Peer Review Report For: MOODMIND: Artificial Intelligence for Major Depressive Disorder Screening in Tuberculosis Patients [version 1; peer review: 1 approved with reservations, 1 not approved]

Background Major Depressive Disorder (MDD) can occur in patients with tuberculosis. The purpose of this research was to develop an early detection system for MDD and conduct an accuracy test. Methods The MOODMIND application uses Natural Language Processing (NLP) with sentiment analysis techniques. MOODMIND offers both speech and text options and is available in Indonesian/English. The screening results were compared with those of the doctor’s autoanamnesis test. Single blinding is used so that doctors are unaware of the application test. Results The app asks open- and closed-ended questions for MDD identification based on the DSM-5. The test results were divided into non-depressive (none or at-risk) and suspected depression groups. MOODMIND showed 67% sensitivity and 100% specificity. Conclusions Ease is advantageous because the steps are simple. MOODMIND has sufficient accuracy, but it can be improved by adding words related to depression in the lexicon adjustment.