Let's replace "trust me" with data.
An AI-driven diagnostics platform reading multi-channel EEG time-series to track neuro-hormonal cycles — giving clinicians and patients an objective, physiological way to understand PMDD, instead of another symptom journal.
PMDD diagnosis still runs on guesswork.
Current diagnostics for PMDD and related neuro-hormonal conditions rely entirely on subjective, retrospective symptom journals — a gap in medical validation that leads to diagnostic delays, misdiagnosis, and ineffective treatment. We didn't guess at this problem — we measured it: across two peer-reviewed studies of the r/PMDD community (41,177 posts, 17,332 users, 12 years of data), depressed mood and physical symptoms are the most-cited complaints, and over 1 in 10 also post in anxiety-disorder communities — diagnostic overlap in practice, not just theory.
The neuroscience already exists: peer-reviewed research shows EEG activity shifts measurably with menstrual-cycle phase and hormone levels, and a 2024 review links this specifically to PMDD. What's new is the machine-learning translation layer that turns raw EEG into a validated, continuous diagnostic index — that translation layer is what we're building.