Integrative Biomedical Research
Integrative Biomedical Research (Journal of Angiotherapy) | Online ISSN 3068-6326
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Volume 9 Number 1 2025
RESEARCH ARTICLE (Open Access)
Deep Mapping of Neuro-phenotypes Using AI-Enhanced MRI: A Contrast-Based Multiplanar Analysis
Moin Uddin Patwary1*, Tanjila Hasan Tulon1, Shamim Al Mamun1, Md. Mortuza Galib1, Ezaz Ahmad Shah1, Antu Das1, Rudro kumar Saha1, Sabrina Sultana Sneha1, Arobi Akter Mim1, Jinia1
Integrative Biomedical Research 9 (1) 1-8 https://doi.org/10.25163/biomedical.9110528
Submitted: 29 September 2025 Revised: 22 November 2025 Accepted: 28 November 2025 Published: 30 November 2025
Abstract
Background: Magnetic Resonance Imaging, or MRI for short, is the workhorse of brain morphometry. However, conventional T1-weighted and T2-weighted scans lack the sensitivity at the voxel level to enable neuro-phenotyping or the early detection of pathologies. This limitation has driven the introduction of artificial intelligence (AI) to enhance internal imaging.
Methods: We got MRI data on 828 participants (aged 5-21) from an open-access dataset. Next, the images underwent some preprocessing with skull stripping, bias field correction, intensity normalization, and finally CLAHE for contrast enhancement. After that, the team applied AI-driven voxel-wise intensity mapping to generate improved heatmaps across axial, coronal, and sagittal plane Voxel intensities of specific regions-of-interest (ROIs) were extracted and compared using one-way ANOVA, which includes original, contrast-enhanced and AI-processed images.
Results: Upon analysis, AI-generated imaging produced a better visualization of the cortical borders, subcortical regions and central structures. By increasing the contrast, some asymmetries and intensity gradients become visible. This is particularly true for the anterior-posterior axis. The voxel intensity across groups differed significantly on the axial (F (2,12) = 41.56, p = 4.03×10-6), coronal (F (2,12) = 50.72, p = 1.40×10-6) and sagittal (F (2,12) = 57.76, p = 6.95×10-7) planes, illustrating the added value of the enhancement pipeline.
Conclusion: This research confirms that the use of AI image enhancement of MRI yields statistically proven improvements in anatomy at the voxel level. The pipeline suggested is a high precision toolbox for neuro-phenotyping and automating the brain shape analysis for researcher in developmental and clinical settings.
Keywords: AI-enhanced MRI, Voxel-based analysis, Brain phenotyping, Contrast mapping, T2-weighted imaging
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