MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.
RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.
CONCLUSION: MRS has a role as a non-invasive tool for the detection of brain metabolite biomarkers that indicate brain metabolic health, which can be integral in the management of AD.
METHODS: Data from the population-based Singapore Cancer Registry for 1968-1992 were used to determine time trends, inter-ethnic differences and the contributions of age, period and cohort effects to the incidence of the disease.
RESULTS: Our results revealed an average annual increase of 3.6% over the 25-year period for all women, form 20.2 per 100,000 women in the period 1968-1972 to 38.8 per 100,000 in 1988-1992. There was a statistically significant difference between the three major ethnic groups, the rate of increase being highest in Malays (4.4%) and lowest in Indians (1.4%). The overall increase was attributable to a strong cohort effect that remained significant when adjusted for time period for Chinese women and for all ethnic groups combined. The risk was observed to increase in successive birth cohorts from the 1890s to 1960s.
CONCLUSIONS: Our results suggest that breast cancer incidence rates are likely to continue to increase more sharply in the future as women born after the mid-20th century reach the high-risk age groups. They also suggest the pattern by which important aetiological factors for the disease in our population have exerted their effects, and provide support for the role of demographic and lifestyle changes as possible risk factors.