. Although significant progress has been achieved, the integration of AI in clinical settings remains limited, as AI-based outputs currently serve as supportive tools rather than definitive diagnostic evidence. We discuss the potential of AI to transform thyroid cancer diagnostics by enhancing reliability and accessibility, while addressing the need for further research to develop a unified, robust and clinically trustworthy AI framework for thyroid cancer diagnosis. [ABSTRACT FROM AUTHOR]

AbstractPotentiostats play a crucial role in the precise control of electrochemical cell potentials. This paper introduces LabStat – a user-friendly and easily constructed potentiostat – developed using Laboratory Virtual Instrument Engineering Workbench (LabVIEW), tailored for three- and two-electrode electrochemical analyses. The LabStat comprises a single-ended potentiostat circuit with an external control for adjusting the current delivered to the working electrode. This device can perform cyclic voltammetry, linear sweep voltammetry, and chronoamperometry, offering an adjustable current range of ±1000 μA and a broad voltage range of ±10 V. Also, LabStat offers high-end noise rejection capabilities with low noise floor (10 nV/Hz), low distortion (0.0003% at 1 kHz), and an excellent signal-to-noise ratio (24.61 dB). Integrating a data acquisition card enables the device to achieve a voltage increment resolution of 10 mV and a current resolution of ±200 pA, making it a versatile and precise tool for electrochemical measurements. To validate its performance, LabStat is compared with a research-grade potentiostat through a series of experiments involving voltammetry and chronoamperometry using potassium ferricyanide as the standard solution. The comparative analysis demonstrated that LabStat performs on par with commercial potentiostats, confirming its reliability for laboratory use. Additionally, the device’s application is further established by detecting and quantifying glucose using a two-electrode sensor. The results demonstrate that LabStat is easy-to-fabricate and robust for conducting electrochemical experiments with real-world sensors. [ABSTRACT FROM AUTHOR]

Fusion of large-scale information is the key strategy for the complete understanding of many nonlinear and complicated industrial and medical processes. This paper presents the proposed multiview (MV) feature fusion-based learning generalizing discriminant correlation analysis (DCA) for assessment and diagnosis of nonlinear processes. The core focus of this algorithm is to explore the effectiveness of MV information embedding in learning models and viable implementation in real-time applications. Our method is capable of incorporating high-dimensional information inherent in MV features generated from available inputs using the proposed DCA-based scheme. The algorithm is tested with two real-time electromyogram data sets, which include three categories of nonlinear data-amyotrophic lateral sclerosis, myopathy, and healthy control subjects. A set of MV features is generated in both the time and the wavelet domain for all of the study groups. The features are subjected to DCA projection and optimization and obtained low-order features are concatenated using DCA-based fusion scheme. The discriminant features are applied for the statistical analysis and the model validation. The model achieves an accuracy of 99.03% with a specificity of 99.58% and sensitivities of 98.50% and 97.59%. However, the accuracy over the second data set is 100% with sensitivities of 100% and the specificity of 100%. Results are further compared with the state-of-the-art methods. The proposed scheme is promising and outperforms many state-of-the-art methods, and thus it ensures the faithfulness for industrial applications. [ABSTRACT FROM AUTHOR]

This study focuses on the application of a direct interface circuit (DIC) with a microcontroller for the measurement of responses from an array of metal oxide semiconductor (MOS) gas sensors and evaluation of its performance. The authors estimate the uncertainty caused by a sensor circuit and DIC parameters in the measurement of output responses from a MOS gas sensor array. In deriving such a setting, an uncertainty principle is utilised to estimate the ranges of the uncertainty of each parameter causing a deviation in measurement from the true value. The authors explore the extent of uncertainty contributed by both the sensor parameters as well as the DIC parameters of the e‐nose system. An experimental investigation demonstrates that the DIC‐interfaced MOS gas sensor‐based e‐nose incorporates various sources of uncertainty. [ABSTRACT FROM AUTHOR]