Artificial Intelligence With Neural Network Algorithms in Pediatric Astrocytoma Diagnosis: A Systematic Review DOI Creative Commons
Floresya K. Farmawati, Della W.A. Nurwakhid, Tifani Antonia Pradhea

et al.

Innovative medicine of Kuban, Journal Year: 2025, Volume and Issue: 10(1), P. 93 - 100

Published: Feb. 26, 2025

Background: Astrocytoma is a common pediatric brain tumor that poses significant health burden. Recent advancements in artificial intelligence (AI), particularly neural network algorithms, have been studied for their precision and efficiency medical diagnostics via effectively analyzing imaging data to identify patterns anomalies. Objective: To systematically review AI-based diagnostic tools with algorithms’ methodologies, sensitivities, specificities, potential clinical integration astrocytoma, providing consolidated perspective on overall performance impact decision-making. Methods: As per PRISMA 2020 guidelines, we conducted comprehensive search PubMed, Scopus, ScienceDirect February 5, 2024. The strategy was guided by PECO question focusing astrocytoma diagnosis using AI algorithms vs computed tomography or magnetic resonance (MRI). Keywords were terms related algorithms. We included studies the accuracy of methods cases (World Health Organization grades 1-3), no restrictions publication year country. excluded papers written languages other than English Bahasa Indonesia nonhuman studies. Data assessed Effective Public Practice Project tool. Results: Of 454 articles screened, 6 met inclusion criteria. These varied design, location, sample size, ranging from 10 135 subjects. showed high sensitivity specificity, often surpassing traditional radiological techniques. Notably, 3-dimensional MRI demonstrated improved compared 2-dimensional (96% 77%). models exhibited levels comparable exceeding expert radiologists, metrics such as classification 92% values area under receiver operating characteristic curve. Conclusions: shows promise enhancing diagnosis. reviewed indicate these advanced can achieve superior specificity conventional Integrating into practice could substantially improve patient outcomes.

Language: Английский

AI-Driven Innovations in Alzheimer's Disease: Integrating Early Diagnosis, Personalized Treatment, and Prognostic Modelling DOI
Mayur B. Kale, Nitu L. Wankhede,

Rupali S. Pawar

et al.

Ageing Research Reviews, Journal Year: 2024, Volume and Issue: unknown, P. 102497 - 102497

Published: Sept. 1, 2024

Language: Английский

Citations

30
Emerging role of immunogenic cell death in cancer immunotherapy: Advancing next-generation CAR-T cell immunotherapy by combination DOI
Zhaokai Zhou, Yumiao Mai, Ge Zhang

et al.

Cancer Letters, Journal Year: 2024, Volume and Issue: 598, P. 217079 - 217079

Published: June 25, 2024

Language: Английский

Citations

21
Deep Convolutional Neural Networks in Medical Image Analysis: A Review DOI Creative Commons
Ibomoiye Domor Mienye, Theo G. Swart, George Obaido

et al.

Information, Journal Year: 2025, Volume and Issue: 16(3), P. 195 - 195

Published: March 3, 2025

Deep convolutional neural networks (CNNs) have revolutionized medical image analysis by enabling the automated learning of hierarchical features from complex imaging datasets. This review provides a focused CNN evolution and architectures as applied to analysis, highlighting their application performance in different fields, including oncology, neurology, cardiology, pulmonology, ophthalmology, dermatology, orthopedics. The paper also explores challenges specific outlines trends future research directions. aims serve valuable resource for researchers practitioners healthcare artificial intelligence.

Language: Английский

Citations

4
Accessible AI Diagnostics and Lightweight Brain Tumor Detection on Medical Edge Devices DOI Creative Commons
Akmalbek Abdusalomov, Sanjar Mirzakhalilov, Sabina Umirzakova

et al.

Bioengineering, Journal Year: 2025, Volume and Issue: 12(1), P. 62 - 62

Published: Jan. 13, 2025

The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight efficient RetinaNet variant tailored edge device deployment. model reduces computational overhead while maintaining high accuracy by replacing the computationally intensive ResNet backbone with MobileNet leveraging depthwise separable convolutions. modified achieves an average precision (AP) 32.1, surpassing state-of-the-art models small tumor (APS: 14.3) large localization (APL: 49.7). Furthermore, significantly costs, making real-time analysis feasible on low-power hardware. Clinical relevance key focus this work. proposed addresses diagnostic challenges small, variable-sized often overlooked existing methods. Its architecture enables portable devices, bridging gap accessibility underserved regions. Extensive experiments BRATS dataset demonstrate robustness across sizes configurations, confidence scores consistently exceeding 81%. advancement holds potential improving early detection, particularly remote areas lacking advanced infrastructure, thereby contributing to better patient outcomes broader AI-driven tools.

Language: Английский

Citations

3
The effects of artificial intelligence on human resource activities and the roles of the human resource triad: opportunities and challenges DOI Creative Commons
Justine Dima, Marie‐Hélène Gilbert, Julie Dextras-Gauthier

et al.

Frontiers in Psychology, Journal Year: 2024, Volume and Issue: 15

Published: June 3, 2024

This study analyzes the existing academic literature to identify effects of artificial intelligence (AI) on human resource (HR) activities, highlighting both opportunities and associated challenges, roles employees, line managers, HR professionals, collectively referred as triad.

Language: Английский

Citations

14
Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment DOI Open Access

John Rafanan,

Nabih Ghani, Sarah Kazemeini

et al.

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 917 - 917

Published: Jan. 22, 2025

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among most challenging malignancies due to their high mortality rates complex neurological effects. Despite advancements surgery chemoradiotherapy, prognosis for glioblastoma multiforme (GBM) metastases remains poor, underscoring need innovative diagnostic strategies. This review highlights recent imaging techniques, liquid biopsies, artificial intelligence (AI) applications addressing current challenges. Advanced including diffusion tensor (DTI) magnetic resonance spectroscopy (MRS), improve differentiation tumor progression from treatment-related changes. Additionally, novel positron emission tomography (PET) radiotracers, such as 18F-fluoropivalate, 18F-fluoroethyltyrosine, 18F-fluluciclovine, facilitate metabolic profiling high-grade gliomas. Liquid biopsy, a minimally invasive technique, enables real-time monitoring biomarkers circulating DNA (ctDNA), extracellular vesicles (EVs), cells (CTCs), tumor-educated platelets (TEPs), enhancing precision. AI-driven algorithms, convolutional neural networks, integrate tools accuracy, reduce interobserver variability, accelerate clinical decision-making. These innovations advance personalized neuro-oncological care, offering new opportunities outcomes patients with central nervous system tumors. We advocate future research integrating these into workflows, accessibility challenges, standardizing methodologies ensure broad applicability neuro-oncology.

Language: Английский

Citations

2
Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook DOI Creative Commons
Bassam Abdul Rasool Hassan, Ali Haider Mohammed, Souheil Hallit

et al.

Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15

Published: Feb. 4, 2025

Background Artificial intelligence (AI) has emerged as a transformative tool in oncology, offering promising applications chemotherapy development, cancer diagnosis, and predicting response. Despite its potential, debates persist regarding the predictive accuracy of AI technologies, particularly machine learning (ML) deep (DL). Objective This review aims to explore role forecasting outcomes related treatment response, synthesizing current advancements identifying critical gaps field. Methods A comprehensive literature search was conducted across PubMed, Embase, Web Science, Cochrane databases up 2023. Keywords included “Artificial Intelligence (AI),” “Machine Learning (ML),” “Deep (DL)” combined with “chemotherapy development,” “cancer diagnosis,” treatment.” Articles published within last four years written English were included. The Prediction Model Risk Bias Assessment utilized assess risk bias selected studies. Conclusion underscores substantial impact AI, including ML DL, on innovation, response for both solid hematological tumors. Evidence from recent studies highlights AI’s potential reduce cancer-related mortality by optimizing diagnostic accuracy, personalizing plans, improving therapeutic outcomes. Future research should focus addressing challenges clinical implementation, ethical considerations, scalability enhance integration into oncology care.

Language: Английский

Citations

2
Decoding the black box: Explainable AI (XAI) for cancer diagnosis, prognosis, and treatment planning-A state-of-the art systematic review DOI

Youssef Alaaeldin Ali Mohamed,

Bee Luan Khoo,

Mohd Shahrimie Mohd Asaari

et al.

International Journal of Medical Informatics, Journal Year: 2024, Volume and Issue: 193, P. 105689 - 105689

Published: Nov. 4, 2024

Language: Английский

Citations

9
Diagnosing Progression in Glioblastoma—Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma DOI Creative Commons
Mason J. Belue, Stephanie A. Harmon, Shreya Chappidi

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 14(13), P. 1374 - 1374

Published: June 28, 2024

Glioblastoma (GBM) is the most aggressive and common primary brain tumor, defined by nearly uniform rapid progression despite current standard of care involving maximal surgical resection followed radiation therapy (RT) temozolomide (TMZ) or concurrent chemoirradiation (CRT), with an overall survival (OS) less than 30% at 2 years. The diagnosis tumor in clinic based on clinical assessment interpretation MRI using Response Assessment Neuro-Oncology (RANO) criteria, which suffers from several limitations including a paucity precise measures progression. Given that imaging modality generates quantitative data capable capturing change over time for GBM, this renders it pivotal optimizing advancing response particularly given lack biomarkers space. In study, we employed artificial intelligence (AI)-derived volumetric parameters segmentation mask output nnU-Net to arrive four classes (background, edema, non-contrast enhancing (NET), contrast-enhancing (CET)) determine if dynamic changes AI volumes detected throughout can be linked PFS features. We identified associations between MR AI-generated independently location, MGMT methylation status, extent while validating CET edema are patient subpopulations separated district rates disease. study provides valuable insights risk stratification, future RT treatment planning, monitoring neuro-oncology.

Language: Английский

Citations

7
Machine Learning and Artificial Intelligence Systems Based on the Optical Spectral Analysis in Neuro-Oncology DOI Creative Commons
Tatiana A. Savelieva, I.D. Romanishkin, A. Ospanov

et al.

Photonics, Journal Year: 2025, Volume and Issue: 12(1), P. 37 - 37

Published: Jan. 4, 2025

Decision support systems based on machine learning (ML) techniques are already empowering neuro-oncologists. These provide comprehensive diagnostics, offer a deeper understanding of diseases, predict outcomes, and assist in customizing treatment plans to individual patient needs. Collectively, these elements represent artificial intelligence (AI) neuro-oncology. This paper reviews recent studies which apply algorithms optical spectroscopy data from central nervous system (CNS) tumors, both ex vivo vivo. We first cover general issues such as the physical basis optical-spectral methods used neuro-oncology, basic spectral signal preprocessing, feature extraction, clustering, supervised classification methods. Then, we review more detail methodology results applying ML fluorescence, elastic inelastic scattering, IR spectroscopy.

Language: Английский

Citations

1