Editorial: Machine learning algorithms for brain imaging: new frontiers in neurodiagnostics and treatment
Abstract
The field of neuroimaging has undergone profound transformation in recent years, driven primarily by rapid 3 advances in machine learning (ML), and especially deep learning (DL), techniques. These computational 4 innovations have been amplified by ongoing improvements in brain imaging modalities, such as structural 5 MRI (sMRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography 6 (PET), which now deliver increasingly high-resolution, multimodal views of brain structure, function, 7 connectivity, and metabolism.The intersection of neuroimaging and machine learning marks an inflection point in neuroscience; 9 a confluence where vast, high-dimensional brain data meet computational power capable of turning 10 complexity into clinically actionable insights. This convergence has led to the rapidly expanding domain of 11 machine learning for neuroimaging, inviting a new generation of studies that leverage ML to transcend the 12 limitations of traditional neuroimaging analysis.Traditional statistical approaches and model-based techniques often struggle with the defining features 14 of modern neuroimaging datasets: extreme high dimensionality, substantial heterogeneity across 15 subjects/scanners/protocols, nonlinear patterns, and large-scale "big data" from consortia and multimodal 16 fusion. In contrast, ML/DL paradigms provide powerful approaches to address the aforementioned 17 challenges by virtue of their ability to learn complex,…
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Evidence Hub reads this as a editorial / letter, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health. Reported size: 15 participants or records.
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