Neural Networks In Medical Neuroimaging: Advancing Detection And Treatment Of Neurological Disorders And Brain Tumors

Abstract

Objective: This paper aims to evaluate the role and performance of Neural Networks (NNs) in medical imaging of neurological disorders such as Alzheimer's disease, Parkinson's disease, stroke, brain tumors, and Multiple Sclerosis. To that end, the study seeks to assess the effectiveness of AI-assisted NNs in enhancing disease accuracy, ease, and treatment outcomes, in addition to unraveling constraints to utilizing such innovation in healthcare facilities.Methods: A convenient sample of 250 healthcare professionals from the subsets of radiologists, neurologists, developers of AI, and healthcare administrators were targeted. The structured questionnaire used quantitative data to gather information on how familiar the respondents are with NNs, how often they use them, and their perception of the concept. Descriptive analysis, reliability analysis (Cronbach's Alpha), normality analysis (Anderson-Darling), and correlation analysis (Pearson's r) were used to analyze significant variables, including familiarity, perceived effectiveness, and likelihood to recommend.Results: The findings indicate that participants are moderately familiar with NNs, where usage differs across neurological disorders. Alzheimer's disease and stroke were identified as the two diseases in which NNs were most beneficial; however, usefulness was low for other diseases such as Parkinson's disease and Multiple Sclerosis. When performing the reliability test, it obtained a low Cronbach's Alpha, which suggests that there is a weak internal consistency between measured items. Moreover, there was low interaction between the level of familiarity, perceived effectiveness, and likelihood of recommending AI technologies, which indicated that different professions may have different beliefs about AI technologies. Challenges that limited the adoption of the models were costly, lack of explanation, and sheer legal issues.

Conclusion: There are opportunities for using NNs in medical imaging of neurological disorders and their treatment, which have significant uses in Alzheimer's and stroke. But, for the most part, using the technologies mentioned earlier largely remains limited due to financial constraints, technological limitations, and existing regulations. More development is still required regarding AI transparency, training, and affordable application to improve the confidence and frequency of use by the large spectrum of healthcare workers. There are also areas for future research: first, the use of AI in less researched neurological disorders; second, increasing the robustness of the AI models and the readability of the results.