An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges

Walter Antonio  Campos-Ugaz; Jessica Paola Palacios Garay; Oriana  Rivera-Lozada; Mitchell Alberto  Alarcón Diaz; Doris Fuster-Guillén; Arístides  Alfonso Tejada Arana

doi:10.18502/ijps.v18i2.12372

Walter Antonio Campos-Ugaz Universidad Nacional Pedro Ruiz Gallo, Lambayeque, Perú.
Jessica Paola Palacios Garay Universidad Privada Norbert Wiener, Lima, Perú.
Oriana Rivera-Lozada Universidad Privada Norbert Wiener, Lima, Perú.
Mitchell Alberto Alarcón Diaz Universidad Nacional Mayor de San Marcos, Lima, Perú.
Doris Fuster-Guillén Universidad Nacional Mayor de San Marcos, Lima, Perú.
Arístides Alfonso Tejada Arana Universidad Privada Norbert Wiener, Lima, Perú.

DOI: https://doi.org/10.18502/ijps.v18i2.12372

Keywords: Bipolar Disorder; Electroencephalogram; Magnetic Resonance Imaging; Machine Learning

Abstract

Objective: Automatic diagnosis of psychiatric disorders such as bipolar disorder (BD) through machine learning techniques has attracted substantial attention from psychiatric and artificial intelligence communities. These approaches mostly rely on various biomarkers extracted from electroencephalogram (EEG) or magnetic resonance imaging (MRI)/functional MRI (fMRI) data. In this paper, we provide an updated overview of existing machine learning-based methods for bipolar disorder (BD) diagnosis using MRI and EEG data.

Method: This study is a short non-systematic review with the aim of describing the current situation in automatic diagnosis of BD using machine learning methods. Therefore, an appropriate literature search was conducted via relevant keywords for original EEG/MRI studies on distinguishing BD from other conditions, particularly from healthy peers, in PubMed, Web of Science, and Google Scholar databases.

Results: We reviewed 26 studies, including 10 EEG studies and 16 MRI studies (including structural and functional MRI), that used traditional machine learning methods and deep learning algorithms to automatically detect BD. The reported accuracies for EEG studies is about 90%, while the reported accuracies for MRI studies remains below the minimum level for clinical relevance, i.e. about 80% of the classification outcome for traditional machine learning methods. However, deep learning techniques have generally achieved accuracies higher than 95%.

Conclusion: Research utilizing machine learning applied to EEG signals and brain images has provided proof of concept for how this innovative technique can help psychiatrists distinguish BD patients from healthy people. However, the results have been somewhat contradictory and we must keep away from excessive optimistic interpretations of the findings. Much progress is still needed to reach the level of clinical practice in this field.