Patent ID: 12213811

Throughout the drawings, the same reference numerals and characters, unless otherwise stated, are used to denote like features, elements, components, or portions of the illustrated embodiments. Moreover, while the subject invention will now be described in detail with reference to the drawings, the description is done in connection with the illustrative embodiments. It is intended that changes and modifications can be made to the described embodiments without departing from the true scope and spirit of the subject invention as defined by the appended claims.

WRITTEN DESCRIPTION

Near-IR spectroscopy, and pulse oximetry calculations, to estimate a percentage of oxygen bound to hemoglobin in the blood (also referred to herein as “hemoglobin oxygen saturation” or “SpO2”) may incorporate calculations using the modified Beer Lambert law (mBLL), which describes changes in hemoglobin absorption related to changes in light intensity of various wavelengths. Under the assumption that bulk tissue has homogenous characteristics, the modified Beer Lambert law can be written as Equation 1:

Δ⁢μa(λ)=-1r*DPF⁡(λ)⁢Id⁡(λ)-Is⁡(λ)Is⁡(λ)=-1r*DPF⁡(λ)⁢Δ⁢O⁢DEquation⁢1Where:Id=diastolic intensity of the fetal pulseIs=systolic intensity of the fetal pulser=source detector distance, which may be given by the geometry of an optical probe or source/detector combinationDPF=differential path length factor, which is not knownΔOD=change in optical densityΔμa=change in absorption coefficientλ=wavelength

Results from this equation may be used to extract values for concentrations of oxygenated hemoglobin (sometimes referred to herein as “c_HbO”) and deoxygenated hemoglobin (sometimes referred to herein as “c__Hb”) 2 according to Equation 2, below.
SpO2(%)=c_HbO/(c_HbO+c_Hb)  Equation 2

Although the modified Beer Lambert Law traditionally serves as the fundamental basis for near-IR spectroscopy, it is limited by several assumptions, including that light absorption within tissue is homogeneous, change in a differential path length factor is negligible, and that the light scattering within tissue is low. In complex in vivo, physio-optical environments of, for example, non-homogenous tissue and/or two or more layers of different types of tissue such as a fetus within a mother, these assumptions may not always hold true and yield accurate calculations. For example, traditional methods for calculating pulse oximetry using the modified Beer Lambert Law assume that photons travel a relatively short distance (e.g., 1 cm) and that there is a negligible change in the differential path length factor (DPF) across this distance. However, when a distance photons travel is larger than 1 cm (e.g., 1.5-10 cm) changes in the DPF can be significant thereby adversely impacting the accuracy SpO2 calculations. For example, in transabdominal fetal application, photons can travel 5 cm, 10 cm, or more and, consequently, changes in the DPF can be significant. To accurately account for DPF variability and calculate oxygen saturation is a complex problem, particularly when multiple wavelengths of light are used. One way to overcome this problem is to calculate SpO2 using a 2-layer description of the modified Beer Lambert Law that can independently calibrate for different layers of tissue.

However, a drawback of the using the two-layer Modified Beer Lambert Law calculations when calculating oxygen saturation of target tissue is that this approach is dependent on having an accurate depth of the target (e.g., a fetus) within the body input in order to accurately calculate the blood oxygen saturation. This may pose a challenge in a clinical situation because measuring (via, for example, an ultrasound or Doppler device) depth of a target (e.g., a fetus, fetal head, or fetal back) within a body is open to clinical interpretation and may not always be reliable, especially when differences of as little as 5 mm can impact the accuracy of calculations. Furthermore, a target's depth that is measured by an ultrasound or Doppler device may not accurately reflect that path of the photons because, for example, the photons are a different signal type (i.e., optical) than the sound waves used by the ultrasound/Doppler device and/or the ultrasound/Doppler device is not positioned where the optical probe is positioned so the ultrasound/Doppler device may be imaging a portion of the surface tissue that is not coincident with the placement of the optical probe. In order to overcome the target depth requirement, machine learning may be deployed as a methodology to develop a model that augments the 2-layered description of the modified Beer Lambert Law to arrive at target SpO2 values without requiring target depth as an input. This model would them be able to accurately determine fetal oximetry values without requiring fetal depth.

Transdermal in vivo measurements of target (e.g., fetal) SpO2 levels may involve placing an optical probe (e.g., one or more light source(s) and photodetector(s)) on the skin of a patient (e.g., a pregnant woman), transmitting an optical signal into the skin of the patient, and collecting resulting optical signals emitted from the skin of the patient via, for example, backscattering and/or transmission through the patient's non-target tissue and target's tissue. In many cases, determining SpO2 involves calculating the amplitudes of the AC signals, normalizing them using (dividing by) the amplitude of the DC signals, and multiplying the normalized AC signals by a calibration factor that takes into account, for example, abdominal and/or fetal tissue scattering properties and/or fetal depth as a function of wavelength of the incident optical signal/light. However, this normalization methodology is, in many cases, too generic of an approach because, for example, the impact of the maternal/fetal tissue on the behavior of the incident light is uniform along the pathlength of the optical signal. However, in many cases, this is inaccurate due to one or more confounding influences of, for example, maternal tissue, maternal physiology, maternal movement, and/or fetal tissue/physiology/movement when the optical probe is being used and/or during measurement of fetal SpO2. Hence, a different normalization methodology for the AC signals may assist with more accurately calculating fetal SpO2.

In some embodiments, using one or more optical probe(s) that include multiple light sources (also called “sources” herein) and/or multiple photodetectors (also called “detectors” herein) that facilitate multiple sets of sources and detectors that have different source-detector distances (i.e., different distances between the source and detector) may provide inputs that can be used to compensate for confounding influences in some situations because, for example, a mean depth of penetration for the light into the patient's tissue (e.g., pregnant mammal's abdomen) gets larger as the source-detector separation increases. Shorter separations between the source and detector result in light that penetrates the tissue less deeply than light from larger separations between the source and detector. Use of signals detected when the source/detector distance is relatively short biases these measurement towards measuring only the patient's non-target (e.g., maternal or abdominal) tissues (which are shallower than the target tissue), because the light detected by relatively close detectors only penetrates the patient's non-target tissue. In some cases, these detected signals may be called short separation signals.

Additionally, or alternatively, by using one or more probes with various source/detector distances both the patient's non-target-only signals (i.e., short separation signals) and a composite signal that includes light incident on both the patient's non-target and target tissue (sometimes referred to herein as a “composite signal”) may enable measurement and/or comparisons of variability of the patient's non-target and/or target tissue. In some instances, comparing detected signals from detectors with a short source/detector distance with detected signals from detectors with a longer source/detector distance may facilitate understanding of how the patient's non-target and/or target tissue my impact the behavior of light incident thereon. This information may be used, for example, to normalize AC signals and/or develop or adjust a calibration factor used to determine target SpO2, which may make calculation of target SpO2 more accurate than previously used techniques.

In, for example, a transabdominal fetal oximetry context, using machine learning as a methodology to develop a model that augments the 2-layered (one maternal and one fetal) description of the modified Beer Lambert Law to arrive at fetal hemoglobin oxygen saturation values without requiring fetal depth as an input and/or incorporates confounding influences of maternal and fetal tissue when determining fetal SpO2 requires a large data set of fetal SpO2 values so that many different scenarios with different fetal depths and/or confounding factors may be understood and factored into a determination of fetal SpO2 in a particular situation. A data set of fetal oximetry and/or SpO2 values calculated using a model, or mathematical simulation, of the fetal and/or maternal tissue, which is sometimes referred to herein as “calculated fetal oximetry values” or “calculated fetal Spo2 values,” may be used to train and/or test a processor to determine fetal SpO2 values. In some instances, the model may be a physiological model of the fetus and mother, which in some cases may include static and time variant tissue layer properties of the fetus and/or pregnant mammal that may calculate how light may behave when transmitted and/or detected with various source-separation distances and light wavelengths. These calculated SpO2 values may, in some cases, represent a simulated light transmission time series data set (“also referred to herein as a “simulated light transmission data set”) that models the optical signals that may be detected by a detector over time and may thereby be available for analysis, manipulation, and input into machine learning models in a manner similar to, for example, actual light transmission data sets collected from a detector and/or actual fetal SpO2 values. In some embodiments, simulated light transmission data used to calculate fetal oximetry and/or SpO2 values may be determined and/or generated using machine learning equipment and/or techniques.

Additionally, or alternatively, the simulated light transmission data may be generated by software designed to build models and/or generate simulated data for light traveling through tissue and/or tissue model(s). Examples of this software are Monte Carlo simulations and Near Infrared Fluorescence and Spectral Tomography (NIRFAST, Dartmouth College, NH) software. Use of modeling software allows for models to be built that incorporate a variety of parameters such as wavelength of light used, DFP, source/detector distance, and/or maternal and/or fetal morphological, geometric and/or physiological parameters such as abdominal wall thickness and/or composition, tissue composition, tissue type, muscular state of the maternal uterus, maternal skin color, fetal skin color, and/or position on the fetus on which the light was incident. At times, the parameters of the data sets and/or inputs used to generate the models may be changed discretely, randomly, pseudo-randomly, and/or selected within a range and/or distribution of values. Additionally, or alternatively, combinations of input parameters may be used to generate the simulated signals. This approach and/or a combination of approaches may provide a random covering of the possible simulated light transmission data sets/time series and/or calculated fetal SpO2 values that may be used for training and testing the machine learning model. Additionally, or alternatively, features may be extracted from simulated light transmission data sets to be used as inputs to the machine learning architecture or models. Examples of potential features that may be extracted from simulated light transmission data sets are correlation amplitudes, FFTs, DC levels, AC levels or other post-processed signal descriptors.

Possible uses and/or advantages of the present invention include, but are not limited to, facilitation of perturbation analysis of the data sets whereby one variable (e.g., maternal heart rate, fetal heart rate, fetal distance, source/detector distance) is changed at a time to determine an impact (if at all) on the calculated fetal SpO2 values. This is a substantial advantage over experimentally determined data sets, or calculated fetal SpO2 values, because it is difficult, in real life, to control only one factor at a time because, often times, multiple factors change at unpredictable rates/times with in vivo situations.

Additionally, or alternatively, the present invention may be used to perform sensitivity analysis, which may allow for changing multiple variables/parameters used to generate the models and/or simulated light transmission data sets so that, for example, the results (e.g., calculated fetal SpO2 values) may be evaluated for accuracy and/or to determine how multiple variables may interact with one another to vary calculated fetal SpO2 values. Model variables that may be modified to perform sensitivity analysis include, but are not limited to, noise, wavelength of light used, DFP, source/detector distance, and/or maternal and/or fetal morphological, geometric and/or physiological parameters such as abdominal wall thickness and/or composition, tissue composition, tissue type, muscular state of the maternal uterus, maternal skin color, fetal skin color, and/or position on the fetus on which the light was incident.

Additionally, or alternatively, an advantage of the present invention is that use of simulated light transmission data sets and/or fetal SpO2 values calculated using the simulated light transmission data sets to train a simulate fetal oximetry model, or teach the machine, reduces the number of experimentally, or measured, in vivo light transmission data sets and/or fetal SpO2 values that are necessary to arrive at an accurately trained model. This, in turn, reduces the need for a very large and difficult to obtain data set of actual fetal SpO2 values determined using measured/in vivo data (e.g., a blood gas analysis).

FIG.1Aprovides an exemplary system10for using machine learning to develop a simulated fetal oximetry model and/or an in vivo fetal oximetry model as disclosed herein. In some cases, the developed simulated fetal oximetry model and/or an in vivo fetal oximetry model may compensate for one or more physio-optical influences that occur when performing transabdominal fetal oximetry. System10includes a cloud computing platform11, a communication network12, a computer13, a display device14, and a database15. In many instances, communication network12is the Internet. The components of system10may be coupled together via wired and/or wireless communication links. In some instances, wireless communication of one or more components of system10may be enabled using short-range wireless communication protocols designed to communicate over relatively short distances (e.g., BLUETOOTH®, near field communication (NFC), radio-frequency identification (RFID), and Wi-Fi) with, for example, a computer or personal electronic device (e.g., tablet computer or smart phone) as described below.

Cloud computing platform11may be any cloud computing platform11configured to run a machine learning program and/or support a machine learning architecture such as TensorFlow. Exemplary cloud computing platforms include, but are not limited to, Amazon Web Service (AWS), Rackspace, and Microsoft Azure. Exemplary machine learning architectures include neural networks, artificial neural networks, Bayesian networks, and/or software or hardware that utilizes artificial intelligence.

Computer13may be configured to act as a communication terminal to cloud computing platform11via, for example, communication network12and may facilitate provision of the results machine learning calculations (e.g., training and/or testing of a simulated fetal oximetry model, tuning of a simulated fetal oximetry model, training and/or testing of a in vivo fetal oximetry model, and/or tuning of the in vivo fetal oximetry model) performed on cloud computing platform11to display device155. Exemplary computers13include desktop and laptop computers, servers, tablet computers, personal electronic devices, mobile devices (e.g., smart phones), and the like. Exemplary display devices155are computer monitors, tablet computer devices, and displays provided by one or more of the components of system10. In some instances, display device155may be resident in computer13. Computer13may be communicatively coupled to database15, which may be configured to store information (e.g., simulated optical inputs, simulated light transmission data sets, levels of a simulated fetal oximetry model, simulated and/or calculated fetal oximetry values, in vivo light transmission data sets, levels of an in vivo fetal oximetry model, model testing results, etc.), or inputs, used for machine learning and/or sets of instructions for computer13and/or cloud computing platform11.

FIG.1Bis a block diagram of an exemplary system100for measuring in vivo light transmission data, measuring in vivo fetal oximetry values, and/or determining in vivo fetal oximetry values. In some embodiments, system100and/or a component thereof, such as computer13, may be communicatively coupled to system10, or a component thereof such as communication network12and/or cloud computing platform11. The components of system100may be coupled together via wired and/or wireless communication links. In some instances, wireless communication of one or more components of system100may be enabled by using short-range wireless communication protocols designed to communicate over relatively short distances (e.g., BLUETOOTH®, near field communication (NFC), radio-frequency identification (RFID), and Wi-Fi) with, for example, a computer or personal electronic device (e.g., tablet computer or smart phone) as described below.

System100includes a light source105and a detector160that, at times, may be housed in a single housing, which may be referred to as a fetal probe115. Light source105may include a single, or multiple light sources and detector160may include a single, or multiple detectors.

Light sources105may transmit light at light of one or more wavelengths, including NIR, into the pregnant mammal's abdomen. Typically, the light emitted by light sources105will be focused or emitted as a narrow beam to reduce spreading of the light upon entry into the pregnant mammal's abdomen. Light sources105may be, for example, a LED, and/or a LASER, a tunable light bulb and/or a tunable LED that may be coupled to a fiber optic cable. On some occasions, the light sources may be one or more fiber optic cables optically coupled to a laser and arranged in an array. In some instances, the light sources105may be tunable or otherwise user configurable while, in other instances, one or more of the light sources may be configured to emit light within a pre-defined range of wavelengths. Additionally, or alternatively, one or more filters (not shown) and/or polarizers may filter/polarize the light emitted by light sources105to be of one or more preferred wavelengths. These filters/polarizers may also be tunable or user configurable.

An exemplary light source105may have a relatively small form factor and may operate with high efficiency, which may serve to, for example, conserve space and/or limit heat emitted by the light source105. In one embodiment, light source105is configured to emit light in the range of 770-850 nm. Exemplary flux ratios for light sources include, but are not limited to a luminous flux/radiant flux of 175-260 mW, a total radiant flux of 300-550 mW and a power rating of 0.6 W-3.5 W.

Detector160may be configured to detect a light signal emitted from the pregnant mammal and/or the fetus via, for example, transmission and/or back scattering. Detector160may convert this light signal into an electronic signal, which may be communicated to a computer or processor and/or an on-board transceiver that may be capable of communicating the signal to the computer/processor. This emitted light might then be processed in order to determine how much light, at various wavelengths, passes through the fetus and/or is reflected and/or absorbed by the fetal oxyhemoglobin and/or de-oxyhemoglobin so that a fetal hemoglobin oxygen saturation level may be determined. This processing will be discussed in greater detail below. In some embodiments, detector160may be configured to detect/count single photons. At times, the optical signals detected by detector160and converted into an electronic signal corresponding to the detected optical signal may be referred to herein as measured, or in vivo, light transmission data and/or a detected electronic signal.

Exemplary detectors include, but are not limited to, cameras, traditional photomultiplier tubes (PMTs), silicon PMTs, avalanche photodiodes, and silicon photodiodes. In some embodiments, the detectors will have a relatively low cost (e.g., $50 or below), a low voltage requirement (e.g., less than 100 volts), and non-glass (e.g., plastic) form factor. In other embodiments, (e.g., contactless pulse oximetry) a sensitive camera may be deployed to receive light emitted by the pregnant mammal's abdomen. For example, detector160may be a sensitive camera adapted to capture small changes in fetal skin tone caused by changes in cardiovascular pressure associated with fetal myocardial contractions. In these embodiments, detector160and/or fetal probe115may be in contact with the pregnant mammal's abdomen, or not, as this embodiment may be used to perform so-called contactless pulse oximetry. In these embodiments, light sources105may be adapted to provide light (e.g., in the visible spectrum, near-infrared, etc.) directed toward the pregnant mammal's abdomen so that the detector160is able to receive/detect light emitted by the pregnant mammal's abdomen and fetus. The emitted light captured by detector160may be communicated to computer13for processing to convert the images to a measurement of fetal hemoglobin oxygen saturation according to, for example, one or more of the processes described herein.

A fetal probe115, light source105, and/or detector160may be of any appropriate size and, in some circumstances, may be sized so as to accommodate the size of the pregnant mammal using any appropriate sizing system (e.g., waist size and/or small, medium, large, etc.). Exemplary lengths for a fetal probe115include a length of 4 cm-40 cm and a width of 2 cm-10 cm. In some circumstances, the size and/or configuration of a fetal probe115, or components thereof, may be responsive to skin pigmentation of the pregnant mammal and/or fetus. In some instances, the fetal probe115may be applied to the pregnant mammal's skin via tape or a strap that cooperates with a mechanism (e.g., snap, loop, etc.) (not shown). In some instances, fetal probe115may act to pre-process or filter detected signals.

System100includes a number of optional independent sensors/probes designed to monitor various aspects of maternal and/or fetal health and may be in contact with a pregnant mammal. These probes/sensors are a NIRS adult hemoglobin probe125, a pulse oximetry probe130, a Doppler and/or ultrasound probe135, and a uterine contraction measurement device140. Not all embodiments of system100will include all of these components. In some embodiments, system100may also include an electrocardiography (ECG) machine (not shown) that may be used to determine the pregnant mammal's and/or fetus's heart rate and/or an intrauterine pulse oximetry probe (not shown) that may be used to determine the fetus's heart rate. The Doppler and/or ultrasound probe135may be configured to be placed on the abdomen of the pregnant mammal and may be of a size and shape that approximates a silver U.S. dollar coin and may provide information regarding fetal position, orientation, and/or heart rate. Pulse oximetry probe130may be a conventional pulse oximetry probe placed on pregnant mammal's hand and/or finger to measure the pregnant mammal's hemoglobin oxygen saturation. NIRS adult hemoglobin probe125may be placed on, for example, the pregnant mammal's 2nd finger and may be configured to, for example, use near infrared spectroscopy to calculate the ratio of adult oxyhemoglobin to adult de-oxyhemoglobin. NIRS adult hemoglobin probe125may also be used to determine the pregnant mammal's heart rate.

Optionally, system100may include a uterine contraction measurement device140configured to measure the strength and/or timing of the pregnant mammal's uterine contractions. In some embodiments, uterine contractions will be measured by uterine contraction measurement device140as a function of pressure (e.g., measured in e.g., mmHg) over time. In some instances, the uterine contraction measurement device140is and/or includes a tocotransducer, which is an instrument that includes a pressure-sensing area that detects changes in the abdominal contour to measure uterine activity and, in this way, monitors frequency and duration of contractions.

In another embodiment, uterine contraction measurement device140may be configured to pass an electrical current through the pregnant mammal and measure changes in the electrical current as the uterus contracts. Additionally, or alternatively, uterine contractions may also be measured via near infrared spectroscopy using, for example, light received/detected by detector160because uterine contractions, which are muscle contractions, are oscillations of the uterine muscle between a contracted state and a relaxed state. Oxygen consumption of the uterine muscle during both of these stages is different and these differences may be detectable using NIRS.

Measurements and/or signals from NIRS adult hemoglobin probe125, pulse oximetry probe130, Doppler and/or ultrasound probe135, and/or uterine contraction measurement device140may be communicated to receiver145for communication to computer13and display on display device155and, in some instances, may be considered secondary signals. As will be discussed below, measurements provided by NIRS adult hemoglobin probe125, pulse oximetry probe130, a Doppler and/or ultrasound probe135, uterine contraction measurement device140may be used in conjunction with fetal probe115to isolate a fetal pulse signal and/or fetal heart rate from a maternal pulse signal and/or maternal heart rate. Receiver145may be configured to receive signals and/or data from one or more components of system100including, but not limited to, fetal probe115, NIRS adult hemoglobin probe125, pulse oximetry probe130, Doppler and/or ultrasound probe135, and/or uterine contraction measurement device140. Communication of receiver145with other components of system may be made using wired or wireless communication.

In some instances, one or more of NIRS adult hemoglobin probe125, pulse oximetry probe130, a Doppler and/or ultrasound probe135, uterine contraction measurement device140may include a dedicated display that provides the measurements to, for example, a user or medical treatment provider. It is important to note that not all of these probes may be used in every instance. For example, when the pregnant mammal is using fetal probe115in a setting outside of a hospital or treatment facility (e.g., at home or work) then, some of the probes (e.g., NIRS adult hemoglobin probe125, pulse oximetry probe130, a Doppler and/or ultrasound probe135, uterine contraction measurement device140) of system100may not be used.

In some instances, receiver145may be configured to process or pre-process received signals so as to, for example, make the signals compatible with computer13(e.g., convert an optical signal to an electrical signal), improve signal to noise ratio (SNR), amplify a received signal, etc. In some instances, receiver145may be resident within and/or a component of computer13. In some embodiments, computer13may amplify or otherwise condition the received detected signal so as to, for example, improve the signal-to-noise ratio.

Receiver145may communicate received, pre-processed, and/or processed signals to computer13. Computer13may act to process the received signals, as discussed in greater detail below, and facilitate provision of the results to a display device155. Exemplary computers13include desktop and laptop computers, servers, tablet computers, personal electronic devices, mobile devices (e.g., smart phones), and the like. Exemplary display devices155are computer monitors, tablet computer devices, and displays provided by one or more of the components of system100. In some instances, display device155may be resident in receiver145and/or computer13. Computer13may be communicatively coupled to database170, which may be configured to store information regarding physiological characteristic and/or combinations of physiological characteristic of pregnant mammals and/or their fetuses, impacts of physiological characteristic on light behavior, information regarding the calculation of hemoglobin oxygen saturation levels, calibration factors, and so on.

In some embodiments, a pregnant mammal may be electrically insulated from one or more components of system100by, for example, an electricity isolator120. Exemplary electricity insulators120include circuit breakers, ground fault switches, and fuses.

In some embodiments, system100may include an electro-cardiogram (ECG) machine175configured to ascertain characteristics of the pregnant mammal's heart rate and/or pulse and/or measure same. These characteristics may be used as, for example, a secondary signal and/or maternal heart rate signal as disclosed herein.

In some embodiments, system100may include a ventilatory/respiratory signal source180that may be configured to monitor the pregnant mammal's respiratory rate and provide a respiratory signal indicating the pregnant mammal's respiratory rate to, for example, computer13. Additionally, or alternatively, ventilatory/respiratory signal source180may be a source of a ventilatory signal obtained via, for example, cooperation with a ventilation machine. Exemplary ventilatory/respiratory signal sources180include, but are not limited to, a carbon dioxide measurement device, a stethoscope and/or electronic acoustic stethoscope, a device that measures chest excursion for the pregnant mammal, and a pulse oximeter. A signal from a pulse oximeter may be analyzed to determine variations in the PPG signal that may correspond to respiration for the pregnant mammal. Additionally, or alternatively, ventilatory/respiratory signal source180may provide a respiratory signal that corresponds to a frequency with which gas (e.g., air, anesthetic, etc.) is provided to the pregnant mammal during, for example, a surgical procedure. This respiratory signal may be used to, for example, determine a frequency of respiration for the pregnant mammal.

In some embodiments, system100may include a timestamping device185. Timestamping device185may be configured to timestamp a signal provided by, for example, fetal probe115, Doppler/ultrasound probe135, pulse oximetry probe130, NIRS adult hemoglobin probe, uterine contraction measurement device140, ECG175, and/or ventilatory/respiratory signal source180with a timestamp that represents, for example, an event (e.g., time, or t, =0, 10, 20, etc.) and/or chronological time (e.g., date and time). Timestamping device185may time stamp a signal via, for example, introducing a ground signal into system100that may simultaneously, or nearly simultaneously, interrupt or otherwise introduce a stamp or other indicator into a signal generated by one or more of, for example, fetal probe115, Doppler/ultrasound probe135, pulse oximetry probe130, NIRS adult hemoglobin probe, uterine contraction measurement device140, ECG175, and/or ventilatory/respiratory signal source180. Additionally, or alternatively, timestamping device185may time stamp a signal via, for example, introducing an optical signal into system100that may simultaneously, or nearly simultaneously, interrupt or otherwise introduce a stamp or other indicator into a signal generated by one or more of, for example, fetal probe115, pulse oximetry probe130, NIRS adult hemoglobin probe, uterine contraction measurement device140. Additionally, or alternatively, timestamping device185may time stamp a signal via, for example, introducing an acoustic signal into system100that may simultaneously, or nearly simultaneously, interrupt or otherwise introduce a stamp or other indicator into a signal generated by one or more of, for example, fetal probe115, Doppler/ultrasound probe135, and/or ventilatory/respiratory signal source180.

A timestamp generated by timestamping device185may serve as a simultaneous, or nearly simultaneous starting point, or benchmark, for the processing, measuring, synchronizing, correlating, and/or analyzing of a signal from, for example, fetal probe115, Doppler/ultrasound probe135, pulse oximetry probe130, NIRS adult hemoglobin probe, uterine contraction measurement device140, ECG175, and/or ventilatory/respiratory signal source180. In some instances, a time stamp may be used to correlate and/or synchronize two or more signals generated by, for example, fetal probe115, Doppler/ultrasound probe135, pulse oximetry probe130, NIRS adult hemoglobin probe, uterine contraction measurement device140, ECG175, and/or ventilatory/respiratory signal source180so that, for example, they align in the time domain.

FIG.2is a flowchart showing an exemplary process200for generating a plurality of sets of simulated light transmission data and corresponding oximetry values using a computer-generated, or simulated, model of animal tissue. Process200may be executed by, for example, system100,10, and/or components thereof.

Initially, in step205, a two and/or three-dimensional model of a portion of animal tissue may be generated and/or received. The layers of the model may each have different optical properties such as absorption and/or reflection characteristics, blood saturation characteristics, in some cases, these optical properties may be dictated by properties of the tissue such as lipid content, water content, density, and/or tissue type. When process200is executed multiple times, one or more aspects of the model received and/or generated in step205may change.

The model(s) of step205may include, for example, 1-8 layers of tissue; some of which may be fetal tissue. In many embodiments, the modeled tissue may include at least two layers one of which corresponds to maternal tissue and the other layer corresponds to fetal tissue.FIG.9provides an image of an exemplary seven-layer two-dimensional model900of a pregnant mammal's abdomen and her fetus that may be received and/or generated at step205. The seven layers of two-dimensional model900are 1) maternal dermal, 2) maternal subdermal, 3) maternal uterus, 4) fetal scalp, 5) fetal arterial, 6) fetal skull, and 7) fetal brain. Each of these layers may have different optical properties based on, for example, light wavelength, light intensity, tissue layer composition, tissue layer thickness, and/or tissue layer geometry.

In some cases, execution of step205may also include receipt and/or selection of parameters or rules for the model, some of which may be machine learning inputs and/or optical properties of one or more layers of the model.FIG.10provides a table of exemplary parameters1000that may be used to generate a model in step205and/or may govern one or more aspects and/or functions of a model that is received. When a generated model is received in step205(as opposed to generated), a table like the table ofFIG.10may also be received so that one or more parameters of the model may be understood. In some embodiments, one or more parameters of a model may dictate behavior of one or more simulated light transmission data sets.

The matrix of properties of table1000may be used to generate a two- and/or three-dimensional model, like exemplary model900, of the pregnant mammal and/or fetus, which may then be used to generate a plurality of simulated light transmission data sets. More specifically, table1000provides exemplary values for wavelength of light emitted by the source, a distance between the source and a detector, fetal cardiac state, maternal cardiac state, fetal depth, fetal SpO2, maternal SpO2, fetal scattering coefficient multiplier, and maternal scattering coefficient multiplier. Additionally, or alternatively, other parameters may be used to generate the models such as tissue composition (e.g., lipid content, water content, muscle cell content, etc.), noise, ambient light, geometry of the portion of the human body being modeled, fetal and maternal cross correlation with heartbeats, DC level, normalization ratios, and/or fetal depth.

In some embodiments, a photoplethysmogram (PPG) modulated signal may be integrated into one or more of the models generated and/or received in step205to simulate cardiac cycles for the pregnant mammal and/or fetus. The PPG modulated signal may have, for example, a variable 1% to 2% change in systolic blood volume for the pregnant mammal and/or fetus.FIG.11provides a graph1100that plots a simulated fetal and maternal PPG signals over time in seconds, wherein a PPG signal for the mother/pregnant mammal1105is shown in black and a PPG signal for the fetus1110is shown in grey. In some embodiments, noise and/or a confounding factor may be added to the PPG signal for the fetus1110and/or pregnant mammal1105as part of, for example, perturbation analysis using the model(s).

In step210, one or more simulated optical inputs for the generation of simulated light transmission data as simulated light corresponding to the simulated optical inputs travels through the model of step205may be selected, received, and/or configured. Exemplary simulated optical inputs include, but are not limited to, simulated light wavelength, intensity, modulation of the light (e.g., a duration of successive light pulses), and/or a range of wavelengths. In some cases, the simulated optical inputs will be for the generation of simulated infra-red and/or near infra-red light.

Next, in step215, a simulation using the model and simulated optical inputs of steps205and210, respectively, may be run wherein simulated light is transmitted through the model and “detected” by a simulated photodetector. A result of execution of step215is the generation of a set of simulated light transmission data. In some cases, a set of simulated light transmission data may correspond to simulated light being transmitted through the model for a period of time (e.g., 15, 30, or 60 seconds; 1, 5, or 10 minutes). Step215may be executed a plurality (e.g., 50,000; 100,000; 500,000; 1,000,000; 5,000,000) of times thereby generating a plurality of sets of simulated light transmission data. The plurality of simulated light transmission data sets may then be stored in a database (step220) like database15and/or170. Simulated light transmission data sets may include a simulation of an electronic signal that may be “detected” by a photodetector responsively to detecting light (in this case, the simulated optical input) as it travels through the animal model.

In step225, an oximetry value, which may correspond to a calculated fetal SpO2 value, for each set of simulated light transmission data may be determined and/or received. The oximetry value may be, for example, a maternal hemoglobin oxygen saturation level, a maternal tissue oxygenation level, a fetal hemoglobin oxygen saturation level, and/or a fetal tissue oxygenation level. When the oximetry values are hemoglobin oxygen saturation levels, the oximetry values may be determined via, for example, inputting the simulated light transmission data into the Beer Lambert Law or a modified version of the Beer Lambert Law as explained above using Equations 1 and 2. When the oximetry values are tissue oxygen saturation levels, the oximetry values may be determined via, for example, diffuse optical tomography (DOT) or another tissue oxygen saturation determination technique. Following step225, the oximetry values and/or correlations between each set of simulated light transmission data and it's respective oximetry value may be stored in a database (step230) like database15and/or170.

FIG.3is a flowchart showing an exemplary process300for generating a plurality of sets of simulated light transmission data and corresponding oximetry values using light transmitted through a physical model of animal tissue. Portions of process300may be executed by, for example, system100,10, and/or components thereof.

In step305, one or more simulated optical inputs for the generation of one or more sets of simulated light transmission data as light travels through a physical model of tissue may be selected, received, and/or configured. The physical model of tissue may comprise one or more layers that have the same or different optical properties. The physical model may be made from for example, gels, aqueous solutions, and lipids. Exemplary optical inputs include, but are not limited to, light wavelength, intensity, modulation of the light (e.g., a duration of successive light pulses), and/or a range of wavelengths. In many cases, the optical inputs will be for the generation of infra-red and/or near infra-red light.

Next, in step310, a plurality of sets of detected electronic signals corresponding to light generated using the optical inputs of step305that has passed through the physical model and been detected by a photodetector such as detector160may be received from the photodetector. In some cases, the detected electronic signals may correspond to light being transmitted through the physical model for a period of time (e.g., 15, 30, or 60 seconds; 1, 5, or 10 minutes). A result of execution of step310may be the generation of a set of simulated light transmission data. Step310may be executed a plurality (e.g., 50,000; 100,000; 500,000; 1,000,000; 5,000,000) of times thereby generating a plurality of sets of simulated light transmission data. The plurality of sets of detected electronic signals may then be stored in a database (step315) like database15and/or170.

In step320, an oximetry value for each set of detected electronic signals may be determined and/or received. The oximetry value may be, for example, a maternal hemoglobin oxygen saturation level, a maternal tissue oxygenation level, a fetal hemoglobin oxygen saturation level, and/or a fetal tissue oxygenation level. When the oximetry values are hemoglobin oxygen saturation levels, the oximetry values may be determined via, for example, the Beer Lambert Law or a modified version of the Beer Lambert Law as explained above using Equations 1 and 2. When the oximetry values are tissue oxygen saturation levels, the oximetry values may be determined via, for example, diffuse optical tomography (DOT) or another tissue oxygen saturation determination technique. Following step320, the oximetry values and/or correlations between each set of detected electronic signals (which may also be referred to herein as simulated light transmission data) and it's respective oximetry value may be stored in a database (step325) like database15and/or170.

FIGS.4A and4Bprovide a flowchart (over two pages) showing an exemplary process400for developing a model to accurately calculate oximetry values for a target tissue within a body, such as a fetus in-utero. Process400may be executed by, for example, system100,10, and/or components thereof.

Initially, in step402, a tissue model may be received (e.g., following execution of process200and/or300) and/or generated using, for example, a process similar to process200and/or300. The tissue model may be a two and/or three-dimensional model of a portion of an animal (e.g., human) body with one or more layers of tissue. The modeled layer(s) of tissue may have different optical properties. An exemplary tissue model is provided by model900shown inFIG.9, as discussed above. For the purposes of discussion, a layer of the tissue model may correspond to a model of abdominal tissue for a pregnant mammal and another layer may correspond to a model of fetal tissue but, it is noted that the models of other parts of the body that have two or more layers may also be received and/or generated in step402. In some cases, execution of step402may also include receipt and/or selection of parameters or rules for the model such as the parameters showing in table1000ofFIG.10, discussed above.

In some embodiments, a photoplethysmogram (PPG) modulated signal may be integrated into one or more of the models generated in step402to simulate cardiac cycles for the pregnant mammal and/or fetus. The PPG modulated signal may have, for example, a variable 1% to 2% change in systolic blood volume for the pregnant mammal and fetus.FIG.11provides a graph1100that plots a simulated fetal and maternal PPG signals over time in seconds, wherein the PPG signal for the mother/pregnant mammal is shown in blue and the PPG signal for the fetus is shown in red. In some embodiments, noise and/or a confounding factor may be added to the PPG signal for the fetus and/or pregnant mammal as part of, for example, perturbation analysis using the models.

In step404, a plurality (e.g., 500,000; 1,000,000; 5,000,000) of simulated light transmission data sets that simulate light traveling, over a period of time (e.g., 10 s, 30 s, 60 s, 5 minutes, etc.), through the models received and/or generated in step402and being detected by a detector like detector160may be generated and/or received. In some embodiments, execution of step404may include generation of one or more time series waveforms with variable fetal (100 to 240 BPM) and/or maternal (50 to 12 BPM) heart-rates, amplitudes, and phases between them. Individual parameters used to generate each of the simulated light transmission data sets may be selected randomly, pseudo randomly, and/or systematically according to, for example, a physiologically appropriate distribution (e.g., likelihood of occurrence within a population) assigned to each parameter.

In some embodiments, execution of step404may include running a plurality (e.g., 50-500) of experiments and/or simulations with different inputs (e.g., fetal and maternal cross correlation with heartbeats, DC level, maternal SpO2, normalization ratios, fetal depth, and/or maternal optical scattering properties), different machine learning architectures. In some cases, different classifiers and/or loss functions may generate a large number (e.g., 2-5 million) of data sets from which fetal oximetry values (e.g., fetal SpO2, fetal tissue oxygen saturation, etc.) may be calculated. Additionally, or alternatively, execution of step404may include running a plurality (e.g., 50-500) of experiments and/or simulations with different inputs that pertain to features of equipment (e.g., detector sensitivity, lag times, light source characteristics, errors or noise that may be introduced into a signal when particular equipment is used, etc.) that may be used when taking in vivo light transmission and/or fetal oximetry measurements.

In some embodiments, execution of step404may include generating simulated light transmission data sets where the light transmission data is “received” from a plurality (e.g., 2, 4, 6, or 8) of different detectors and/or is transmitted by a plurality (e.g., 2, 4, 6, or 8) of sources. Additionally, or alternatively, the simulated detector signals may correspond to light of different wavelengths and/or from different types of light sources. Additionally, or alternatively, execution of step404may include generating desired features of the simulated light transmission data sets such as, for example, correlation amplitudes, DC levels, and/or fast Fourier transforms (FFTs). In some embodiments, execution of step404may include calculating one or more correlation amplitudes for the simulated light transmission data sets using, for example, time series data.

The received and/or generated simulated light transmission data sets may then be stored in a database like database15and/or170(step406). Optionally, in step408, the simulated light transmission data sets may then be divided into a training set (e.g., 60%, 70%, or 80% of the data sets) and a testing set (e.g., 40%, 30%, or 20% of the data sets).

Optionally, in step410, inputs to the machine learning architecture and/or software program for determining fetal oximetry values may be selected. Exemplary inputs include, but are not limited to, fetal depth, fetal heart rate, maternal heart rate, equipment characteristics, background noise characteristics, maternal geometrical characteristics, maternal physiological characteristics, fetal geometrical characteristics, fetal physiological characteristics and/or maternal oximetry values (e.g., SpO2). In some embodiments, one or more inputs may be received from a component of system100such as ECG175, Doppler/ultrasound probe135, pulse oximetry probe130, NIRS adult hemoglobin probe125, and/or ventilator/ventilatory signal device. Input features may be normalized to standard mean and/or variance values, such as zero mean and unit variance, and, in some instances, may be combined into composite features that are then input into the machine learning architecture. In some cases, the machine learning architecture disclosed herein may be a deep learning network architecture that may include convolutional nets and engineered feature layers. Additionally, or alternatively, the machine learning architecture may be a neural network, an artificial neural network, a Bayesian network, and/or software or hardware that utilizes artificial intelligence. In some embodiments, execution of step410may include downsampling and/or activating one or convolutional layers of the machine learning architecture and/or a model (e.g., a simulated fetal oximetry model) generated by the machine learning architecture. In some cases, execution of step410may also include adding one or more engineered features, bias, and/or classifier layers to the machine learning architecture and/or a model (e.g., a simulated fetal oximetry model) generated by the machine learning architecture. Additionally or alternatively, models (e.g., simulated fetal oximetry models) generated by process400may include tree-based models or ensembles of layered and/or tree-based models. Additionally, or alternatively, models (e.g., simulated fetal oximetry models) generated by process400may incorporate K-fold cross-validation to, for example, generate the expected error, receiver operating characteristic (ROC), and/or area under the curve (AUC) values for the model.

In some embodiments, execution of step410may include selection of one or more types of outputs that may be incorporated into the machine learning architecture. Exemplary outputs include predicted fetal oximetry (e.g., SpO2 and/or fetal tissue oxygen saturation) values and a binary fetal hypoxia, fetal hypoxemia, fetal non-hypoxia, and/or fetal non-hypoxemia (e.g., fetal SpO2 above/below 30%) indication.

In step412, the simulated light transmission data sets and/or training data set (when step408is executed) may be input into the machine learning architecture to generate and/or train a first version of a fetal oximetry model that may be configured to, for example, to predict a first set of outputs (e.g., fetal SpO2 values, fetal tissue oxygen saturation, and/or fetal hypoxemia or non-hypoxemia determinations). The first version of the simulated fetal oximetry model may include a plurality of layers and/or functions and, in some cases, may include one or more small layered network(s), sub-networks, and/or a Support Vector Machine. In some embodiments, execution of step412may include communication of the machine learning inputs and/or machine learning architecture to, for example, a machine learning computer platform and/or neural network such as a machine learning platform resident on/within cloud computing platform11. In step414, the first version of the simulated fetal oximetry model may be stored in a database such as database15and/or170.

Optionally, in step416, the first version of the simulated fetal oximetry model and/or first set of outputs may be tested using, for example, the testing data set from step408. The results of the testing may then be evaluated (step418) and used to modify the first version of the simulated fetal oximetry model thereby generating a second version of the simulated fetal oximetry model (step420) via, for example, training and/or tuning the first version of the simulated fetal oximetry algorithm using the machine learning architecture. The second version of the simulated fetal oximetry model may be used to predict a second set of outputs. In some embodiments, the second version of the simulated fetal oximetry model may be similar, or identical to, the first version of the fetal oximetry model.

Then, in step422, a set of measured, or actual, in vivo light transmission data sets and corresponding output data (e.g., fetal oximetry values such as fetal SpO2 and/or fetal tissue oxygenation saturation) may be received. The in vivo light transmission data sets may be received from a fetal oximetry probe such as fetal oximetry probe115and each corresponding output data/oximetry value may be calculated using a corresponding in vivo light transmission data set received in step422. In one embodiment, the set of measured in vivo light transmission data sets and corresponding measured output data may include 200-10,000 datasets/output values. In the case of a pregnant human, the measured output data may be light transmission data sets taken over an interval of time (e.g., 30 or 60 seconds) and the measured, or actual, output values may be measured in vivo fetal oximetry values corresponding, in time, to when the light transmission data sets were measured. The In this example, measured in vivo fetal oximetry values may be within the range of, for example, of 10-70%. In some cases, the set of set of measured, or actual, output data may be converted into a format compatible with the predicted outputs so that a valid comparison between them may be made.

In step424, instructions to adapt the first or second (when steps416-420are performed) version of the simulated fetal oximetry model for use in the generation of a first version of an in vivo fetal oximetry model. The first version of the in vivo fetal oximetry model may be generated by training, for example, the first/second version of the simulated fetal oximetry model using a plurality of measured in vivo light transmission data sets and corresponding measured in vivo fetal oximetry values.

Exemplary instructions received in step424include instructions to train, or update, only certain portions (e.g., layers, functions, networks, and/or sub-networks) of the first/second version of the simulated fetal oximetry model and fix, or hold constant, other portions of the fetal first/second version of the simulated fetal oximetry model as needed. Typically, the initial input layer or layers of the network would be fixed to preserve the features found in the simulations. Additionally, or alternatively, portions of the first/second version of the simulated fetal oximetry model that may remain fixed include portions of the first/second version of the simulated fetal oximetry model that are generally applicable to the in vivo fetal oximetry model such as, for example, layers pertaining to calibration factors, maternal and/or fetal physiology and/or geometry, and equipment parameters.

Optionally, in step425, the measured in vivo light transmission data sets and corresponding output values (e.g., fetal oximetry values) may be divided into a measured training set and a measured testing set. In step426, the in vivo light transmission data sets and corresponding output data (e.g., oximetry values) and/or the training set of in vivo light transmission data sets and corresponding output data (when step424is executed) may be input into an adapted (according to the instructions of step424) version of the first/second version of the simulated fetal oximetry model so that one or more portions (e.g., layers or functions) of the first/second fetal oximetry model may be tuned or updated using the in vivo light transmission data and corresponding output values thereby generating an in vivo fetal oximetry model and, optionally, a third set of predicted output values generated by the in vivo fetal oximetry model.

In step428, the third set of predicted output values may be compared with the corresponding measured output values to determine differences between them (step428). Results of the comparison may then be evaluated (step430) and used to update the in vivo fetal oximetry model (step432). Execution of step432may also include storing the updated in vivo fetal oximetry model in a database such as the databases disclosed herein.

Optionally, when step425is performed, the testing set of measured light transmission data and corresponding output values may then be run through the in vivo fetal oximetry model to generate a fourth set of predicted output values (step434). The fourth set of predicted output values may then be compared with the corresponding measured output values from the testing set of output values to determine differences between them (step436). Results of the comparison may then be evaluated (step438) and used to generate an updated in vivo fetal oximetry model to predict output values (step440) using the machine learning architecture. The updated in vivo fetal oximetry model may also be stored in step440. Then, the in vivo fetal oximetry model and/or an indication of the comparison(s), evaluation(s), and/or predicted output values may be provided to the user (step442).

In some embodiments, process400and/or portions thereof may be repeated on a periodic, as-needed, and/or continuous basis to, for example, improve the accuracy of the predictions the model yields, perform perturbation analysis, and/or perform sensitivity analysis. When step434is not performed, process400may end at step432.

FIG.5is a flowchart illustrating an exemplary process500for the generation of a simulated fetal oximetry model and/or a tuned simulated fetal oximetry model. Process500may be performed by, for example, any of the systems or system components disclosed herein and may use data, determinations, and/or models generated and/or used by any of the processes disclosed herein.

Initially, a plurality (e.g., 10,000-10 million) of sets of simulated light transmission data and corresponding oximetry values for each set of simulated light transmission data may be received by a processor or network of processors such as cloud computing platform11(step505). Each set of the simulated light transmission data may have been generated by simulating a transmission of light of one more wavelengths and/or intensities through a model of animal tissue that may have been generated and/or received via, for example, execution of process200and/or300. The oximetry values corresponding to each set of simulated light transmission data may have been generated via, for example, execution of process200and/or300and/or may be calculated as part of execution of step505using the simulated light transmission data. In some embodiments, the model of animal tissue may include at least two layers of animal tissue, one of which is fetal tissue (e.g., skin, bone, brain, blood, etc.). Optionally, in step505, additional information regarding one or more of sets of simulated light transmission data and/or oximetry values may be received. The additional information may pertain to, for example, one or more of the following: fetal depth, source/detector separation distance, a thickness of maternal tissue, a type of maternal tissue, maternal and/or fetal skin color and/or melanin content, a thickness of fetal tissue, a type of fetal tissue, a type of light used, an intensity of light used, a light scattering property of layer of tissue in the model, a light absorption property of layer of tissue in the model, a fetal age, and/or calibration factor(s) associated with equipment used to obtain the simulated light transmission data, environmental conditions when the simulated light transmission data is collected.

Optionally, the plurality of sets of simulated light transmission data and corresponding fetal oximetry values may be divided into a training set of simulated data and a test set of simulated data (step510). The plurality of sets of simulated light transmission data and corresponding fetal oximetry values may be divided along any appropriate ratio including, for example, 90:10 training/testing; 80:20 training/testing; or 70:30 training/testing. In some embodiments, execution of step510may be similar to execution of step408.

In step515, machine learning inputs for the generation of a simulated fetal oximetry model may be determined, set, and/or selected for input into a machine learning program and/or architecture such as herein described. In some embodiments, execution of step515may resemble execution of step410. Then, in step520, a simulated fetal oximetry model may be trained using all or most of the data (in all or most combinations) received in step505and/or the training set of data of step510when step510is executed. Step520may be executed via, for example, inputting the simulated light transmission data, simulated detected electronic signals, corresponding oximetry values and/or addition information and/or a training set thereof (when step510is executed) into the machine learning architecture once it is set up with the machine learning inputs of step515. The simulated fetal oximetry model may be configured to receive a plurality of sets of simulated light transmission data included in the training set of simulated data and determine an oximetry value a fetus for each set of simulated light transmission data included in the training set of simulated data. This determined oximetry value may then be compared with the corresponding oximetry value received in step505to determine any differences therebetween. Results of this comparison may be used to iteratively update/train the simulated fetal oximetry model during execution of step520. Training of the simulated fetal oximetry model may be complete (step525) when, for example, a number or proportion (e.g., 60-99%) of the oximetry values calculated by the simulated fetal oximetry model using one or more simulated light transmission data sets received in step505are sufficiently close to (e.g., within a standard of deviation, within 0.5 standards of deviation, within 0.1 standards of deviation, and/or within 60-99% of the associated oximetry value) of the oximetry values associated each of the respective simulated light transmission data sets. When the training of the simulated fetal oximetry model is not complete (step525), step520may be repeated. When step510is not executed, process500may end following a determination that the training of the simulated fetal oximetry model in step525is complete.

In some embodiments, the simulated fetal oximetry model includes a plurality of layers, factors, calibrations, and/or functions (referred to herein collectively as “layers”) that are used to calculate oximetry values using the simulated light transmission data. Layers may include functions that account for and/or factor in, for example, fetal depth, source/detector separation distance, a thickness of maternal tissue, a type of maternal tissue, maternal and/or fetal skin color and/or melanin content, a thickness of fetal tissue, a type of fetal tissue, a type of light used, an intensity of light used, a fetal age, and/or calibration factor(s) associated with equipment that may be used in clinical applications to obtain in vivo measurements of light transmission data, environmental conditions that may be present during clinical applications when in vivo measurements of light transmission data is collected.

When the training of the simulated fetal oximetry model is complete (step525), the simulated fetal oximetry model may be tested with the testing set of simulated data (step530). In some embodiments, execution of step530may be similar to execution of step416. Results of the testing of the simulated fetal oximetry model may then be evaluated (step535) to, for example, determine how accurately the simulated fetal oximetry model calculated oximetry values. In some cases, the testing of step530may be iterative. When the testing of the simulated fetal oximetry model is complete (step540), the simulated fetal oximetry model may be tuned responsively to one or more results of the testing and/or evaluation of the tests (step545) thereby generating a tuned simulated fetal oximetry model and process500may end. When the testing of the simulated fetal oximetry model is not complete (step540), process500may proceed to step530.

FIG.6is a flowchart illustrating an exemplary process600for the generation of an in vivo fetal oximetry model and/or a tuned in vivo fetal oximetry model. Process600may be performed by, for example, any of the systems or system components disclosed herein and may use data, determinations, and/or models generated and/or used by any of the processes disclosed herein. In some embodiments, process600may be performed subsequently to performance of process500and, on occasion, may be executed by the same systems and/or processors.

In step605, a tuned simulated fetal oximetry model, such as the tuned simulated fetal oximetry model generated by process500, may be received by, for example, a processor or network of processors such as cloud computing platform11. Additionally, or alternatively, when, for example, steps530-545of process500are executed, a tuned simulated fetal oximetry model may be received in step605. For ease of discussion, the following discussion of process600will refer to a “simulated fetal oximetry model” as referring to both the simulated fetal oximetry model (of, for example, step525) and the tuned simulated fetal oximetry model (of, for example, step545).

Instructions to adapt the simulated fetal oximetry model for transfer to an in vivo fetal oximetry model may then be received (step610). In some cases, the instructions to adapt the simulated fetal oximetry model for transfer to an in vivo fetal oximetry model may include instructions to fix one or more layers, or functions, of the simulated fetal oximetry model that may be generally applicable to the in vivo fetal oximetry model. Exemplary layers and/or functions of the tuned simulated fetal oximetry model that may be fixed include, but are not limited to, how one or more of a source/detector distance, a wavelength of light, a fetal depth, maternal skin color, fetal skin color, maternal tissue composition, fetal tissue composition and/or a calibration factor impact (e.g., weights in the model), an oximetry calculation.

Then, the tuned simulated fetal oximetry model may be adapted for transfer to an in vivo fetal oximetry model responsively to the instructions (step615). In some cases, the adapting of step615may include determining, setting, and/or selecting one or more machine learning inputs for a machine learning architecture for the generation of an in vivo fetal oximetry model. Additionally, or alternatively, the adapting of step615may include fixing one or more layers, or functions, of the tuned simulated fetal oximetry model so that it remains fixed during the in vivo fetal oximetry model training process (step630, which is discussed below).

In step620, a plurality (e.g., 1,000-10 million) of sets of in vivo light transmission data and corresponding fetal oximetry values for each set of in vivo light transmission may be received. The plurality of sets of in vivo light transmission data may be received from, for example, a fetal oximetry probe like fetal oximetry probe115and the corresponding fetal oximetry values may be calculated using, for example, Equations 1 and 2 as discussed herein.

Optionally, in step625, the plurality of sets of in vivo light transmission data and corresponding fetal oximetry values may then be divided into a training set of in vivo data and a test set of in vivo data. The plurality of sets of in vivo light transmission data and corresponding fetal oximetry values may be divided along any appropriate ratio including, for example, 90:10 training/testing; 80:20 training/testing; or 70:30 training/testing. In some embodiments, execution of step625may have one or more similarities with execution of step408and/or510.

In step630, an in vivo fetal oximetry model may be trained using the training set of in vivo data and the adapted simulated fetal oximetry model. Step630may be executed via, for example, inputting the training set of in vivo data into the machine learning architecture once it is set up with the adapted simulated fetal oximetry model of step615. In some embodiments, the in vivo fetal oximetry model may be configured to receive a plurality of sets of in vivo light transmission data included in the plurality of sets of measured in vivo data and/or training set of in vivo data and determine an oximetry value of a fetus for each respective set of in vivo light transmission data. This determined oximetry value may then be compared with the oximetry value associated with the in vivo light transmission data to determine any differences therebetween. These differences may be used to, for example, iteratively update/train the in vivo fetal oximetry model during execution of step630. Training of the in vivo fetal oximetry model may be complete when, for example, a number or proportion (e.g., 60-99%) of the oximetry values calculated by the in vivo fetal oximetry model using one or more in vivo light transmission data sets received in step620are sufficiently close to (e.g., within a standard of deviation, within 0.5 standards of deviation, within 0.1 standards of deviation, and/or within 60-99% of the associated oximetry value) to the oximetry values associated with each of the respective in vivo transmitted light data sets. When the training of the in vivo fetal oximetry model is not complete, step630may be repeated and/or may continue to be executed.

In some embodiments, the in vivo fetal oximetry model includes a plurality of layers, factors, calibrations, and/or functions (referred to herein collectively as “layers”) that are used to calculate oximetry values using the in vivo light transmission data. Exemplary layers include functions that factor in, account for, and/or are associated with, for example, fetal depth, source/detector separation distance, a thickness of maternal tissue, a type of maternal tissue, maternal and/or fetal skin color and/or melanin content, a thickness of fetal tissue, a type of fetal tissue, a type of light used, an intensity of light used, a fetal age, and/or calibration factor(s) associated with equipment used to obtain the in vivo light transmission data, environmental conditions when the in vivo light transmission data is collected.

When the training of the in vivo fetal oximetry model is complete, process600may optionally proceed to step650. Alternatively, and optionally, when the training of the in vivo fetal oximetry model is complete, the in vivo fetal oximetry model may be tested with the testing set of in vivo data (step635). Optionally, results of the testing of the in vivo fetal oximetry model may then be evaluated (step640) to, for example, determine how accurate the in vivo fetal oximetry model calculated oximetry values are. In some cases, the testing of step635may be iterative. When the testing of the in vivo fetal oximetry model is complete, the in vivo fetal oximetry model may be tuned and/or updated responsively to one or more results of the testing and/or evaluation of the tests (step645) thereby generating a tuned in vivo fetal oximetry model. The tuned in vivo fetal oximetry model may then be finalized and/or stored and process600may end.

FIG.7is a flowchart illustrating an exemplary process700for the generation of an in vivo fetal oximetry model. Process700may be performed by, for example, any of the systems or system components disclosed herein and may use data, determinations, and/or models generated and/or used by any of the processes disclosed herein.

Initially, a plurality (e.g., 100,000-10 million) of sets of simulated light transmission data and corresponding oximetry values for each set of simulated light transmission data may be received by a processor or network of processors such as cloud computing platform11(step705). Each set of the simulated light transmission data may have been generated by simulating a transmission of light of one more wavelengths and/or intensities through a model of animal tissue that may have been generated and/or received via, for example, execution of process200and/or300. The simulated light transmission data sets may resemble those received in, for example, step404. In some embodiments, the oximetry values corresponding to each set of simulated light transmission data may have been generated via, for example, execution of process200and/or300and/or may be calculated as part of execution of step705using the simulated light transmission data. In some embodiments, the model of animal tissue may include at least two layers of animal tissue, one of which is fetal tissue (e.g., skin, bone, brain, blood, etc.). On some occasions, execution of step705may resemble execution of step505.

In step710, machine learning inputs for the generation of a simulated fetal oximetry model may be determined, set, and/or selected for input into a machine learning program and/or architecture such as TensorFlow. In some embodiments, execution of step710may resemble execution of step410and/or515. Then, in step715, a simulated fetal oximetry model may be trained using the simulated light transmission data sets and corresponding oximetry values. Step715may be executed via, for example, inputting the simulated light transmission data and corresponding oximetry values into the machine learning architecture once it is set up with the machine learning inputs of step710. At times, execution of step715may resemble execution of step520.

The simulated fetal oximetry model may be trained and/or configured to receive a plurality of sets of simulated light transmission data and determine an oximetry value for a fetus that may be associated with each set of simulated light transmission data. This determined oximetry value may then be compared with the oximetry value associated with respective sets of simulated light transmission data received in step705to determine any differences therebetween. Results of this comparison may be used to iteratively update/train the simulated fetal oximetry model during execution of step715. Training of the simulated fetal oximetry model may be complete (step720) when, for example, a number or proportion (e.g., 60-99%) of the oximetry values calculated by the simulated fetal oximetry model using one or more simulated light transmission data sets received in step705are sufficiently close to (e.g., within a standard of deviation, within 0.5 standards of deviation, within 0.1 standards of deviation, and/or within 60-99% of the associated oximetry value) of the oximetry values associated each of the respective simulated light transmission data sets. When the training of the simulated fetal oximetry model is not complete (step720), step715may be iteratively repeated. In some embodiments, execution of step720may resemble execution of step525.

In some embodiments, the simulated fetal oximetry model includes a plurality of layers, factors, calibrations, and/or functions (referred to herein collectively as “layers”) that are used to calculate oximetry values using the simulated light transmission data. Layers may include, for example, functions that account for and/or factor in, for example, fetal depth, source/detector separation distance, a thickness of maternal tissue, a type of maternal tissue, maternal and/or fetal skin color and/or melanin content, a thickness of fetal tissue, a type of fetal tissue, a type of light used, an intensity of light used, a fetal age, and/or calibration factor(s) associated with equipment that may be used in clinical applications to obtain in vivo measurements of light transmission data, environmental conditions that may be present during clinical applications when in vivo measurements of light transmission data is collected. When the training of the simulated fetal oximetry model is complete (step720), it may be stored in a database like database15and/or170(step725).

In step730, instructions to adapt the simulated fetal oximetry model for transfer to an in vivo fetal oximetry model may then be received. In some cases, the instructions to adapt the simulated fetal oximetry model for transfer to an in vivo fetal oximetry model may include instructions to fix one or more layers, or functions, of the simulated fetal oximetry model that may be generally applicable to the in vivo fetal oximetry model so that these fixed layers/functions do not change during the training process. Exemplary layers and/or functions of the simulated fetal oximetry model that may be fixed include, but are not limited to, how one or more of a source/detector distance, a wavelength of light, a fetal depth, maternal skin color, fetal skin color, maternal tissue composition, fetal tissue composition and/or a calibration factor impact (e.g., weights in the model), an oximetry calculation. In some embodiments, execution of step730may resemble execution of step424and/or610.

Then, the simulated fetal oximetry model may be adapted for transfer to an in vivo fetal oximetry model responsively to the instructions (step735). In some cases, the adapting of step735may include determining, setting, and/or selecting one or more machine learning inputs for a machine learning architecture for the generation of an in vivo fetal oximetry model. Additionally, or alternatively, the adapting of step735may include fixing one or more layers, or functions, of the simulated fetal oximetry model so that it remains fixed during the in vivo fetal oximetry model training process (step745, which is discussed below).

In step740, a plurality (e.g., 1,000-10 million) of sets of in vivo light transmission data and corresponding fetal oximetry values for each set of in vivo light transmission may be received. The plurality of sets of in vivo light transmission data may be received from, for example, a fetal oximetry probe like fetal oximetry probe115and the corresponding fetal oximetry values may be calculated using, for example, Equations 1 and 2 as discussed herein. Then, in step745, an in vivo fetal oximetry model may be generated and/or trained using the in vivo data and the adapted simulated fetal oximetry model of step735. Step745may be executed via, for example, inputting a plurality of sets of in vivo data into the machine learning architecture once it is set up with the adapted simulated fetal oximetry model of step735. The in vivo fetal oximetry model may be configured to receive a plurality of sets of in vivo light transmission data and determine an oximetry value of a fetus for each set of in vivo light transmission data included in the training set of in vivo data. This determined oximetry value may then be compared with the oximetry value associated with a respective set of in vivo light transmission data that may be received in step740to determine any differences therebetween. These differences may be used to, for example, iteratively update/train the in vivo fetal oximetry model during execution of step745. Training of the in vivo fetal oximetry model may be complete (step750) when, for example, a number or proportion (e.g., 60-99%) of the oximetry values calculated by the in vivo fetal oximetry model using one or more in vivo light transmission data sets received in step740are sufficiently close to (e.g., within a standard of deviation, within 0.5 standards of deviation, within 0.1 standards of deviation, and/or within 60-99% of the associated oximetry value) to the oximetry values associated with each of the respective in vivo transmitted light data sets.

In some embodiments, the in vivo fetal oximetry model includes a plurality of layers, factors, calibrations, and/or functions (referred to herein collectively as “layers”) that are used to calculate oximetry values using the in vivo light transmission data. Exemplary layers include functions that factor in and/or account for associated with, for example, fetal depth, source/detector separation distance, a thickness of maternal tissue, a type of maternal tissue, maternal and/or fetal skin color and/or melanin content, a thickness of fetal tissue, a type of fetal tissue, a type of light used, an intensity of light used, a fetal age, and/or calibration factor(s) associated with equipment used to obtain the in vivo light transmission data, environmental conditions when the in vivo light transmission data is collected.

When the training of the in vivo fetal oximetry model is not complete, (step745) may be repeated and/or may continue to be iteratively executed. When the training of the in vivo fetal oximetry model is complete (step745), the vivo fetal oximetry model may then be finalized and stored (step755) and process700may end or proceed to step805of process800discussed below.

FIG.8is a flowchart illustrating an exemplary process800for the determination of a fetal oximetry value for a fetus using an in vivo fetal oximetry model that may be generated via, for example, execution of process600and/or700. Process800may be performed by, for example, any of the systems or system components disclosed herein.

Initially, in step805, light transmission data for a pregnant mammal's abdomen and fetus may be received from, for example, a photodetector like detector160and/or a probe like fetal hemoglobin probe115. The light may be transmitted from a light source, through the pregnant mammal's abdomen and fetus and/or backscattered from the abdominal/fetal tissue, and detected by the photodetector. The light transmission data received in step805may then be put into and/or processed by the in vivo fetal oximetry model (which may be the finalized in vivo fetal oximetry model of step650in step810. In some embodiments, the light transmission data received in step805may be pre-processed prior to execution of step810. The pre-processing may include, for example, filtering with, for example, a Kalman or bandpass filter, application of a noise reduction model, removal of a portion of the light transmission data that is incident only the pregnant mammal (i.e., not incident on the fetus), and/or isolation of a portion of the light incident on the fetus from the received light transmission data. On some occasions, removal of a portion of the light transmission data that is incident only the pregnant mammal (i.e., not incident on the fetus), and/or isolation of a portion of the light incident on the fetus from the received light transmission data may be accomplished by, for example, receiving a maternal heartrate signal, using the maternal heart rate signal to identify the portion of the light transmission data contributed by the pregnant mammal and then subtracting the portion of the light transmission data contributed by the pregnant mammal from the light transmission data. Additionally, or alternatively, isolation of the fetal portion of the light transmission data may be accomplished by, for example, receiving a fetal heartrate signal, using the fetal heart rate signal to identify the portion of the light transmission data contributed by the fetus and then subtracting the remainder of light transmission data and/or amplifying the portion of the light transmission data contributed by the fetus. Additionally, or alternatively, isolation of the fetal portion of the light transmission data may include determining a fetal position and/or fetal depth and then

In step815, an oximetry value for the fetus within the pregnant mammal's abdomen may be determined and/or output by the in vivo fetal oximetry model. The oximetry value may be, for example, a fetal hemoglobin oxygen saturation level, a fetal tissue oxygen saturation level, an indication of fetal hypoxia, an indication of fetal hypoxemia, and/or an alert condition indicating that a fetal oximetry value indicates the fetus may be in distress. The oximetry value may then be communicated to a display device like display device14and/or155for display to a user such as a clinician and/or the pregnant mammal.