Whole-Brain Exploratory Analysis of Functional Task Response Following Erythropoietin Treatment in Mood Disorders: A Supervised Machine Learning Approach

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Reproducibility of developmental neuroplasticity in in vitro brain tissue models

The current prevalence of neurodevelopmental, neurodegenerative diseases, stroke and brain injury stimulates studies aimed to identify new molecular targets, to select the drug candidates, to complete the whole set of preclinical and clinical trials, and to implement new drugs into routine neurological practice. Establishment of protocols based on microfluidics, blood-brain barrier- or neurovascular unit-on-chip, and microphysiological systems allowed improving the barrier characteristics and analyzing the regulation of local microcirculation, angiogenesis, and neurogenesis. Reconstruction of key mechanisms of brain development and even some aspects of experience-driven brain plasticity would be helpful in the establishment of brain in vitro models with the highest degree of reliability. Activity, metabolic status and expression pattern of cells within the models can be effectively assessed with the protocols of system biology, cell imaging, and functional cell analysis. The next generation of in vitro models should demonstrate high scalability, 3D or 4D complexity, possibility to be combined with other tissues or cell types within the microphysiological systems, compatibility with bio-inks or extracellular matrix-like materials, achievement of adequate vascularization, patient-specific characteristics, and opportunity to provide high-content screening. In this review, we will focus on currently available and prospective brain tissue in vitro models suitable for experimental and preclinical studies with the special focus on models enabling 4D reconstruction of brain tissue for the assessment of brain development, brain plasticity, and drug kinetics.