scholarly journals Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Selim ◽  
Hatem Khater
Keyword(s):  
Clinical Diagnosis ◽  
Human Error ◽  
Low Cost ◽  
False Negative ◽  
False Negative Rate ◽  
Principal Component ◽  
Clinical Samples ◽  
Support Vector ◽  
Proposed Model ◽  
Better Than

Alzheimer's disease (AD) is a significant regular type of dementia that causes damage in brain cells. Early detection of AD acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. Our proposed model, in the first stage, apply the medical dataset to a composite hybrid feature selection (CHFS) to extract new features for select the best features to improve the performance of the classification process due to eliminating obscures features. In the second stage, we applied a dataset to a stacked hybrid classification system to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. All experiments conducted on a laptop with an Intel Core i7- 8750H CPU at 2.2 GHz and 16 G of ram running on windows 10 (64 bits). The dataset evaluated using an explorer set of weka data mining software for the analysis purpose. The experimental show that the proposed model of ‏(CHFS) feature extraction ‏performs better than principal component analysis (PCA), and lead to effectively reduced the false-negative rate with a relatively high overall accuracy with support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% which is considerably better than the previous state-of-the-art result. The receiver operating characteristic (ROC) curve was equal to 95.5%. Also, the experiment on MRI images Kaggle dataset of CNN classification process with 80.21% accuracy result. The results of the proposed model show an accurate classify Alzheimer's clinical samples against MRI neuroimaging for diagnoses AD at a low cost.

scholarly journals Applying Artificial Intelligence Techniques to Improve Clinical Diagnosis of Alzheimer’s Disease

2020 ◽  
Vol 3 (2) ◽  
pp. 58-79
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Ibrahim Selim ◽  
Hatem Awad A. Khater
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Diagnosis ◽  
Human Error ◽  
False Negative ◽  
False Negative Rate ◽  
Clinical Samples ◽  
Support Vector ◽  
Proposed Model ◽  
Better Than

Alzheimer's disease (AD) is a significant regular type of dementia that causes damage in brain cells. Early detection of AD acting as an essential role in global health care due to misdiagnosis and sharing many clinical sets with other types of dementia, and costly monitoring the progression of the disease over time by magnetic reasoning imaging (MRI) with consideration of human error in manual reading. Our proposed model in the first stage, apply the medical dataset to a composite hybrid feature selection (CHFS) to extract new features for select the best features to improve the performance of the classification process due to eliminating obscures. In the second stage, we applied a dataset to a stacked hybrid classification system to combine Jrip and random forest classifiers with six model evaluations as meta-classifier individually to improve the prediction of clinical diagnosis. All experiments conducted on a laptop with an Intel Core i7- 8750H CPU at 2.2 GHz and 16 G of ram running on windows 10 (64 bits). The dataset evaluated using an explorer set of WEKA data mining software for the analysis purpose. The experimental show that the proposed model of (CHFS) feature extraction performs better than proncipal component analysis (PCA), and lead to effectively reduced the false-negative rate with a relatively high overall accuracy with support vector machine (SVM) as meta-classifier of 96.50% compared to 68.83% which is considerably better than the previous state-of-the-art result. The receiver operating characteristic (ROC) curve was equal to 95.5%. Also, the experiment on MRI images Kaggle dataset of CNN classification process with 80.21% accuracy result. The results of the proposed model show an accurate classify Alzheimer's clinical samples against MRI neuroimaging for diagnoses AD at a low cost.

Drug Target Group Prediction with Multiple Drug Networks

2020 ◽  
Vol 23 (4) ◽  
pp. 274-284 ◽  
Author(s):  
Jingang Che ◽  
Lei Chen ◽  
Zi-Han Guo ◽  
Shuaiqun Wang ◽  
Aorigele
Keyword(s):  
Drug Target ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Multiple Drug ◽  
Property A ◽  
Multiple Networks ◽  
Proposed Model ◽  
The One

Background: Identification of drug-target interaction is essential in drug discovery. It is beneficial to predict unexpected therapeutic or adverse side effects of drugs. To date, several computational methods have been proposed to predict drug-target interactions because they are prompt and low-cost compared with traditional wet experiments. Methods: In this study, we investigated this problem in a different way. According to KEGG, drugs were classified into several groups based on their target proteins. A multi-label classification model was presented to assign drugs into correct target groups. To make full use of the known drug properties, five networks were constructed, each of which represented drug associations in one property. A powerful network embedding method, Mashup, was adopted to extract drug features from above-mentioned networks, based on which several machine learning algorithms, including RAndom k-labELsets (RAKEL) algorithm, Label Powerset (LP) algorithm and Support Vector Machine (SVM), were used to build the classification model. Results and Conclusion: Tenfold cross-validation yielded the accuracy of 0.839, exact match of 0.816 and hamming loss of 0.037, indicating good performance of the model. The contribution of each network was also analyzed. Furthermore, the network model with multiple networks was found to be superior to the one with a single network and classic model, indicating the superiority of the proposed model.

scholarly journals Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1407
Author(s):  
Matyas Bukva ◽  
Gabriella Dobra ◽  
Juan Gomez-Perez ◽  
Krisztian Koos ◽  
Maria Harmati ◽  
...  
Keyword(s):  
Lumbar Disc ◽  
Area Under The Curve ◽  
Principal Component ◽  
Extracellular Vesicle ◽  
Classification Performance ◽  
Cns Tumors ◽  
Clinical Samples ◽  
Support Vector ◽  
Serum Samples ◽  
Raman Spectroscopic

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis–Support Vector Machine (PCA–SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9–92.5% CA, 80–95% sensitivity and 80–90% specificity. AUC scores in the range of 0.82–0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

scholarly journals Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2506 ◽  
Author(s):  
Yunfeng Chen ◽  
Yue Chen ◽  
Xuping Feng ◽  
Xufeng Yang ◽  
Jinnuo Zhang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Spectroscopic Technique ◽  
Variety Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Discriminant Models ◽  
The Fourier Transform ◽  
Better Than

The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000–550 cm−1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

Catheter-Based Computed Tomography Angiography in Anterolateral Thigh Perforator Mapping of Chinese Patients

2018 ◽  
Vol 35 (03) ◽  
pp. 221-228 ◽  
Author(s):  
Zhongjie Wang ◽  
Jiqiang He ◽  
Liming Qing ◽  
Zhengbing Zhou ◽  
Youming Zhang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Computed Tomography Angiography ◽  
False Negative ◽  
False Negative Rate ◽  
Chinese Patients ◽  
Trauma Patients ◽  
Anterolateral Thigh ◽  
Limb Trauma ◽  
Extremity Reconstruction ◽  
Better Than

Background During reconstructive surgery, anterolateral thigh (ALT) flap harvest is challenging due to variation and uncertainty in perforator distribution. We performed a pilot study to identify the predictive value of catheter-based computed tomography angiography (C-CTA) and traditional CTA (T-CTA) in ALT perforator mapping for patients whose ALT perforators were difficult to identify. Methods Thirty-four consecutive T-CTA/C-CTA-mapped ALT flaps were evaluated for extremity reconstruction. The perforator location, origin, and course were compared between T-CTA/C-CTA imaging and intraoperative findings. The mapping efficiency of T-CTA and C-CTA was compared thoroughly. Results Among the 34 ALT thigh flaps, 117 (36) of the 130 perforators identified intraoperatively were visible on C-CTA (T-CTA) in a subgroup of Chinese limb trauma patients with limited activity. C-CTA showed a satisfactory efficiency in perforator mapping, which was much better than the efficiency of T-CTA. C-CTA also showed a much better sensitivity (90.00 vs. 27.69%), specificity (94.74 vs. 66.67%), and accuracy (91.07 vs. 36.69%), and a much lower false-positive (1.68 vs. 26.53%), and false-negative rate (10.00 vs. 72.31%). Moreover, C-CTA could accurately predict the origin and septocutaneous or intramuscular course in all identified perforators. All flaps were elevated successfully and survived. Conclusion C-CTA outperforms T-CTA in the preoperative perforator mapping of ALT flaps in a subgroup of Chinese limb trauma patients. C-CTA should be the method of choice for perforator mapping in patients whose ALT flaps are intended for extremity reconstruction.

scholarly journals Predicting cardiometabolic disturbances from waist-to-height ratio: findings from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) baseline

2018 ◽  
Vol 21 (6) ◽  
pp. 1028-1035 ◽  
Author(s):  
Marcelo Castanheira ◽  
Dóra Chor ◽  
José Uéleres Braga ◽  
Letícia de Oliveira Cardoso ◽  
Rosane Härter Griep ◽  
...  
Keyword(s):  
Longitudinal Study ◽  
False Negative ◽  
False Negative Rate ◽  
Cross Sectional Study ◽  
Discriminatory Power ◽  
Adult Health ◽  
Height Ratio ◽  
Cross Sectional ◽  
Waist To Height Ratio ◽  
Better Than

AbstractObjectiveTo evaluate the performance of waist-to-height ratio (WHtR) in predicting cardiometabolic outcomes and compare cut-off points for Brazilian adults.DesignCross-sectional study. WHtR areas under the curve (AUC) were compared with those for BMI, waist circumference (WC) and waist-to-hip ratio (WHR). The outcomes of interest were hypertension, diabetes, hypertriacylglycerolaemia and presence of at least two components of metabolic syndrome (≥2 MetS). Cut-offs for WHtR were compared and validity measures were estimated for each point.SettingTeaching and research institutions in six Brazilian state capitals, 2008–2010.SubjectsWomen (n 5026) and men (n 4238) aged 35–54 years who participated in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) at baseline.ResultsWHtR age-adjusted AUC ranged from 0·68 to 0·72 in men and 0·69 to 0·75 in women, with smaller AUC for hypertriacylglycerolaemia and the largest for ≥2 MetS. WHtR performed better than BMI for practically all outcomes; better than WHR for hypertension in both sexes; and displayed larger AUC than WC in predicting diabetes mellitus. It also offered better discriminatory power for ≥2 MetS in men; and was better than WC, but not WHR, in women. Optimal cut-off points of WHtR were 0·55 (women) and 0·54 (men), but they presented high false-negative rate compared with 0·50.ConclusionsWe recommend using WHtR (which performed similarly to, or better than, other available indices of adiposity) as an anthropometric index with good discriminatory power for cardiometabolic outcomes in Brazilian adults, indicating the already referenced limit of WHtR≥0·50.

scholarly journals Microfluidic Nano-Scale qPCR Enables Ultra-Sensitive and Quantitative Detection of SARS-CoV-2

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1425
Author(s):  
Xin Xie ◽  
Tamara Gjorgjieva ◽  
Zaynoun Attieh ◽  
Mame Massar Dieng ◽  
Marc Arnoux ◽  
...  
Keyword(s):  
Viral Infections ◽  
Limit Of Detection ◽  
False Negative ◽  
False Negative Rate ◽  
Clinical Samples ◽  
Quantitative Detection ◽  
Rt Pcr ◽  
Viral Loads ◽  
Nano Scale ◽  
Negative Rate

A major challenge in controlling the COVID-19 pandemic is the high false-negative rate of the commonly used RT-PCR methods for SARS-CoV-2 detection in clinical samples. Accurate detection is particularly challenging in samples with low viral loads that are below the limit of detection (LoD) of standard one- or two-step RT-PCR methods. In this study, we implemented a three-step approach for SARS-CoV-2 detection and quantification that employs reverse transcription, targeted cDNA preamplification, and nano-scale qPCR based on a commercially available microfluidic chip. Using SARS-CoV-2 synthetic RNA and plasmid controls, we demonstrate that the addition of a preamplification step enhances the LoD of this microfluidic RT-qPCR by 1000-fold, enabling detection below 1 copy/µL. We applied this method to analyze 182 clinical NP swab samples previously diagnosed using a standard RT-qPCR protocol (91 positive, 91 negative) and demonstrate reproducible and quantitative detection of SARS-CoV-2 over five orders of magnitude (<1 to 106 viral copies/µL). Crucially, we detect SARS-CoV-2 with relatively low viral load estimates (<1 to 40 viral copies/µL) in 17 samples with negative clinical diagnosis, indicating a potential false-negative rate of 18.7% by clinical diagnostic procedures. In summary, this three-step nano-scale RT-qPCR method can robustly detect SARS-CoV-2 in samples with relatively low viral loads (<1 viral copy/µL) and has the potential to reduce the false-negative rate of standard RT-PCR-based diagnostic tests for SARS-CoV-2 and other viral infections.

scholarly journals Individual Detection of Citrus and Avocado Trees Using Extended Maxima Transform Summation on Digital Surface Models

2020 ◽  
Vol 12 (10) ◽  
pp. 1633 ◽  
Author(s):  
Daniel G. García-Murillo ◽  
J. Caicedo-Acosta ◽  
G. Castellanos-Dominguez
Keyword(s):  
Low Cost ◽  
False Negative ◽  
False Negative Rate ◽  
Seed Selection ◽  
Individual Tree ◽  
Local Maxima ◽  
Commission Errors ◽  
Tree Detection ◽  
Surface Models ◽  
Cumulative Summation

Individual tree detection (ITD) locates plants from images to estimate monitoring parameters, helping the management of forestry and agriculture systems. As a low-cost solution to help farm monitoring, digital surface models are increasingly involved together with mathematical morphology techniques within the framework of ITD tasks. However, morphology-based approaches are prone to omission and commission errors due to the shape and size of structuring elements. To reduce the error rate in ITD tasks, we introduce a morphological transform that is based on the local maxima segmentation (Cumulative Summation of Extended Maxima transform (SEMAX)) with the aim to enhance the seed selection by extracting information collected from different heights. Validation is performed on data collected from the plantations of citrus and avocado using different measures of precision. The results obtained by the SEMAX approach show that the devised ITD algorithm provides enough accuracy, and achieves the lowest false-negative rate than other compared state-of-art approaches do.

Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network

2020 ◽  
Vol 32 (4) ◽  
pp. 741-758
Author(s):  
Xugang Xi ◽  
Wenjun Jiang ◽  
Seyed M. Miran ◽  
Xian Hua ◽  
Yun-Bo Zhao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Surface Electromyography ◽  
Contractile Activity ◽  
Random Network ◽  
Principal Component ◽  
Small World ◽  
Support Vector ◽  
Echo State Network ◽  
Better Than ◽  
Semg Signals

Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals. In this letter, we propose a feature extraction method of sEMG signals based on the improved small-world leaky echo state network (ISWLESN). The reservoir of leaky echo state network (LESN) is connected by a random network. First, we improved the reservoir of the echo state network (ESN) by these networks and used edge-added probability to improve these networks. That idea enhances the adaptability of the reservoir, the generalization ability, and the stability of ESN. Then we obtained the output weight of the network through training and used it as features. We recorded the sEMG signals during different activities: falling, walking, sitting, squatting, going upstairs, and going downstairs. Afterward, we extracted corresponding features by ISWLESN and used principal component analysis for dimension reduction. At the end, scatter plot, the class separability index, and the Davies-Bouldin index were used to assess the performance of features. The results showed that the ISWLESN clustering performance was better than those of LESN and ESN. By support vector machine, it was also revealed that the performance of ISWLESN for classifying the activities was better than those of ESN and LESN.

Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals

2020 ◽  
Vol 234 (8) ◽  
pp. 794-811
Author(s):  
Harikrishna Mulam ◽  
Malini Mudigonda
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
False Positive Rate ◽  
False Negative ◽  
False Negative Rate ◽  
Principal Component ◽  
The Neural Network ◽  
Marquardt Algorithm ◽  
Positive Rate ◽  
Sensitivity Specificity

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human–computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg–Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient.

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