# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
## 4 Discussion
### Study Limitations  
The models developed in this study were limited to analyzing 2D representations of structural MRI brain scans, using normalized axial slices to simplify processing and reduce computational demands. This approach ensured consistency and reproducibility across the dataset, making it suitable for a proof-of-concept classification framework designed primarily for research purposes.  
However, converting 3D (volumetric) MRI data into 2D slices inevitably results in losing some part of the spatial information.  
Dementia-related neurodegeneration affects multiple brain regions, such as the hippocampus, medial temporal lobes, and parietal cortex, in a spatially complex and heterogeneous manner. The atrophy patterns can either be dispersed throughout the entire brain volume or localized in several specific anatomically distinct regions, making the 2D approach insufficient for capturing the full extent and distribution of these changes.  
While suitable for preliminary experimentation, the models developed may not satisfy the requirements of real-life clinical applications, where a comprehensive spatial context is necessary for accurate diagnosis.  
In this regard, 3D CNNs offer a promising alternative, as they can directly analyze volumetric brain data and capture the intricate spatial patterns of atrophy and pathological changes across the whole brain. Furthermore, techniques like voxel-based morphometry (VBM) have also been explored for dementia research to identify statistically significant regional differences in gray and white matter volume between symptomatic patients and CN individuals [49; 50]. Incorporating VBM into the classification framework could enable a voxel-wise, whole-brain assessment of structural changes, which is particularly valuable in cases where the atrophy is subtle, diffuse, and not confined to predefined ROIs [51; 52].  
Future work will explore the application of 3D CNN-based models and assess the diagnostic utility of incorporating additional anatomical planes, such as sagittal and coronal, to understand better the contribution of different spatial orientations to the models' classification performance and diagnostic accuracy. Integrating DL-based models with insights from established neuroimaging techniques such as VBM may enhance both performance and interpretability, contributing to more robust and clinically meaningful applications.  
Additionally, in the context of dementia diagnosis, relying solely on imaging findings to construct a robust CDSS is not feasible. Although complex volumetric analyses, which involve extracting and processing radiomic features, can play a significant role in such a system, it is essential to recognize that the diagnostic process for dementia is multifaceted. It requires not only neuroimaging but also various cognitive assessments, laboratory testing results, and the detection of specific biomarkers (in blood, CSF, and even genes). These factors represent a fraction of the broader patient profile that must be considered for a comprehensive evaluation.  
Although the findings of this exploration may not yet be directly applicable in real-life clinical settings, they provide a strong foundation for further research and development in the area of a potential AI-based dementia-related CDSS. Despite their limitations, they can still deliver essential insights and help guide clinicians in selecting more specialized diagnostic tests, potentially improving the accuracy of dementia diagnosis, speeding it up, and optimizing the quality of disease management.