[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118662-en":3,"doc-seo-118662-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118662,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Prospects of machine learning applications in affective disorders","Mental disorders represent a major medical and social challenge worldwide, affecting nearly one billion people, with more than 300 million diagnosed with depression or bipolar disorder. Rapid progress in digital technologies—especially artificial intelligence, including machine learning and deep learning—has intensified interest in psychiatric care. This review summarizes current and promising AI directions for clinical practice in patients with depression and bipolar disorder.","REVIEW Vol. 6 (1) 2025 Digital Diagnostics  \nDOI: [https://doi.org/10.17816/DD634885](https://doi.org/10.17816/DD634885)  \nProspects of machine learning applications in affective disorders  \nEkaterina S. Mosolova 1 , Alexander E. Alfimov2 , Elena G. Kostyukova 1 , Sergey N. Mosolov 1,3  \n1 V. Serbsky National Medical Research Centre for Psychiatry and Narcology, Moscow, Russia;  \n2 Sechenov First Moscow State Medical University, Moscow, Russia;  \n3 Russian Medical Academy of Continuous Professional Education, Moscow, Russia  \nABSTRACT  \nMental 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.  \nA 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\"), «психическоерасстройство» (\"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.  \nStudies 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. Welldesigned 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.  \nKeywords: artificial intelligence; machine learning; deep learning; psychiatry; depression; recurrent depressive disorder; bipolar disorder.  \nTo cite this article:  \nMosolova ES, Alfimov AE, Kostyukova EG, Mosolov SN. Prospects of machine learning applications in affective disorders. Digital Diagnostics. 2025;6(1):97–115 . DOI: [https://doi.org/10.17816/DD634885](https://doi.org/10.17816/DD634885)  \nReceived: 06.08.2024 Accepted: 06.12.2024 Published online: 28.01.2025  \nArticle can be used under the CC BY-NC-ND 4.0 ","cbCaif0M9VmJmsKM","https://ap.wps.com/l/cbCaif0M9VmJmsKM","pdf",896269,1,16,"English","en",105,"# Abstract\n## Scope and clinical focus\n## Methods and literature search\n## Common model types and data sources\n## Key limitations for clinical implementation\n## Outlook and future research needs","[{\"question\":\"Which psychiatric conditions does the review focus on?\",\"answer\":\"The review focuses on patients with depression and bipolar disorder, outlining current and promising AI applications for clinical practice.\"},{\"question\":\"How was the literature search conducted?\",\"answer\":\"A search was conducted from January to February 2024 in PubMed, Google Scholar, and eLibrary using both Russian and English keyword sets related to psychiatry, affective disorders, and machine/deep learning.\"},{\"question\":\"What data types are commonly used for machine learning models in affective disorders?\",\"answer\":\"Reviewed models commonly use neuroimaging (MRI and electroencephalography), text, audio, video, electronic device data, molecular-genetic markers, and clinical indicators, trained on mono- or multimodal datasets.\"},{\"question\":\"Why is clinical implementation of AI in psychiatry still limited?\",\"answer\":\"Clinical translation is constrained by significant limitations such as small sample sizes, poor representativeness and standardization, inclusion of noisy or correlated variables, and lack of validation on independent datasets.\"}]","Prospects of machine learning applications in affective disorders | 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