Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease

Ortíz, Andrés; Munilla, Jorge; Górriz, Juan Manuel; Ramı́rez, Javier

doi:10.1142/s0129065716500258

Cited by 326 publications

(135 citation statements)

References 46 publications

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“…Numerous predictive approaches have been developed for diagnosis of AD, most of them derived using Cox Regression (Barnes et al, 2014, Derby et al, 2013, Ewers et al, 2012, Okereke et al, 2012, Seshadri et al, 2010), and Logistic Regression (Barnes et al, 2010, Bauer et al, 2018, Chary et al 2013, Wolfsgruber et al, 2014). In the past decade, there has also been growing interest toward the application of SVM (Casanova et al, 2015, Cui et al, 2011, Klöppel et al, 2008, Ritter et al, 2015, Weygandt et al, 2011), RF (Gray et al, 2013, Sarica et al, 2017) as well as deep neural network models for AD diagnostics (Ortiz, Munilla, Gorriz, & Ramirez, 2016, Shen, Wu, & Suk, 2017). The SVM-based models have been developed for both differential diagnosis and assessment of AD severity using neuroimaging, genome-based, and blood-based biomarkers (Klöppel et al, 2008, Laske et al, 2011, Smith-Vikos & Slack, 2013, Weygandt et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Bucholc

Ding

Wang

et al. 2019

Preprint

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1Computerized clinical decision support systems can help to provide objective, standardized, 2 and timely dementia diagnosis. However, current computerized systems are mainly based 3 on the group analysis, discrete classification of disease stages, or expensive and not readily 4 accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and 5 functional assessments (CFA). In this study, we developed a computational framework using 6 a suite of machine learning tools for identifying key markers in predicting the severity of 7 Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine 8 learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression 9 (SVR), and k-Nearest Neighbor (kNN reg ) for regression and Support Vector Machine (SVM), 10Random Forest (RF), and k-Nearest Neighbor (kNN class ) for classification, were used for the 11 development of predictive models. We demonstrated high predictive power of CFA. 12Predictive performance of models incorporating CFA was shown to be consistently higher 13 accuracy than those based solely on biomarker modalities. We found that KRR and SVM 14 were the best performing regression and classification methods respectively. The optimal 15 SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, 16MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the 17 best performance of the KRR model was reported with combined CFA and MRI 18 neuroimaging data, i.e., R 2 = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power 19 of CFA and their widespread use in clinical practice, we then designed a data-driven and 20 self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating 21 the severity of AD of an individual on a continuous spectrum. The system implemented an 22 automated computational approach for data pre-processing, modelling, and validation and 23 used exclusively the scores of selected cognitive measures as data entries. Taken together, 24we have developed an objective and practical CDSS to aid AD diagnosis. 25Keywords: dementia; Alzheimer's disease; decision support system; machine learning; 26 diagnosis support; cognitive impairment 27 97 CDSS for evaluating the severity of AD of an individual on a continuous spectrum. In order to 98 achieve this, we first utilize a suite of machine learning techniques to extract useful 99 information from large volumes of patient data and provide a disease outcome prediction for 100 different types and combinations of AD markers. We demonstrate that CFA can reliably and 101 accurately provide prediction of AD severity. Next, we design a CDSS that incorporates an 102 automated computational approach for data pre-processing, modelling, and validation and 103 uses selected CFA scores as data input. Since our system was designed to utilize 104 information from readily available and cost-effective CFA markers, it can be easily 105 implemented in general cli...

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Section: Discussionmentioning

confidence: 99%

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Bucholc

Ding

Wang

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…In several reports, deep learning with convolutional neural networks as AI has been used in medicine . The accuracies of this method with deep learning have been published and were 0.997 for histopathological diagnosis of breast cancer, 0.90‐0.83 for the early diagnosis of Alzheimer's disease, 0.83 for urological dysfunctions, 0.72 and 0.50 for colposcopy, 0.83 for the diagnostic imaging of orthopedic trauma, and 0.98 for the morphological quality of blastocysts and evaluation by embryologist . In one report, embryos with fair‐quality images that were classified as poor and good quality were scored as 0.509 and 0.614, respectively, for the likelihood of achieving a positive live birth .…”

Section: Discussionmentioning

confidence: 99%

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

Miyagi

Habara

Hirata

et al. 2019

Reprod Medicine & Biology

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Purpose To identify artificial intelligence (AI) classifiers in images of blastocysts to predict the probability of achieving a live birth in patients classified by age. Results are compared to those obtained by conventional embryo (CE) evaluation. Methods A total of 5691 blastocysts were retrospectively enrolled. Images captured 115 hours after insemination (or 139 hours if not yet large enough) were classified according to maternal age as follows: <35, 35‐37, 38‐39, 40‐41, and ≥42 years. The classifiers for each category and a classifier for all ages were related to convolutional neural networks associated with deep learning. Then, the live birth functions predicted by the AI and the multivariate logistic model functions predicted by CE were tested. The feasibility of the AI was investigated. Results The accuracies of AI/CE for predicting live birth were 0.64/0.61, 0.71/0.70, 0.78/0.77, 0.81/0.83, 0.88/0.94, and 0.72/0.74 for the age categories <35, 35‐37, 38‐39, 40‐41, and ≥42 years and all ages, respectively. The sum value of the sensitivity and specificity revealed that AI performed better than CE ( P = 0.01). Conclusions AI classifiers categorized by age can predict the probability of live birth from an image of the blastocyst and produced better results than were achieved using CE.

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“…Because there are 160 images in this study, we studied machine learning except deep learning. The accuracies with the deep learning have been reported such as 0.997 for histopathological diagnosis of breast cancer , 0.90‐0.83 for the early diagnosis of the Alzheimer's disease , 0.83 for urological dysfunctions , 0.72 , and 0.50 for the colposcopy, 0.83 for the diagnostic imaging of orthopedic trauma . In practical sterility, there are some clinical disincentives for the embryo to achieve live birth; the uterine factors —(intrauterine adhesions , uterine myomas , and endometrial polyps), endometriosis , ovarian function , oviduct obstruction , female diseases such as diabetes mellitus , immune disorder , and uterine microbiota .…”

Section: Discussionmentioning

confidence: 99%

Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image

Miyagi

Habara

Hirata

et al. 2019

Reprod Medicine & Biology

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Purpose To make the artificial intelligence (AI) classifiers of the image of the blastocyst implanted later in order to predict the probability of achieving live birth. Methods A system for using the machine learning approaches, which are logistic regression, naive Bayes, nearest neighbors, random forest, neural network, and support vector machine, of artificial intelligence to predict the probability of live birth from a blastocyst image was developed. Eighty images of blastocysts that led to live births and 80 images of blastocysts that led to aneuploid miscarriages were used to create an AI‐based method with 5‐fold cross‐validation retrospectively for classifying embryos. Results The logistic regression method showed the best results. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.65, 0.60, 0.70, 0.67, and 0.64, respectively. Area under the curve was 0.65 ± 0.04 (mean ± SE). Estimated probability of belonging to the live birth category was found significantly related to the probability of live birth ( P < 0.005). Conclusions Classifiers using artificial intelligence applied toward a blastocyst image have a potential to show the probability of live birth being the outcome.

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Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease

Cited by 326 publications

References 46 publications

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

A practical computerized decision support system for predicting the severity of Alzheimer’s disease of an individual

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image

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