A method and device for measuring intra-abdominal pressure in a pregnant woman to assess likelihood or occurrence of pre-eclampsia. The method includes providing a catheter having first and second lumens and a balloon, inserting the catheter into a bladder of the patient, injecting gas into the first lumen of the catheter to expand the balloon, obtaining a first pressure reading of the bladder based on deformation of the balloon to thereby monitor pressure within an abdomen of the mother to assess if pre-eclampsia is occurring or likely to occur and transmitting the first pressure reading to an external monitor connected to the catheter. The pressure reading is indicative of the presence and/or risk of pre-eclampsia to determine when intervention should occur to prevent morbidity and mortality of the woman and baby.

The present disclosure generally relates to pregnancy user interfaces. In some examples, a computer system displays one or more user interfaces for customizing notifications related to a pregnancy event after receiving a request to add the pregnancy event to an account of the computer system. In some examples, a computer system displays cycle tracking indicators corresponding to respective days with different appearances based on whether an account of the computer system includes an indication that a user of the account is pregnant. In some examples, a computer system displays a graphical representation of first health data corresponding to a first duration and, based on a determination that an account of the computer system includes an indication that a user is pregnant, an indication that a difference between second health data corresponding to a second duration and the first health information is based on the user being pregnant.

A device may include an artificial intelligence (AI) model for pregnancy classification. The AI model may be trained by inputting labeled training data. During training, the AI model may determine, using a loss function, an error margin for the binary classification AI model based on inputting the labeled training data. The loss function may impose, for false positive pregnancy classifications, a first penalty factor that is weighted relative to a menstruation start date and that is greater than a reward factor for true positive pregnancy classifications. The loss function may impose a second penalty factor for classification confidences that change by a threshold amount between two consecutive days. The AI model may adjust one or more parameters of the binary classification AI model based on the error margin determined using the loss function.

A method for assessing the risk of preeclampsia in a pregnant subject is provided. The method can comprise: obtaining at least one blood pressure pulse waveform from at least one arterial site of the subject; determining a risk of preeclampsia in the subject, based at least in part on the at least one blood pressure pulse waveform; and generating an output indicative of the determined risk.

A method can include receiving, at a computer system, characteristic values of a pregnancy of a subject. As an example, the characteristic values can include a numerical value for a live birth order of the pregnancy for the subject. The computer system can store a machine learning model that receives a first set of input features and provides a second set of one or more output values. In some embodiments, the first set of input features can correspond to the characteristic values of the pregnancy of the subject. The second set of one or more output values can include a probability of a Cesarean delivery. The characteristic values can be input into the machine learning model to obtain the probability of the Cesarean delivery being required for the subject during an attempt of a vaginal delivery. The Cesarean delivery can be performed based on the probability.

Systems and methods are provided for assigning a quality parameter to a reproductive cellular structure. An image of the reproductive cellular structure is obtained. The image of the reproductive cellular structure is provided to a neural network to generate a value representing a morphology of the reproductive cellular structure. The value is compared to a predefined standard to provide a quality assurance metric representing one of a medical personnel, a facility, a growth medium, and an identity of the reproductive cellular structure.

Apparatus and methods are described including using a sensor to monitor a sleeping subject and generate a signal in response to the monitoring. A noise control device is controlled in response to the signal.