Interpretable convolutional neural network for autism diagnosis support in children using structural magnetic...

Autism is one of the most common neurodevelopmental conditions, and it is characterized by restricted, repetitive behaviors and social difficulties that affect daily functioning. It is challenging to provide an early and accurate diagnosis due to the wide diversity of symptoms and the developmental changes that occur during childhood. We evaluate the feasibility of an explainable deep learning (DL) model using structural MRI (sMRI) to identify meaningful brain biomarkers relevant to autism in children and thus support its diagnosis.

A total of 452 $T1$ -weighted sMRI scans from children aged 9 to 11 years were obtained from the Autism Brain Imaging Data Exchange database. A DL model was trained to differentiate between autistic and typically developing children. Model explainability was assessed using saliency maps to identify key brain regions contributing to classification. Model performance was evaluated across 20 folds and compared with traditional machine learning models trained with regional volumetric features extracted from the sMRI scans.

The model achieved a mean area under the receiver operating curve of 71.2%. The saliency maps highlighted brain regions that are known neuroanatomical and functional biomarkers associated with autism, such as the cuneus, pericalcarine, ventricles, lingual, vermal lobules, caudate, and thalamus.

We show the potential of interpretable DL models trained on sMRI data to aid in autism diagnosis within a narrowly defined pediatric age group. Our findings contribute to the field of explainable artificial intelligence methods in neurodevelopmental research and may help in clinical decision-making for autism and other neurodevelopmental conditions.