Detection of Alzheimer's Disease in Neuroimages using Vision Transformers: A Systematic Review and Meta-Analysis (Preprint) DOI Creative Commons
Vivens Mubonanyikuzo,

Hongjie Yan,

Temitope Emmanuel Komolafe

et al.

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 27, P. e62647 - e62647

Published: Nov. 30, 2024

Background Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications the detection diagnosis of AD. Objective This review systematically examines recent studies on application ViTs detecting AD, evaluating diagnostic accuracy impact network architecture model performance. Methods We conducted systematic search across major databases, including China National Knowledge Infrastructure, CENTRAL (Cochrane Central Register Controlled Trials), ScienceDirect, PubMed, Web Science, Scopus, covering publications from January 1, 2020, to March 2024. A manual was also performed include relevant gray literature. The included papers used ViT for AD versus healthy controls based neuroimaging data, magnetic resonance imaging positron emission tomography. Pooled estimates, sensitivity, specificity, likelihood ratios, odds were derived using random-effects models. Subgroup analyses comparing performance different architectures performed. Results meta-analysis, encompassing 11 95% CIs P values, demonstrated pooled accuracy: sensitivity 0.925 (95% CI 0.892-0.959; P<.01), specificity 0.957 0.932-0.981; positive ratio 21.84 12.26-38.91; negative 0.08 0.05-0.14; P<.01). area under curve notably high at 0.924. findings highlight effective tools early accurate diagnosis, offering insights future neuroimaging-based approaches. Conclusions provides valuable evidence utility distinguishing patients controls, thereby contributing advancements methodologies. Trial Registration PROSPERO CRD42024584347; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=584347

Language: Английский

Review on Pharmaceutical Industries Production of Medicines for Lung Cancer Diseases Prevention and Side Effects DataSets View and Analysis with Orange Software Visualization Techniques DOI Open Access

V. Srinivasan,

S. Soumya

International Journal of Management Technology and Social Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 17 - 44

Published: Jan. 30, 2025

Purpose: The purpose of this research is to explore how Orange, a powerful information extraction and predictive modeling software, can be applied in the pharmaceutical industry assess visualize effectiveness cancer prevention medicines. By focusing on companies like Genentech Inc. (USA), AstraZeneca Pharmaceutical PLC (UK), Boehringer Ingelheim (Germany), Chugai Co. Ltd. (Japan), study seeks evaluate which cancer-preventing drugs from these provide best efficacy while minimizing side effects for patients. goal assist healthcare professionals (doctors pharmacists) making informed decisions about most suitable medications prevention, ensuring patient safety optimal treatment outcomes. Design/Methodology/Approach: This utilizes Orange software’s machine learning data visualization tools, specifically scatterplot graphs, analyze complex datasets related drugs. using scatterplots concurrently examine multiple parameters, such as Company Name, Drug Class, Medicine Prevention Cancer Diseases, Side Effects Percentage aims identify patterns correlations that help drug safety. approach involves analyzing relationship between characteristics effects, providing actionable insights into different treatments might interact with health conditions. Findings/Results: findings suggest Orange’s visualizations valuable various medicines across companies. enabling simultaneous analysis software helps are effective preventing effects. provides clearer understanding characteristics, outcomes, supporting data-driven decision-making development practices. Originality/Value: originality lies application mining capabilities relationships within datasets. use efficacy, an innovative offers richer, more nuanced contributes optimizing choice strategies, ultimately improving therapeutic Paper Type: analytical paper applies techniques focuses tools extract interpret data, professionals.

Language: Английский

Citations

0
Digital Biomarkers for Parkinson's Disease: A Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait (Preprint) DOI Creative Commons
Wenhao Qi,

S. Shen,

Chaoqun Dong

et al.

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

Language: Английский

Citations

0
Detection of Alzheimer's Disease in Neuroimages using Vision Transformers: A Systematic Review and Meta-Analysis (Preprint) DOI Creative Commons
Vivens Mubonanyikuzo,

Hongjie Yan,

Temitope Emmanuel Komolafe

et al.

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 27, P. e62647 - e62647

Published: Nov. 30, 2024

Background Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications the detection diagnosis of AD. Objective This review systematically examines recent studies on application ViTs detecting AD, evaluating diagnostic accuracy impact network architecture model performance. Methods We conducted systematic search across major databases, including China National Knowledge Infrastructure, CENTRAL (Cochrane Central Register Controlled Trials), ScienceDirect, PubMed, Web Science, Scopus, covering publications from January 1, 2020, to March 2024. A manual was also performed include relevant gray literature. The included papers used ViT for AD versus healthy controls based neuroimaging data, magnetic resonance imaging positron emission tomography. Pooled estimates, sensitivity, specificity, likelihood ratios, odds were derived using random-effects models. Subgroup analyses comparing performance different architectures performed. Results meta-analysis, encompassing 11 95% CIs P values, demonstrated pooled accuracy: sensitivity 0.925 (95% CI 0.892-0.959; P<.01), specificity 0.957 0.932-0.981; positive ratio 21.84 12.26-38.91; negative 0.08 0.05-0.14; P<.01). area under curve notably high at 0.924. findings highlight effective tools early accurate diagnosis, offering insights future neuroimaging-based approaches. Conclusions provides valuable evidence utility distinguishing patients controls, thereby contributing advancements methodologies. Trial Registration PROSPERO CRD42024584347; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=584347

Language: Английский

Citations

0