ArticleBMC psychiatry2025
MentalAId: an improved DenseNet model to assist scalable psychosis assessment.
Article in BMC psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- A scoping review of the use of artificial intelligence as a psychological assessment tool.Translational psychiatry · 2026Article
- Automated Kellgren-Lawrence grading of knee osteoarthritis using a multi-scale attention-based deep learning framework.Frontiers in medicine · 2026Article
- Correction: MentalAId: an improved DenseNet model to assist scalable psychosis assessment.BMC psychiatry · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
backgroundThe 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.
methodsWe 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 demographic variables (sex and age) across 28,746 individuals spanning four distinct cohorts: psychotic inpatients (n = 9,271), non-psychotic inpatients with various diseases (n = 14,508), healthy controls (n = 1,826), and drug-naïve first-episode psychosis (FEP) patients (n = 3,141).
resultsThe MentalAId model achieved high accuracy in generally discriminating psychoses from both healthy individuals and patients with other physical diseases, achieving 93.3% accuracy and AUC of 0.983. Further validating its robustness, MentalAId demonstrated high performance under real-world clinical conditions, accurately handling extreme values and missing values, with accuracies of 92.4% and 92.0%, respectively. Even encompassing the drug-naïve FEPs, MentalAId maintained an accuracy of 91.9%, underscoring its translational potential for early FEP recognition. Interpretability analyses identified indirect bilirubin, direct bilirubin, and basophil ratio as potential metabolic indicators.
conclusionsCollectively, MentalAId offers an accessible, affordable, and scalable solution to assist timely psychosis assessment and monitoring, irrespective of the complexity of pathology or manifestation of symptoms. By requiring standard blood tests solely, it can be easily integrated into existing healthcare workflow. This empowers long-term and population-wide monitoring of disease progression and prognosis, particularly during public health crises like COVID-19.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.