In undertaking my research project, I’ve faced numerous challenges and considerations, particularly regarding data access and methodological choices. This reflection explores how these elements shape my approach to answering the core research question:
“How can deep learning methods enhance patient experiences and outcomes in the early detection and preventive screening of Alzheimer’s disease?”
The Role of Data in Research Projects
The data required to support my original research idea included clinical datasets such as MRIs or retinal images. However, the lack of access to this data presented a significant hurdle, prompting me to pivot my project to a systematic review. Instead of working with raw datasets, I now rely on data from previously conducted studies to synthesize insights.
While this change has shifted the scope of my research, the focus remains on leveraging deep learning in medical diagnostics. Conducting a systematic review allows me to explore the effectiveness of different approaches without the need for direct access to proprietary clinical data.
The Impact of Privacy on Research Progress
During a recent lecture and Ted Talk, the concept of privacy restrictions slowing research progress resonated with me deeply. The constraints on accessing medical data, such as MRI or retinal imaging databases, are a prime example. These restrictions directly impacted my project, forcing me to reconsider my methodology.
Although respecting privacy and consent is crucial, I believe that loosening some restrictions on medical data—while maintaining strong ethical safeguards—could accelerate innovation and improve clinical outcomes. This delicate balance is particularly relevant in fields like mine, where timely access to diverse datasets could significantly enhance research quality.
Selecting the Right Methodological Approach
The choice of research methods is critical to the success of any project. For my work, I delved into several methodological strategies to contextualise my quantitative-dominant approach, even though primary data collection was not feasible. These strategies included:
- Sequential Explanatory: Starting with quantitative data analysis, followed by qualitative insights to expand or clarify findings.
- Concurrent Triangulation: Simultaneously analysing quantitative and qualitative data to cross-validate results.
- Concurrent Nested: Employing one method as the primary focus while integrating the other in a supporting capacity.
- Concurrent Transformative: Using data collection framed by a specific theoretical lens—such as ethics or public health—to examine broader societal implications.
For my current work, a quantitative focus aligned best with my goals, particularly analysing retinal imaging datasets. By uncovering patterns within this data, my aim is to advance early detection of Alzheimer’s disease. While a mixed-methods approach could add depth, the constraints of my systematic review necessitated prioritising quantitative methods.
Strategies for Data Collection and Analysis
Initially, I attempted to source data from platforms like Kaggle and by contacting authors of relevant academic papers. However, these efforts proved challenging due to privacy restrictions and data availability. For my revised project, I will rely on research papers sourced from databases such as PubMed, IEEE Xplore, and Scopus.
The analysis will predominantly involve quantitative methods, focusing on biomarkers identified in retinal imaging studies. This approach allows me to evaluate the effectiveness of various deep learning models in identifying early signs of Alzheimer’s disease.
Conclusion
Reflecting on my journey so far, I’ve gained a deeper appreciation for the complexities of data access and methodological decision-making in research. While I’ve had to adapt my approach to work within these constraints, the process has reinforced the importance of interdisciplinary collaboration and innovative thinking. By focusing on retinal imaging and deep learning, I aim to contribute valuable insights into the early detection and prevention of Alzheimer’s disease, paving the way for advancements in both research and patient care.