Deep learning-based differential diagnosis of major depression and bipolar disorder using microglia-cellular sensors and patient-derived small extracellular vesicles.

The diagnosis of major depressive disorder (MDD) and bipolar disorder (BD) relies on symptom-based evaluations. Both MDD as well as BD present episodes of depressed mood, often leading to misdiagnosis and treatment delays. Our study presents a novel deep learning–based diagnostic approach that employs microglial cells as biosensors to identify disease-specific image features induced by patient-derived plasma small extracellular vesicles (sEVs), enabling differentiation among MDD, BD, and control (CTRL) groups. Microglial morphological changes in response to plasma sEVs were captured using fluorescence microscopy. Individual cell images were grouped into structured M×M arrays and processed through a DenseNet121 convolutional neural network (CNN). To enhance classification robustness, P image arrays per subject were generated using random cell image permutations and affine transformations. Final diagnoses were assigned through weighted voting across all arrays. Model performance was assessed using repeated subject-disjoint random splits. The CNN-based image analysis framework accurately distinguished between MDD, BD, and CTRL subjects. The best model configuration correctly classified 44 out of 45 individuals across the five cross-validations. By combining deep learning and microglial cell-based biosensing, our results support the proof-of-concept for building a novel diagnostic platform for the differential diagnosis between MDD and BD.