An artificial‐intelligence‐based age‐specific template construction framework for brain structural analysis using magnetic resonance images

Abstract

It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age‐specific brain variations and tendencies across different ages. In this article, we present an AI‐based age‐specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1‐weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5‐year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.

uRP: An integrated research platform for one-stop analysis of medical images

“…To emphasize, this segmentation process depends on VB-Net, achieving efficient, precise, and end-to-end segmentation of multiple sub-regions. The model was trained on T1 images of 1,800 subjects and tested on 295 subjects with an average Dice of 0.92, where the images were acquired from the Consortium for Reliability and Reproducibility (CoRR) dataset ( 65 ) and Chinese brain molecular and functional mapping (CBMFM) project ( 66 ). Based on the segmentation results, the volume, volume ratio of each sub-region, and the asymmetry index of paired sub-region are calculated quantitatively and compared to the relevant parameters from the gender- and age-matched normal dataset ( Figure 5B ).…”

A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach

“…Recently, MRI techniques were successfully used to diagnose lung cancer, liver cancer, prostate, and breast cancer cells, and AI was also integrated into these techniques, allowing them to be integrated into multidisciplinary applications allowing patient-specific medicine to be personalized [ 137 , 138 , 139 ]. This investigation clearly demonstrated that AI should be integrated into designing the magnetic materials for MRI imaging and the obtained MRI images successfully enhance the diagnostic capabilities [ 140 ].…”

A Cogitation on the ChatGPT Craze from the Perspective of Psychological Algorithm Aversion and Appreciation

“…It is worth noting that research has found that as the degree of artificial intelligence anthropomorphism increases, the moral responsibility attributed to it also increases, and this effect is achieved through the enhancement of artificial intelligence’s perceived free will caused by anthropomorphism. 33 Therefore, although a higher level of anthropomorphism can lead to algorithm appreciation, excessive anthropomorphism can also result in algorithm aversion and even generate the “uncanny valley effect” (the feeling of unease when AI becomes too intelligent, leading to a decrease in preference). 34 …”