Predicting new onset thought disorder in early adolescence with optimized deep learning implicates environmental-putamen interactions.

ABSTRACT:

Background

Thought disorder (TD) is a sensitive and specific marker of risk for schizophrenia onset. Specifying factors that predict TD onset in adolescence is important to early identification of youth at risk. However, there is a paucity of studies prospectively predicting TD onset in unstratified youth populations.

Study design

We used deep learning optimized with artificial intelligence (AI) to analyze 5,777 multimodal features obtained at 9-10 years from youth and their parents in the ABCD study, including 5,014 neural metrics, to prospectively predict new onset TD cases at 11-12 years. The design was replicated for all prevailing TD cases at 11-12 years.

Study results

Optimizing performance with AI, we were able to achieve 92% accuracy and F1 and 0.96 AUROC in p

SUBMITTER: de Lacy N 

PROVIDER: S-EPMC10635181 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature