Animated pain assessment method

Abstract

Disclosed herein are systems and methods for evaluating pain in a subject. These systems and methods include generating or selecting an adjustable pain animation, allowing the user to adjust the pain animation, and connecting pain descriptor words to each pain animation. The systems and methods further include evaluating and predicting current and future pain conditions, pain types, and pain characteristics of a subject. Additional physiological inputs like energy level, mood, pain level rating, and mobility can also be entered into the systems and methods. The systems and methods also include evaluating and predicting appropriate pain medications or addiction levels based on input. The systems and methods include AI / ML evaluation of physiological condition inputs.

Description

ANIMATED PAIN ASSESSMENT METHODCROSS-REFERENCE

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 631,299, filed April 8, 2024, which is incorporated by reference herein in its entirety.STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

[0002] This present disclosure was made with government support under grant number 1R03HL145193-01 awarded by the National Heart, Lung and Blood Institute of the NIH. The government has certain rights in the disclosure.BACKGROUND

[0003] Pain is the primary reason for healthcare utilization in the US and has a high impact on quality of life. Chronic pain impairs daily functioning and elevates the risk of depression, anxiety, and opioid dependence. Although the impact of pain on health outcomes is substantial, its assessment in healthcare settings is often inadequate. Pain is a multidimensional experience encompassing both sensory and emotional aspects, making communication challenging. Existing unidimensional pain measures, such as numeric or visual analog scales, oversimplify the complexity of pain by reducing it to a single number representing severity. This oversimplification hinders the assessment of physiological mechanisms and overlooks the interplay of thoughts, mood, and affect during the pain experience. Multidimensional pain scales that assess affective pain qualities using adjectives and phrases have limited application in clinical settings due to respondent and administrative burden, lack of evidence to support their psychometric properties, and inconsistent correlation with clinical pain outcomes.

[0004] Physiological condition animations, such as pain animations, coupled with a comprehensive physiological condition assessment system capable of generating one or more physiological condition assessment results, including pain assessment results, can improve pain assessment in clinical settings and non-clinical settings, and can provide comprehensive analytic and predictive functions to those experiencing pain, including chronic pain.SUMMARY

[0005] In one aspect, disclosed herein are computer-implemented methods for assessing physiological condition in a subject, the method comprising: (a) generating a graphical representation of the subject’s body on an interactive graphical user interface (GUI); (b)receiving one or more inputs from the subject through the GUI wherein the one or more inputs comprise selection of an area within the graphical representation of the subject’s body; (c) generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; (d) receiving one or more pain feedback inputs from the subject, the one or more pain feedback inputs comprising selection of the one or more visual animations of the one or more pain characteristics, or adjustments to the one or more visual animations of the one or more pain characteristics; and (e) generating one or more outputs comprising one or more pain assessment results from the selected animations

[0006] In some embodiments, the one or more inputs from the subject comprise one or more input types selected from the list comprising: drawing a shape around the selected area of the subject’s body, drawing inside the selected area of the subject’s body, tapping on the selected area of the subject’s body, zooming in on the selected area of the subject’s body, or any combination thereof.

[0007] In some embodiments, the method further comprises generating one or more physiological condition-associated pain assessment results, wherein generating the one or more physiological condition-associated pain assessment results further comprises receiving one or more physiological condition inputs from the subject associated with the one or more pain feedback inputs. In some embodiments, the one or more physiological condition inputs comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof.

[0008] In some embodiments, the method further comprises iteratively applying one or more algorithms of a pain forecast model to the one or more pain feedback inputs to generate one or more outputs comprising one or more pain forecast predictions for the selected area of the subject’s body.

[0009] In some embodiments, the method further comprises transmitting the one or more outputs comprising one or more pain assessment results to an external device.

[0010] In some embodiments, the method further comprises generating the one or more visual animations of the one or more pain characteristics using a virtual reality device, wherein the virtual reality device maps the selected area of the subject’s body to the subject’s body and displays the one or more visual animations on the subject’s body.

[0011] In some embodiments, the method further comprises transmitting data to an external healthcare application and / or integrating received data from an external healthcare application. Insome embodiments, adjustments to the one or more visual animations of the one or more pain characteristics further comprise manual adjustments made by the subject.

[0012] In another aspect, disclosed herein are computer-implemented methods for generating one or more suggested diagnoses from pain assessment in a subject, the computer-implemented method comprising: (a) generating a predictive diagnosis model, comprising a library of potential predicted diagnoses, wherein the potential predicted diagnoses have been selected by a method comprising: (i) extracting pain descriptor terms from one or more pain feedback inputs from the subject, and (ii) applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for one or more of the potential predicted diagnoses; and (b) generating one or more outputs comprising one or more of the potential predicted diagnoses.

[0013] In yet another aspect, disclosed herein are computer-implemented methods for selecting an optimized opioid treatment from pain assessment in a subject, the computer-implemented method comprising: (a) generating a predictive optimized opioid treatment model, comprising a library of opioids, wherein the one or more predicted optimized opioid treatments are generated by a method comprising: (i) extracting pain descriptor terms from one or more pain feedback inputs from the subject, and (ii) applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for the types of pain alleviated by the one or more opioids; and (b) generating one or more outputs comprising one or more of the predicted optimized opioid treatments.

[0014] In some embodiments, the method further comprises generating a predictive opioid addiction model, wherein the predictive opioid addiction model comprises the library of opioids, wherein each opioid is further associated with one or more addiction risk scores, and wherein the predictive opioid addiction model generates one or more outputs comprising one or more of the predicted opioid addiction risk scores.

[0015] In yet another aspect, disclosed herein are computer-implemented methods for generating one or more optimized customized pain assessment animations for a subject, the computer- implemented method comprising: (a) generating an interactive graphical user interface (GUI) displaying a representation of the subject’s body; (b) receiving one or more inputs from the subject, the one or more inputs comprising selection of an area of the subject’s body being displayed on the GUI; (c) generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; (d) generating a generative pain animation customization model, wherein the pain animation customization model generates customized, optimized pain animations with improved accuracy to the subject’s pain characteristics, wherein the optimized customized visual animations of one or more paincharacteristics associated with the selected area of the subject’s body are generated by a method comprising: (i) receiving one or more signals from the subject, the one or more signals comprising adjustment instructions for the one or more visual animations of the one or more pain characteristics, (ii) applying one or more algorithms to iteratively modify one or more elements of the one or more visual animations of the one or more pain characteristics based on the adjustment instructions, (iii) generating one or more optimized customized visual animations of the one or more pain characteristics incorporating the modified elements of the one or more visual animations; and (e) generating one or more optimized outputs comprising one or more pain assessment results.

[0016] In some embodiments, the one or more signals comprising adjustment instructions for the one or more visual animations further comprise audio instructions, text instructions, drawn instructions, or any combination thereof. In some embodiments, the one or more signals comprising adjustment instructions for the one or more visual animations further comprise concrete instructions, descriptive instructions, abstract instructions, or any combination thereof.

[0017] In yet another aspect, disclosed herein are one or more computer-implemented systems for assessing physiological condition in a subject, the computer-implemented system comprising: (a) an interactive graphical user interface (GUI) onto which the computer-implemented system generates a graphical representation of the subject’s body, wherein the GUI is configured to receive one or more inputs from the subject, wherein the one or more inputs comprise selection of an area within the graphical representation of the subject’s body; (b) a processor configured to perform operations comprising generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; (c) the processor configured to perform operations further comprising receiving one or more pain feedback inputs from the subject, the one or more pain feedback inputs comprising selection of the one or more visual animations of the one or more pain characteristics, or adjustments to the one or more visual animations of the one or more pain characteristics; and (d) one or more system outputs comprising one or more pain assessment results from the selected animations.

[0018] In some embodiments, the one or more inputs from the subject comprise one or more input types selected from the list comprising: drawing a shape around the selected area of the subject’s body, drawing inside the selected area of the subject’s body, tapping on the selected area of the subject’s body, zooming in on the selected area of the subject’s body, or any combination thereof. In some embodiments, the processor is configured to perform operations further comprising generating one or more physiological condition-associated pain assessment results, wherein generating the one or more physiological condition-associated pain assessment results further comprises receiving one or more physiological condition inputs from the subjectassociated with the one or more pain feedback inputs. In some embodiments, the one or more physiological condition inputs comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof. In some embodiments, the one or more physiological condition inputs comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof.

[0019] In some embodiments, the computer-implemented system further comprises a pain forecast model configured to perform operations comprising iteratively applying one or more algorithms to the one or more pain feedback inputs to generate one or more outputs comprising one or more pain forecast predictions for the selected area of the subject’s body.

[0020] In some embodiments, the computer-implemented system further comprises a virtual reality device, wherein the virtual reality device maps the selected area of the subject’s body to the subject’s body and displays the one or more visual animations on the subject’s body.

[0021] In some embodiments, the computer-implemented system further comprises one or more external healthcare applications, wherein the one or more external healthcare applications transmit data to the system and / or integrate output from the system. In some embodiments, adjustments to the one or more visual animations of the one or more pain characteristics further comprise manual adjustments made by the subject.

[0022] In yet another aspect, disclosed herein are computer-implemented systems for generating one or more suggested diagnoses from pain assessment in a subject, the computer-implemented system comprising: (a) a predictive diagnosis model, comprising a library of potential predicted diagnoses, wherein the potential predicted diagnoses have been selected by a method comprising: (i) extracting pain descriptor terms from one or more pain feedback inputs from the subject, and (ii) applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for one or more of the potential predicted diagnoses; and (b) one or more outputs comprising one or more of the potential predicted diagnoses.

[0023] In yet another aspect, disclosed herein are computer-implemented systems for selecting an optimized opioid treatment from pain assessment in a subject, the computer-implemented system comprising: (a) a predictive optimized opioid treatment model, comprising a library of opioids, wherein the one or more predicted optimized opioid treatments are generated by amethod comprising: (i) extracting pain descriptor terms from one or more pain feedback inputs from the subject, and (ii) applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for the types of pain alleviated by the one or more opioids; and (b) one or more outputs comprising one or more of the predicted optimized opioid treatments.

[0024] In some embodiments, the computer-implemented system further comprises a predictive opioid addiction model, wherein the predictive opioid addiction model comprises the library of opioids, wherein each opioid is further associated with one or more addiction risk scores, and wherein the predictive opioid addiction model generates one or more outputs comprising one or more of the predicted opioid addiction risk scores.

[0025] In yet another aspect, disclosed herein are computer-implemented systems for generating one or more optimized customized pain assessment animations for a subject, the computer- implemented system comprising: (a) an interactive graphical user interface (GUI) displaying a representation of the subject’s body; (b) a processor configured to perform operations comprising receiving one or more inputs from the subject, the one or more inputs comprising selection of an area of the subject’s body being displayed on the GUI; (c) the processor configured to perform operations further comprising generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; (d) a generative pain animation customization model, wherein the pain animation customization model generates customized, optimized pain animations with improved accuracy to the subject’s pain characteristics, wherein the optimized customized visual animations of one or more pain characteristics associated with the selected area of the subject’s body are generated by a method comprising: (i) receiving one or more signals from the subject, the one or more signals comprising adjustment instructions for the one or more visual animations of the one or more pain characteristics, (ii) applying one or more algorithms to iteratively modify one or more elements of the one or more visual animations of the one or more pain characteristics based on the adjustment instructions, (iii) generating one or more optimized customized visual animations of the one or more pain characteristics incorporating the modified elements of the one or more visual animations; and (e) one or more optimized outputs comprising one or more pain assessment results.

[0026] In some embodiments, the one or more signals comprising adjustment instructions for the one or more visual animations further comprise audio instructions, text instructions, drawn instructions, or any combination thereof. In some embodiments, the one or more signals comprising adjustment instructions for the one or more visual animations further comprise concrete instructions, descriptive instructions, abstract instructions, or any combination thereof.

[0027] In yet another aspect, described herein is a computer-implemented method for determining resting-state functional connectivity patterns of a pain characteristic profile of a subject, the computer-implemented method comprising: generating one or more visual animations of one or more pain characteristics associated with a selected area of the subject’s body; receiving one or more pain feedback inputs from the subject, the one or more pain feedback inputs comprising selection of the one or more visual animations of the one or more pain characteristics, or adjustments to the one or more visual animations of the one or more pain characteristics; generating the pain characteristic profile of the subject based on the received one or more pain feedback inputs from the subject; and generating a functional connectivity profile based at least in part on analyzing functional connectivity data between a first region of a brain of the subject and a second region of the brain of the subject, wherein the functional connectivity profile is associated with the pain characteristic profile of the subject.

[0028] In some cases, the pain characteristic profile comprises one or more of: a pain area, a pain sensation type, a pain intensity, or a pain duration, or any combination thereof.

[0029] In some embodiments, the functional connectivity data comprises one or more functional connectivity values.

[0030] In some cases, each of the one or more functional connectivity values comprise correlation values between activation of the first region of the brain and activation of the second region of the brain.

[0031] In some cases, the functional connectivity data comprises an increase in connectivity between the first region and the second region of the brain.

[0032] In some cases, the indication of the change in the one or more characteristics of connectivity comprises a decrease in connectivity between the first region and the second region of the brain.

[0033] In some embodiments, the first region is located at a first hemisphere of the brain and the second region is located at a second hemisphere of the brain.

[0034] In some embodiments, the first region and the second region are located at the same hemisphere of the brain.

[0035] In some embodiments, the functional connectivity profile comprises multiple functional connectivity values relating to two or more sets of functionally connected regions.

[0036] In some cases, each of the two or more sets of functionally connected regions comprises the first region and the second region.

[0037] In some cases, the first region or the second region, or both, are different between the two or more sets of functionally connected regions.

[0038] In some embodiments, the functional connectivity profile comprises one or more increases in connectivity between one or more sets of functionally connected regions, or one or more decreases in connectivity between one or more sets of functionally connected regions, or both.

[0039] In some cases, the one or more increases in connectivity and the one or more decreases in connectivity are determined relative to baseline connectivity values of control participants.

[0040] In some embodiments, the functional connectivity data is determined based at least in part on functional imaging data.

[0041] In some embodiments, the functional imaging data comprises functional magnetic resonance imaging (fMRI) data.

[0042] In some embodiments, the computer-implemented method further comprises generating a functional connectivity report.

[0043] In some embodiments, the functional connectivity report comprises one or more changes in connectivity associated with one or more pain types of the pain characteristic profile of the subject.

[0044] Another aspect of the present disclosure provides a non-transitory computer readable medium comprising machine executable code that, upon execution by one or more computer processors, implements any of the methods above or elsewhere herein.

[0045] Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory comprises machine executable code that, upon execution by the one or more computer processors, implements any of the methods above or elsewhere herein.

[0046] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.INCORPORATION BY REFERENCE

[0047] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated byreference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which:

[0049] FIG. 1 shows a non-limiting example of a computing device; in this case, a device with one or more processors, memory, storage, and a network interface, per one or more embodiments herein.

[0050] FIG. 2 shows a non-limiting diagram of exemplary functional blocks for assessing physiological conditions using body area-specific pain animations.

[0051] FIG. 3 shows a non-limiting diagram of exemplary functional blocks for assessing physiological conditions using customized body area-specific pain animations.

[0052] FIG. 4 shows a non-limiting diagram of exemplary functional blocks for predicting pain forecast, suggested diagnoses, opioid addiction, and optimized opioid treatment using body areaspecific pain animations.

[0053] FIG. 5 shows a non-limiting diagram of exemplary functional aspects for predicting physiological conditions and predicting physiological aspects using pain animations.

[0054] FIG. 6 shows non-limiting examples of static depictions of exemplary animation blocks with associated pain characteristics.

[0055] FIG. 7 shows non-limiting examples of features of the physiological condition assessment system and method, as well as an exemplary display of the graphic user interface (GUI) of the physiological assessment method and system.

[0056] FIG. 8 shows further non-limiting examples of features of the physiological condition assessment system and method, as well as an exemplary display of the GUI of the body area selection module.

[0057] FIGs. 9A-9B show non-limiting examples of an introductory screen and informed consent screen as part of the physiological condition assessment system and method. FIG. 9A shows a non-limiting example of the introductory screen and informed consent of the system and method. FIG. 9B depicts a non-limiting example of the informed consent of the system and method, as well as the signature area and submission feature.

[0058] FIGs. 10A-10F show non-limiting examples of a GUI representative depiction of a subject’s body, with body areas selected. FIG. 10A depicts a non-limiting example of a GUI representing a depiction of the front view of the subject’s body, with the shoulder areas selected. FIG. 10B depicts a non-limiting example of a GUI representing a depiction of the front view and a depiction of the back view of the subject’s body, the front view having the shoulder areas selected, and the back view having the lower back area and side of right leg area selected. FIG. 10C depicts a non-limiting example of a GUI representing a depiction of the front view of the subject’s body, in an exemplary state where no body area is selected, and an exemplary state where the hand area and left lower leg area are selected. FIG. 10D depicts a non-limiting example of a GUI representing a depiction of a refined location of the subject’s body, with the thumb area selected. FIGs. 10E-10F depict non-limiting examples of GUIs representing depictions of the subject’s body and being programmed to associate a body area with a pain animation.

[0059] FIG. 11 shows non-limiting examples of GUIs representing depictions of the subject’s front of body and back of body, with various body areas selected, and an associated exemplary animation set of pain animations coupled with an exemplary pain intensity scale for the selected animation, and an exemplary area for reviewing or editing a saved pain animation.

[0060] FIGs. 12A-12D show non-limiting examples of static depictions of pain animations. FIG. 12A depicts non-limiting examples of static depictions of pain animations coupled with associated pain characteristics. FIG. 12B depicts a non-limiting example of a pain animation selection interface allowing selection of multiple static depictions of pain animations. FIG. 12C depicts non-limiting examples of static depictions of various pain animations. FIG. 12D depicts non-limiting examples of static depictions of various pain animations coupled with a nonlimiting example of a pain intensity scale and save option.

[0061] FIG. 13 shows a non-limiting example of a computer GUI depicting static depictions of pain animations, a pain intensity scale, a depiction of the front view and back view of the subject’s body with body areas selected, and non-limiting examples of characteristics of the systems and methods.

[0062] FIGs. 14A-14B show non-limiting examples of GUIs representing selectable mood physiological characteristics. FIG. 14A depicts non-limiting examples of selectable scored mood physiological states. FIG. 14B depicts non-limiting examples of selectable mood physiological states, in an unselected and selected condition.

[0063] FIGs. 15A-15B show non-limiting examples of GUIs representing selectable energy level physiological characteristics. FIG. 15A depicts a non-limiting example of a selectableenergy level chart in an unselected state. FIG. 15B depicts non-limiting examples of selectable energy level charts with various energy levels selected.

[0064] FIG. 16 shows non-limiting examples of GUIs representing selectable pain level physiological characteristics in an unselected and selected state.

[0065] FIG. 17 shows a non-limiting example of a GUI representing selectable mobility level and status answers.

[0066] FIG. 18 shows a non-limiting example of a GUI representing a satisfaction survey for the user interface.

[0067] FIG. 19 shows a non-limiting example of a GUI representing selected mood physiological characteristics and selected energy level physiological characteristics, with input option examples including voice recording and writing.

[0068] FIG. 20 shows non-limiting examples of GUIs depicting analytical results of physiological characteristics, as well as selected energy level.

[0069] FIGs. 21A-21B show non-limiting examples of aggregate data analysis of one or more subjects. FIG. 21 A depicts non-limiting examples of aggregate physiological characteristic data for one or more subjects. FIG. 21B depicts non-limiting examples of aggregate physiological characteristic data coupled to body area selection and predictive diagnostic and treatment information.

[0070] FIGs. 22A-22D show data concerning pain diagnosis and physiological characteristic data utilized in the systems and methods.

[0071] FIGs. 23A-23B show data concerning pain and psychosocial outcomes and physiological characteristic data utilized in the systems and methods.

[0072] FIG. 24 depicts a non-limiting example of connected machine learning algorithms utilized in the systems and methods.

[0073] FIG. 25 illustrates a non-limiting example of a GUI of a pain assessment application according to the systems and methods disclosed herein.

[0074] FIG. 26 shows an exemplary diagram of a study cohort of participants in a pain study.

[0075] FIG. 27 illustrates exemplary data concerning affirmative responses to questions stratified by education level for a study cohort.

[0076] FIGs. 28A-28B show exemplary data relating to scaled questions regarding experiences of a cohort with pain animation GUIs, methods, and systems described herein. FIG. 28A illustrates exemplary data of responses of participants to questions concerning utility of the pain animation elements. FIG. 28B illustrates exemplary data of responses of participants to questions concerning burden of the pain animation elements.

[0077] FIG. 29 illustrates exemplary data of severity rating by individuals related to a visual analogue scale (VAS) score of the individuals.

[0078] FIGs. 30A-30B show exemplary data relating to stratified group VAS score analysis, grouped by pain type animation chosen or not chosen. FIG. 30A illustrates exemplary data showing VAS score analysis for a cramping animation chosen group and a cramping animation not chosen group. FIG. 30B shows exemplary data showing VAS score analysis, for a stabbing animation type chosen group or not chosen group.

[0079] FIG. 31 shows exemplary data concerning demographic information for participants.

[0080] FIG. 32 shows exemplary data concerning characteristic measurements for participants, including pain descriptions.

[0081] FIG. 33 illustrates an exemplary diagram of a subject describing pain sensations according to methods and systems described herein and resting-state functional connectivity of the subject’s brain.

[0082] FIG. 34 shows an exemplary workflow diagram according to methods and systems described herein, illustrating differences in functional connectivity metrics between individuals having Sickle Cell Disease pain and control individuals.

[0083] FIGs. 35A-35E illustrate exemplary data showing functional connectivity of the brain in a default mode network (DMN). FIG. 35A shows exemplary diagrams relating to connectivity in control individual brains and brains of individuals having Sickle Cell Disease (SCD). FIG. 35B shows exemplary connectivity matrix data for control individuals. FIG. 35C shows exemplary connectivity differences between control individuals and individuals having SCD. FIG. 35D shows an exemplary connectivity matrix data for individuals having SCD. FIG. 35E shows an exemplary connectivity difference value heatmap between control individuals and individuals having SCD.

[0084] FIGs. 36A-36E show exemplary data illustrating functional connectivity in a salience network (SAN). FIG. 36A illustrates exemplary diagrams relating to connectivity in control individual brains and brains of individuals having Sickle Cell Disease (SCD). FIG. 36B illustrates exemplary connectivity matrix data for control individuals in a SAN. FIG. 36C illustrates exemplary connectivity differences between control individuals and individuals having SCD. FIG. 36D illustrates an exemplary connectivity matrix data of brain regions of the SAN for individuals having SCD. FIG. 36E illustrates an exemplary connectivity difference value heatmap between control individuals and individuals having SCD.

[0085] FIGs. 37A-37E illustrate exemplary data showing functional connectivity in a somatosensory network (SMN). FIG. 37A illustrates exemplary diagrams relating to connectivity in control individual brains and brains of individuals having Sickle Cell Disease(SCD). FIG. 37B illustrates exemplary connectivity matrix data for control individuals in a SMN. FIG. 37C illustrates exemplary connectivity differences between control individuals and individuals having SCD. FIG. 37D illustrates an exemplary connectivity matrix data of brain regions of the SMN for individuals having SCD. FIG. 37E illustrates an exemplary connectivity difference value heatmap between control individuals and individuals having SCD.

[0086] FIGs. 38A-38F show exemplary diagrams illustrating neuropathic descriptive correlations in the default mode network (DMN), salience network (SAN), and somatosensory network (SMN) for various categories of selected pain sensation animations. FIG. 38A illustrates a connectivity map in the DMN of individuals who selected a burning animation to describe their pain sensations. FIG. 38B illustrates a connectivity map in the DMN of individuals who selected a stabbing animation to describe their pain sensations. FIG. 38C illustrates a connectivity map in the SAN of individuals who selected an electrifying animation to describe their pain sensations. FIG. 38D illustrates a connectivity map in the SAN of individuals who selected a stabbing animation to describe their pain sensations. FIG. 38E illustrates a connectivity map in the SMN of individuals who selected an electrifying animation to describe their pain sensations. FIG. 38F illustrates a connectivity map in the SMN of individuals who selected a burning animation to describe their pain sensations.

[0087] FIGs. 39A-39F show exemplary diagrams illustrating neuropathic descriptive correlations in the default mode network (DMN), salience network (SAN), and somatosensory network (SMN) for various categories of selected pain sensation animations. FIG. 39A illustrates a connectivity map in the DMN of individuals who selected a cramping animation to describe their pain sensations. FIG. 39B illustrates a connectivity map in the DMN of individuals who selected a throbbing animation to describe their pain sensations. FIG. 39C illustrates a connectivity map in the SAN of individuals who selected a cramping animation to describe their pain sensations. FIG. 39D illustrates a connectivity map in the SAN of individuals who selected a throbbing animation to describe their pain sensations. FIG. 39E illustrates a connectivity map in the SMN of individuals who selected a cramping animation to describe their pain sensations. FIG. 39F illustrates a connectivity map in the SMN of individuals who selected a throbbing animation to describe their pain sensations.

[0088] FIGs. 40A-40B illustrate exemplary correlation analysis charts between functional connectivity and a pain intensity score. FIG. 40A shows exemplary data illustrating connectivity data corresponding to a cramping pain intensity score for various subjects. FIG. 40B shows exemplary data illustrating connectivity data corresponding to a stabbing pain intensity score for various subjects.

[0089] FIGs. 41A-41F show exemplary diagrams illustrating neuropathic descriptive correlations in the default mode network (DMN), salience network (SAN), and somatosensory network (SMN) for various categories of selected pain sensation animations. FIG. 41A illustrates a connectivity map in the DMN of individuals who selected an electrifying animation to describe their pain sensations. FIG. 41B illustrates a connectivity map in the DMN of individuals who selected a shooting animation to describe their pain sensations. FIG. 41C illustrates a connectivity map in the SAN of individuals who selected a shooting animation to describe their pain sensations. FIG. 41D illustrates a connectivity map in the SAN of individuals who selected a burning animation to describe their pain sensations. FIG. 41E illustrates a connectivity map in the SMN of individuals who selected a stabbing animation to describe their pain sensations. FIG. 41F illustrates a connectivity map in the SMN of individuals who selected a shooting animation to describe their pain sensations.

[0090] FIGs. 42A-42C show exemplary diagrams illustrating nociceptive descriptive correlations in the default mode network (DMN), salience network (SAN), and somatosensory network (SMN) for selected pain sensation animations. FIG. 42A illustrates a connectivity map in the DMN of individuals who selected a pounding animation to describe their pain sensations. FIG. 42B illustrates a connectivity map in the SAN of individuals who selected a pounding animation to describe their pain sensations. FIG. 42C illustrates a connectivity map in the SMN of individuals who selected a pounding animation to describe their pain sensations.DETAILED DESCRIPTION

[0091] While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.Terms and Definitions

[0092] As used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Any reference to “or” herein is intended to encompass “and / or” unless otherwise stated.

[0093] As used herein, the phrases “at least one,” “one or more,” and “and / or” are open-ended expressions that are both conjunctive and disjunctive in operation. For example, each of the expressions “at least one of A, B and C,” “at least one of A, B, or C,” “one or more of A, B, and C”, “one or more of A, B, or C” and “A, B, and / or C” means A alone, B alone, C alone,A and B together, A and C together, B and C together, or A, B and C together. As used herein, the phrase “at most three” can mean less than one, one, two, or three.

[0094] Reference throughout this specification to “some embodiments,” “further embodiments,” or “a particular embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in some embodiments,” or “in further embodiments,” or “in a particular embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0095] The terms "subject," "individual," and "patient" may be used interchangeably and refer to humans, as well as non-human mammals (e.g., non-human primates, canines, equines, felines, porcines, bovines, ungulates, lagomorphs, rodents, and the like). In various embodiments, the subject can be a human (e.g., adult male, adult female, adolescent male, adolescent female, male child, female child) under the care of a physician or other health worker in a hospital, as an outpatient, or other clinical context. In certain embodiments, the subject may not be under the care or prescription of a physician or other health worker. In some embodiments, the subject may be under the care of a dental professional.

[0096] As used herein, “treatment” or “treating” refers to an approach for obtaining beneficial or desired results with respect to a disease, disorder, or medical condition including, but not limited to, a therapeutic benefit and / or a prophylactic benefit. In certain embodiments, treatment or treating involves administering a therapeutic to a subject. A therapeutic benefit may include the eradication or amelioration of the underlying disorder being treated. Also, a therapeutic benefit may be achieved with the eradication or amelioration of one or more of the physiological symptoms associated with the underlying disorder, such as observing an improvement in the subject, notwithstanding that the subject may still be afflicted with the underlying disorder.

[0097] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0098] As used herein, the term “about” in some cases refers to an amount that is approximately the stated amount, in some cases near the stated amount by 10%, 5%, or 1%, including increments therein, and in some cases, in reference to a percentage, refers to an amount that is greater or less the stated percentage by 10%, 5%, or 1%, including increments therein.

[0099] References throughout this specification to “some embodiments,” “further embodiments,” or “a particular embodiment,” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in some embodiments,” or “in furtherembodiments,” or “in a particular embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.Computing Systems

[0100] Referring to FIG. 1, a block diagram is shown depicting an exemplary machine that includes a computer system 100 (e.g., a processing or computing system) within which a set of instructions can execute for causing a device to perform or execute any one or more of the aspects and / or methodologies for static code scheduling of the present disclosure. The components in FIG. 1 are examples only and do not limit the scope of use or functionality of any hardware, software, embedded logic component, or a combination of two or more such components implementing particular embodiments.

[0101] Computer system 100 may include one or more processors 101, a memory 103, and a storage 108 that communicate with each other, and with other components, via a bus 140. The bus 140 may also link a display 132, one or more input devices 133 (which may, for example, include a keypad, a keyboard, a mouse, a stylus, etc.), one or more output devices 134, one or more storage devices 135, and various tangible storage media 136. All of these elements may interface directly or via one or more interfaces or adaptors to the bus 140. For instance, the various tangible storage media 136 can interface with the bus 140 via storage medium interface 126. Computer system 100 may have any suitable physical form, including but not limited to one or more integrated circuits (ICs), printed circuit boards (PCBs), mobile handheld devices (such as mobile telephones or PDAs), laptop or notebook computers, distributed computer systems, computing grids, or servers.

[0102] Computer system 100 includes one or more processor(s) 101 (e.g., central processing units (CPUs) or general-purpose graphics processing units (GPGPUs)) that carry out functions. Processor(s) 101 optionally contains a cache memory unit 102 for temporary local storage of instructions, data, or computer addresses. Processor(s) 101 are configured to assist in execution of computer readable instructions. Computer system 100 may provide functionality for the components depicted in FIG. 1 as a result of the processor(s) 101 executing non-transitory, processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 103, storage 108, storage devices 135, and / or storage medium 136. The computer-readable media may store software that implements particular embodiments, and processor(s) 101 may execute the software. Memory 103 may read the software from one or more other computer-readable media (such as mass storage device(s) 135, 136) or from one ormore other sources through a suitable interface, such as network interface 120. The software may cause processor(s) 101 to carry out one or more processes or one or more steps of one or more processes described or illustrated herein. Carrying out such processes or steps may include defining data structures stored in memory 103 and modifying the data structures as directed by the software.

[0103] The memory 103 may include various components (e.g., machine readable media) including, but not limited to, a random access memory component (e.g., RAM 104) (e.g., static RAM (SRAM), dynamic RAM (DRAM), ferroelectric random access memory (FRAM), phasechange random access memory (PRAM), etc.), a read-only memory component (e.g., ROM 105), and any combinations thereof. ROM 105 may act to communicate data and instructions unidirectionally to processor(s) 101, and RAM 104 may act to communicate data and instructions bidirectionally with processor(s) 101. ROM 105 and RAM 104 may include any suitable tangible computer-readable media described below. In one example, a basic input / output system 106 (BIOS), including basic routines that help to transfer information between elements within computer system 100, such as during start-up, may be stored in the memory 103.

[0104] Fixed storage 108 is connected bidirectionally to processor(s) 101, optionally through storage control unit 107. Fixed storage 108 provides additional data storage capacity and may also include any suitable tangible computer-readable media described herein. Storage 108 may be used to store operating system 109, executable(s) 110, data 111, applications 112 (application programs), and the like. Storage 108 can also include an optical disk drive, a solid-state memory device (e.g., flash-based systems), or a combination of any of the above. Information in storage 108 may, in appropriate cases, be incorporated as virtual memory in memory 103.

[0105] In one example, storage device(s) 135 may be removably interfaced with computer system 100 (e.g., via an external port connector (not shown)) via a storage device interface 125. Particularly, storage device(s) 135 and an associated machine-readable medium may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for the computer system 100. In one example, software may reside, completely or partially, within a machine-readable medium on storage device(s) 135. In another example, software may reside, completely or partially, within processor(s) 101.

[0106] Bus 140 connects a wide variety of subsystems. Herein, reference to a bus may encompass one or more digital signal lines serving a common function, where appropriate. Bus 140 may be any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures. As an example and not by way of limitation, such architectures include an Industry Standard Architecture (ISA) bus, an Enhanced ISA (EISA) bus, a MicroChannel Architecture (MCA) bus, a Video Electronics Standards Association local bus (VLB), a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, an Accelerated Graphics Port (AGP) bus, HyperTransport (HTX) bus, serial advanced technology attachment (SATA) bus, and any combinations thereof.

[0107] Computer system 100 may also include an input device 133. In one example, a user of computer system 100 may enter commands and / or other information into computer system 100 via input device(s) 133. Examples of an input device(s) 133 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), a touchpad, a touch screen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combinations thereof. In some embodiments, the input device is a Kinect, Leap Motion, or the like. Input device(s) 133 may be interfaced to bus 140 via any of a variety of input interfaces 123 (e.g., input interface 123) including, but not limited to, serial, parallel, game port, USB, FIREWIRE, THUNDERBOLT, or any combination of the above.

[0108] In particular embodiments, when computer system 100 is connected to network 130, computer system 100 may communicate with other devices, specifically mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, and the like, connected to network 130. Communications to and from computer system 100 may be sent through network interface 120. For example, network interface 120 may receive incoming communications (such as requests or responses from other devices) in the form of one or more packets (such as Internet Protocol (IP) packets) from network 130, and computer system 100 may store the incoming communications in memory 103 for processing. Computer system 100 may similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more packets in memory 103 and communicated to network 130 from network interface 120. Processor(s) 101 may access these communication packets stored in memory 103 for processing.

[0109] Examples of the network interface 120 include, but are not limited to, a network interface card, a modem, and any combination thereof. Examples of a network 130 or network segment 130 include, but are not limited to, a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combinations thereof. A network, such as network 130, mayemploy a wired and / or a wireless mode of communication. In general, any network topology may be used.

[0110] Information and data can be displayed through a display 132. Examples of a display 132 include, but are not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display, a plasma display, and any combinations thereof. The display 132 can interface to the processor(s) 101, memory 103, and fixed storage 108, as well as other devices, such as input device(s) 133, via the bus 140. The display 132 is linked to the bus 140 via a video interface 122, and transport of data between the display 132 and the bus 140 can be controlled via the graphics control 121. In some embodiments, the display is a video projector. In some embodiments, the display is a headmounted display (HMD) such as a VR headset. In further embodiments, suitable VR headsets include, by way of non-limiting examples, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headset, and the like. In still further embodiments, the display is a combination of devices such as those disclosed herein.

[0111] In addition to a display 132, computer system 100 may include one or more other peripheral output devices 134 including, but not limited to, an audio speaker, a printer, a storage device, and any combinations thereof. Such peripheral output devices may be connected to the bus 140 via an output interface 124. Examples of an output interface 124 include, but are not limited to, a serial port, a parallel connection, a USB port, a FIREWIRE port, a THUNDERBOLT port, and any combinations thereof.

[0112] In addition, or as an alternative, computer system 100 may provide functionality as a result of logic hardwired or otherwise embodied in a circuit, which may operate in place of or together with software to execute one or more processes or one or more steps of one or more processes described or illustrated herein. Reference to software in this disclosure may encompass logic, and reference to logic may encompass software. Moreover, reference to a computer- readable medium may encompass a circuit (such as an IC) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. The present disclosure encompasses any suitable combination of hardware, software, or both.

[0113] Those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrativecomponents, blocks, modules, circuits, and steps have been described above generally in terms of their functionality.

[0114] The various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0115] The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by one or more processor(s), or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0116] In accordance with the description herein, suitable computing devices include, by way of non-limiting examples, cloud computing platforms, distributed computing platforms, server clusters, server computers, desktop computers, laptop computers, notebook computers, subnotebook computers, netbook computers, and netpad computers.

[0117] In some embodiments, the computing device includes an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device’s hardware and provides services for execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of nonlimiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU / Linux®. In some embodiments, the operating system is providedby cloud computing. Those of skill in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research in Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.Non-transitory Computer Readable Storage Medium

[0118] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system of an optionally networked computing device. In further embodiments, a computer readable storage medium is a tangible component of a computing device. In still further embodiments, a computer readable storage medium is optionally removable from a computing device. In some embodiments, a computer readable storage medium includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, distributed computing systems including cloud computing systems and services, and the like. In some cases, the program and instructions are permanently, substantially permanently, semipermanently, or non-transitorily encoded on the media.Computer Programs

[0119] In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program, or use of the same. A computer program includes a sequence of instructions, executable by one or more processor(s) of the computing device’s CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), computing data structures, and the like, which perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those of skill in the art will recognize that a computer program may be written in various versions of various languages.

[0120] The functionality of the computer readable instructions may be combined or distributed as desired in various environments. In some embodiments, a computer program comprises one sequence of instructions. In some embodiments, a computer program comprises a plurality of sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from a plurality of locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.Software Modules

[0121] In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, a distributed computing resource, a cloud computing resource, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, a plurality of distributed computing resources, a plurality of cloud computing resources, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non-limiting examples, a web application, a mobile application, a standalone application, and a distributed or cloud computing application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on a distributed computing platform such as a cloud computing platform. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.Databases

[0122] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of information, for example customer incident data. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object- oriented databases, object databases, entity -relationship model databases, associative databases, XML databases, document-oriented databases, and graph databases. Further non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, Sybase, and MongoDB. In some embodiments, a database is Internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing based. In a particular embodiment, a database is a distributed database. In other embodiments, a database is based on one or more local computer storage devices.Physiological Condition Assessment Methods

[0123] In one aspect, disclosed herein are computer-implemented methods for assessing physiological condition in a subject, the method can comprise: (a) generating a graphical representation of the subject’s body on an interactive graphical user interface (GUI) (FIGs. 10A- 10D); (b) receiving one or more inputs from the subject through the GUI wherein the one or more inputs can comprise selection of an area within the graphical representation of the subject’s body (FIGs. 10A-10D); (c) generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body (FIGs. 10E-11); (d) receiving one or more pain feedback inputs from the subject, the one or more pain feedback inputs can comprise selection of the one or more visual animations of the one or more pain characteristics, or adjustments to the one or more visual animations of the one or more pain characteristics (FIGs. 12A-13); and (e) generating one or more outputs which can comprise one or more pain assessment results from the selected animations (FIGs. 20-21B).

[0124] In some embodiments, the subjects can read and sign an informed consent agreement before undergoing assessment (FIGs. 9A-9B).

[0125] In some embodiments, the one or more inputs from the subject can comprise one or more input types. In some cases, the one or more inputs can comprise one input type, two input types, three input types, four input types, five input types, six input types, seven input types, eight input types, nine input types, ten input types, or more than ten input types. In some embodiments, the one or more input types can be selected from the list comprising: drawing a shape around the selected area of the subject’s body, drawing inside the selected area of the subject’s body, tapping on the selected area of the subject’s body, zooming in on the selected area of the subject’s body, or any combination thereof. In some cases, the shape drawn around the selected area of the subject’s body can be a round shape, an angular shape, a circle, an oval, a rectangle, a trapezoid, a triangle, a hexagon, a septagon, an octagon, or any other geometric shape. In some cases, drawing inside the selected area of the subject’s body can comprise drawing within the general area of the selected area of the subject’s body, in some cases within the boundary edges of the body area, and in some cases outside the boundary edges of the body area. In some cases, tapping on the selected area of the subject’s body can comprise a long or short tap. In some cases, tapping on the selected area of the subject’s body can comprise one tap or more than one tap. In some cases, tapping on the selected area of the subject’s body can comprise one tap, two taps, three taps, four taps, five taps, six taps, seven taps, eight taps, nine taps, ten taps, or more than ten taps. In some cases, zooming in on the selected area of the subject’s body can comprise zooming in lx, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, lOx, or more than lOx.

[0126] In some embodiments, the method can further comprise generating one or more physiological condition-associated pain assessment results. In some cases, the method can further comprise generating one result, two results, three results, four results, five results, six results, seven results, eight results, nine results, ten results, or more than ten results. In some embodiments, generating the one or more physiological condition-associated pain assessment results can comprise receiving one or more physiological condition inputs from the subject. In some cases, the one or more physiological condition inputs can comprise one input, two inputs, three inputs, four inputs, five inputs, six inputs, seven inputs, eight inputs, nine inputs, ten inputs, or more than ten inputs. In some embodiments, receiving the one or more physiological condition inputs from the subject can be associated with the one or more pain feedback inputs In some cases, the one or more pain feedback inputs can be one input, two inputs, three inputs, four inputs, five inputs, six inputs, seven inputs, eight inputs, nine inputs, ten inputs, or more than ten inputs. In some embodiments, the one or more physiological condition inputs can comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof. In some cases, one or more input types can comprise one input type, two input types, three input types, four input types, five input types, six input types, seven input types, eight input types, nine input types, ten input types, or more than 10 input types.

[0127] In some embodiments, the method can comprise iteratively applying one or more algorithms of a pain forecast model to the one or more pain feedback inputs. In some cases, one algorithm, two algorithms, three algorithms, four algorithms, five algorithms, six algorithms, seven algorithms, eight algorithms, nine algorithms, or more than nine algorithms can be applied. In some cases, the algorithms can comprise a random forest algorithm, a linear regression algorithm, a gradient boosting algorithm, an MLP regressor algorithm, and any combination thereof. In some embodiments, applying one or more algorithms of a pain forecast model to the one or more pain feedback inputs can generate one or more outputs. In some cases, one or more pain feedback inputs can comprise selection of a pain animation, selection of a pain intensity, selection of a pain characteristic, selection of a choice to modify a pain animation, selection of a choice not to modify a pain animation, audio input, tactile input, or written input, and any combination thereof. In some cases, one or more outputs can comprise pain animation type selection data, pain animation intensity selection data, mood selection data, pain level selection data, energy level selection data, mobility data, medication data, or any combination thereof. In some cases, applying one or more algorithms of a pain forecast model to the one or more painfeedback inputs can generate one output, two outputs, three outputs, four outputs, five outputs, six outputs, seven outputs, eight outputs, nine outputs, ten outputs, eleven outputs, twelve outputs, thirteen outputs, fourteen outputs, fifteen outputs, sixteen outputs, seventeen outputs, eighteen outputs, nineteen outputs, twenty outputs, twenty-one outputs, twenty -two outputs, twenty-three outputs, twenty-four outputs, twenty-five outputs, twenty-six outputs, twenty-seven outputs, twenty-eight outputs, twenty-nine outputs, thirty outputs, thirty-one outputs, thirty -two outputs, thirty -three outputs, thirty -four outputs, thirty -five outputs, thirty-six outputs, thirtyseven outputs, thirty-eight outputs, thirty-nine outputs, forty outputs, forty-one outputs, forty -two outputs, forty-three outputs, forty-four outputs, forty-five outputs, forty-six outputs, forty-seven outputs, forty-eight outputs, forty-nine outputs, fifty outputs, fifty-one outputs, fifty -two outputs, fifty -three outputs, fifty-four outputs, fifty -five outputs, fifty-six outputs, fifty-seven outputs, fifty-eight outputs, fifty-nine outputs, sixty outputs, sixty-one outputs, sixty -two outputs, sixty- three outputs, sixty-four outputs, sixty-five outputs, sixty-six outputs, sixty-seven outputs, sixtyeight outputs, sixty -nine outputs, seventy outputs, seventy-one outputs, seventy -two outputs, seventy-three outputs, seventy-four outputs, seventy-five outputs, seventy-six outputs, seventyseven outputs, seventy-eight outputs, seventy -nine outputs, eighty outputs, eighty-one outputs, eighty-two outputs, eighty-three outputs, eighty-four outputs, eighty-five outputs, eighty-six outputs, eighty-seven outputs, eighty-eight outputs, eighty -nine outputs, ninety outputs, ninety- one outputs, ninety -two outputs, ninety -three outputs, ninety-four outputs, ninety -five outputs, ninety-six outputs, ninety-seven outputs, ninety-eight outputs, ninety-nine outputs, one hundred outputs, or more than one hundred outputs. In some embodiments, the one or more outputs can comprise one or more pain forecast predictions for one or more selected areas of the subject’s body (FIG. 21B). In some cases, the pain forecast predictions can be for one selected area of the subject’s body or multiple selected areas of the subject’s body. In some cases, the pain forecast predictions can span about one minute, five minutes, 20 minutes, one hour, two hours, three hours, five hours, eight hours, ten hours, twelve hours, twenty four hours, two days, four days, five days, six days, seven days, eight days, nine days, ten days, eleven days, twelve days, thirteen days, fourteen days, fifteen days, sixteen days, seventeen days, eighteen days, nineteen days, twenty days, twenty-one days, twenty-two days, twenty-three days, twenty-four days, twenty- five days, twenty-six days, twenty-seven days, twenty-eight days, twenty-nine days, thirty days, forty days, fifty days, 100 days, 200 days, one year, two years, three years, four years, five years, six years, seven years, eight years, nine years, ten years, or more than 10 years (FIG. 21 A).

[0128] In some embodiments, the method can comprise transmitting the one or more outputs comprising one or more pain assessment results to an external device. In some cases, the external device can be a medical device. In some cases, the external device can be a smartphone. In somecases, the external device can be a device comprising a processor. In some cases, the external device can be a virtual reality device. In some cases, the external device can be a computer. In some cases, the external device can be a smart wearable device, such as a smart watch.

[0129] In some embodiments, the method can further comprise generating the one or more visual animations of the one or more pain characteristics using a virtual reality device. In some embodiments, the virtual reality device can map the selected area of the subject’s body to the subject’s body. In some embodiments, the virtual reality device can display the one or more visual animations on the subject’s body. In some cases, the virtual reality device can be a wearable virtual reality device. In some cases, the virtual reality device can be a smartphone. In some cases, the virtual reality device can be a medical device. In some cases, the virtual reality device can be a computer. In some cases, the virtual reality device can be connected to a computer. In some cases, the virtual reality device can be connected to a smartphone. In some cases, the virtual reality device can be connected to a medical device. In some cases, the virtual reality device can project an image onto the body. In some cases, the image can be a pain animation. In some cases, movement of the subject can modify the virtual reality visual animations. In some cases, touching of the body area can modify the virtual reality visual animations. In some cases, audio or tactile feedback can modify the virtual reality visual animations.

[0130] In some embodiments, the method can comprise transmitting data to an external healthcare application. In some embodiments, the method can comprise integrating received data from an external healthcare application. In some embodiments, the method can comprise both transmitting data to an external healthcare application and integrating received data from an external healthcare application. In some cases, the external healthcare application can comprise an application on a computer, a smartphone, a virtual reality device, a medical device, a wearable device, a smart watch, or any combination thereof. In some embodiments, adjustments to the one or more visual animations of the one or more pain characteristics can comprise manual adjustments made by the subject. In some embodiments, the manual adjustments can comprise tactile selection adjustments. In some embodiments, the manual adjustments can comprise audio adjustments. In some embodiments, the manual adjustments can comprise written adjustments. In some embodiments, the manual adjustments can comprise interacting with the system GUI.

[0131] In another aspect, disclosed herein are computer-implemented methods for generating one or more suggested diagnoses from pain assessment in a subject, the computer-implemented method can comprise: (a) generating a predictive diagnosis model, which can comprise a library of potential predicted diagnoses, wherein the potential predicted diagnoses can be selected by a method which can comprise: (i) extracting pain descriptor terms from one or more pain feedbackinputs from the subject, and (ii) applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for one or more of the potential predicted diagnoses; and (b) generating one or more outputs which can comprise one or more of the potential predicted diagnoses. In some cases, the library of potential predicted diagnoses can comprise a library of medical diagnoses for pain conditions, chronic conditions, temporary conditions, other medical conditions, psychological conditions, or any combination thereof. In some cases, pain descriptor terms can be derived from subject pain inputs, which can comprise pain animation selection, audio feedback, tactile feedback, or written feedback. In some cases, the one or more pain feedback inputs can comprise one input, two inputs, three inputs, four inputs, five inputs, six inputs, seven inputs, eight inputs, nine inputs, ten inputs, eleven inputs, twelve inputs, thirteen inputs, fourteen inputs, fifteen inputs, sixteen inputs, seventeen inputs, eighteen inputs, nineteen inputs, twenty inputs, or more than twenty inputs.

[0132] In yet another aspect, disclosed herein are computer-implemented methods for selecting an optimized opioid treatment from pain assessment in a subject, the computer-implemented method can comprise: (a) generating a predictive optimized opioid treatment model, which can comprise a library of opioids, wherein the one or more predicted optimized opioid treatments can be generated by a method comprising: (i) extracting pain descriptor terms from one or more pain feedback inputs from the subject, and (ii) applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for the types of pain alleviated by the one or more opioids; and (b) generating one or more outputs which can comprise one or more of the predicted optimized opioid treatments. In some cases, the library of opioids can comprise oral opioids, injectable opioids, or both. In some cases, assessment predicted optimized opioid treatments can comprise evaluation of selection of one or more pain animations, one or more mood scores, one or more energy level scores, one or more pain level scores, one or more mobility scores, one or more opioid medication notes, one or more treatment notes, and any combination thereof.

[0133] In some embodiments, the method can comprise generating a predictive opioid addiction model. In some embodiments, the predictive opioid addiction model can comprise the library of opioids. In some embodiments, each opioid can be further associated with one or more addiction risk scores. In some embodiments, the predictive opioid addiction model can generate one or more outputs which can comprise one or more of the predicted opioid addiction risk scores. In some cases, the one or more outputs can be generated from data comprising selection of one or more pain animations, one or more mood scores, one or more energy level scores, one or more pain level scores, one or more mobility scores, one or more opioid medication notes, one or more treatment notes, and any combination thereof (FIGs. 14A-17). In some cases, the one or moreaddiction risk scores can comprise one opioid addiction risk score, two opioid addiction risk scores, three opioid addiction risk scores, four opioid addiction risk scores, five opioid addiction risk scores, six opioid addiction risk scores, seven opioid addiction risk scores, eight opioid addiction risk scores, nine opioid addiction risk scores, ten opioid addiction risk scores, eleven opioid addiction risk scores, twelve opioid addiction risk scores, thirteen opioid addiction risk scores, fourteen opioid addiction risk scores, fifteen opioid addiction risk scores, sixteen opioid addiction risk scores, seventeen opioid addiction risk scores, eighteen opioid addiction risk scores, nineteen opioid addiction risk scores, twenty opioid addiction risk scores, or more than twenty opioid addiction risk scores.

[0134] In yet another aspect, disclosed herein are computer-implemented methods for generating one or more optimized customized pain assessment animations for a subject, the computer- implemented method can comprise: (a) generating an interactive graphical user interface (GUI) displaying a representation of the subject’s body; (b) receiving one or more inputs from the subject, the one or more inputs can comprise selection of an area of the subject’s body being displayed on the GUI; (c) generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; (d) generating a generative pain animation customization model, wherein the pain animation customization model can generate customized, optimized pain animations with improved accuracy to the subject’s pain characteristics, wherein the optimized customized visual animations of one or more pain characteristics associated with the selected area of the subject’s body can be generated by a method comprising: (i) receiving one or more signals from the subject, the one or more signals can comprise adjustment instructions for the one or more visual animations of the one or more pain characteristics, (ii) applying one or more algorithms to iteratively modify one or more elements of the one or more visual animations of the one or more pain characteristics based on the adjustment instructions, (iii) generating one or more optimized customized visual animations of the one or more pain characteristics incorporating the modified elements of the one or more visual animations; and (e) generating one or more optimized outputs which can comprise one or more pain assessment results. In some cases, the subject can be given a satisfaction survey (FIG. 18).

[0135] In some embodiments, the one or more signals comprising adjustment instructions for the one or more visual animations can comprise audio instructions, text instructions, drawn instructions, or any combination thereof. In some cases, the instructions can comprise spoken instructions. In some cases, the instructions can comprise tactile instructions. In some cases, the instructions can comprise written instructions. In some embodiments, the one or more signals comprising adjustment instructions for the one or more visual animations can comprise concreteinstructions, descriptive instructions, abstract instructions, or any combination thereof. In some cases, the instructions can comprise visual instructions, audio instructions, written instructions, tactile instructions, drawn instructions, and any combination thereof (FIG. 19).Physiological Condition Assessment Systems

[0136] In yet another aspect as shown in FIGs. 2-4, disclosed herein are one or more computer- implemented systems for assessing physiological condition in a subject, the computer- implemented system can comprise: (a) an interactive graphical user interface (GUI) (201) onto which the computer-implemented system can generate a graphical representation of the subject’s body (202), wherein the GUI can be configured to receive one or more inputs from the subject, wherein the one or more inputs can comprise selection of an area within the graphical representation of the subject’s body (204); (b) a processor configured to perform operations which can comprise generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body (207); (c) the processor configured to perform operations which can comprise receiving one or more pain feedback inputs from the subject (212), the one or more pain feedback inputs can comprise selection of the one or more visual animations of the one or more pain characteristics (212), or adjustments to the one or more visual animations of the one or more pain characteristics (312); and (d) one or more system outputs which can comprise one or more pain assessment results from the selected animations (213). In some cases, the selected body area information (205) can be transferred from the body area selection module (200) to the pain assessment module (206).

[0137] In some embodiments, the one or more inputs from the subject can comprise one or more input types selected from the list comprising: drawing a shape around the selected area of the subject’s body, drawing inside the selected area of the subject’s body, tapping on the selected area of the subject’s body, zooming in on the selected area of the subject’s body, or any combination thereof (204). In some cases, the shape drawn around the selected area of the subject’s body can be a round shape, an angular shape, a circle, an oval, a rectangle, a trapezoid, a triangle, a hexagon, a septagon, an octagon, or any other geometric shape. In some embodiments, the processor (206) can be configured to perform operations which can comprise generating (211) one or more physiological condition-associated pain assessment results (213), wherein generating the one or more physiological condition-associated pain assessment results (213) can comprise receiving one or more physiological condition inputs from the subject (212) associated with the one or more pain feedback inputs. In some cases, drawing inside the selected area of the subject’s body can comprise drawing within the general area of the selected area of the subject’s body, in some cases within the boundary edges of the body area, and in some cases outside the boundary edges of the body area. In some cases, tapping on the selected area of thesubject’s body can comprise a long or short tap. In some cases, tapping on the selected area of the subject’s body can comprise one tap or more than one tap. In some cases, tapping on the selected area of the subject’s body can comprise one tap, two taps, three taps, four taps, five taps, six taps, seven taps, eight taps, nine taps, ten taps, or more than ten taps. In some cases, zooming in on the selected area of the subject’s body can comprise zooming in lx, 2x, 3x, 4x, 5x, 6x, 7x, 8x, 9x, lOx, or more than lOx.

[0138] In some cases, the system can further comprise one or more prediction modules (412). In some cases, the one or more prediction modules can comprise a pain forecast prediction model (413), a suggested diagnoses model (414), an opioid addiction model (415), and an optimized opioid treatment model (416). In some embodiments, the one or more physiological condition inputs can comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof (405). In some cases, the one or more pain feedback inputs can be one input, two inputs, three inputs, four inputs, five inputs, six inputs, seven inputs, eight inputs, nine inputs, ten inputs, or more than ten inputs. In some embodiments, the one or more physiological condition inputs can comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof (405). In some cases, one or more input types can comprise one input type, two input types, three input types, four input types, five input types, six input types, seven input types, eight input types, nine input types, ten input types, or more than ten input types.

[0139] In some embodiments, the computer-implemented system can further comprise a pain forecast model (413) configured to perform operations which can comprise iteratively applying one or more algorithms to the one or more pain feedback inputs which can generate one or more outputs comprising one or more pain forecast predictions for the selected area of the subject’s body. In some cases, the algorithms can comprise a random forest algorithm, a linear regression algorithm, a gradient boosting algorithm, an MLP regressor algorithm, and any combination thereof. In some cases, one or more pain feedback inputs can comprise selection of a pain animation, selection of a pain intensity, selection of a pain characteristic, selection of a choice to modify a pain animation, selection of a choice not to modify a pain animation, audio input, tactile input, or written input, and any combination thereof. In some cases, one or more outputscan comprise pain animation type selection data, pain animation intensity selection data, mood selection data, pain level selection data, energy level selection data, mobility data, medication data, or any combination thereof. In some cases, applying one or more algorithms of a pain forecast model (413) to the one or more pain feedback inputs (311) can generate one output, two outputs, three outputs, four outputs, five outputs, six outputs, seven outputs, eight outputs, nine outputs, ten outputs, eleven outputs, twelve outputs, thirteen outputs, fourteen outputs, fifteen outputs, sixteen outputs, seventeen outputs, eighteen outputs, nineteen outputs, twenty outputs, twenty-one outputs, twenty -two outputs, twenty -three outputs, twenty -four outputs, twenty-five outputs, twenty-six outputs, twenty-seven outputs, twenty-eight outputs, twenty-nine outputs, thirty outputs, thirty-one outputs, thirty -two outputs, thirty -three outputs, thirty-four outputs, thirty-five outputs, thirty-six outputs, thirty-seven outputs, thirty-eight outputs, thirty-nine outputs, forty outputs, forty-one outputs, forty -two outputs, forty-three outputs, forty-four outputs, forty -five outputs, forty-six outputs, forty-seven outputs, forty-eight outputs, forty -nine outputs, fifty outputs, fifty-one outputs, fifty -two outputs, fifty -three outputs, fifty -four outputs, fifty-five outputs, fifty-six outputs, fifty-seven outputs, fifty-eight outputs, fifty-nine outputs, sixty outputs, sixty-one outputs, sixty -two outputs, sixty -three outputs, sixty -four outputs, sixty- five outputs, sixty-six outputs, sixty-seven outputs, sixty-eight outputs, sixty-nine outputs, seventy outputs, seventy-one outputs, seventy -two outputs, seventy -three outputs, seventy-four outputs, seventy-five outputs, seventy-six outputs, seventy-seven outputs, seventy-eight outputs, seventy -nine outputs, eighty outputs, eighty-one outputs, eighty -two outputs, eighty -three outputs, eighty -four outputs, eighty-five outputs, eighty-six outputs, eighty-seven outputs, eighty-eight outputs, eighty-nine outputs, ninety outputs, ninety-one outputs, ninety -two outputs, ninety -three outputs, ninety-four outputs, ninety -five outputs, ninety-six outputs, ninety-seven outputs, ninety-eight outputs, ninety-nine outputs, one hundred outputs, or more than one hundred outputs.

[0140] In some embodiments, the computer-implemented system can comprise a virtual reality device, wherein the virtual reality device can map the selected area of the subject’s body to the subject’s body and can display the one or more visual animations on the subject’s body. In some cases, the virtual reality device can be a wearable virtual reality device. In some cases, the virtual reality device can be a smartphone. In some cases, the virtual reality device can be a medical device. In some cases, the virtual reality device can be a computer. In some cases, the virtual reality device can be connected to a computer. In some cases, the virtual reality device can be connected to a smartphone. In some cases, the virtual reality device can be connected to a medical device. In some cases, the virtual reality device can project an image onto the body. In some cases, the image can be a pain animation. In some cases, movement of the subject canmodify the virtual reality visual animations. In some cases, touching of the body area can modify the virtual reality visual animations. In some cases, audio or tactile feedback can modify the virtual reality visual animations.

[0141] In some embodiments, the computer-implemented system can comprise one or more external healthcare applications, wherein the one or more external healthcare applications can transmit data to the system. In some embodiments, the one or more external healthcare applications can integrate output from the system. In some embodiments, the one or more external healthcare applications can both integrate output from the system and transmit data to the system. In some cases, the external healthcare application can comprise an application on a computer, a smartphone, a virtual reality device, a medical device, a wearable device, a smart watch, or any combination thereof. In some embodiments, adjustments to the one or more visual animations of the one or more pain characteristics can comprise manual adjustments made by the subject (311). In some embodiments, the manual adjustments can comprise tactile selection adjustments. In some embodiments, the manual adjustments can comprise audio adjustments. In some embodiments, the manual adjustments can comprise written adjustments (311). In some embodiments, the manual adjustments can comprise interacting with the system GUI (301).

[0142] In yet another aspect, disclosed herein are computer-implemented systems for generating one or more suggested diagnoses from pain assessment in a subject, the computer-implemented system can comprise: (a) a predictive diagnosis model (414), which can comprise a library of potential predicted diagnoses, wherein the potential predicted diagnoses can be selected by a method comprising: (i) extracting pain descriptor terms from one or more pain feedback inputs (212), (311) from the subject, and (ii) applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for one or more of the potential predicted diagnoses; and (b) one or more outputs which can comprise one or more of the potential predicted diagnoses. In some cases, the library of potential predicted diagnoses can comprise a library of medical diagnoses for pain conditions, chronic conditions, temporary conditions, other medical conditions, psychological conditions, or any combination thereof. In some cases, pain descriptor terms can be derived from subject pain inputs, which can comprise pain animation selection, audio feedback, tactile feedback, or written feedback. In some cases, the one or more pain feedback inputs can comprise one input, two inputs, three inputs, four inputs, five inputs, six inputs, seven inputs, eight inputs, nine inputs, ten inputs, eleven inputs, twelve inputs, thirteen inputs, fourteen inputs, fifteen inputs, sixteen inputs, seventeen inputs, eighteen inputs, nineteen inputs, twenty inputs, or more than twenty inputs.

[0143] In yet another aspect, disclosed herein are computer-implemented systems for selecting an optimized opioid treatment from pain assessment in a subject, the computer-implementedsystem can comprise: (a) a predictive optimized opioid treatment model (416), which can comprise a library of opioids, wherein the one or more predicted optimized opioid treatments can be generated by a method which can comprise: (i) extracting pain descriptor terms from one or more pain feedback inputs from the subject (212), (311), and (ii) applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for the types of pain alleviated by the one or more opioids; and (b) one or more outputs which can comprise one or more of the predicted optimized opioid treatments. In some cases, the library of opioids can comprise oral opioids, injectable opioids, or both. In some cases, assessment predicted optimized opioid treatments can comprise evaluation of selection of one or more pain animations, one or more mood scores, one or more energy level scores, one or more pain level scores, one or more mobility scores, one or more opioid medication notes, one or more treatment notes, and any combination thereof.

[0144] In some embodiments, the computer-implemented system can comprise a predictive opioid addiction model (415), wherein the predictive opioid addiction model can comprise the library of opioids, wherein each opioid can be associated with one or more addiction risk scores. In some embodiments, the predictive opioid addiction model can generate one or more outputs which can comprise one or more of the predicted opioid addiction risk scores. In some cases, the one or more outputs can be generated from data comprising selection of one or more pain animations, one or more mood scores, one or more energy level scores, one or more pain level scores, one or more mobility scores, one or more opioid medication notes (406), one or more treatment notes (406), and any combination thereof (405). In some cases, the one or more addiction risk scores can comprise one opioid addiction risk score, two opioid addiction risk scores, three opioid addiction risk scores, four opioid addiction risk scores, five opioid addiction risk scores, six opioid addiction risk scores, seven opioid addiction risk scores, eight opioid addiction risk scores, nine opioid addiction risk scores, ten opioid addiction risk scores, eleven opioid addiction risk scores, twelve opioid addiction risk scores, thirteen opioid addiction risk scores, fourteen opioid addiction risk scores, fifteen opioid addiction risk scores, sixteen opioid addiction risk scores, seventeen opioid addiction risk scores, eighteen opioid addiction risk scores, nineteen opioid addiction risk scores, twenty opioid addiction risk scores, or more than twenty opioid addiction risk scores.

[0145] In some cases, the computer-implemented system can comprise a body area selection module (300) and a pain assessment module (306) that can generate custom animations (313). In yet another aspect, disclosed herein are computer-implemented systems for generating one or more optimized customized pain assessment animations for a subject, the computer-implemented system can comprise: (a) an interactive graphical user interface (GUI) displaying a representationof the subject’s body (301); (b) a processor configured to perform operations which can comprise receiving one or more inputs from the subject (302), the one or more inputs (303) can comprise selection of an area of the subject’s body (303) being displayed on the GUI (304); (c) the processor configured to perform operations (309) which can comprise generating one or more visual animations (310) of one or more pain characteristics associated with the selected area of the subject’s body (305); (d) a generative pain animation customization model (312), wherein the pain animation customization model can generate customized, optimized pain animations (313) with improved accuracy to the subject’s pain characteristics (314), wherein the optimized customized visual animations (313) of one or more pain characteristics associated with the selected area of the subject’s body (305) can be generated by a method comprising: (i) receiving one or more signals from the subject (303), the one or more signals can comprise adjustment instructions (311) for the one or more visual animations of the one or more pain characteristics(310), (ii) applying one or more algorithms (309) to iteratively modify one or more elements of the one or more visual animations (310) of the one or more pain characteristics based on the adjustment instructions (311), (iii) generating one or more optimized customized visual animations (313) of the one or more pain characteristics incorporating the modified elements(311) of the one or more visual animations (310); and (e) one or more optimized outputs (315) which can comprise one or more pain assessment results (315).

[0146] In yet another aspect, described herein is a computer-implemented method for determining resting-state functional connectivity patterns of a pain characteristic profile of a subject, the computer-implemented method comprising generating one or more visual animations of one or more pain characteristics associated with a selected area of the subject’s body. The computer-implemented method can further comprise receiving one or more pain feedback inputs from the subject, the one or more pain feedback inputs comprising selection of the one or more visual animations of the one or more pain characteristics, or adjustments to the one or more visual animations of the one or more pain characteristics. The computer-implemented method can further comprise generating the pain characteristic profile of the subject based on the received one or more pain feedback inputs from the subject. The computer-implemented method can further comprise and generating a functional connectivity profile based at least in part on analyzing functional connectivity data between a first region of a brain of the subject and a second region of the brain of the subject. In some cases, the functional connectivity profile can be associated with the pain characteristic profile of the subject.

[0147] In some cases, the pain characteristic profile can comprise one or more of a pain area, a pain sensation type, a pain intensity, or a pain duration, or any combination thereof.

[0148] In some embodiments, the functional connectivity data can comprise one or more functional connectivity values. In some cases, each of the one or more functional connectivity values can comprise correlation values between activation of the first region of the brain and activation of the second region of the brain. In some cases, the functional connectivity data can comprise an increase in connectivity between the first region and the second region of the brain. In some cases, the indication of the change in the one or more characteristics of connectivity can comprise a decrease in connectivity between the first region and the second region of the brain.

[0149] In some embodiments, the first region can be located at a first hemisphere of the brain and the second region can be located at a second hemisphere of the brain.

[0150] In some embodiments, the first region and the second region can be located at the same hemisphere of the brain.

[0151] In some embodiments, the functional connectivity profile can comprise multiple functional connectivity values relating to two or more sets of functionally connected regions.

[0152] In some cases, each of the two or more sets of functionally connected regions can comprise the first region and the second region. In some cases, the first region or the second region, or both, can be different between the two or more sets of functionally connected regions.

[0153] In some embodiments, the functional connectivity profile can comprise one or more increases in connectivity between one or more sets of functionally connected regions, or one or more decreases in connectivity between one or more sets of functionally connected regions, or both. In some cases, the one or more increases in connectivity and the one or more decreases in connectivity can be determined relative to baseline connectivity values of control participants.

[0154] In some embodiments, the functional connectivity data can be determined based at least in part on functional imaging data.

[0155] In some embodiments, the functional imaging data can comprise functional magnetic resonance imaging (fMRI) data.

[0156] In some embodiments, the computer-implemented method can further comprise generating a functional connectivity report.

[0157] In some embodiments, the functional connectivity report can comprise one or more changes in connectivity associated with one or more pain types of the pain characteristic profile of the subject.

[0158] In some embodiments, the one or more signals comprising adjustment instructions (311) for the one or more visual animations can comprise audio instructions, text instructions, drawn instructions, or any combination thereof. In some cases, the instructions can comprise spoken instructions. In some cases, the instructions can comprise tactile instructions. In some cases, the instructions can comprise written instructions. In some embodiments, the one or more signalscomprising adjustment instructions (311) for the one or more visual animations can comprise concrete instructions, descriptive instructions, abstract instructions, or any combination thereof. In some cases, the instructions (311) can comprise visual instructions, audio instructions, written instructions, tactile instructions, drawn instructions, and any combination thereof. In some embodiments, the one or more signals can comprise adjustment instructions for the one or more visual animations can comprise concrete instructions, descriptive instructions, abstract instructions, or any combination thereof.EXAMPLES

[0159] The following examples are provided to further illustrate some embodiments of the present disclosure but are not intended to limit the scope of the disclosure; it will be understood by their exemplary nature that other procedures, methodologies, or techniques may alternatively be used.Example 1: Pain Outcomes and Associated Pain Animations

[0160] In one non-limiting example, subjects completed a battery of questionnaires and tracked their pain intensity (FIG. 11). Participants also tracked their mood daily (FIGs. 14A-14B). Subjects interacted with a GUI displaying two-dimensional body images paintable to indicate areas affected by pain (FIGs. 10A-10D). Subjects selected from multiple abstract animations corresponding to a variety of represented pain characteristics with adjustable intensity (FIGs. 11- 13). Subjects were categorized into “Shooting Pain” and “No Shooting Pain” groups based on their pain animation selections. Subjects were also organized into groups based on whether body area selected was less or more than the median. Multivariable regressions were used to estimate covariate-adjusted associations between daily pain, Patient Reported Outcomes Measurement Information System (PROMIS) pain interference scale, Pain Catastrophizing Scale (PCS), Current Opioid Misuse Measure (COMM-9), and Adults Sickle Cell Quality of Life Measurement Information System (ASCQ-Me) pain severity and frequency (FIGs. 23A-23B).Example 2: Digital Pain Assessment Using Abstract Animations

[0161] In another non-limiting example, subjects were interviewed using directed storytelling to determine contexts in which subjects had experienced pain, as well as successful and unsuccessful clinical interactions. Subjects verbalized their thought processes while doing specific pain scale tasks including the Wong-Baker faces scale and Numeric rating scale. Subjects were also given a recall interview prompt to describe using scales in past to describepain to medical providers. Clinician interviews were also conducted and transcribed using directed storytelling to gather information about expertise and experience interacting with and treating patients with pain. The transcripts were open coded to determine thematic patterns, resulting in an inductive and deductive analysis and set of criteria for pain communication solutions.

[0162] Drawing exercises were conducted to develop visual depictions of more commonly used pain adjectives derived from the McGill Pain Questionnaire Short Form. These words included stabbing, pounding, and shooting, and the words were drawn in a low, medium, and high version (FIG. 6). Words from the McGill Pain Questionnaire (MPQ) Short Form were clustered into groups, including throbbing, shooting, cramping, with additional categories applied to other qualities, such as deep and dull. Visual variables were also defined, including speed, saturation, focus, and size, among others. Modification of visual variables changed the intensity of the pain animations depicted. A survey was performed to determine the pain characteristics that the pain animations evoked in subjects. Wireframes were created to provide context for the pain animations (FIG. 12A).

[0163] The pain animations were modified based on input from the subjects, and the pain animations were labeled with pain characteristics based on descriptors the animations were intended to represent. Subjects with Sickle Cell Disease (SCD) pain and self-reported chronic pain were presented with about eight pain animations and determined applicability of the pain animations to their pain sensations. Data was also collected from subjects on types of experienced pain, pain tracking methods, and pain communication history with medical providers. About 10 subjects were interviewed. Subjects selected wireframe of pain animations that they felt best reflected their pain sensations, and intensity could be increased or decreased. The pain animations could also be dragged and dropped to save them (FIG. 12B).

[0164] Each pain animation was labeled using pain characteristic descriptors. To confirm accuracy and usability of pain animations, the pain animations were tested on subjects with selfreported chronic pain to determine subjective accuracy.Example 3: Validation Against McGill Pain Questionnaire and PainDETECT Questionnaire

[0165] In another non-limiting example, pain animations were tested on subjects currently receiving treatment for chronic pain. Subjects signed electronic consent forms and completed a battery of electronic questionnaires, uploaded to a REDCap database. Subjects completed demographic information and clinical history of their pain, including a self-report of whether they currently had pain, severity of current pain on a visual analog scale (VAS), duration of paincondition, and type of pain condition. Type of pain condition included nerve damage, arthritis, sickle cell, fibromyalgia, back pain, neck pain, headache, migraine, joint pain, chronic pain, abdominal pain, or additional conditions.

[0166] Subjects completed the MPQ and PainDETECT questionnaire, and the computer- implemented systems and methods for assessing physiological condition. Questionnaires were randomized. Word descriptors from the MPQ, including sensory, affective, and evaluative, were correlated with each pain animation. Words subjects used to describe their pain were mapped onto pain animations they selected. Subject satisfaction with each questionnaire and the computer-implemented systems and methods for assessing physiological condition was evaluated.

[0167] The MPQ, PainDETECT questionnaire, and the computer-implemented systems and methods for assessing physiological condition were evaluated for differences by age, sex, race / ethnicity, and location of pain to determine differences in pain intensity and quality. The pain animation selection of each subject was compared to their pain diagnosis, and differences in recorded pain quality. For example, neuropathic pain, self-reported nerve damage, and nonneuropathic pain categories were compared in evaluating pain scores and pain diagnosis, using means and Pearson correlations.

[0168] Distributions of pain scores were measured using descriptive measures of central tendency and using Pearson correlation coefficients for continuous measures and phi correlation coefficients. PainDETECT values were measured against the computer-implemented systems and methods for assessing physiological conditions using chi-squared analysis, including measurement of self-reported nerve damage versus corresponding pain animation selection.

[0169] Additionally, to test sensitivity and specificity of the computer-implemented systems and methods for assessing physiological condition compared to PainDETECT, receiver operating characteristic (ROC) curves were derived using logistic regression analyses, quantified by area under the curve (AUC). For the computer-implemented systems and methods for assessing physiological condition, animation intensity measured in speed and saturation was transformed into a continuous 0 to 100 score by modeling nonselection of the electrifying pain animation as “0” normalized against the selection of the “electrifying” pain animation coupled with an intensity value.

[0170] Confidence levels of the AUCs were tested to determine overlap, and linear regression was performed with pain characteristics designated as response variables, and measurement type, comprising the computer-implemented systems and methods for assessing physiological condition or PainDETECT being the independent variable. Correlations between the “electrifying” pain animation and qualitative descriptors on the PainDETECT scale weremeasured by assessing the association of the animation with each questionnaire item. The intensity mean of the computer-implemented systems and methods for assessing physiological condition was calculated with 95% confidence intervals along with interquartile ranges. Also evaluated was whether changes in the resulting values of the PainDETECT questionnaire were associated with increased probability of subject selection of the “electrifying” animation.

[0171] Correlation matrices were determined for associations between self-report pain diagnosis, results of the computer-implemented systems and methods for assessing physiological condition, and painDETECT in 170 subjects (FIG. 22A). Correlation matrices were also determined for associations between pain diagnoses, the “electrifying” pain animation, and dichotomized PainDETECT scores (FIGs. 22B-22C). Correlation matrices were also determined for pain animation associations with specific MPQ pain quality descriptors chosen by at least 10 participants (FIG. 22D).

[0172] ROC analysis was performed to determine the ability of the computer-implemented systems and methods for assessing physiological condition to discriminate neuropathic pain as opposed to non-neuropathic pain, and in comparison to PainDETECT. Subject satisfaction was taken after subject completion of the pain scoring.Example 4: Feature Engineering

[0173] In yet another non-limiting example, current pain level as an outcome as predicted using pain location and pain percent variable, as well as pain type and intensity variable, in the following non-limiting example:

[0174] {

[0175] Calculate cur_pain_level using the following:

[0176] pn_pain locations (back-left-foot = 30, back-right-foot = 81);

[0177] pn_pain types (burning = 1);

[0178] }

[0179] Calculate cur_pain_level using linear regression, random forest, gradientboosting, and MLPRegressor;

[0180] Calculate cur_pain_level using a regularized deep artificial neural network architecture wherein:

[0181] {

[0182] Epochs = 1000;

[0183] Batch size = 16;

[0184] Activation = ‘relu’;

[0185] Optimizer = ‘adam’

[0186] }

[0187] Split data into 95% train data and 5% test data.

[0188] Mean squared error was used to evaluate machine learning models. Feature ranking analysis was performed to analyze top features associated with “cur_pain_level”.Example 5: Analysis and Predictive Machine Learning Models

[0189] In yet another non-limiting example, a system of interconnected machine learning algorithms can be used to analyze and predict current and future pain states (FIG. 24). The interconnected machine learning modules can, for example, perform steps including:

[0190] {

[0191] Import necessary open-source libraries in Python;

[0192] Load dataset from Painimation Excel file(s);

[0193] Preprocess Data;

[0194] Perform feature selection or feature engineering, encode labels, normalize data;

[0195] Define and initialize machine learning models, wherein:

[0196] Machine learning models can include a linear regression model, a random forest model, a gradient boosting regressor model, and a neural network regressor model;

[0197] Split data into train and test sets;

[0198] Train and evaluate each machine learning model;

[0199] Select the highest performing machine learning model, wherein the performance can be evaluated via r-squared analysis;

[0200] Perform additional analysis of specific machine learning models, wherein:

[0201] Additional analysis can include feature importance analysis, coefficient analysis, or an additional analysis type;

[0202] Display evaluation results and / or visualization of data, wherein:

[0203] Visualization of data can include visualization of actual collected data or predictive data;

[0204] Interpret the machine learning model and all outcomes;

[0205] Store all results, wherein:

[0206] All or some of the results can be stored in a dictionary module.

[0207] }Example 6: Validation in Sickle Cell Disease (SCD) Individuals

[0208] In yet another non-limiting example, pain animations and pain evaluation systems and methods as described herein were validated and evaluated in a control cohort and a cohort of individuals having SCD. The pain described by the individuals with SCD can be, for example,pain from vaso-occlusive episodes (VOEs) or accumulated tissue damage resulting from the SCD. In some cases, for example, the pain sensations indicated by individuals having SCD can be caused by inflammation, degenerative disease, opioid-induced hyperalgesia, neuropathic pain, or any combination thereof. In some cases, pain perception and reporting may be influenced by the cultural environment of an individual or cohort of individuals. Pain perception and reporting may be disparate across multiple diverse populations of individuals having SCD.

[0209] In one exemplary study, participants aged 10 years and older were recruited from the Ghana Institute of Clinical Genetic (GICG) adult sickle cell clinic and the Department of Health pediatric sickle cell clinic at Korle-Bu Teaching Hospital (KBTH). Participants from GICG adult SCD clinic were recruited from both the outpatient clinic and the day hospital. The day hospital, similar to an outpatient infusion unit, treats patients aged 13 and up presenting with acute SCD issues and provides clinical assessment, intervention (i.e. IV pain medications), and monitoring. Participants who consented to the study completed the study activities during their normal clinic waiting time or after the pain had subsided substantially based on clinician and patient report.

[0210] A feasibility study was performed along with a validity study to assess various pain assessment tools, systems, and methods described herein. To assess validity, participants completed various pain assessment tools while timed, and provided responses regarding their clinical history. Patients were asked to self-report whether they felt they experienced SCD- related pain or a SCD acute vaso-occlusive pain episode (VOE) on the day of the survey. For the pain assessments, patients were asked to describe their pain in the past 24 hours. Patients who reported 0 out of 10 pain in the past 24 hours were asked to describe the pain of their last VOE on a Painimation platform as a demonstration of the technology. Participants were then asked a series of 11 survey questions about the feasibility of their regular use of a Painimation platform. The questions were paired with binary response options or Likert scale response options.

[0211] Participants utilized a software platform having a GUI as illustrated in, for example, FIG. 25. Descriptive statistical endpoints were analyzed and included, for example, frequencies, mean, median, standard deviation, and range of responses. Data points had a denominator of 38 participant responses unless otherwise stated. Agreement was defined as the combined responses ‘agree’ and ‘strongly agree’ and disagreement was defined as the combined responses ‘disagree’ and ‘strongly disagree’. Data was captured into a Redcap database and analyzed with SPSS software version 29.0.2.0.

[0212] A total of 38 patients (25 female, 13 male) were included in the cohort. The median age was 16 years (range 10-49 years). Adults aged 18 years and older accounted for 37% (n=14) of the participants, while adolescents aged 10-17 comprised 63% (n=24). Education level of senior high school (SHS) or greater was seen in 45% (n=17 / 38) of participants (subgroup age range 15-42) while 55% of participants had less than a SHS education (subgroup age range 10-49). The majority of responses (84%, n=32) were from participants presenting to the outpatient clinic, with the remaining 16% (n=6) from patients presenting to the day hospital. Many participants (95%, n=36) reported having had at least one VOE in the past 12 months. Genotypes of participants included HbSS (87%), HbSC (8%), HbS / B+ (3%, n=l) and HbS / BO (3%, n=l).

[0213] Of the individuals in the cohort who reported pain in the past 24 hours (n=26), the majority of pain were located in the lower limb (46%), chest (42%), arm (42%), back (42%), hand (38%), abdomen (31%). Some participants also reported pain in the head (23%) and foot (19%). The median VAS intensity score was 3.55 (IQR: 0, 6.125). Individuals chose to describe their pain with one animation (30.7%), two animations (53.8%), and three animations (15.4%). The most frequently chosen animations were throbbing (50%) and stabbing (35%). Individuals also described their pain as burning (27%), shooting (19%), and cramping (19%).

[0214] Demographics of the cohort are illustrated in Table 1.Table 1: Participant Demographics Grouped by Adult and Pediatric

[0215] As shown in the CONSORT diagram of FIG. 26, for example, all participants who completed study activities generated data that was analyzed.

[0216] Responses by participants were grouped into stratification groups by education level as shown in FIG. 27, for example. The questions included, for example, whether the individual used the touchscreen device to select a pain animation daily, comfort of the individual with the touchscreen interface, if the platform was easy to use, if the platform was enjoyable to use, if the individual was able to find a representative animation for their pain sensations, if the body image of the platform improves the individual’s communication with their healthcare provider, and if the animation(s) of the platform improve the individual’s communication with their healthcare provider.

[0217] Responses by participants given using a Likert scale were analyzed as shown in, for example, FIGs. 28A-28B. Participants were asked questions to validate and analyze the utility of the platform, as shown in FIG. 28A. The questions included, for example, whether the participant was able to find animations that represented their pain well, whether the length of the animation was reasonable and appropriate, whether the animation would be helpful in improvingcommunication between the participant and their healthcare provider, and whether the participant would like to change the intensity of the image. Answers from participants on the Likert scale regarding burdensomeness of the platform were also used to assess feasibility, as shown in FIG. 28B, for example. Answers by participants addressed burdensomeness of the platform use, difficulty of the use of features such as body shading, and difficulty of choosing an animation to describe the individual’s pain sensations.

[0218] The time used to complete the pain sensation reporting by participants on the platform was less than 2 minutes, making it longer to complete than the 0-10 scale but significantly shorter to complete than most multidimensional scales.Example 7: Validation in Pediatric Sickle Cell Disease (SCD) Cohorts

[0219] In yet another non-limiting example, pain animations and pain evaluation systems and methods as described herein were validated and evaluated in a pediatric cohort having SCD. Patients with SCD experience pain more often at younger ages and at earlier stages of cognitive development than their peers. The ability to understand and communicate pain symptoms vary depending on, for example, factors such as age, developmental stage, and the presence of additional neurologic sequelae often associated with SCD. These aspects of pediatric SCD make the communication of a potentially complex and traumatic experiences like feeling pain sensations difficult. Adding to this complexity, the pain phenotype typically changes throughout a lifetime of pediatric individuals having SCD, with up to 30-40% of adolescents with SCD developing chronic neuropathic pain. Providers play a role in the barriers to pain symptom communication. In pediatric SCD, both families and providers perceive race to play a part in clinical care. Racial bias in medical care has been clearly documented, especially around pain and the medical management of pain. For example, black patients communicating similar symptom severity as white patients often have longer wait times to opioid administration and are given fewer opioid prescriptions.

[0220] The study enrolled, for example, eligible children aged 10 years or older with a diagnosis of SCD and being actively followed by the pediatric SCD team at UPMC Children's Hospital. Children were required to understand written or spoken English and have no clinically significant cognitive impairment per investigator judgment. Characteristics of the pediatric participants are illustrated below in Table 2.Table 2: Characteristics of Participating Pediatric Individuals

[0221] The study materials were presented on an electronic tablet. All participants completed the Painimation platform use and generated a visual analogue scale (VAS) Score, followed by a survey distributed via REDCap (Research Electronic Data Capture). The survey included questions regarding the usability of the Painimation pain assessment platform, clinical questions regarding their disease, Lansky Play-Performance Scale (LPP), and validated PRO tools (PedsQL, Ped-PRO-CTCAE, PROMIS).

[0222] For example, the platform included a VAS pain rating (0-10), a front and back 2- dimensional body image that can be shaded to indicate areas affected by pain, and eight abstract animations intended to represent different pain qualities (tingling, shooting, stabbing, throbbing, pounding, cramping, electrifying, and burning). The participant chose up to three animations and the speed of animation movement was adjusted using a non-labeled sliding bar. The animations were presented to the participant without labeling the intended quality.

[0223] Feasibility of use of the platform by the participants was evaluated using statements that included, for example, (1) the Painimation platform is easy to use, (2) I enjoyed Painimation, and (3) I would use the Painimation platform to communicate my / my child’s pain with my / their provider. Items were scored on a five-point Likert scale ranging from 0 (strongly disagree) to 4 (strongly agree). All participants were presented with a list of pain descriptor words extractedfrom the McGill Pain Questionnaire, including the intended effect of all eight animations.Results comprising mean ratings are illustrated in Table 3.Table 3: Mean Ratings of Feasibility Statements on a Likert Scale

[0224] The Lansky’s Play Performance (LPP) scale was presented to all participants. It was coded with 0 being “Fully active, normal” and 10 being “unresponsive”. The remaining options were coded in rank order accordingly.

[0225] In analyzing symptoms of the pediatric participants, a Ped-PRO-CTAE survey was used. The six symptoms comprise, for example, headache, abdominal pain, nausea, numbness / tingling, general pain, and fatigue with a seven-day recall period. Items were scored on a four-point Likert scale ranging from 0 (none) to 3 (severe). Each symptom was captured in 2-3 questions about the severity, frequency, and interference of the symptom, for example. The 2-3 responses were then converted into a single measure, using an algorithm.

[0226] Additionally, the PROMIS pain interference question form, for example, was completed by the participants. This eight-question form was administered to patients with a recall period of seven days. Items were scored on a five-point Likert scale ranging from 0 (never) to 4 (always). The eight responses were then summed for a total score ranging from 0 to 32 with higher scores indicating greater interference.

[0227] Additionally, for example, a PedsQL General Health form and General Well-Being form were provided to participants for completion, for example. The General Health form is a one item measure regarding the child’s general health over the last 1 month. The General Well-Being form is a six-item measure regarding psychosocial aspects of the child’s life over the last seven days. Both items were scored on a five-point Likert scale ranging from 0 (never) to 4 (always). These scores were then converted into scores out of 100 as described by the published scoring guide, and the average was calculated to produce a single report.

[0228] Descriptive measures of central tendency and dispersion were used to examine age distribution, pain location, VAS score, and LPP status. Each animation was compared to their intended pain descriptor and percent agreement was obtained. The animations were also assessedfor patterns of clustering based on specific characteristics such as age, VAS score, and selfreported neuropathic pain, for example. The maximum animation severity for each participant was compared to the respective participant’s VAS score. Then, for example, correlation was assessed via Spearman’s rho (p). Criteria for correlation were defined as negligible (p< 0.2), weak (0.2< p<0.4), moderate (0.4< p<0.6), strong (0.6< p<0.8), and very strong (p>0.8).

[0229] The cohort enrolled 44 pediatric participants. The mean age was 16 years-old with a range of 11 to 22 years old. The cohort included 23 male and 21 female participants. Forty -three of the patients were outpatient and 1 was inpatient. The SCD phenotypes were 30 (68%) patients with HbSS, 3 (7%) patients with HbS beta-thalassemia0, 1 (0.02%) patient with HbS beta- thalassemia+, and 10 (23%) with HbSC.

[0230] Responses of participants were analyzed with data regarding the agreement of each pain animation with the corresponding intended pain descriptor. VAS scores for various pain locations were also analyzed and compared to analyzed values for agreement with the intended pain descriptor as shown in Table 4.Table 4: Participant Responses for Pain Animation Agreement Associated with VAS Scores

[0231] Severity rating responses for the severity of pain sensations experienced by the pediatric participant cohort was analyzed and plotted with associated VAS scores as shown in FIG. 29. Maximum pain severity values from 0 to 100 were plotted and correlated with VAS scores from 0 to 10 as illustrated in FIG. 29.

[0232] Next, groups of cohorts were formed, for example, based on a chosen type of pain animation. As shown in FIG. 30A, for example, VAS score data from participants was plotted and stratified into a group of VAS scores for participants who chose the cramping pain sensation animation to describe their pain and a separate group of VAS scores for participants who did not choose the cramping pain sensation animation to describe their pain. As shown in FIG. 30B, for example, VAS score data from participants was plotted and stratified into a group of VAS scores for participants who chose the stabbing pain sensation animation to describe their pain and a separate group of VAS scores for participants who did not choose the stabbing pain sensation animation to describe their pain.Example 8: Analysis of Functional Connectivity with Digital Pain Assessment

[0233] In yet another non-limiting example, neuroimaging findings of functional connectivity were associated with participant-reported digital pain assessment data to analyze brain functionality dynamics in groups of participants indicating an experience of different types of pain sensations. For example, the study comprised a control cohort of participants and a cohort of participants having Sickle Cell Disease (SCD).

[0234] The study examined, for example, central sensitization (CS), a heightened and persistent state of pain processing within the central nervous system (CNS) leading to increased pain perception as a hallmark of SCD. For example, CS can be a dysfunction in pain processing leading to hyperalgesia and allodynia, creating sleep and mood disorders, and affecting the quality of life of patients with SCD. Functional imaging data of brain activity was utilized to determine impaired connectivity and abnormal connectivity patterns of various neuronal areas of the brain that have been associated with CS.

[0235] Healthy controls (HbAA, n=30) from the University of Pittsburgh Clinical and Translational Science Institute Pitt+Me Registry, and community brochures. Healthy controls were age, sex, and race matched with patients with SCD. Twenty-eight patients with SCD (HbSS, n=14; HbSC n=12; HbSb+, n=2) from the University of Pittsburgh Medical Center (UPMC) Adult Sickle Cell Program outpatient clinic. All participants were older than 18 yearsand of Black / African American descent. Eligibility criteria required participants to be: 1) English-speaking and 2) actively receiving routine follow-up care through the UPMC Adult Sickle Cell Program. Participants, both patients with SCD and controls, were excluded if they were pregnant (determined by a positive urine human chorionic gonadotropin test), lactating, or had medical conditions associated with neurocognitive or brain dysfunction unrelated to SCD. These included conditions such as diabetes mellitus, coronary artery disease, peripheral vascular disease, and cerebral vasculitis caused by disorders like systemic lupus erythematosus (SLE). Additional exclusion criteria included any contraindications to MRI, such as electronic or magnetically activated implants, tattoos above the shoulders, or brain implants. Enrolled participants with SCD were mean age 38.33±11.19 years (63% females) to determine associations between their pain neural pathways and descriptors of pain. The enrolled controls were aged-matched with a mean age 39±10.18 years (46%, females) and had no significant difference in age (p-value = 0.815, t-stat = - 0.235) and sex from patients with SCD (p-value = 0.217, %2 = 1.521).

[0236] Demographic information for participants in the study cohort is shown in FIG. 31 including, for example, ethnicity of participants, race of participants, employment status of participants, and education level of participants in a control group and an SCD group.

[0237] Participant reported data, for example descriptions of pain recorded with the Painimation platform were integrated with functional imaging data to determine how the descriptions of pain may influence functional connectivity of neural pain pathways in patients having SCD. For example, FIG. 32 illustrates various demographic and pain sensation characteristics of the control cohort of participants and the SCD cohort of participants. The demographic and pain sensation characteristics can include, for example, age, sex, sickle cell type, hemoglobin levels, or pain descriptions, or any combination thereof.

[0238] For example, FIG. 33 illustrates an exemplary diagram of a subject describing pain sensations according to methods and systems described herein, and resting-state functional connectivity of the subject’s brain. For example, a subject having SCD can utilize the Painimation platform to describe pain sensations, including varied sensations of chronic pain and selecting corresponding animations of pain sensations descriptions. As shown in FIG. 33, for example, corresponding neural pathways can be mapped and matched to the corresponding pain descriptions to generate an analysis of a relationship between the pain descriptions and restingstate functional connectivity.

[0239] Prior to MRI data collection, participants with SCD were asked to fill the Painimation platform questionnaire on an electronic tablet. Participants utilized a paintable body image to identify locations affected by pain and review seven pain sensation animations to clearlydescribe and indicate their pain experience. These animations can include, for example, animations intended to be associated with burning, electrifying, shooting, stabbing (neuropathic descriptors) and cramping, throbbing, and pounding (nociceptive descriptors). Participants chose animations they connected with and best represented their pain experience and assigned an intensity value to each selection experiences in patients. The body area affected by pain was assessed by calculating the percentage of the body in pain, determined by the proportion of selected pixels.

[0240] Next, for example, structural and functional MRI images can be collected using a 7T MRI scanner (Siemens Magnetom, Germany) with a first-generation radiofrequency (RF) head coil customized with 16 transmit (combined into one for use in the single transmit mode) and 32 receive channels. Tl-weighted (Tlw) Magnetization Prepared Rapid Gradient Echo (MPRAGE) was acquired with echo time (TE) and repetition time (TR) of 2.17 and 3000 ms respectively. The bandwidth was, for example, 391 Hz / Px with an acceleration factor of 2 and the time of acquisition was 5:02 mins. T2-weighted (T2w) images were obtained with TE / TR = 61 / 10060 ms, acceleration factor of 2 and a bandwidth of 264. Resting-state echo planar imaging (EPI) functional data was acquired with TE / TR = 20 / 2500 ms and 1.5 isotopic voxel size. The slice thickness was 1.5 mm, and the rotation was 180 degrees. The resting-state acquisition time was 5:45 mins.

[0241] The resting state functional data was preprocessed and analyzed using CONN version 22a, MATLAB R2023b, and SPM12. The Tlw and EPI functional images underwent preprocessing using a flexible pipeline. For example, functional images were first realigned using SPM realign and unwarping. For example, all scans were co-registered to a reference image, which was the first scan of the first session through a least-squares approach and a six- parameter rigid-body transformation. Resampling of the data was performed using b-spline interpolation. Temporal misalignment between slices, acquired in interleaved Siemens order, was corrected using the SPM slice-timing correction (STC) procedure. This correction implemented since interpolation to adjust the blood-oxygenation-level-dependent (BOLD) timeseries to a consistent mid-acquisition time.

[0242] Reference BOLD images for each subject were generated by averaging all scans while excluding outliers, defined as framewise displacement greater than 9 mm and global BOLD signal exceeding 5 standard deviations. Functional and anatomical data were then normalized to standard MNI space and segmented into gray matter, white matter, and cerebrospinal fluid (CSF) tissue classes to enhance signal interpretation and improve spatial normalization. The data were resampled to 2 mm isotropic voxels. For example, to enhance spatial resolution, functional data were smoothed using a Gaussian kernel with a 3 mm full width half maximum (FWHM).

[0243] Following preprocessing, a standard denoising pipeline was applied to the functional data to minimize noise and confounding effects. Confound regression was performed to remove signal contributions from white matter (10 CompCor components), CSF (5 CompCor components), motion parameters and their first-order derivatives (12 factors), outlier scans (up to 48 factors), session and linear trends (2 factors per functional run). Bandpass filtering was then applied to the BOLD timeseries to retain signal frequencies between, for example, 0.09 Hz and 0.12 Hz, a range associated with low frequency fluctuations and reflecting neural processes and functional connectivity at rest while minimizing high frequency-noise from respiration and movement.

[0244] An exemplary methodology used is shown in FIG. 34, where the methodology illustrates differences in functional connectivity metrics between participants with SCD and control participants. The BOLD timeseries data was extracted with Nileam toolbox for each subject using the Schaefer 2018 atlas including 17 different brain networks and 100 brain parcellations. As shown in FIG. 34, the time series data can include signals between two brain regions within a network. The default mode network (DMN), salience network (SAN) and somatosensory network (SMN) were used to perform functional connectivity (FC) analysis. For example, the FC analysis was used to assess changes caused by SCD and networks that are known to be associated with disruptions in pain. The networks associated with pain can be, for example, specifically chronic pain, and pain descriptors. Specifically, for each subject, the timeseries data representing brain activity was correlated between selected brain regions for each network as shown in FIG. 34. The correlation matrices for each subject were calculated using the Pearson correlation metric and normalized to Fisher’s r-to-z transformation to stabilize variance.

[0245] Increased connectivity, for example as shown in the heatmap of FIG. 34, was shown as high strength and number of significant correlations between two brain regions of interest (ROI), indicating a stronger connection (in the form of z-values). Higher correlations represented the number of significant connections made between ROIs. A 70% thresholding was adopted for glass brain representations to avoid visual clutter and show stronger connections (z >0.7) between brain regions from the connectivity matrix shown in FIG. 34.

[0246] Mean correlation matrices for each subject cohort were obtained for group level comparisons in various modes. Differences in connectivity between controls and patients with SCD were analyzed, and independent t-tests were conducted for each ROI-ROI pair in various modes as illustrated in, for example, FIGs. 35A-39F.

[0247] FIGs. 35A-35E illustrate exemplary data showing functional connectivity of the brain in a default mode network (DMN). FIG. 35A shows exemplary diagrams, for example glass brain diagrams relating to connectivity in control individual brains and brains of individuals havingSickle Cell Disease (SCD). The diagrams illustrate connectivity in control cohort functional imaging data and SCD cohort functional imaging data. For example, a 70% thresholding value was applied to the data.

[0248] FIG. 35B shows exemplary connectivity matrix data for control individuals. The connectivity matrix illustrates, for example, strength of connectivity between brain regions in DMN for control cohort participants.

[0249] FIG. 35C shows exemplary connectivity differences between control individuals and individuals having SCD in the DMN regions. A color bar represents z-scores. Positive and negative values of z-scores represent, for example, increased and decreased connectivity respectively. Connectivity matrices included heat maps of the color scores.

[0250] FIG. 35D shows an exemplary connectivity matrix data for individuals having SCD in the DMN brain regions.

[0251] FIG. 35E shows an exemplary connectivity difference value heatmap between control individuals and individuals having SCD. Statistical significance of connectivity differences were evaluated in the heatmap, and FDR-corrected p-values were generated, representing cooler colors in the heatmap at about p<0.05, for example. For connectivity differences, positive z- scores indicated increased connectivity in controls and decreased connectivity in patients with SCD. Negative z-scores, conversely, represent increased connectivity in patients with SCD in the DMN brain regions.

[0252] FIGs. 36A-36E show exemplary data illustrating functional connectivity in a salience network (SAN). FIG. 36A shows exemplary diagrams, for example glass brain diagrams relating to connectivity in control individual brains and brains of individuals having Sickle Cell Disease (SCD). The diagrams illustrate connectivity in control cohort functional imaging data and SCD cohort functional imaging data. For example, a 70% thresholding value was applied to the data.

[0253] FIG. 36B shows exemplary connectivity matrix data for control individuals. The connectivity matrix illustrates, for example, strength of connectivity between brain regions in SAN for control cohort participants.

[0254] FIG. 36C shows exemplary connectivity differences between control individuals and individuals having SCD in the SAN brain regions. A color bar represents z-scores. Positive and negative values of z-scores represent, for example, increased and decreased connectivity respectively. Connectivity matrices included heat maps of the color scores.

[0255] FIG. 36D shows an exemplary connectivity matrix data for individuals having SCD in the SAN brain regions.

[0256] FIG. 36E shows an exemplary connectivity difference value heatmap between control individuals and individuals having SCD in the SAN brain region. Statistical significance ofconnectivity differences were evaluated in the heatmap, and FDR-corrected p-values were generated, representing cooler colors in the heatmap at about p<0.05, for example. For connectivity differences, positive z-scores indicated increased connectivity in controls and decreased connectivity in patients with SCD. Negative z-scores, conversely, represent increased connectivity in patients with SCD in the SAN brain regions.

[0257]

[0258] FIGs. 37A-37E illustrate exemplary data showing functional connectivity in a somatosensory network (SMN). FIG. 37A shows exemplary diagrams, for example glass brain diagrams relating to connectivity in control individual brains and brains of individuals having Sickle Cell Disease (SCD). The diagrams illustrate connectivity in control cohort functional imaging data and SMN cohort functional imaging data. For example, a 70% thresholding value was applied to the data.

[0259] FIG. 37B shows exemplary connectivity matrix data for control individuals. The connectivity matrix illustrates, for example, strength of connectivity between brain regions in SMN for control cohort participants.

[0260] FIG. 37C shows exemplary connectivity differences between control individuals and individuals having SCD in the SMN brain regions. A color bar represents z-scores. Positive and negative values of z-scores represent, for example, increased and decreased connectivity respectively. Connectivity matrices included heat maps of the color scores.

[0261] FIG. 37D shows an exemplary connectivity matrix data for individuals having SCD in the SMN brain regions.

[0262] FIG. 37E shows an exemplary connectivity difference value heatmap between control individuals and individuals having SCD in the SMN brain region. Statistical significance of connectivity differences were evaluated in the heatmap, and FDR-corrected p-values were generated, representing cooler colors in the heatmap at about p<0.05, for example. For connectivity differences, positive z-scores indicated increased connectivity in controls and decreased connectivity in patients with SCD. Negative z-scores, conversely, represent increased connectivity in patients with SCD in the SMN brain regions.

[0263] Additionally, participants with SCD were categorized based on their descriptors of pain, for example burning, electrifying, shooting, stabbing, cramping, throbbing, and pounding recorded in the Painimation platform. Next, for example, mean correlation matrices were acquired from the participants to examine FC differences between brain networks and the different pain descriptors. For example, FIGs. 38A-38F show exemplary diagrams illustrating neuropathic descriptive correlations in the default mode network (DMN), salience network(SAN), and somatosensory network (SMN) for various categories of selected pain sensation animations.

[0264] Unthresholded p-values were obtained for both controls and patients, group differences and the different groups of pain descriptors for patients with SCD. A false discovery rate threshold (FDR-correction, p<0.05) was applied to group-level differences to adjust for multiple comparisons. Percentages of both nociceptive and neuropathic descriptors were measured to determine the frequency of pain experiences in patients with SCD. Linear regression was used to evaluate the relationship between the connectivity values of patients and the intensity scores included by patients when selecting a visual description in Painimation. The Mann-Whitney U test was adopted to compare the ages, education, employment and marital statuses between patients with SCD and controls.

[0265] FIG. 38A illustrates a connectivity map in the DMN of individuals who selected a burning animation to describe their pain sensations. The connectivity map is a visual glass brain diagram, for example. The diagrams represent the two highest neuropathic descriptive correlations in the DMN, SAN, and SMN with 70% thresholding.

[0266] FIG. 38B illustrates a connectivity map in the DMN of individuals who selected a stabbing animation to describe their pain sensations.

[0267] FIG. 38C illustrates a connectivity map in the SAN of individuals who selected an electrifying animation to describe their pain sensations.

[0268] FIG. 38D illustrates a connectivity map in the SAN of individuals who selected a stabbing animation to describe their pain sensations.

[0269] FIG. 38E illustrates a connectivity map in the SMN of individuals who selected an electrifying animation to describe their pain sensations.

[0270] FIG. 38F illustrates a connectivity map in the SMN of individuals who selected a burning animation to describe their pain sensations. The connectivity maps can be utilized to indicate increased and decreased connectivity of the map in SCD participants.

[0271] FIGs. 39A-39F show exemplary diagrams illustrating neuropathic descriptive correlations in the default mode network (DMN), salience network (SAN), and somatosensory network (SMN) for various categories of selected pain sensation animations.

[0272] FIG. 39A illustrates a connectivity map in the DMN of individuals who selected a cramping animation to describe their pain sensations. The connectivity map can be a glass brain representation. The connectivity map is a representation of the two highest nociceptive correlations in the DMN, SAN, and SMN. A thresholding value of 70% can be used.

[0273] FIG. 39B illustrates a connectivity map in the DMN of individuals who selected a throbbing animation to describe their pain sensations.

[0274] FIG. 39C illustrates a connectivity map in the SAN of individuals who selected a cramping animation to describe their pain sensations.

[0275] FIG. 39D illustrates a connectivity map in the SAN of individuals who selected a throbbing animation to describe their pain sensations.

[0276] FIG. 39E illustrates a connectivity map in the SMN of individuals who selected a cramping animation to describe their pain sensations.

[0277] FIG. 39F illustrates a connectivity map in the SMN of individuals who selected a throbbing animation to describe their pain sensations. The connectivity maps can be utilized to indicate increased and decreased connectivity of the map in SCD participants.

[0278] For example, the relationship between the intensity values associated with the selected pain descriptors from patients with SCD and connectivity results from DMN, SAN, and SMN were analyzed. FIGs. 40A-40B illustrate exemplary correlation analysis charts between functional connectivity and a pain intensity score.

[0279] FIG. 40A shows exemplary data illustrating connectivity data corresponding to a cramping pain intensity score for various subjects. The exemplary data illustrates, for example, a negative correlation (r) between the left precentral gyrus (PreCG) connected to the left central operculum cortex connectivity (COC) in the somatosensory network and cramping pain intensity, which is, for example, a nociceptive descriptor.

[0280] FIG. 40B shows exemplary data illustrating connectivity data corresponding to a stabbing pain intensity score for various subjects. The exemplary data illustrates, for example, a negative correlation (r) between the left central operculum cortex (COC) connection to the left parietal operculum. The connectivity in the somatosensory network is, for example, connectivity in the SMN network and stabbing pain intensity, which can be a neuropathic descriptors.

[0281] FIGs. 41A-41F show exemplary diagrams illustrating neuropathic descriptive correlations in the default mode network (DMN), salience network (SAN), and somatosensory network (SMN) for various categories of selected pain sensation animations.

[0282] FIG. 41A illustrates a connectivity map in the DMN of individuals who selected an electrifying animation to describe their pain sensations.

[0283] FIG. 41B illustrates a connectivity map in the DMN of individuals who selected a shooting animation to describe their pain sensations.

[0284] FIG. 41C illustrates a connectivity map in the SAN of individuals who selected a shooting animation to describe their pain sensations.

[0285] FIG. 41D illustrates a connectivity map in the SAN of individuals who selected a burning animation to describe their pain sensations.

[0286] FIG. 41E illustrates a connectivity map in the SMN of individuals who selected a stabbing animation to describe their pain sensations.

[0287] FIG. 41F illustrates a connectivity map in the SMN of individuals who selected a shooting animation to describe their pain sensations.

[0288] FIGs. 42A-42C show exemplary diagrams illustrating nociceptive descriptive correlations in the default mode network (DMN), salience network (SAN), and somatosensory network (SMN) for selected pain sensation animations.

[0289] FIG. 42A illustrates a connectivity map in the DMN of individuals who selected a pounding animation to describe their pain sensations.

[0290] FIG. 42B illustrates a connectivity map in the SAN of individuals who selected a pounding animation to describe their pain sensations.

[0291] FIG. 42C illustrates a connectivity map in the SMN of individuals who selected a pounding animation to describe their pain sensations.

[0292] Of the recorded pain descriptors, throbbing and cramping (nociceptive descriptors), shooting and stabbing pain (neuropathic descriptors) were amongst the most selected animations within the Painimation platform by patients with SCD. Throbbing and cramping pain can be associated with acute pain episodes superimposed on chronic pain conditions in SCD. In a recent study by Dyal et al, patients that selected throbbing pain were classified by having central or mixed sensitization. This asserts that patients that selected this description in addition to other animations may have been experiencing acute pain daily. Shooting and stabbing pain are mostly characterized by neuropathic pain, which are known descriptors amongst patients with cancer pain, Shooting can be associated to the affective and physiological elements of pain in SCD. The use of animated visuals of descriptions enables patients to effectively communicate their pain to healthcare providers for accurate treatment.

[0293] Functional connectivity was analyzed for participants that used the platform. This information was gathered into the neural correlates of patients’ true descriptions and patterns of pain. By linking the true subjective pain experience with objective brain connectivity data, understanding of pain perception in SCD through the connectivity of well-known resting networks can be enhanced. Studying how their connectivity remains intact or alters when experiencing pain can be utilized. For the DMN, patients with SCD showed decreased connectivity between the lateral, dorsal, ventral and medial prefrontal cortex when compared with controls. These brain regions are involved in descending pain modulation pathways.

[0294] Next, connectivity differences between the insula and most salience brain network regions of the brain (paracentral and frontal medial areas) were analyzed, with diminishedactivity in SCD during resting-state. The para central medial area and the rostral ventromedial medulla, connected to insula connectivity, are involved in the descending pathway of pain.

[0295] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

CLAIMSWHAT IS CLAIMED IS:

1. A computer-implemented method for assessing physiological condition in a subject, the computer-implemented method comprising: a) generating a graphical representation of the subject’s body on an interactive graphical user interface (GUI); b) receiving one or more inputs from the subject through the GUI wherein the one or more inputs comprise selection of an area within the graphical representation of the subject’s body; c) generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; d) receiving one or more pain feedback inputs from the subject, the one or more pain feedback inputs comprising selection of the one or more visual animations of the one or more pain characteristics, or adjustments to the one or more visual animations of the one or more pain characteristics; and e) generating one or more outputs comprising one or more pain assessment results from the selected animations.

2. The computer-implemented method of claim 1, wherein the one or more inputs from the subject comprise one or more input types selected from the list comprising: drawing a shape around the selected area of the subject’s body, drawing inside the selected area of the subject’s body, tapping on the selected area of the subject’s body, zooming in on the selected area of the subject’s body, or any combination thereof.

3. The computer-implemented method of claim 1, further comprising generating one or more physiological condition-associated pain assessment results, wherein generating the one or more physiological condition-associated pain assessment results further comprises receiving one or more physiological condition inputs from the subject associated with the one or more pain feedback inputs.

4. The computer-implemented method of claim 3, wherein the one or more physiological condition inputs comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof.

5. The computer-implemented method of claim 1, further comprising iteratively applying one or more algorithms of a pain forecast model to the one or more pain feedback inputs to generate one or more outputs comprising one or more pain forecast predictions for the selected area of the subject’s body.

6. The computer-implemented method of claim 1, further comprising transmitting the one or more outputs comprising one or more pain assessment results to an external device.

7. The computer-implemented method of claim 1, further comprising generating the one or more visual animations of the one or more pain characteristics using a virtual reality device, wherein the virtual reality device maps the selected area of the subject’s body to the subject’s body, and displays the one or more visual animations on the subject’s body.

8. The computer-implemented method of claim 1, further comprising transmitting data to an external healthcare application and / or integrating received data from an external healthcare application.

9. The computer-implemented method of claim 1, wherein adjustments to the one or more visual animations of the one or more pain characteristics further comprise manual adjustments made by the subject.

10. A computer-implemented method for generating one or more suggested diagnoses from pain assessment in a subject, the computer-implemented method comprising: a) generating a predictive diagnosis model, comprising a library of potential predicted diagnoses, wherein the potential predicted diagnoses have been selected by a method comprising: i. extracting pain descriptor terms from one or more pain feedback inputs from the subject, and ii. applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for one or more of the potential predicted diagnoses; and b) generating one or more outputs comprising one or more of the potential predicted diagnoses.

11. A computer-implemented method for selecting an optimized opioid treatment from pain assessment in a subject, the computer-implemented method comprising: a) generating a predictive optimized opioid treatment model, comprising a library of opioids, wherein the one or more predicted optimized opioid treatments are generated by a method comprising: i. extracting pain descriptor terms from one or more pain feedback inputs from the subject, andii. applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for the types of pain alleviated by the one or more opioids; and b) generating one or more outputs comprising one or more of the predicted optimized opioid treatments.

12. The computer-implemented method of claim 11, further comprising generating a predictive opioid addiction model, wherein the predictive opioid addiction model comprises the library of opioids, wherein each opioid is further associated with one or more addiction risk scores, and wherein the predictive opioid addiction model generates one or more outputs comprising one or more of the predicted opioid addiction risk scores.

13. A computer-implemented method for generating one or more optimized customized pain assessment animations for a subject, the computer-implemented method comprising: a) generating an interactive graphical user interface (GUI) displaying a representation of the subject’s body; b) receiving one or more inputs from the subject, the one or more inputs comprising selection of an area of the subject’s body being displayed on the GUI; c) generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; d) generating a generative pain animation customization model, wherein the pain animation customization model generates customized, optimized pain animations with improved accuracy to the subject’s pain characteristics, wherein the optimized customized visual animations of one or more pain characteristics associated with the selected area of the subject’s body are generated by a method comprising: i. receiving one or more signals from the subject, the one or more signals comprising adjustment instructions for the one or more visual animations of the one or more pain characteristics, ii. applying one or more algorithms to iteratively modify one or more elements of the one or more visual animations of the one or more pain characteristics based on the adjustment instructions, iii. generating one or more optimized customized visual animations of the one or more pain characteristics incorporating the modified elements of the one or more visual animations; and e) generating one or more optimized outputs comprising one or more pain assessment results.

14. The computer-implemented method of claim 13, wherein the one or more signals comprising adjustment instructions for the one or more visual animations further comprise audio instructions, text instructions, drawn instructions, or any combination thereof.

15. The computer-implemented method of claim 13, wherein the one or more signals comprising adjustment instructions for the one or more visual animations further comprise concrete instructions, descriptive instructions, abstract instructions, or any combination thereof.

16. A computer-implemented system for assessing physiological condition in a subject, the computer-implemented system comprising: a) an interactive graphical user interface (GUI) onto which the computer- implemented system generates a graphical representation of the subject’s body, wherein the GUI is configured to receive one or more inputs from the subject, wherein the one or more inputs comprise selection of an area within the graphical representation of the subject’s body; b) a processor configured to perform operations comprising generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; c) the processor configured to perform operations further comprising receiving one or more pain feedback inputs from the subject, the one or more pain feedback inputs comprising selection of the one or more visual animations of the one or more pain characteristics, or adjustments to the one or more visual animations of the one or more pain characteristics; and d) one or more system outputs comprising one or more pain assessment results from the selected animations.

17. The computer-implemented system of claim 16, wherein the one or more inputs from the subject comprise one or more input types selected from the list comprising: drawing a shape around the selected area of the subject’s body, drawing inside the selected area of the subject’s body, tapping on the selected area of the subject’s body, zooming in on the selected area of the subject’s body, or any combination thereof.

18. The computer-implemented system of claim 16, wherein the processor is configured to perform operations further comprising generating one or more physiological condition- associated pain assessment results, wherein generating the one or more physiological condition-associated pain assessment results further comprises receiving one or morephysiological condition inputs from the subject associated with the one or more pain feedback inputs.

19. The computer-implemented system of claim 18, wherein the one or more physiological condition inputs comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof.

20. The computer-implemented system of claim 18, wherein the one or more physiological condition inputs comprise one or more input types selected from the list comprising: mood of the subject, energy level of the subject, mental health state of the subject, ease of movement of the subject, motivation of the subject, notes input by the subject, annotations input by the subject, one or more therapies undertaken by the subject, one or more medications taken by the subject, or any combination thereof.

21. The computer-implemented system of claim 16, further comprising a pain forecast model configured to perform operations comprising iteratively applying one or more algorithms to the one or more pain feedback inputs to generate one or more outputs comprising one or more pain forecast predictions for the selected area of the subject’s body.

22. The computer-implemented system of claim 16, further comprising a virtual reality device, wherein the virtual reality device maps the selected area of the subject’s body to the subject’s body, and displays the one or more visual animations on the subject’s body.

23. The computer-implemented system of claim 16, further comprising one or more external healthcare applications, wherein the one or more external healthcare applications transmit data to the system and / or integrate output from the system.

24. The computer-implemented system of claim 16, wherein adjustments to the one or more visual animations of the one or more pain characteristics further comprise manual adjustments made by the subject.

25. A computer-implemented system for generating one or more suggested diagnoses from pain assessment in a subject, the computer-implemented system comprising: a) a predictive diagnosis model, comprising a library of potential predicted diagnoses, wherein the potential predicted diagnoses have been selected by a method comprising: i. extracting pain descriptor terms from one or more pain feedback inputs from the subject, andii. applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for one or more of the potential predicted diagnoses; and b) one or more outputs comprising one or more of the potential predicted diagnoses.

26. A computer-implemented system for selecting an optimized opioid treatment from pain assessment in a subject, the computer-implemented system comprising: a) a predictive optimized opioid treatment model, comprising a library of opioids, wherein the one or more predicted optimized opioid treatments are generated by a method comprising: i. extracting pain descriptor terms from one or more pain feedback inputs from the subject, and ii. applying one or more algorithms to iteratively match the one or more pain feedback inputs with descriptor terms for the types of pain alleviated by the one or more opioids; and b) one or more outputs comprising one or more of the predicted optimized opioid treatments.

27. The computer-implemented system of claim 26, further comprising a predictive opioid addiction model, wherein the predictive opioid addiction model comprises the library of opioids, wherein each opioid is further associated with one or more addiction risk scores, and wherein the predictive opioid addiction model generates one or more outputs comprising one or more of the predicted opioid addiction risk scores.

28. A computer-implemented system for generating one or more optimized customized pain assessment animations for a subject, the computer-implemented system comprising: a) an interactive graphical user interface (GUI) displaying a representation of the subject’s body; b) a processor configured to perform operations comprising receiving one or more inputs from the subject, the one or more inputs comprising selection of an area of the subject’s body being displayed on the GUI; c) the processor configured to perform operations further comprising generating one or more visual animations of one or more pain characteristics associated with the selected area of the subject’s body; d) a generative pain animation customization model, wherein the pain animation customization model generates customized, optimized pain animations with improved accuracy to the subject’s pain characteristics, wherein the optimizedcustomized visual animations of one or more pain characteristics associated with the selected area of the subject’s body are generated by a method comprising: i. receiving one or more signals from the subject, the one or more signals comprising adjustment instructions for the one or more visual animations of the one or more pain characteristics, ii. applying one or more algorithms to iteratively modify one or more elements of the one or more visual animations of the one or more pain characteristics based on the adjustment instructions, iii. generating one or more optimized customized visual animations of the one or more pain characteristics incorporating the modified elements of the one or more visual animations; and e) one or more optimized outputs comprising one or more pain assessment results.

29. The computer-implemented system of claim 28, wherein the one or more signals comprising adjustment instructions for the one or more visual animations further comprise audio instructions, text instructions, drawn instructions, or any combination thereof.

30. The computer-implemented system of claim 28, wherein the one or more signals comprising adjustment instructions for the one or more visual animations further comprise concrete instructions, descriptive instructions, abstract instructions, or any combination thereof.

31. A computer-implemented method for determining resting-state functional connectivity patterns of a pain characteristic profile of a subject, the computer-implemented method comprising: a) generating one or more visual animations of one or more pain characteristics associated with a selected area of the subject’s body; b) receiving one or more pain feedback inputs from the subject, the one or more pain feedback inputs comprising selection of the one or more visual animations of the one or more pain characteristics, or adjustments to the one or more visual animations of the one or more pain characteristics; c) generating the pain characteristic profile of the subject based on the received one or more pain feedback inputs from the subject; and d) generating a functional connectivity profile based at least in part on analyzing functional connectivity data between a first region of a brain of the subject and a second region of the brain of the subject, wherein the functional connectivity profile is associated with the pain characteristic profile of the subject.

32. The computer-implemented method of claim 31, wherein the pain characteristic profile comprises one or more of: a pain area, a pain sensation type, a pain intensity, or a pain duration, or any combination thereof.

33. The computer-implemented method of claim 31, wherein the functional connectivity data comprises one or more functional connectivity values.

34. The computer-implemented method of claim 33, wherein each of the one or more functional connectivity values comprise correlation values between activation of the first region of the brain and activation of the second region of the brain.

35. The computer-implemented method of claim 31, wherein the functional connectivity data comprises an increase in connectivity between the first region and the second region of the brain.

36. The computer-implemented method of claim 31, wherein the indication of the change in the one or more characteristics of connectivity comprises a decrease in connectivity between the first region and the second region of the brain.

37. The computer-implemented method of claim 31, wherein the first region is located at a first hemisphere of the brain and the second region is located at a second hemisphere of the brain.

38. The computer-implemented method of claim 31, wherein the first region and the second region are located at the same hemisphere of the brain.

39. The computer-implemented method of claim 31, wherein the functional connectivity profile comprises multiple functional connectivity values relating to two or more sets of functionally connected regions.

40. The computer-implemented method of claim 39, wherein each of the two or more sets of functionally connected regions comprises the first region and the second region.

41. The computer-implemented method of claim 39, wherein the first region or the second region, or both, are different between the two or more sets of functionally connected regions.

42. The computer-implemented method of claim 31, wherein the functional connectivity profile comprises one or more increases in connectivity between one or more sets of functionally connected regions, or one or more decreases in connectivity between one or more sets of functionally connected regions, or both.

43. The computer-implemented method of claim 42, wherein the one or more increases in connectivity and the one or more decreases in connectivity are determined relative to baseline connectivity values of control participants.

44. The computer-implemented method of claim 31, wherein the functional connectivity data is determined based at least in part on functional imaging data.

45. The computer-implemented method of claim 44, wherein the functional imaging data comprises functional magnetic resonance imaging (fMRI) data.

46. The computer-implemented method of claim 31, further comprising generating a functional connectivity report.

47. The computer-implemented method of claim 46, wherein the functional connectivity report comprises one or more changes in connectivity associated with one or more pain types of the pain characteristic profile of the subject.

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