Application of Northern Goshawk Back-Propagation Artificial Neural Network in the Prediction of Monohydroxycarbazepine Concentration in Patients with Epilepsy

INTRODUCTION

A northern goshawk back-propagation artificial neural network (NGO-BPANN) model was established to predict monohydroxycarbazepine (MHD) concentration in patients with epilepsy.

METHODS

The data were collected from 108 Han Chinese patients with epilepsy on oxcarbazepine monotherapy. The results of 14 genotype variates were selected as the input layer in the first BPANN model, and the variables that had a more significant impact on the plasma concentration of MHD were retained. With demographic characteristics and clinical laboratory test results, the genotypes of SCN1A rs2298771 and SCN2A rs17183814 were used to construct the BPANN model. The BPANN model was comprehensively validated and used to predict the MHD plasma concentration of five patients with epilepsy in our hospital.

RESULTS

The model demonstrated favorable fitness metrics, including a mean squared error of 0.00662, a gradient magnitude of 0.00753, an absence of validation tests amounting to zero, and a correlation coefficient of 0.980. Sex, BMI, and the genotype SCN1A rs2298771 were ranked highest by the absolute mean impact value (MIV), which is primarily associated with the concentration of MHD. The test group exhibited a range of - 20.84% to 31.03% bias between the predicted and measured values, with a correlation coefficient of 0.941 between the two. With BPANN, the MHD nadir concentration could be predicted precisely.

CONCLUSION

The NGO-BPANN model exhibits exceptional predictive capability and can be a practical instrument for forecasting MHD concentration in patients with epilepsy.

CLINICAL TRIAL REGISTRATION

www.chiCTR-OOC-17012141 .

Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach

BACKGROUND

Adequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine learning.

MATERIALS AND METHODS

Cephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. Back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences comparison.

RESULTS

The regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and II.

CONCLUSIONS

Rotation of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.

Enhanced anaerobic digestion of swine manure via a coupled microbial electrolysis cell

Microbial electrolysis cell coupled anaerobic digestion (MEC-AD) is a new technology in energy recovery and waste treatment, which could be used to recycle swine manure. Here, different applied voltage effects were studied using MEC-AD with swine manure as a substrate. The maximum cumulative biogas and methane yields, both occurring with 0.9 V, were 547.3 mL/g total solid (TS) and 347.7 mL/g TS, respectively. The increased energy can counterbalance the electrical input. First order, logistic, gompertz, and back-propagation artificial neural network (BP-ANN) models were used to study cumulative biogas and methane yields. The BP-ANN model was superior to the other three models. The maximum degradation rate of hemicellulose, cellulose, and lignin was 60.97%, 48.59%, and 31.59% at 0.9 V, respectively. The BP-ANN model establishes a model for cumulative biogas and methane yields using MEC-AD. Thus, MEC-AD enhanced biogas and methane production and accelerated substrate degradation at a suitable voltage.

Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images

BACKGROUND

After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images.

METHODS

The database consists of a total of 1005 MRI images with evolutive lymphoma and residual masses. More specifically, we propose a novel approach that leverages: (1)-The complementarity of the functional and anatomical criteria of MRI images through a fusion step based on the discrete wavelet transforms (DWT). (2)- The automatic segmentation of the lesions, their localization, and their enumeration using the Chan-Vese algorithm. (3)- The generation of the parametric image which contains the apparent diffusion coefficient value named ADC map. (4)- The features selection through the application of the sequential forward selection (SFS), Entropy, Symmetric uncertainty and Gain Ratio algorithm on 72 extracted features. (5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN). Moreover, this study is achieved with an evaluation of the classification using 335 DW-MR images where 80% of them are used for the training and the remaining 20% for the test.

RESULTS

The obtained accuracy for the five classifiers recorded a slight superiority to the proposed method based on the back-propagation 3-9-1 ANN model which reaches 96,5%. In addition, we compared the proposed method to five other works from the literature. The proposed method gives much better results in terms of SE, SP, accuracy, F₁-measure, and geometric-mean which reaches respectively 96.4%, 90.9%, 95.5%, 0.97, and 91.61%.

CONCLUSIONS

Our initial results suggest that Combining functional, anatomical, and morphological features of ROI's have very good accuracy (97.01%) for evolutive lymphoma and residual masses recognition when we based on the new proposed approach using the back-propagation 3-9-1 ANN model. Proposed method based on machine learning gives less than Deep learning CNN, which is 98.5%.

Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy

The main purpose of this research is to check the relative importance of methods fuzzy-logic and back-propagation neural network to evaluate the performance of wire electric discharge machine (WEDM) of aeronautics super alloy. It has been confirmed that BP-ANN method reveals significant result over the fuzzy logic method for the evaluation of surface roughness and waviness of the WEDM of aeronautic super alloy. On the basis of Taguchi analysis, it has been established that the variable pulse-on, interaction amid the pulse-on and pulse-off time, wire tension and spark-gap voltage have a superlative influence on the surface roughness. The waviness is influenced prominently by pulse-on time, pulse-off time and spark-gap voltage. The thickness of recast layer is minimized up to 9.434 μm.

Influence and prediction of meteorological factors on epidemic situation of digestive system infectious diseases in drought area

AIM: To explore the key meteorological factors which affect the infectious diseases of digestive system and establish the back-propagation (BP) neural network prediction model of digestive system infectious diseases in drought conditions.

METHODS: The data on incidence of digestive system infectious diseases and meteorological factors in drought area (Chaoyang, Liaoning Province) from 1981 to 1994 were collected and analyzed using SPSS 15.0. The prediction model of BP artificial neural network was built by Matlab 6.5.

RESULTS: The incidence of bacillary dysentery was negatively correlated to the annual mean atmospheric pressure and annual mean precipitation (r = -0.770, -0.591; P = 0.001, 0.026), and was positively correlated to the annual mean evaporation (r = 0.703, P = 0.005). The incidence of viral hepatitis was negatively correlated to the annual mean atmospheric pressure (r = -0.570, P = 0.033), but positively correlated to the maximum temperature (r = 0.722, P = 0.004). The incidence of typhoid fever and paratyphoid fever was negatively correlated to the annual mean atmospheric pressure (r = -0.713, P = 0.004), but positively correlated to the annual mean evaporation and maximum temperature (r = 0.655, 0.562; P = 0.011, 0.037). The predictive precision of bacillary dysentery, viral hepatitis and typhoid fever and paratyphoid fever BP model was 24.3%, 3.5% and 8.3%, respectively.

CONCLUSION: The incidence of digestive system infectious diseases is correlated to the atmospheric pressure, precipitation, evaporation and maximum temperature. The BP neural network model fits very well in the study of digestive system infectious diseases.