Apple's latest Artificial Intelligence (AI) model can now identify pregnancy with 92 per cent accuracy, using behavioural data collected from iPhones and Apple Watches, according to a new study.
The study titled "Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions" revealed that the new AI model could detect certain health signs such as sleep quality, heart rate variability, mobility, and other high-level metrics.
The Wearable Behaviour Model (WBM) could also track changes in certain health conditions during pregnancy. Over 2.5 billion hours of wearable data were used to train a new foundation model, which they demonstrated could match or even surpass the performance of prior models based on low-level sensor data.
Researchers created a pregnancy dataset using data from 430 pregnancies, ending in either a vaginal or cesarean delivery. The WBM collected data from the Apple Health app, HealthKit, and heart rate sensor data (PPG).
The data revealed that the nine months leading up to childbirth and one month after delivery were "positive" weeks, as women were likely going through physiological changes related to pregnancy or postpartum recovery. The other times were marked as "negative" weeks, said the study.
Researchers also collected data from over 24,000 women under 50 who were not pregnant as part of the study to bring more accuracy to the results.
Apple Watch collects a lot of raw sensor data, but that data can be noisy and hard to understand.
The study said, "Unlike raw sensors, these higher-level behavioural metrics are calculated using carefully validated algorithms derived from the raw sensors. These metrics are intentionally chosen by experts to align with physiologically relevant quantities and health states."
The new WBM model shows clear data by tracking and predicting health changes over time, according to 9to5mac.
