A Nigerian professor based in the United States, Nchebe-Jah Iloanusi, has developed an Artificial Intelligence system capable of predicting the onset of depression in real time before clinical symptoms become visible.
Iloanusi, who lectures in the Department of Biology at the College of Staten Island, City University of New York, unveiled the breakthrough system, which fuses heart rate signals, sleep data, and speech patterns to detect early signs of depression.
The innovation uses a transformer-based AI model to process heart rate variability, sleep architecture, and speech biomarkers, captured through wearable devices and voice inputs.
Iloanusi, who lectures in the Department of Biology at the College of Staten Island, City University of New York, unveiled the breakthrough system, which fuses heart rate signals, sleep data, and speech patterns to detect early signs of depression.
The innovation uses a transformer-based AI model to process heart rate variability, sleep architecture, and speech biomarkers, captured through wearable devices and voice inputs.
Iloanusi maintained that the technology could be integrated into smartphones, wearables, and telehealth platforms, enabling clinicians to monitor patients remotely and intervene before depression escalates.
“This system bridges a critical gap in mental healthcare by detecting changes before clinical thresholds are reached,” he noted.
The professor also stressed that the AI tool is not a replacement for psychiatric diagnosis.
Mental health professionals have hailed the development as a promising step toward proactive and data-driven mental healthcare, especially in underserved populations where access to mental health services is limited.
Credit: Punch Healthwise
“This system bridges a critical gap in mental healthcare by detecting changes before clinical thresholds are reached,” he noted.
The professor also stressed that the AI tool is not a replacement for psychiatric diagnosis.
Mental health professionals have hailed the development as a promising step toward proactive and data-driven mental healthcare, especially in underserved populations where access to mental health services is limited.
Credit: Punch Healthwise
