scholarly journals Early prediction of putamen imaging features in HIV-associated neurocognitive impairment syndrome

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yu Qi ◽  
Man Xu ◽  
Wei Wang ◽  
Yuan-Yuan Wang ◽  
Jiao-Jiao Liu ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Treatment Group ◽  
Early Stage ◽  
Neurocognitive Impairment ◽  
Classification Model ◽  
Support Vector ◽  
Imaging Features ◽  
Asymptomatic Neurocognitive Impairment ◽  
Clinical Variables ◽  
The Mean

Abstract Background To explore the correlation between the volume of putamen and brain cognitive impairment in patients with HIV and to predict the feasibility of early-stage HIV brain cognitive impairment through radiomics. Method Retrospective selection of 90 patients with HIV infection, including 36 asymptomatic neurocognitive impairment (ANI) patients and 54 pre-clinical ANI patients in Beijing YouAn Hospital. All patients received comprehensive neuropsychological assessment and MRI scanning. 3D Slicer software was used to acquire volume of interest (VOI) and radiomics features. Clinical variables and volume of putamen were compared between patients with ANI and pre-clinical ANI. The Kruskal Wallis test was used to analysis multiple comparisons between groups. The relationship between cognitive scores and VOI was compared using linear regression. For radiomics, principal component analysis (PCA) was used to reduce model overfitting and calculations and then a support vector machine (SVM) was used to build a binary classification model. For model performance evaluation, we used an accuracy, sensitivity, specificity and receiver operating characteristic curve (ROC). Result There were no significant differences in clinical variables between ANI group and pre-clinical-ANI group (P>0.05). The volume of bilateral putamen was significantly different between AHI group and pre-clinical group (P<0.05), but there was only a trend in the left putamen between ANI-treatment group and pre-clinical treatment group(P = 0.063). Reduced cognitive scores in Verbal Fluency, Attention/Working Memory, Executive Functioning, memory and Speed of Information Processing were negatively correlated with the increased VOI (P<0.05), but the correlation was relatively low. In diagnosing the ANI from pre-clinical ANI, the mean area under the ROC curves (AUC) were 0.85 ± 0.22, the mean sensitivity and specificity were 63.12 ± 5.51 and 94.25% ± 3.08%. Conclusion The volumes of putamen in patients with ANI may be larger than patients with pre-clinical ANI, the change of the volume of the putamen may have a certain process; there is a relationship between putamen and cognitive impairment, but the exact mechanism is unclear. Radiomics may be a useful tool for predicting early stage HAND in patients with HIV.

Prevalence of neurocognitive impairment and its associated factors among patients with HIV in Indonesia

2020 ◽  
Vol 16 (6) ◽  
pp. 258-264
Author(s):  
Linlin Lindayani ◽  
Diwa Agus Sudrajat ◽  
Chanti Melnawati ◽  
Dian Anggarini
Keyword(s):  
Cognitive Impairment ◽  
Associated Factors ◽  
High School Graduates ◽  
Cross Sectional Study ◽  
Neurocognitive Impairment ◽  
Hiv Positive ◽  
Study Participation ◽  
Cross Sectional ◽  
The Mean ◽  
Living With Hiv

Background: Patients with HIV are susceptable to developing HIV-associated neurocognitive disorder (HAND). However, few studies have explored the prevalence of neurocognitive impairment and its associated factors among patients with HIV in Indonesia. Aims: This study aimed to explore the prevalence of neurocognitive impairment in patients with HIV in Indonesia. Methods: A cross-sectional study was conducted among patients with HIV in West Java, Indonesia. Patients were eligible for study participation if they were HIV-positive and were aged over 20 years. The International HIV Dementia Scale (IHDS) was used to measure probable HIV–related dementia. Descriptive categorisations and statistics, using mean scores, standard deviation (SD) calculations or frequency rates, were applied to participant characteristics and major variables. Findings: There were a total 150 participants in this study. The mean age was 38.09 years (SD=3.99); 74% were male; 80.7% were high school graduates. The mean of CD4 counts was 493.3 (SD=139.8), and all participants had been living with HIV for more than 9 years (SD=3.84). The mean of total scores using the IHDS was 10.2 (SD=1.58), and 49.30% of patients with HIV were reported to have cognitive impairment. There was an increased prevalence of cognitive impairment among males with a higher BMI and higher systolic blood pressure Conclusions: Nearly half of patients with HIV in Indonesia are at high risk of cognitive impairment, as measured by IHSD. Health professionals caring for patients with HIV may consider routine neurocognitive impairment screenings during clinical visits to address this risk.

scholarly journals A Strategy for Identifying the Risk of Developing Alzheimer's Disease From Healthy Controls: Distance to Novelty Detection Boundary

2020 ◽  
Author(s):  
Jiangbing Mao ◽  
Qinyong Ye ◽  
Hongqing Yang ◽  
Magda Bucholc ◽  
Shuo Liu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Early Stage ◽  
Novelty Detection ◽  
Imaging Biomarkers ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Healthy Controls

Abstract Background:Machine learning (ML) techniques are expected to tackle the problem of the high prevalence of Alzheimer’s disease (AD) we are facing worldwide. However, few studies of novelty detection (ND), a typical ML technique for safety-critical systems especially in healthcare, were engaged for identifying the risk of developing cognitive impairment from healthy controls (HC) population.Materials and Methods: Two independent datasets were used for this study, including the Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) and the Fujian Medical University Union Hospital (FMUUH), China datasets. Multiple feature selection methods were applied to identify the most relevant features for predicting the severity of AD. Four easily interpretable ND algorithms, including k nearest neighbor, Mixture of Gaussian (MoG), KMEANS, and support vector data description were used to construct predictive models. The models were visualized by drawing their decision boundaries tightly surrounding the HC data. A distance to boundary (DtB) strategy was proposed to differentiate individuals with mild cognitive impairment (MCI) and AD from HC. Results: The best overall MCI&AD detection performance in both AIBL and FMUUH was obtained on the cognitive and functional assessments (CFA) modality only using MoG-based ND with AUC of 0.8757 and 0.9443, respectively. The highest sensitivity of MCI was presented by using a combination of CFA and brain imaging modality. The DTB value reflects the risk of developing cognitive impairment for HC and the dementia severity of MCI/AD.Conclusions: Our findings suggest that applying some non-invasive and cost-effective features can significantly detect cognitive decline in an early stage. The visualized decision boundary and the proposed DtB strategy illustrated the severity of cognitive decline of potential MCI&AD patients in an early stage. The results would help inform future guidelines for developing a clinical decision-making support system aiming at an early diagnosis and prognosis of MCI&AD.

scholarly journals Assessment of Tomato Maturity in Different Layers by Spatially Resolved Spectroscopy

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7229
Author(s):  
Yuping Huang ◽  
Wan Si ◽  
Kunjie Chen ◽  
Ye Sun
Keyword(s):  
Model Performance ◽  
Classification Model ◽  
Support Vector ◽  
Eating Quality ◽  
Spatially Resolved ◽  
The Mean ◽  
Spatially Resolved Spectroscopy ◽  
Fruit Shelf Life ◽  
Measurement Location ◽  
Two Sides

Tomato maturity is important to determine the fruit shelf life and eating quality. The objective of this research was to evaluate tomato maturity in different layers by using a newly developed spatially resolved spectroscopic system over the spectral region of 550–1650 nm. Thirty spatially resolved spectra were obtained for 600 tomatoes, 100 for each of the six maturity stages (i.e., green, breaker, turning, pink, light red, and red). Support vector machine discriminant analysis (SVMDA) models were first developed for each of individual spatially resolved (SR) spectra to compare the classification results of two sides. The mean spectra of two sides with the same source-detector distances were employed to determine the model performance of different layers. SR combination by averaging all the SR spectra was also subject to comparison with the classification model performance. The results showed large source-detector distances would be helpful for evaluating tomato maturity, and the mean_SR 15 obtained excellent classification results with the total classification accuracy of 98.3%. Moreover, the classification results were distinct for two sides of the probe, which demonstrated even if in the same source-detector distances, the classification results were influenced by the measurement location due to the heterogeneity for tomato. The mean of all SR spectra could only improve the classification results based on the first three mean_SR spectra, but could not obtain the accuracy as good as the following mean_SR spectra. This study demonstrated that spatially resolved spectroscopy has potential for assessing tomato maturity in different layers.

scholarly journals Signal and Texture Features from T2 Maps for the Prediction of Mild Cognitive Impairment to Alzheimer’s Disease Progression

Healthcare ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 941
Author(s):  
Alejandro I. Trejo-Castro ◽  
Ricardo A. Caballero-Luna ◽  
José A. Garnica-López ◽  
Fernando Vega-Lara ◽  
Antonio Martinez-Torteya
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Texture Features ◽  
Mean Value ◽  
Absolute Difference ◽  
Imaging Features ◽  
Machine Learning Methods ◽  
The Mean

Early detection of Alzheimer’s disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an effective biomarker of AD. To test these claims, a study was conducted using T2 maps, a sequence not previously studied, of 40 patients with mild cognitive impairment (MCI) from the Alzheimer’s Disease Neuroimaging Initiative database, who either progressed to AD (18) or remained stable (22). From these maps, the mean value and absolute difference of 37 signal and texture imaging features for 40 contralateral pairs of regions were measured. We used seven machine learning methods to analyze whether, by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify patients who will progress to AD. The predictive models improved with the addition of signal and texture features. Additionally, features related to the signal and texture of the images were much more relevant than volumetric ones. Our results suggest that contralateral signal and texture features should be further investigated as potential biomarkers for the prediction of AD.

Wearable IoT based diagnosis of prostate cancer using GLCM-multiclass SVM and SIFT-multiclass SVM feature extraction strategies

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Swetha Parvatha Reddy Chandrasekhara ◽  
Mohan G. Kabadi ◽  
Srivinay Srivinay
Keyword(s):  
Prostate Cancer ◽  
Image Processing ◽  
Feature Extraction ◽  
Sensitivity And Specificity ◽  
Early Stage ◽  
Classification Model ◽  
Support Vector ◽  
Data Set ◽  
Content Type ◽  
Multiclass Svm

Purpose This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life. Design/methodology/approach The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer. Findings The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set. Originality/value The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

scholarly journals The invasion depth measurement of bladder cancer using T2-weighted magnetic resonance imaging

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Yang Liu ◽  
Haojie Zheng ◽  
Xiaopan Xu ◽  
Xi Zhang ◽  
Peng Du ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Bladder Wall ◽  
Classification Model ◽  
Support Vector ◽  
Dice Similarity Coefficient ◽  
Wall Region ◽  
Feature Subset ◽  
Invasion Depth ◽  
Depth Measurement ◽  
The Mean

Abstract Background Invasion depth is an important index for staging and clinical treatment strategy of bladder cancer (BCa). The aim of this study was to investigate the feasibility of segmenting the BCa region from bladder wall region on MRI, and quantitatively measuring the invasion depth of the tumor mass in bladder lumen for further clinical decision-making. This retrospective study involved 20 eligible patients with postoperatively pathologically confirmed BCa. It was conducted in the following steps: (1) a total of 1159 features were extracted from each voxel of both the certain cancerous and wall tissues with the T2-weighted (T2W) MRI data; (2) the support vector machine (SVM)-based recursive feature elimination (RFE) method was implemented to first select an optimal feature subset, and then develop the classification model for the precise separation of the cancerous regions; (3) after excluding the cancerous region from the bladder wall, the three-dimensional bladder wall thickness (BWT) was calculated using Laplacian method, and the invasion depth of BCa was eventually defined by the subtraction of the mean BWT excluding the cancerous region and the minimum BWT of the cancerous region. Results The segmented results showed a promising accuracy, with the mean Dice similarity coefficient of 0.921. The “soft boundary” defined by the voxels with the probabilities between 0.1 and 0.9 could demonstrate the overlapped region of cancerous and wall tissues. The invasion depth calculated from proposed segmentation method was compared with that from manual segmentation, with a mean difference of 0.277 mm. Conclusion The proposed strategy could accurately segment the BCa region, and, as the first attempt, realize the quantitative measurement of BCa invasion depth.

scholarly journals The invasion depth measurement of bladder cancer using T2-weighted magnetic resonance imaging

2020 ◽  
Author(s):  
Yang Liu ◽  
Haojie Zheng ◽  
Xiaopan Xu ◽  
Xi Zhang ◽  
Peng Du ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Bladder Wall ◽  
Classification Model ◽  
Support Vector ◽  
Dice Similarity Coefficient ◽  
Wall Region ◽  
Feature Subset ◽  
Invasion Depth ◽  
Depth Measurement ◽  
The Mean

Abstract Background: Invasion depth is an important index for staging and clinical treatment strategy of bladder cancer (BCa). The aim of this study was to investigate the feasibility of segmenting the BCa region from bladder wall region on MRI, and quantitatively measuring the invasion depth of the tumor mass in bladder lumen for further clinical decision-making. This retrospective study involved 20 eligible patients with postoperatively pathologically-confirmed BCa. It was conducted in the following steps: (1) a total of 1159 features were extracted from each voxel of both the certain cancerous and wall tissues with the T2-weighted (T2W) MRI data; (2) the support vector machine (SVM)-based recursive feature elimination (RFE) method was implemented to first select an optimal feature subset, and then develop the classification model for the precise separation of the cancerous regions; (3) after excluding the cancerous region from the bladder wall, the three-dimensional bladder wall thickness (BWT) was calculated using Laplacian method, and the invasion depth of BCa was eventually defined by the subtraction of the mean BWT excluding the cancerous region and the minimum BWT of the cancerous region.Results: The segmented results showed a promising accuracy, with the mean Dice similarity coefficient of 0.921. The “soft boundary” defined by the voxels with the probabilities between 0.1 and 0.9 could demonstrate the overlapped region of cancerous and wall tissues. The invasion depth calculated from proposed segmentation method was compared with that from manual segmentation, with a mean difference of 0.277 mm.Conclusion: The proposed strategy could accurately segment the BCa region, and, as the first attempt, realize the quantitative measurement of BCa invasion depth.

scholarly journals The Invasion Depth Measurement of Bladder Cancer based on MRI

2020 ◽  
Author(s):  
Yang Liu ◽  
Haojie Zheng ◽  
Xiaopan Xu ◽  
Xi Zhang ◽  
Peng Du ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Bladder Wall ◽  
Classification Model ◽  
Support Vector ◽  
Dice Similarity Coefficient ◽  
Wall Region ◽  
Feature Subset ◽  
Invasion Depth ◽  
Depth Measurement ◽  
The Mean

Abstract Background: Invasion depth is an important index for staging and clinical treatment strategy of bladder cancer (BCa). The aim of this study was to investigate the feasibility of segmenting the BCa region from bladder wall region on MRI, and quantitatively measuring the invasion depth of the tumor mass in bladder lumen for further clinical decision-making. This retrospective study involved 20 eligible patients with postoperatively pathologically-confirmed BCa. It was conducted in the following steps: (1) a total of 1159 features were extracted from each voxel of both the certain cancerous and wall tissues with the T2-weighted (T2W) MRI data; (2) the support vector machine (SVM)-based recursive feature elimination (RFE) method was implemented to first select an optimal feature subset, and then develop the classification model for the precise separation of the cancerous regions; (3) after excluding the cancerous region from the bladder wall, the three-dimensional bladder wall thickness (BWT) was calculated using Laplacian method, and the invasion depth of BCa was eventually defined by the subtraction of the minimum BWT of the cancerous region and the mean BWT other than the cancerous region.Results: The segmented results showed a promising accuracy, with the mean Dice similarity coefficient of 0.921. The “soft boundary” defined by the voxels with the probabilities between 0.1 and 0.9 could demonstrate the overlapped region of cancerous and wall tissues. The invasion depth calculated from proposed segmentation method was compared with that from manual segmentation, with a mean difference of 0.277 mm.Conclusion: The proposed strategy could accurately segment the BCa region, and, as the first attempt, realize the quantitative measurement of BCa invasion depth.

scholarly journals The Invasion Depth Measurement of Bladder Cancer using T2-weighted Magnetic Resonance Imaging

2020 ◽  
Author(s):  
yang liu ◽  
Haojie Zheng ◽  
Xiaopan Xu ◽  
Xi Zhang ◽  
Peng Du ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Bladder Wall ◽  
Classification Model ◽  
Support Vector ◽  
Dice Similarity Coefficient ◽  
Wall Region ◽  
Feature Subset ◽  
Invasion Depth ◽  
Depth Measurement ◽  
The Mean

Abstract Background: Invasion depth is an important index for staging and clinical treatment strategy of bladder cancer (BCa). The aim of this study was to investigate the feasibility of segmenting the BCa region from bladder wall region on MRI, and quantitatively measuring the invasion depth of the tumor mass in bladder lumen for further clinical decision-making. This retrospective study involved 20 eligible patients with postoperatively pathologically-confirmed BCa. It was conducted in the following steps: (1) a total of 1159 features were extracted from each voxel of both the certain cancerous and wall tissues with the T2-weighted (T2W) MRI data; (2) the support vector machine (SVM)-based recursive feature elimination (RFE) method was implemented to first select an optimal feature subset, and then develop the classification model for the precise separation of the cancerous regions; (3) after excluding the cancerous region from the bladder wall, the three-dimensional bladder wall thickness (BWT) was calculated using Laplacian method, and the invasion depth of BCa was eventually defined by the subtraction of the mean BWT excluding the cancerous region and the minimum BWT of the cancerous region.Results: The segmented results showed a promising accuracy, with the mean Dice similarity coefficient of 0.921. The “soft boundary” defined by the voxels with the probabilities between 0.1 and 0.9 could demonstrate the overlapped region of cancerous and wall tissues. The invasion depth calculated from proposed segmentation method was compared with that from manual segmentation, with a mean difference of 0.277 mm.Conclusion: The proposed strategy could accurately segment the BCa region, and, as the first attempt, realize the quantitative measurement of BCa invasion depth.

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