Early detection of Alzheimer disease using Gadolinium material

Soundarya, S.; Sruthi, M. S.; Bama, S. Sathya; Kiruthika, S.; Dhiyaneswaran, J.

doi:10.1016/j.matpr.2020.03.189

Cited by 13 publications

(6 citation statements)

References 16 publications

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“…The results acquired showed the accuracy by 90% on using the SCRL dataset. Soundarya et al ( 2020 ) proposed the methodology in which description of shrink brain tissue is used for the ancient analysis of Alzheimer’s disease. They have implemented various machine and deep learning algorithms.…”

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

411

173

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Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

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Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

411

173

View full text Add to dashboard Cite

show abstract

“…There are various gadolinium complexes ( Figure 29 ) [ 16 , 83 , 196 ] and manganese-based [ 197 ] contrast agents that have been applied to visualize different amyloidoses, including AD, in humans and animals, but do not have an affinity to the fibrillar structures. These agents are hydrophilic and distribute in the tissues surrounding the plaques, but cannot penetrate the hydrophobic amyloid deposits.…”

Section: Molecular Design Of Mri Fibril-binding Probesmentioning

confidence: 99%

Molecular Design of Magnetic Resonance Imaging Agents Binding to Amyloid Deposits

Nikiforova

Sedov

2023

IJMS

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The ability to detect and monitor amyloid deposition in the brain using non-invasive imaging techniques provides valuable insights into the early diagnosis and progression of Alzheimer’s disease and helps to evaluate the efficacy of potential treatments. Magnetic resonance imaging (MRI) is a widely available technique offering high-spatial-resolution imaging. It can be used to visualize amyloid deposits with the help of amyloid-binding diagnostic agents injected into the body. In recent years, a number of amyloid-targeted MRI probes have been developed, but none of them has entered clinical practice. We review the advances in the field and deduce the requirements for the molecular structure and properties of a diagnostic probe candidate. These requirements make up the base for the rational design of MRI-active small molecules targeting amyloid deposits. Particular attention is paid to the novel cryo-EM structures of the fibril aggregates and their complexes, with known binders offering the possibility to use computational structure-based design methods. With continued research and development, MRI probes may revolutionize the diagnosis and treatment of neurodegenerative diseases, ultimately improving the lives of millions of people worldwide.

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“…Hidden layer 2 has 400 epochs and provides 88.6% accuracy, surpassing machine learning models such as Decision Tree, K-Nearest Neighbor (KNN), Random Forest, Logistic Regression, SVM, etc. In 2021, Soundarya et al [ 13 ] used ANN to compare with machine learning models to detect Alzheimer’s Disease (AD) and found that ANN achieved the highest accuracy with sufficient data. Pasha et al [ 14 ] used ANN to improve the prediction accuracy of cardiovascular disease.…”

Section: Structured Data Algorithmsmentioning

confidence: 99%

Popular deep learning algorithms for disease prediction: a review

Wang

Wan

et al. 2022

Cluster Comput

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Due to its automatic feature learning ability and high performance, deep learning has gradually become the mainstream of artificial intelligence in recent years, playing a role in many fields. Especially in the medical field, the accuracy rate of deep learning even exceeds that of doctors. This paper introduces several deep learning algorithms: Artificial Neural Network (NN), FM-Deep Learning, Convolutional NN and Recurrent NN, and expounds their theory, development history and applications in disease prediction; we analyze the defects in the current disease prediction field and give some current solutions; our paper expounds the two major trends in the future disease prediction and medical field—integrating Digital Twins and promoting precision medicine. This study can better inspire relevant researchers, so that they can use this article to understand related disease prediction algorithms and then make better related research.

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Early detection of Alzheimer disease using Gadolinium material

Cited by 13 publications

References 16 publications

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Molecular Design of Magnetic Resonance Imaging Agents Binding to Amyloid Deposits

Popular deep learning algorithms for disease prediction: a review

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