Acute kidney injury (AKI) is a commonly encountered medical problem that is associated with poor health
outcomes in survivors, including increased mortality and re-admission to the hospital. Despite their high-risk
status, only a small fraction (<10%) of patients receive specialized nephrologist follow-up after AKI episode.
The low rate of follow-up care is due to lack of clear guidelines as well as reluctance on part of patients due
to several reasons such as hospital fatigue, long travel times and unwillingness to add more doctors to the
care team. To address the gap in care for AKI survivors, we propose an artificial intelligence (AI) enabled,
MUlti-modal SEnsor (MUSE) platform for at-home use that can monitor patient health automatically, perform
risk assessment for AKI recurrence, and alert the patient to seek specialized care. MUSE comprises of – 1)
a colorimetric dipstick for capturing concentration of bio-markers (creatinine, urea, pH and lactate) in urine;
2) a near-field communication (NFC) powered stretchable, battery-less, single-lead electrocardiogram (ECG)
skin patch that records ECG since cardiovascular complications is a strong predictor for AKI recurrence; 3)
an AI-enabled mobile application that acquires sensor data (from urine sample and ECG) and runs an on-device deep learning fusion AI model to combine sensor data and patient medical record (past co-morbidities
and demographics) for precision and personalized predictions. We will harness capabilities of smartphone
for several key tasks - a) capture images of the dipstick sensor with built-in camera; b) act as NFC reader
for ECG patch; c) run the computer vision and AI algorithms natively on-board without requiring network
connection, and encrypt patient data locally. The AI model will be trained and validated on a large
retrospective dataset collected from patients at Mayo Clinic Hospital, and the sensor system functionality will
be validated with an observational study on 20 adult participants (10 healthy and 10 AKI patients) at Mayo.
The proposed research has the potential to drive innovations in producing the next generation of intelligent
wearables that performs fusion of multi-modal sensor data and EMR for early detection of underlying health
issues with high accuracy. A successful realization of the proposal aims will pave the way for a future, large-scale clinical trial with our sensor platform.
