Machine learning approaches to distinguish bipolar disorder from borderline personality disorder: a scoping review.
Borderline personality disorder (BPD) and bipolar disorder (BD) are debilitating psychiatric illnesses with significant rates of misdiagnosis. This scoping review explores the potential of machine learning (ML) approaches in distinguishing individuals diagnosed with BD from those with BPD, reporting the performance metrics of various predictive models.
We searched Ovid MEDLINE, PubMed, Scopus, and Web of Science from inception to March 2025 for studies involving the terms "bipolar disorder," "borderline personality disorder," "machine learning", and "artificial intelligence." Peer-reviewed research was included without restriction on publication date or language. Of 60 studies screened, 5 met the inclusion criteria. The review followed the PCC framework, JBI Reviewer's Manual, and PRISMA guidelines.
This study identified five studies that applied predictive models to data from 591 participants to differentiate individuals with BD and BPD. Classification accuracy ranged from 61.7% to 89%. While ML models outperformed DSM-based categorical approaches overall, accuracy differed markedly by diagnosis: correctly 87.8% for BD compared with 57.7% for BPD, illustrating the persistent diagnostic challenges for BPD. Models were more accurate in distinguishing patients with both BD and BPD from those with BD alone (79.6%) than from those with BPD alone (61.7%). ML techniques based on brain imaging features achieved 80% accuracy, while mood ratings collected via smartphone enabled the differentiation of BD, BPD, and controls with 75% accuracy.
Currently, few predictive models have been developed to distinguish between BD and BPD. The findings of this review suggest that ML algorithms show moderate to good performance in clinical differentiation of BD and BPD. Further research is warranted to refine and validate predictive tools that aim to improve diagnostic precision in BD and BPD clinical practice.
We searched Ovid MEDLINE, PubMed, Scopus, and Web of Science from inception to March 2025 for studies involving the terms "bipolar disorder," "borderline personality disorder," "machine learning", and "artificial intelligence." Peer-reviewed research was included without restriction on publication date or language. Of 60 studies screened, 5 met the inclusion criteria. The review followed the PCC framework, JBI Reviewer's Manual, and PRISMA guidelines.
This study identified five studies that applied predictive models to data from 591 participants to differentiate individuals with BD and BPD. Classification accuracy ranged from 61.7% to 89%. While ML models outperformed DSM-based categorical approaches overall, accuracy differed markedly by diagnosis: correctly 87.8% for BD compared with 57.7% for BPD, illustrating the persistent diagnostic challenges for BPD. Models were more accurate in distinguishing patients with both BD and BPD from those with BD alone (79.6%) than from those with BPD alone (61.7%). ML techniques based on brain imaging features achieved 80% accuracy, while mood ratings collected via smartphone enabled the differentiation of BD, BPD, and controls with 75% accuracy.
Currently, few predictive models have been developed to distinguish between BD and BPD. The findings of this review suggest that ML algorithms show moderate to good performance in clinical differentiation of BD and BPD. Further research is warranted to refine and validate predictive tools that aim to improve diagnostic precision in BD and BPD clinical practice.