Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges DOI Open Access
Sheerin Zadoo, Yashwant Singh, Pradeep Kumar Singh

et al.

International Journal on Smart Sensing and Intelligent Systems, Journal Year: 2024, Volume and Issue: 17(1)

Published: Jan. 1, 2024

Abstract Parkinson's disease (PsD) is a prevalent neurodegenerative malady, which keeps intensifying with age. It acquired by the progressive demise of dopaminergic neurons existing in substantia nigra pars compacta region human brain. In absence single accurate test, and due to dependency on doctors, intensive research being carried out automate early detection predict severity also. this study, detailed review various artificial intelligence (AI) models applied different datasets across modalities has been presented. The emotional (EI) modality, can be used for help maintaining comfortable lifestyle, identified. EI predominant, emerging technology that detect PsD at initial stages enhance socialization patients their attendants. Challenges possibilities assist bridging differences between fast-growing technologies meant actual implementation automated model are presented research. This highlights prominence using support vector machine (SVM) classifier achieving an accuracy about 99% many such as magnetic resonance imaging (MRI), speech, electroencephalogram (EEG). A 100% achieved EEG handwriting modality convolutional neural network (CNN) optimized crow search algorithm (OCSA), respectively. Also, 95% progression Bagged Tree, (ANN), SVM. maximum attained K-nearest Neighbors (KNN) Naïve Bayes classifiers signals EI. most widely dataset identified Progression Markers Initiative (PPMI) database.

Language: Английский

Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice DOI Creative Commons
Hamid Reza Saeidnia, Faezeh Firuzpour, Marcin Kozak

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Jan. 25, 2025

Language: Английский

Citations

4
Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation DOI Creative Commons
Runqiu Huang, Xiaolin Meng, Xiaoxuan Zhang

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 6, 2025

Abstract Artificial intelligence (AI) is rapidly advancing, yet its applications in radiology remain relatively nascent. From a spatiotemporal perspective, this review examines the forces driving AI development and integration with medicine radiology, particular focus on advancements addressing major diseases that significantly threaten human health. Temporally, advent of foundational model architectures, combined underlying drivers development, accelerating progress interventions their practical applications. Spatially, discussion explores potential evolving methodologies to strengthen interdisciplinary within medicine, emphasizing four critical points imaging process, as well application disease management, including emergence commercial products. Additionally, current utilization deep learning reviewed, future through multimodal foundation models Generative Pre‐trained Transformer are anticipated.

Language: Английский

Citations

2
AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review DOI Creative Commons
Aanuoluwapo Clement David-Olawade, David B. Olawade, Laura Vanderbloemen

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(6), P. 689 - 689

Published: March 11, 2025

The widespread use of medical imaging techniques such as X-rays and computed tomography (CT) has raised significant concerns regarding ionizing radiation exposure, particularly among vulnerable populations requiring frequent imaging. Achieving a balance between high-quality diagnostic minimizing exposure remains fundamental challenge in radiology. Artificial intelligence (AI) emerged transformative solution, enabling low-dose protocols that enhance image quality while significantly reducing doses. This review explores the role AI-assisted imaging, CT, X-ray, magnetic resonance (MRI), highlighting advancements deep learning models, convolutional neural networks (CNNs), other AI-based approaches. These technologies have demonstrated substantial improvements noise reduction, artifact removal, real-time optimization parameters, thereby enhancing accuracy mitigating risks. Additionally, AI contributed to improved radiology workflow efficiency cost reduction by need for repeat scans. also discusses emerging directions AI-driven including hybrid systems integrate post-processing with data acquisition, personalized tailored patient characteristics, expansion applications fluoroscopy positron emission (PET). However, challenges model generalizability, regulatory constraints, ethical considerations, computational requirements must be addressed facilitate broader clinical adoption. potential revolutionize safety, optimizing quality, improving healthcare efficiency, paving way more advanced sustainable future

Language: Английский

Citations

2
The Potential of cfDNA as Biomarker: Opportunities and Challenges for Neurodegenerative Diseases DOI Creative Commons
Şeyma Aydın, Selçuk Özdemir, Ahmet Adıgüzel

et al.

Journal of Molecular Neuroscience, Journal Year: 2025, Volume and Issue: 75(1)

Published: March 13, 2025

Abstract Neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s (PD), multiple sclerosis (MS), and amyotrophic lateral (ALS), are characterized by the progressive gradual degeneration of neurons. The prevalence rates these disorders rise significantly with age. As life spans continue to increase in many countries, number cases is expected grow foreseeable future. Early precise diagnosis, along appropriate surveillance, continues pose a challenge. high heterogeneity neurodegenerative diseases calls for more accurate definitive biomarkers improve clinical therapy. Cell-free DNA (cfDNA), fragmented released into bodily fluids via apoptosis, necrosis, or active secretion, has emerged as promising non-invasive diagnostic tool various diseases. cfDNA can serve an indicator ongoing cellular damage mortality, neuronal loss, may provide valuable insights processes, progression, therapeutic responses. This review will first cover key aspects then examine recent advances its potential use biomarker disorders.

Language: Английский

Citations

1
Brain tumour detection using machine and deep learning: a systematic review DOI
Novsheena Rasool, Javaid Iqbal Bhat

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 23, 2024

Language: Английский

Citations

6
Enhanced Colon Cancer Segmentation and Image Synthesis through Advanced Generative Adversarial Networks based-Sine Cosine Algorithm DOI Creative Commons

Alawi Alqushaibi,

Mohd Hilmi Hasan, Said Jadid Abdulkadir

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 105354 - 105369

Published: Jan. 1, 2024

Colorectal cancer (CRC) is a prevalent and life-threatening malignancy, demanding early diagnosis effective treatment for improved patient outcomes. Accurate segmentation of colon in medical images challenging task due to the complexity its morphology limited annotated data availability. This paper presents an efficient approach image synthesis, combining Attention U-Net Pix2Pix Generative Adversarial Network (Pix2Pix-GAN) guided by Sine Cosine Algorithm (SCA) hyperparameter tuning within GAN framework. The utilization SCA plays pivotal role optimizing delicate balance between generator discriminator dynamics, resulting enhanced convergence stability. Our method achieved state-of-the-art results with mean Dice score 0.9514, Intersection over Union 0.9123, F beta 0.9636, similarity index 0.9430 outperforming existing methods. Moreover, Mean Absolute Error reached minimal value 0.01583. proposed shows promise enhancing accuracy robustness which could lead better cancer.

Language: Английский

Citations

5
Data-driven classification and explainable-AI in the field of lung imaging DOI Creative Commons
Syed Taimoor Hussain Shah, Syed Adil Hussain Shah,

Iqra Iqbal Khan

et al.

Frontiers in Big Data, Journal Year: 2024, Volume and Issue: 7

Published: Sept. 19, 2024

Detecting lung diseases in medical images can be quite challenging for radiologists. In some cases, even experienced experts may struggle with accurately diagnosing chest diseases, leading to potential inaccuracies due complex or unseen biomarkers. This review paper delves into various datasets and machine learning techniques employed recent research disease classification, focusing on pneumonia analysis using X-ray images. We explore conventional methods, pretrained deep models, customized convolutional neural networks (CNNs), ensemble methods. A comprehensive comparison of different classification approaches is presented, encompassing data acquisition, preprocessing, feature extraction, vision, learning, explainable-AI (XAI). Our highlights the superior performance transfer learning-based methods CNNs models/features classification. addition, our offers insights researchers other domains too who utilize radiological By providing a thorough overview techniques, work enables establishment effective strategies identification suitable wide range challenges. Currently, beyond traditional evaluation metrics, emphasize importance XAI models their applications tasks. incorporation helps gaining deeper understanding decision-making processes, improved trust, transparency, overall clinical decision-making. serves as valuable resource practitioners seeking not only advance field detection but also from diverse domains.

Language: Английский

Citations

4
A database of magnetic resonance imaging‐transcranial ultrasound co‐registration DOI Creative Commons
Maryam Alizadeh, D. Louis Collins, Marta Kersten‐Oertel

et al.

Medical Physics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 7, 2025

As a portable and cost-effective imaging modality with better accessibility than Magnetic Resonance Imaging (MRI), transcranial sonography (TCS) has demonstrated its flexibility potential utility in various clinical diagnostic applications, including Parkinson's disease cerebrovascular conditions. To understand the information TCS for data analysis acquisition, MRI can provide guidance efficient neuronavigation systems confirmation of disease-related abnormality. In these cases, MRI-TCS co-registration is crucial, but relevant public databases are scarce to help develop related algorithms software systems. This dataset comprises manually registered ultrasound volumes from eight healthy subjects. Three raters each subject's scans, based on visual inspection image feature correspondence. Average transformation matrices were computed all raters' alignments subject. Inter- intra-rater variability transformations conducted by presented validate accuracy consistency manual registration. addition, population-averaged brain vascular atlas provided facilitate development computer-assisted acquisition software. The both NIFTI MINC formats publicly available OSF repository: https://osf.io/zdcjb/. provides first resource assessment registration ground truths, as well resources establishing TCS. These technical advancements could greatly boost an tool applications diagnosis neurological conditions such disorders.

Language: Английский

Citations

0
Diagnostic accuracy of ultrasound in hyperthyroidism: A comprehensive review of recent studies DOI

Dawei Wang,

Chao Xie,

Xuena Zheng

et al.

Journal of Radiation Research and Applied Sciences, Journal Year: 2025, Volume and Issue: 18(2), P. 101370 - 101370

Published: Feb. 26, 2025

Language: Английский

Citations

0
MCU-Net: A multi-prior collaborative deep unfolding network with gates-controlled spatial attention for accelerated MRI reconstruction DOI
Xiaoyu Qiao, Weisheng Li, Guofen Wang

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: 633, P. 129771 - 129771

Published: Feb. 26, 2025

Language: Английский

Citations

0