IL313100A - System and method for performing bpp - Google Patents

Description

48495/24 BPP SYSTEM AND METHOD Field of the Invention The present invention relates to a method for obtaining a biophysical profile (BPP) using a combination of acoustic and ultrasound data.

Background of the Invention A biophysical profile (BPP) test measures the fetus's health during pregnancy. A BPP test usually includes a non-stress test with electronic fetal heart monitoring (cardiotocography (CTG)) and fetal ultrasound imaging.

Fetal heart monitoring tracks the heart rate of the fetus during the pregnancy. This helps healthcare professionals (HCPs) assess the fetus's condition and detect early signs of distress.

The fetal heart rate and the mother's contractions are monitored to see how the fetus responds. Two types of monitoring—external or internal—can be used. Instruments that detect fetal heartbeats are placed around the pregnant woman's abdomen for external monitoring. An example is a belt with ultrasonic piezo or similar transducers or ECG electrodes. For internal monitoring, electrodes that measure fetal heartbeats are connected to the fetus's scalp.

Using ultrasound imaging, the BPP assesses the fetus' heart rate, muscle tone, movement, breathing, and amniotic fluid. A health practitioner may do it in the third trimester or earlier for high-risk pregnancies or if there are concerns about the fetus’ health, decreased fetal movement, fetal growth problems, or if the pregnancy goes past 42 weeks.

The BPP score 48495/24 The biophysical profile combines two tests to check the fetus's overall health: a nonstress test and an ultrasound.

The nonstress test checks the fetus’ heart rate and contractions. Devices are used to monitor the fetus’ movements and heart rate. The test evaluates fetal health and determines if the fetus is at risk for complications. The presence of fetal heart rate acceleration or deceleration is critical, and the test follows a systematic approach for interpretation. It does not hold predictive value and only indicates fetal hypoxemia at the time of the test. An example of guidelines for this test can be found at "Maternity - Fetal heart rate monitoring, GL2018_025, Ministry of Health, Public Health System, NSW, Australia" or at guidelines from the ACOG (American College of Gynecology), USA.

The biophysical profile test [National Library of Medicine, National Center for Biotechnology Information, Ultrasound Biophysical Profile, January 2022] evaluates fetal breathing, movement, tone, and amniotic fluid volume. Each area is given a score of 0 or 2, with a total score ranging from 0 to 10. A score of 8 to 10 is reassuring, 4 to 6 may require further testing or delivery, and a score of 0 or 2 almost always leads to immediate delivery. The test can help Health Care Professionals (HCP) decide if early delivery or medication is necessary.

Summary of the Invention The invention relates to a system adapted to perform BPP analysis in a non-clinical setup, comprising: a) an ultrasound apparatus adapted to scan a pregnant individual to obtain data relative to a fetus; 48495/24 b) a plurality of microphones adapted to receive sounds pertaining to a fetus and/or the pregnant individual; c) processing components adapted to receive and process data received both from said ultrasound apparatus and said plurality of microphones (wired or wireless). d) optionally, additional connected (wired or wireless, single use or reusable) external sensors, such as blood pressure, spo2 sensors, ECG stickers, etc.

Wherever the term “microphone” is mentioned, it should be understood in the broadest possible way, to include also other external sensors, such as piezo belts, for example. The microphones can be, for example, constructed from a film, piezo, or PVDF, with a sticker that includes gel (for good attachment). For wireless transmission, it includes a button battery, a flexible pCB, and a Bluetooth transmitter. For a wired setup, it includes two wires that connect all microphones (could be three, five, or more) to a specific connection in the cradle, such as 300 in Fig. 3.

In embodiments of the invention, the ultrasound apparatus is a home scanner, e.g., a hand- held scanner operatable by the patient. In other embodiments a scanner operatable by a healthcare professional is used.

When a home handheld device, such as the Pulsenmore ES device, is used, the processing components can be provided integrally to a smart device associated with the handheld device.

In some embodiments the processing components are located remotely to a smart device and are in wireless communication therewith or with the ultrasound scanner.

Also encompassed by the invention is a method for performing BPP analysis, comprising: 48495/24 a) scanning a pregnant individual to obtain data relative to a fetus using an ultrasound apparatus; b) recording using a plurality of microphone sounds pertaining to a fetus and/or the pregnant individual; c) processing data received both from said ultrasound apparatus and said plurality of microphones and /or other external sensors; and d) correlating data received from the plurality of microphones with that received from the ultrasound scanner, to obtain an improved analysis of the fetus and/or the pregnant individual status.

Brief Description of the Drawings In the drawings: Fig. 1 schematically illustrates a woman in an advanced month of pregnancy, preparing for a medical test according to the invention; Fig. 2 shows the woman of Fig. 1 in a lying position; and Fig. 3 is an example of an ultrasound device adapted for home use.

Detailed Description of the Invention Combining fetal heart rate (FHR), maternal heart rate (MHR), contractions, and maternal blood pressure with ultrasound imaging, according to the present invention, offers a comprehensive dataset for machine learning applications, especially but not only in a home environment, enhancing diagnostic accuracy and early detection of complications. 48495/24 Using multiple signal sources enriches machine learning datasets, resulting in models better suited for predicting complications like preterm labor and fetal distress. By analyzing patterns and anomalies in both physiological data and ultrasound images, this holistic approach allows for more accurate predictions and timely interventions. It reduces false positives and negatives while providing detailed visual confirmation of issues, thereby enhancing diagnostic accuracy and patient monitoring. For example, correlating images that contain low amniotic fluid with measurements of the fetus's heartbeat can generate an indication of an anomaly.

However, by measuring the blood pressure of the mother and correlating it with the abovementioned signals, it can be demonstrated that if the mother’s blood pressure is low, then probably, there is no anomaly. This can be further enhanced by machine learning.

Advanced signal processing techniques can detect subtle signs of distress in physiological signals that might be missed with ultrasound alone. Machine learning algorithms can recognize complex patterns associated with fetal or maternal distress. Understanding the time lag between physiological signals and ultrasound changes helps in identifying causative relationships, providing predictive insights for potential distress events.

For example, for high BMI ( >35 ), it is required to measure the contractions (low-frequency signals) against the fetus's heartbeat and or fetus movement to analyze the fetus's status; however, this is not the full picture of the fetus's viability. By adding additional inputs such as placenta location, AFI (Amniotic fluid index), or MVP (Maximum Vertical Pocket) that change during the contraction, all these changes are relatively small in comparison to a pregnant woman with BMI<35; hence, suitable algorithm and machine learning can provide a clear distinction between anomalies and non-anomalies cases. The same can be applied, for example, to pregnant women with a genetic disorder that may contain high amniotic fluid, for example, to pregnant women with a genetic disorder that may have high amniotic fluid 48495/24 (MVP>8cm). A suitable algorithm and machine learning can provide a clear distinction between anomalies and non-anomalies cases (fetal heartbeat>160 is an anomaly) during the contraction where the volume change and heartbeat rise to create a false negative or positive as the cases may be (as we know that this case creates abnormal amniotic fluid). As persons skilled in the art can understand, an algorithm of machine learning can be enriched by different anomalies to predict and generate the accurate status of the fetus.

Machine learning can also analyze ultrasound images to detect signs of fetal distress, such as abnormal movements or changes in amniotic fluid levels. Image segmentation can isolate specific anatomical features, while anomaly detection algorithms identify irregularities indicating distress. Object detection algorithms enhance monitoring by tracking key indicators like fetal breathing movements and blood flow in the umbilical cord .

Particularly (but not only) in a home environment, combining these technologies significantly improves maternal and fetal health monitoring. Home ultrasound devices and systems for measuring FHR, MHR, doppler ultrasound, contractions, and blood pressure make advanced monitoring accessible outside clinical settings. Continuous data collection at home allows for real-time monitoring and early detection of complications, reducing the need for frequent hospital visits. Integrating this data with telemedicine services ensures continuous care and timely interventions, enhancing outcomes for both mothers and babies .

Leveraging the combined data from multiple signal sources significantly enhances maternal and fetal health monitoring, leading to better health outcomes and more comprehensive prenatal care. For instance : - A complete Biophysical Profile (BPP) requires ultrasound imaging and continuous FHR monitoring, making home BPP feasible with this combined approach . 48495/24 - Non-stress tests (NSTs) assess fetal heart rate patterns in response to fetal movements.

Integrating continuous FHR monitoring with ultrasound imaging provides a more detailed picture of fetal well-being, identifying responses to maternal contractions and other stimuli that might be missed by FHR monitoring alone.

- Combining maternal blood pressure monitoring with FHR, MHR, and ultrasound imaging allows for the detection of early signs of preeclampsia and its impact on fetal health, enabling timely intervention and management .

- Regular monitoring of fetal growth and development is crucial for identifying developmental issues. Combining ultrasound measurements of fetal size and amniotic fluid levels with continuous FHR monitoring provides a comprehensive assessment, allowing for early detection of growth abnormalities and appropriate intervention .

- Continuous FHR monitoring combined with detailed ultrasound imaging of the fetal heart can improve the detection and diagnosis of fetal arrhythmias, allowing for early intervention and better management outcomes .

- Simultaneously monitoring MHR, FHR, contractions, and blood pressure, along with ultrasound imaging, helps clinicians better understand the interplay between maternal and fetal physiological states, leading to more personalized and effective care strategies.

In accordance with the above, the invention provides a system consisting of an ultrasound device in a working relationship with a plurality of microphones. Microphones that can produce useful inputs by recording sounds generated by the body of a pregnant woman and her fetus are known in the arts and are, therefore, not discussed herein for the sake of brevity.

However, the invention provides for the combined analysis of sounds generated by the aforementioned microphones and ultrasound scans to generate an enriched data set 48495/24 providing critical information on the status of both the mother and the fetus. This combined information permits to obtain a reliable BPP, which can be generated anywhere, including at home.

The ultrasound device can be of any kind, but in one embodiment of the invention, it is a handheld device adapted for use by a patient or other unskilled person. One such ultrasound device is shown in Fig. 3, which shows the Pulsenmore ES device manufactured by Pulsenmore Ltd. (https://pulsenmore.com/prenatal/). The device, generally indicated at 300 in the figure houses and connects with a smart device 301.

In a typical use case, the woman 100 (Figs. 1 and 2) affixes a plurality of microphones 101 to her belly, either using biocompatible adhesives or by affixing a harness which achieves it fixed position of the microphones it comprises. The microphones can, of course, be single-use or multi-use. The microphone can be passive or active (i.e., contain preamplification capacities and or wireless capabilities such as Bluetooth), but the bottom elements that come into contact with the woman's skin are preferably disposable.

In embodiments of the invention, the microphones are in communication with the smart device or cradle 301, which is equipped with software adapted to receive and analyze the signals generated by them. Furthermore, the smart device can be equipped with AI or machine learning components adapted to perform an integrative analysis of all inputs received by both the microphones and the ultrasound scanner, and to derive BPP information therefrom. In embodiments of the invention, the smart device 301 is in wireless communication with remote computing systems, which can perform part or all the analysis of the data received by the smart device. 48495/24 The spread of a microphone array over the belly can assist in roughly identifying the location of the fetal heart. Thus, the system (using the input from the microphones) will provide rough navigation instructions to the (lay) user to move the ultrasound transducer until it reaches close to the fetus' heart. The certainty that this is the fetus’ heart and further fine navigation is achieved, for example, by AI analysis of the ultrasound images .

This automated system for home use enables a lay user to replace a health care professional in conducting the scan for locating the fetus’ heart.

Claims (6)

48495/24 - 10 - Claims

1. A system adapted to perform BPP analysis, comprising: a) an ultrasound apparatus adapted to scan a pregnant individual to obtain data relative to a fetus; b) a plurality of microphones adapted to receive sounds pertaining to a fetus and/or the pregnant individual; c) processing components adapted to receive and process data received both from said ultrasound apparatus and said plurality of microphones and or sensors.

2. A system according to claim 1, wherein the ultrasound apparatus is a home scanner.

3. A system according to claim 1, wherein additional connected (wired or wireless, single-use or reusable) external sensors are provided, such as blood pressure, spo sensors, etc.

4. A system according to claim 1, wherein the processing components are integral to a smart device.

5. A system according to claim 1, wherein the processing components are located remotely to a smart device and are in wireless communication therewith or with the ultrasound scanner.

6. A method for performing BPP analysis, comprising: a) scanning a pregnant individual to obtain data relative to a fetus using an ultrasound apparatus; 48495/24 - 11 - b) recording using a plurality of microphone and /or other external sensors sounds pertaining to a fetus and/or the pregnant individual ; c) processing data received both from said ultrasound apparatus and said plurality of microphones; and d) correlating data received from the plurality of microphones with that received from the ultrasound scanner, to obtain an improved analysis of the fetus and/or the pregnant individual status.