Prospects of machine learning applications in affective disorders

Mental disorders are a significant medical and social issue globally. Currently, approximately 970 million individuals suffer from mental disorders, with over 300 million diagnosed with depression or bipolar disorder. Recently, there has been significant advancement in digital technologies, particularly in artificial intelligence, encompassing machine learning and deep learning. Given the growing interest in their use in psychiatry and the need to develop new approaches to psychiatric care. This review explores the current and promising directions for the application of artificial intelligence technologies in clinical practice, focusing on patients with depression and bipolar disorder.

A literature search was conducted from January to February 2024 in the databases PubMed, Google Scholar, and eLibrary using the following keywords: «психиатрия» ("psychiatry"), «психическое здоровье» ("mental health"), «психическое расстройство» ("psychiatric disorder"), «депрессия» ("depression"), «депрессивный эпизод» ("depressive episode"), «рекуррентное депрессивное расстройство» ("recurrent brief depression"), «биполярное расстройство» ("bipolar disorder"), «машинное обучение» ("machine learning"), «глубокое обучение» ("deep learning"), «искусственный интеллект» ("artificial intelligence"); "psychiatry", "mental health", "psychiatric disorder", "depression", "depressive episode", "major depressive disorder", "bipolar disorder", "machine learning", "deep learning", "artificial intelligence". Studies on the use of artificial intelligence technologies in patients with depression and bipolar disorders and review articles discussing the difficulties of their application in psychiatry were excluded. Publications in Russian and English in the past 10 years were selected.

The most commonly used machine learning models for diagnosing patients with affective disorders utilize neuroimaging data (primarily magnetic resonance imaging and electroencephalography), text, audio, and video data and data from electronic devices, molecular-genetic markers, and clinical indicators. The models were trained using mono- or multimodal datasets. Notably, many of the reviewed studies have significant limitations, making the implementation of artificial intelligence technologies in clinical practice challenging. These include small sample sizes, low representativeness and standardization, inclusion of “noise” and correlated variables, and absence of validation using independent datasets.

Studies on machine learning methods have demonstrated promising results in the early diagnosis of affective episodes and in predicting treatment responses. However, their clinical application is limited, owing to insufficient validation. Well-designed prospective cohort studies and the creation of extensive, high-quality datasets and models capable of uncovering new relationships between variables are required to address this limitation.