Predicting Age Groups using Brain Imaging Quality Data

Brain imaging quality of data is important to confirm their liability of brain imaging studies. Previous literature confirmed that some confounding factors such as movement, age, and gender may impact brain imaging quality. Automatic quality control (QC) applications may not be able to properly calculate their reliability due to confounding factors.There are a few studies on brain imaging quality data and relevant confounding factors such as age or gender. Methods: Open data from a previous study was used to conduct this study. In total 26 participants were recruited. Random Forest (RF) and Neural Networks (NN) machine learning (ML) methods were used to predict age groups (cut-off age: 16). Patients were grouped by age groups. Then, the age group was predicted with RF and NN machine learning (ML) models. Goal of study: The goal of the study was to predict age groups using brain imaging quality data. Results: We found that according to NNs, the age group was predicted with an accuracy of over 60% (accuracy: 64%, sensitivity: 50%, specificity: 71%, area under curve (AUC): 55%,). Furthermore, the RFML model found that the age group was predicted with an accuracy of 64% (sensitivity: 50%, specificity: 71%, AUC: 86.6%). Conclusion: Our study showed that age groups can be predicted using the brain imaging quality of the data. Further studies should investigate the relationship between other brain imaging parameters related to the quality of data and age.