Creutzfeldt-Jakob Disease Prediction Using Machine Learning Techniques

Bhakta, Arnav; Byrne, Carolyn

doi:10.1109/ichi52183.2021.00101

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…A popular variant of CJD is caused by consumption of cattle products contaminated with bovine spongiform encephalopathy (BSE) . With the use of elastic net regression, recurrent neural networks, and random forests , Bhakta and Byrne 2021 [ 56 ] learned the predictive causes of the CJD epidemic in the United States. Their results indicated that beer consumption, obesity, and tobacco use are strongly associated with CJD.…”

Section: Contact-based Zoonosesmentioning

confidence: 99%

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

2022

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Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.

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Section: Contact-based Zoonosesmentioning

confidence: 99%

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

2022

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Quantitative analysis of prion disease using an AI-powered digital pathology framework

Salvi,

Molinari,

Ciccarelli

et al. 2023

Sci Rep

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Prion disease is a fatal neurodegenerative disorder characterized by accumulation of an abnormal prion protein (PrPSc) in the central nervous system. To identify PrPSc aggregates for diagnostic purposes, pathologists use immunohistochemical staining of prion protein antibodies on tissue samples. With digital pathology, artificial intelligence can now analyze stained slides. In this study, we developed an automated pipeline for the identification of PrPSc aggregates in tissue samples from the cerebellar and occipital cortex. To the best of our knowledge, this is the first framework to evaluate PrPSc deposition in digital images. We used two strategies: a deep learning segmentation approach using a vision transformer, and a machine learning classification approach with traditional classifiers. Our method was developed and tested on 64 whole slide images from 41 patients definitively diagnosed with prion disease. The results of our study demonstrated that our proposed framework can accurately classify WSIs from a blind test set. Moreover, it can quantify PrPSc distribution and localization throughout the brain. This could potentially be extended to evaluate protein expression in other neurodegenerative diseases like Alzheimer's and Parkinson's. Overall, our pipeline highlights the potential of AI-assisted pathology to provide valuable insights, leading to improved diagnostic accuracy and efficiency.

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Creutzfeldt-Jakob Disease Prediction Using Machine Learning Techniques

Cited by 2 publications

References 13 publications

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Artificial Intelligence Models for Zoonotic Pathogens: A Survey

Quantitative analysis of prion disease using an AI-powered digital pathology framework

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