Optimisation of an enzymatic method to obtain modified artichoke pectin and pectic oligosaccharides using artificial neural network tools. In silico and in vitro assessment of the antioxidant activity

Intraoral adjustment of ceramic prostheses involving cutting process is a central procedure in restorative dentistry because the quality of ceramic prostheses depends on the cutting process. In this paper, an artificial neural network (ANN) model was developed for the first time to forecast the dynamic forces in dental cutting process as functions of clinical operational parameters. The predicted force values were compared with the measured values in in vitro dental cutting of porcelain prostheses obtained using a novel two-degrees-of-freedom computer-assisted testing apparatus with a high-speed dental handpiece and diamond burs. The results indicate that there existed nonlinear relationships between the cutting forces and clinical operational parameters. It is found that the ANN-forecasted forces were in good agreement with the experiment-measured values. This indicates that the established ANN model can provide insights into the force-related process assessment and forecast for clinical dental cutting of ceramic prostheses.

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Non-Invasive Optical Blood Glucose Measurement based on Discrete Fourier Transform and Fast Artificial Neural Network: Fasting Normal Glucose Participants Case Study

Journal of Medical Devices ◽

10.1115/1.4044336 ◽

2019 ◽

Author(s):

Renan Prasta Jenie ◽

Evy Damayanthi ◽

Irzaman Irzaman ◽

Rimbawan Rimbawan ◽

Dadang Sukandar ◽

...

Keyword(s):

Neural Network ◽

Clinical Trial ◽

Artificial Neural Network ◽

Fourier Transform ◽

Blood Glucose ◽

Discrete Fourier Transform ◽

Glucose Measurement ◽

Non Invasive ◽

Artificial Neural

A prototype non-invasive blood glucose level measurement optical device (NI-BGL-MOD) has been developed. The NI-BGL-MOD uses a discrete Fourier transform (DFT) method and a fast artificial neural network algorithm to optimize device performance. The appropriate light-emitting diode for the sensory module was selected based on near-infrared spectrophotometry of a blood glucose model and human blood. DFT is implemented in an analog-to-digital converter module. An in vitro trial using the blood glucose model along with a clinical trial involving 110 participants were conducted to evaluate the performance of the prototype. The root-mean-square error of the prototype was 10.8 mg/dl in the in vitro trial and 3.64 mg/dl in the clinical trial, which is lower than the ISO-15197:2016 mandated value of 10 mg/dl. In each trial, consensus error grid analysis indicated that the measurement error was within the safe range. The sensitivity and specificity of the prototype were 0.83 (0.36, 1.00) and 0.90 (0.55, 1.00) in the in vitro trial and 0.81 (0.75, 0.85) and 0.83 (0.78, 0.87) in the clinical trial, respectively. In general, the proposed NI-BGL-MOD demonstrated good performance than gold-standard measurement. Key words: Non-invasive blood glucose measurement, optical device, discrete Fourier transform, multi-formulatric regression, fast artificial neural network

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