MentalAId: an improved DenseNet model to assist scalable psychosis assessment

Abstract Background: The escalating mental health crisis during and post-COVID-19 underscores the urgent need for scalable, timely, cost-effective assessment solutions for general psychotic disorders. Regretfully, traditional symptom-based, one-to-one assessment face inherent limitations in large-scale and longitudinal screening, likely delaying early intervention. Methods: We developed MentalAId, an improved densely connected convolutional network (DenseNet) model, to assist automated psychosis recognition, leveraging accessible routine laboratory data without requiring additional specialized tests. MentalAId learned subtle variations in 49 routine clinical hematological tests and two