Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review

Tomography, Journal Year: 2024, Volume and Issue: 10(8), P. 1238 - 1262

Published: Aug. 12, 2024

The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) deep (DL) integration has transformed field, providing advanced models for brain age estimation. However, achieving precise prediction across all ages remains significant analytical challenge. This comprehensive review scrutinizes advancements in ML- DL-based prediction, analyzing 52 peer-reviewed studies 2020 to 2024. It assesses various model architectures, highlighting their effectiveness nuances lifespan studies. By comparing ML DL, strengths forecasting methodological limitations are revealed. Finally, key findings reviewed articles summarized number major issues related ML/DL-based discussed. Through this study, we aim at synthesis current state emphasizing both persistent challenges, guiding future research, technological advancements, improving early intervention strategies diseases.

Language: Английский

LSTGINet: Local Attention Spatio-Temporal Graph Inference Network for Age Prediction

Algorithms, Journal Year: 2025, Volume and Issue: 18(3), P. 138 - 138

Published: March 3, 2025

There is a close correlation between brain aging and age. However, traditional neural networks cannot fully capture the potential age due to limited receptive field. Furthermore, they are more concerned with deep spatial semantics, ignoring fact that effective temporal information can enrich representation of low-level semantics. To address these limitations, local attention spatio-temporal graph inference network (LSTGINet) was developed explore details association aging, taking into account both perspectives. First, multi-scale branches used increase field model simultaneously, achieving perception static correlation. Second, feature graphs reconstructed, large topographies constructed. The node aggregation transfer functions hidden dynamic A new module embedded in component global context establish dependencies interactivity different features, balance differences distribution We use newly designed weighted loss function supervise learning entire prediction framework strengthen process final experimental results show MAE on baseline datasets such as CamCAN NKI 6.33 6.28, respectively, better than current state-of-the-art methods, provides basis for assessing state adults.

Language: Английский

Enhanced Data Mining and Visualization of Sensory-Graph-Modeled Datasets through Summarization

Sensors, Journal Year: 2024, Volume and Issue: 24(14), P. 4554 - 4554

Published: July 14, 2024

The acquisition, processing, mining, and visualization of sensory data for knowledge discovery decision support has recently been a popular area research exploration. Its usefulness is paramount because its relationship to the continuous involvement in improvement healthcare other related disciplines. As result this, huge amount have collected analyzed. These are made available community various shapes formats; their representation study form graphs or networks also an which many scholars focused on. However, large size such graph datasets poses challenges mining visualization. For example, from Bio–Mouse–Gene dataset, over 43 thousand nodes 14.5 million edges, non-trivial job. In this regard, summarizing provided useful alternative. Graph summarization aims provide efficient analysis complex large-sized data; hence, it beneficial approach. During summarization, all that similar structural properties merged together. doing so, traditional methods often overlook importance personalizing summary, would be helpful highlighting certain targeted nodes. Personalized context-specific scenarios require more tailored approach accurately capturing distinct patterns trends. Hence, concept personalized acquire concise depiction graph, emphasizing connections closer proximity specific set given target paper, we present faster algorithm (PGS) problem, named IPGS; designed facilitate enhanced effective domains, including biosensors. Our objective obtain compression ratio as one by state-of-the-art PGS algorithm, but manner. To achieve improve execution time current using weighted, locality-sensitive hashing, through experiments on eight publicly datasets. demonstrate effectiveness scalability IPGS while providing way, our contributes perspective summarization. We presented detailed was conducted investigate domain

Language: Английский