Early Prediction of Alzheimer’s Disease Progression Using Variational Autoencoders

Basu, Sayantani; Wagstyl, Konrad; Zandifar, Azar; Collins, D. Louis; Romero, Adriana; Precup, Doina

doi:10.1007/978-3-030-32251-9_23

Cited by 17 publications

(12 citation statements)

References 22 publications

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“…Traditional Caroli and Frisoni, 2010;Jack et al, 2011Jack et al, , 2012 Machine learning Generative Fonteijn et al, 2012;Chen et al, 2016;Khanna et al, 2018;Oxtoby et al, 2018;Basu et al, 2019;De Jong et al, 2019;Gootjes-Dreesbach et al, 2019;Martinez-Murcia et al, 2019 Discriminative Supervised Hinrichs et al, 2010;Magnin et al, 2010;Rao et al, 2011;Zhang et al, 2011;Da et al, 2013;Li et al, 2013Unsupervised Nettiksimmons et al, 2014Gamberger et al, 2017;Toschi et al, 2019 We subdivide data-driven integrative AD models which into two subgroups. While the first group uses simple statistical approaches (e.g., simple linear models), the second group uses more advanced techniques (e.g., machine learning).…”

Section: Data-driven Integrative Ad Models Referencesmentioning

confidence: 99%

“…In essence, an autoencoder is a neural network that aims to encode the input data into a lower dimensional representation and from that decode it again, reconstructing the original input. It has successfully been applied for different tasks on AD cohorts (Basu et al, 2019;Martinez-Murcia et al, 2019). The two main applications of this approach in the field consist of classifying patients based on AD diagnosis (Basu et al, 2019) and clustering of patient trajectories into subgroups (De Jong et al, 2019).…”

Section: Generative Modelsmentioning

confidence: 99%

“…It has successfully been applied for different tasks on AD cohorts (Basu et al, 2019;Martinez-Murcia et al, 2019). The two main applications of this approach in the field consist of classifying patients based on AD diagnosis (Basu et al, 2019) and clustering of patient trajectories into subgroups (De Jong et al, 2019). These strategies are especially interesting for patient classification and stratification tasks in datasets where information is sparse.…”

Section: Generative Modelsmentioning

confidence: 99%

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Challenges of Integrative Disease Modeling in Alzheimer's Disease

Khatami

Robinson

Birkenbihl

et al. 2020

Front. Mol. Biosci.

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Dementia-related diseases like Alzheimer's Disease (AD) have a tremendous social and economic cost. A deeper understanding of its underlying pathophysiologies may provide an opportunity for earlier detection and therapeutic intervention. Previous approaches for characterizing AD were targeted at single aspects of the disease. Yet, due to the complex nature of AD, the success of these approaches was limited. However, in recent years, advancements in integrative disease modeling, built on a wide range of AD biomarkers, have taken a global view on the disease, facilitating more comprehensive analysis and interpretation. Integrative AD models can be sorted in two primary types, namely hypothetical models and data-driven models. The latter group split into two subgroups: (i) Models that use traditional statistical methods such as linear models, (ii) Models that take advantage of more advanced artificial intelligence approaches such as machine learning. While many integrative AD models have been published over the last decade, their impact on clinical practice is limited. There exist major challenges in the course of integrative AD modeling, namely data missingness and censoring, imprecise human-involved priori knowledge, model reproducibility, dataset interoperability, dataset integration, and model interpretability. In this review, we highlight recent advancements and future possibilities of integrative modeling in the field of AD research, showcase and discuss the limitations and challenges involved, and finally, propose avenues to address several of these challenges.

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Section: Data-driven Integrative Ad Models Referencesmentioning

confidence: 99%

Section: Generative Modelsmentioning

confidence: 99%

Section: Generative Modelsmentioning

confidence: 99%

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Challenges of Integrative Disease Modeling in Alzheimer's Disease

Khatami

Robinson

Birkenbihl

et al. 2020

Front. Mol. Biosci.

View full text Add to dashboard Cite

show abstract

“…Based on CNNs, more advanced neural networks have been proposed such as fully convolution network (FCN) [15], generative adversarial network (GAN) [16], and variational auto-encoder (VAE) [17], and so on. Many researchers applied these neural networks in medical fields and achieved promising results in identifying bone fracture [18] and tumor region [19], screening high-risk subjects of a specific disease [20], locating biomarkers [21], segmenting tissues and organs [22,23,24,25], and so on.…”

Section: Introductionmentioning

confidence: 99%

An Artificial Intelligence Pipeline for Diagnosing Hepatocellular Carcinoma Patients with Bile Duct Tumor Thrombus

Liu

Bai

et al. 2021

Preprint

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Background and purpose: Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. The preoperative diagnosis of BDTT is usually based on identification of dilated bile ducts (DBDs) on medical images (eg., CT and MRI images). However, it is easy for doctors to ignore DBDs when reporting the imaging scan result, leading to a high misdiagnosis rate in practice. The aim of the present study was to develop an artificial intelligence (AI) pipeline for diagnosing HCC patients with BDTT using medical images. Methods: The proposed AI pipeline included two stages. First, the object detection neural network Faster R-CNN was adopted to identify DBDs; then, an HCC patient was diagnosed to have BDTT if the proportion of images with at least one identified DBD exceeds some threshold value. Four-fold cross validation was used to evaluate the performance of the proposed AI pipeline. Results: The proposed AI pipeline was applied on a real dataset consisting of CT images collected from 34 HCC patients (16 with BDTT and 18 without BDTT). The average true positive rate for identifying DBDs per patient was 0.92, while the patient-level true positive rate for diagnosing BDTT was 0.94. The AUC value of patient-level diagnosis of BDTT was 0.92 (95% CI: 0.83, 1.00), compared with 0.71 (95% CI: 0.52, 0.89) by random forest. Conclusions: This study first proposes an AI pipeline to identify DBDs and diagnose BDTT, and the high accuracies demonstrate that it is successful in the diagnosis of BDTT.

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“…Based on CNNs, more advanced neural networks have been proposed such as fully convolution network (FCN) 15 , generative adversarial network (GAN) 16 , and variational auto-encoder (VAE) 17 , and so on. Many researchers applied these neural networks in medical fields and achieved promising results in identifying bone fracture 18 and tumor region 19 , screening high-risk subjects of a specific disease 20 , locating biomarkers 21 , segmenting tissues and organs [22][23][24][25] , and so on.…”

Section: Introductionmentioning

confidence: 99%

Preoperative Diagnosis of Hepatocellular Carcinoma Patients with Bile Duct Tumor Thrombus using Deep Learning Method

Liu

Bai

et al. 2021

Preprint

View full text Add to dashboard Cite

Preoperative diagnosis of bile duct tumor thrombus (BDTT) is clinically important as the surgical prognosis of hepatocellular carcinoma (HCC) patients with BDTT is significantly different from that of patients without BDTT. The current diagnosis of BDTT is usually based on identifying dilated bile ducts (DBDs) on medical images (eg., CT and MRI images). However, it is easy for doctors to ignore DBDs when reporting imaging scan results, leading to a high misdiagnosis rate in practice. The aim of the present study was to develop an artificial intelligence (AI) pipeline for diagnosing HCC patients with BDTT using medical images. The proposed AI pipeline includes two stages. First, the object detection neural network Faster R-CNN is adopted to identify DBDs; then, an HCC patient is diagnosed to have BDTT if the proportion of images with at least one identified DBD exceeds some threshold value. The proposed AI pipeline was applied to a real dataset consisting of 2,611 CT images collected from 34 HCC patients (16 with BDTT and 18 without BDTT). The average true positive rate for identifying DBDs per patient was 0.92, while the patient-level true positive rate for diagnosing BDTT was 0.94. The area under ROC curve for patient-level diagnosis of BDTT was 0.92 (95% CI: 0.83, 1.00), compared with 0.71 (95% CI: 0.52, 0.89) by random forest based on preoperative clinical variables. These results demonstrated that the proposed AI pipeline is successful in the diagnosis of BDTT. The automatic detection of DBDs is a key step in early diagnosis of HCC patients with BDTT, and is helpful in the treatment and prognosis of these patients.

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Early Prediction of Alzheimer’s Disease Progression Using Variational Autoencoders

Cited by 17 publications

References 22 publications

Challenges of Integrative Disease Modeling in Alzheimer's Disease

Challenges of Integrative Disease Modeling in Alzheimer's Disease

An Artificial Intelligence Pipeline for Diagnosing Hepatocellular Carcinoma Patients with Bile Duct Tumor Thrombus

Preoperative Diagnosis of Hepatocellular Carcinoma Patients with Bile Duct Tumor Thrombus using Deep Learning Method

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