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Artificial intelligence (AI) is increasingly being used in the medical field, specifically for brain cancer imaging. In this review, we explore how AI-powered medical imaging can impact the diagnosis, prognosis, and treatment of brain cancer. We discuss various AI techniques, including deep learning and causality learning, and their relevance. Additionally, we examine current applications that provide practical solutions for detecting, classifying, segmenting, and registering brain tumors. Although challenges such as data quality, availability, interpretability, transparency, and ethics persist, we emphasise the enormous potential of intelligent applications in standardising procedures and enhancing personalised treatment, leading to improved patient outcomes. Innovative AI solutions have the power to revolutionise neuro-oncology by enhancing the quality of routine clinical practice.
RESUMO
Background: The Mini-Mental State Examination (MMSE) is a simple informative and validated screening test of cognitive functions. No data of MMSE scores has been published about elderly Jordanian population. Objective: To study the MMSE scores in the elderly Jordanian population (defined as age of 50 years or more). Methods: This was a cross sectional study. A 250 healthy elderly Jordanians were interviewed and tested for their MMSE score. Their scores were analyzed and the effect of age, gender, education, marital status, diabetes mellitus (DM), hypertension (HTN), smoking, dyslipidemia, heart disease (HD), and family history of dementia (FHD) on the score was studied. Results: MMSE scores of 236 elderly subjects, aged 63±8.4 years (range 50-86 years) were analyzed. There were 111 (47%) males. There was a direct correlation between MMSE score and education level. People with a higher education (college) had the highest scores in comparison with people who are illiterates or have a high school level. The score correlated negatively with age (Pearson correlation r = -0.23, p = 0.00). Other studied variables did not correlate with score on multivariate analysis. Conclusion: Jordanians ≥50 years old with no previous brain disease scored 26.7 ± 3.2. Education was the most important determining factor of this score. the score showed also a negative correlation with age. We suggest that in this population a cut off score of 24 for those with high education beyond high school and 21 for those with up to high school education and it is not appropriate for illiterate.
