Podiatric scanning system, method and apparatus

Abstract

A system, method and device for early detection of lower extremity injuries in patients with neuropathy who may have impaired pain sensation. The system comprises a sensor apparatus with multiple sensors that measure physiological parameters at specific locations on a patient's foot and lower limb. A connected computing entity receives and analyzes sensor data to identify potential injury markers such as temperature anomalies, tissue oxygenation changes, and swelling. Using machine learning algorithms that improve through continuous training, the system determines injury likelihood based on personalized baselines and historical data at a patient level. The computing entity generates an intuitive user interface displaying injury likelihood assessments for healthcare providers. This technology enables early intervention through non-invasive monitoring, potentially preventing serious complications in a vulnerable populations.

Description

PODIATRIC SCANNING SYSTEM, METHOD AND APPARATUSCROSS-REFERENCE TO RELATED APPLICATIONSThis application claims priority to provisional application number 63 / 571642, filed March 29, 2024 and titled PODIATRIC SCANNING SYSTEM, METHOD AND APPARATUS, the entire content of which is incorporated herein by reference.BACKGROUND

[0001] Diabetes is a chronic metabolic disorder that affects millions of people worldwide. One of the most severe complications of diabetes is lower extremity amputation, which can have a profound impact on a patient's quality of life.

[0002] Pathophysiology of Diabetes-Related Amputations: Diabetes can lead to several complications that increase the risk of lower extremity amputation. The two primary factors are peripheral neuropathy and peripheral artery disease (PAD).

[0003] 1. Peripheral Neuropathy: Peripheral neuropathy is a common complication of diabetes, affecting up to 50% of patients. It occurs due to prolonged exposure to high blood glucose levels, which damage the nerves in the extremities. This damage can lead to numbness, tingling, and loss of sensation in the feet. As a result, patients may not notice minor injuries, such as blisters or cuts, which can develop into serious infections.

[0004] 2. Peripheral Artery Disease (PAD): PAD is another significant risk factor for diabetes-related amputations. It occurs when the arteries that supply blood to the legs become narrowed or blocked due to atherosclerosis. This reduces blood flow to the feet, making it difficult for wounds to heal and increasing the risk of infection. PAD is more common in people with diabetes, and the combination of peripheral neuropathy and PAD significantly increases the risk of amputation.

[0005] There are a number problems with detecting neuropathy in the lower extremities

[0006] 1. Reduced sensation: Neuropathy can cause a loss of sensation in the feet, making it difficult for patients to feel pain, pressure, or temperature changes that may indicate an injury.

[0007] 2. Delayed presentation: Due to the lack of sensation, patients may not notice injuries until they have progressed to a more serious stage, such as infection or ulceration.

[0008] 3. Atypical presentation: Neuropathy can alter the typical signs and symptoms of injury, making it harder for healthcare providers to recognize and diagnose problems.

[0009] 4. Impaired healing: Diabetes can impair the body's ability to heal wounds, increasing the risk of complications and prolonging recovery time.

[0010] 5. Increased risk of infection: Neuropathy and diabetes can compromise the immune system, making patients more susceptible to infections in the feet and lower extremities.

[0011] 6. Charcot foot: Neuropathy can lead to Charcot foot, a condition characterized by weakening of the bones in the foot, which can cause fractures and deformities that may be mistaken for other injuries.

[0012] 7. Vascular complications: Diabetes can cause peripheral artery disease, reducing blood flow to the feet and lower extremities, which can complicate the detection and healing of injuries.

[0013] 8. Limited diagnostic tools: Traditional diagnostic methods, such as X-rays or MRIs, may not always detect early stages of injury in patients with diabetes-related neuropathy.

[0014] 9. Lack of patient awareness: Some patients with diabetes may not be aware of the importance of regular foot exams and may not report injuries or changes in their feet to their healthcare provider.

[0015] 10. Comorbidities: Patients with diabetes often have other health conditions, such as vision problems or obesity, which can make it more challenging to detect and manage lower extremity injuries.

[0016] Current Injury Detection Regimes: Early detection and management of foot problems are crucial in preventing diabetes-related amputations. The following are the current injury detection regimes:

[0017] 1. Regular Foot Examinations: Patients with diabetes should undergo regular foot examinations by healthcare professionals. These examinations should include a visual inspection of the feet, assessment of sensation using a monofilament test, and palpation of foot pulses to detect PAD. The American Diabetes Association recommends that patients with diabetes have a comprehensive foot examination at least annually, and more frequently if they have additional risk factors.

[0018] 2. Self-Monitoring: Patients with diabetes should be educated on the importance of daily self-monitoring of their feet. They should be taught to inspect their feet daily for any signs of injury, redness, swelling, or infection. Patients should also be advised to report any concerning findings to their healthcare provider promptly.

[0019] 3. Glycemic Control: Maintaining good glycemic control is essential in reducing the risk of diabetes-related complications, including amputations. The American Diabetes Association recommends a target HbAlc of less than 7% for most patients with diabetes. Regular monitoring of blood glucose levels and adjusting treatment accordingly can help achieve this goal.

[0020] 4. Vascular Assessment: Patients with diabetes should undergo regular vascular assessments to detect PAD. This may include measuring the ankle-brachial index (ABI), which compares the blood pressure in the ankle to the blood pressure in the arm. An ABI of less than 0.9 is suggestive of PAD. Other imaging modalities, such as duplex ultrasound or angiography, may be used to further evaluate the extent of PAD.

[0021] 5. Wound Care: Prompt and appropriate wound care is essential in preventing infections and promoting healing. Patients with diabetes who develop foot ulcers should be referred to a specialized wound care team. Treatment may include offloading the affected area, debridement of necrotic tissue, and the use of advanced wound dressings.

[0022] 6. Patient Education: Patient education is a critical component of preventing diabetes- related amputations. Patients should be taught about the importance of proper foot care, including wearing appropriate footwear, avoiding walking barefoot, and keeping their feet clean and dry. They should also be educated on the signs and symptoms of foot problems and the importance of seeking prompt medical attention when necessary.

[0023] 7. Multidisciplinary Approach: The management of patients with diabetes at risk for lower extremity amputation requires a multidisciplinary approach. This may include collaboration between primary care physicians, endocrinologists, vascular surgeons, podiatrists, wound care specialists, and other healthcare professionals. A coordinated approach can help ensure that patients receive comprehensive care and that any potential complications are identified and managed promptly.REFERENCE TO A TABLE / PROGRAM LISTING / OTHERBRIEF SUMMARY

[0024] This solution provides a device, a method, and a system for real-time lower extremity health monitoring, particularly beneficial for patients with conditions like diabetes who may have reduced sensation in their feet. The system combines contact and non-contact sensor arrays to collect physiological data from patients' feet and lower limbs, enabling early detectionof potential issues. The solution is deployed in clinical settings as well as a consumer home monitoring solution and allows for a continuum of care to be enabled at a patient level.

[0025] The solution technology utilizes a multi-modal approach with various sensor types and combinations. Contact-based sensors include load cells, temperature sensors, pressure sensors, humidity sensors and biometric sensors integrated into devices like weight scales. Non-contact sensors encompass infrared thermal imaging, Doppler ultrasound, pulse oximetry, laser Doppler flowmeters, and multispectral imaging systems.

[0026] The solution detects early signs of non-visible injury. Vascular obstructions are identified by sudden temperature drops in specific areas compared to previous measurements or the contralateral foot. Trauma or injury is detected through temperature spikes and complementary measurements like swelling detection and tissue oxygenation assessment. Potential ulcers are signaled by persistent temperature differences of four degrees or more between corresponding points on both feet for three consecutive days. The solution also leverages machine learning algorithms that continuously improve by training on historical data, treatment variables, demographics, and treatment outcomes and integrates with electronic health record systems and third-party platforms via APIs. The infrastructure utilizes cloud computing, ensuring data resilience through multiple levels of backups and security protocols.BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0027] To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.

[0028] FIG. 1 illustrates a schematic overview of an exemplary system for detecting lower extremity injury in patients with neuropathy, showing the principal components including the sensor apparatus, computing entity, and display device.

[0029] FIG. 2 illustrates a functional block diagram showing the interplay between contact and non-contact sensor types deployed in the sensor apparatus, particularly demonstrating temperature, pressure, moisture, and optical sensor integration.

[0030] FIG. 3 illustrates a network architecture diagram of a distributed computing environment for implementing the lower extremity injury detection system, showing clientserver relationships and data flow pathways.

[0031] FIG. 4 illustrates a machine learning implementation diagram showing the training and operational phases of the neural network used to analyze physiological parameters for injury detection.

[0032] FIG. 5 illustrates a functional block diagram of a data platform architecture that supports the storage, processing, and analysis of sensor data for injury detection.

[0033] FIG. 6 illustrates a hardware implementation diagram of a machine implementing the lower extremity injury detection system, showing processors, memory components, and I / O interfaces.

[0034] FIG. 7 illustrates a multiprocessor processing environment diagram showing the specialized processors that handle sensor data acquisition, analysis, and user interface generation.

[0035] FIG. 8 illustrates a software architecture stack diagram showing the hierarchical arrangement of operating system components, libraries, frameworks, and applications that support the injury detection functionality.

[0036] FIG. 9 illustrates a thermal imaging visualization of a foot showing temperature patterns indicative of vascular obstruction, demonstrating how the system detects reduced blood flow through temperature differential analysis.

[0037] FIG. 10 illustrates an aspect of the subject matter in accordance with one variant of the solution showing the detection of foot trauma.

[0038] FIG. 11 illustrates an aspect of the subject matter in accordance with one embodiment showing the detection of a a potential ulcer forming.DETAILED DESCRIPTION

[0039] The disclosed healthcare technology encompasses real-time physiological data processing systems, distributed medical computing methodologies, and computer program commodities at varying degrees of clinical integration. Such a computer program commodity may comprise a machine-readable storage medium (or multiple mediums) bearing machineexecutable instructions to prompt a processor to execute components of the specified real-time foot health monitoring technology, including patient data acquisition, computational analysis of temperature and vascular patterns, and dynamic update mechanisms for clinical alerts.

[0040] This machine-readable medium is a physical entity capable of maintaining and storing instructions to be utilized by an instruction execution apparatus, including clinical data servers,diagnostic computational engines, and healthcare visualization systems. The medium could be, for example, but not restricted to, electronic, magnetic, optical, electromagnetic, semiconductor storage devices, or a fusion of these. A non-limiting list of specific instances of the machine- readable medium includes portable computer diskettes, hard drives, RAM, ROM, EPROM or Flash memory, SRAM, CD-ROMs, DVDs, memory sticks, floppy disks, and mechanical devices with embedded instructions. It should be clarified that the aforementioned medium does not consider transitory signals in isolation, like free-propagating electromagnetic waves or electrical signals over wires.

[0041] The machine-executable instructions detailed can be transferred to diverse medical computational devices, including healthcare system servers and clinical user devices, from the machine-readable medium or an external medical computer or storage via networks like the Internet, hospital LANs, healthcare WANs, or secure wireless networks. Such networks may integrate copper or optical fibers, HIPAA-compliant wireless transmission mechanisms, routers, firewalls, switches, gateway computers, and edge servers for point-of-carc deployment. Within each clinical computational device, a network interface or adapter fetches the instructions from the network, forwarding them for retention in the device's machine-readable medium and patient time-series database.

[0042] Instructions facilitating operations of this healthcare technology might be encoded as medical data normalization algorithms, physiological parameter weighting formulas, diagnostic calculation procedures, or code (both source and object) in diverse programming languages. Examples include but aren't restricted to clinical data processing languages like Python, Java, C++, and procedural ones like the "C" language. These instructions might operate wholly on a local healthcare system server, partly on local and remote clinical components, or entirely across the distributed healthcare network. Remote components can be linked via secure healthcare networks, inclusive of the Internet via HIPAA-compliant ISPs. In certain cases, specialized medical processing hardware such as clinical data warehouse systems or patient monitoring analytics engines could employ the instructions, utilizing their state data to actualize facets of the foot health monitoring technology.

[0043] The technology's facets are expounded with reference to flowcharts and block diagrams of methods, systems, and computer program products per its real-time patient monitoring solution variants. Each block in these can be realized via machine-executable instructions, including physiological data acquisition protocols and diagnostic computationalalgorithms. These instructions could be presented to a processor in general-purpose medical computers, specialized clinical data processing computers, or other programmable medical data apparatuses, crafting a machine that institutes the functions denoted in the diagrams. Furthermore, these instructions could be conserved within a patient time-series database directing system components to operate in a specific clinical fashion. The instructions could also be loaded onto a healthcare system server or clinical visualization device to prompt a sequence of tasks producing a patient data-driven process.

[0044] The depicted flowcharts and diagrams exhibit potential healthcare system, method, and product architectures and functionalities per the technology's real-time foot health monitoring solution variants. It's essential to note that these blocks, or their combinations, can be realized by specialized medical data processing systems designed for those tasks or combinations of healthcare hardware and machine instructions that ensure regulatory compliance while delivering accurate diagnostic information to clinicians and patients.

[0045] In FIG. 1, the solution engages with a Patient 102 who interacts with a comprehensive monitoring infrastructure that includes both Contact Sensor Array 104 and Non Contact Sensor Array 106. During the initial setup, the patient's demographic information and relevant medical history are entered into the patient profile database, creating a new patient profile.

[0046] The sensor arrays collect data from the patient's foot and lower limbs, which is then processed through Sensor Quality processing 108. The data acquisition module receives and digitizes this raw sensor data. A Neural Network 110 analyzes the received data for usability and quality using data quality assessment algorithms. If the data is deemed not useful, the User Feedback Generator 124 creates instructions for the user on how to obtain better measurements, and the system requests a rc-mcasurcmcnt.

[0047] Once data quality is satisfactory, a CPU in the Cloud Computing Instance 122 processes the digitized data using machine learning algorithms, comparing it against the Stored Diagnostic Parameters Data 112 in the reference database and the patient's historical data from Unique Patient Parameters Data 114) or directly from their EHR Databases 116 to create a set of personalized learning algorithms.

[0048] The personalized learning algorithms analyze the patient's current data in the context of their historical patterns and trends, identifying any significant deviations or anomalies. The adaptive threshold generator adjusts the expected parameters for the patient based on their personalized data, enabling more precise anomaly detection.

[0049] If the collected data falls outside the patient- specific expected parameters, the Cloud Computing Instance 122 will notifies the appropriate healthcare professionals for further evaluation or intervention. The Correlation and Display Computing Module 118 generates a report containing the relevant diagnostic parameters, taking into account the patient's personalized data and trends.

[0050] The patient-specific recommendation engine generates tailored recommendations and care plans based on the patient's unique data patterns and medical history. The user interface displays the report and personalized recommendations to the healthcare professional, who can interpret the results and make informed decisions about further testing, treatment, or referrals.

[0051] The system also features an API to 3rd Pty systems 120 for integration with external platforms. All processing is done supported by a Cloud Computing Instance 122. The machine stores the patient's latest data, results, generated user feedback, professional alerts, and personalized recommendations in the patient profile database, updating their historical record.

[0052] The system scans a Scanned Patient 202, who interacts with a comprehensive monitoring infrastructure of the solution that includes both No Contact Sensor measurement 204 and / or Contact Sensor Measurement 206. These sensor arrangements allow for flexibility in measuring either an unweighted or weighted part of the Patient. This distinction is crucial because inflammation fluctuates throughout the day, requiring inflammatory marker readings to be taken upon awakening. Additionally, blood perfusion of the superficial skin of the foot caused by trauma (which manifests as temperature changes) must be measured first thing upon awakening. If standing or walking pressure occurs before measurement, blood perfusion buildup will be ejected from the foot, contaminating temperature readings.

[0053] For a Scanned Patient 202 requiring a waking measurement, a No Contact Sensor measurement 204 may be used. The Contact Sensor Array 104includes various sensors integrated into devices like weight scales:1. Load cells that convert mechanical force into electrical signals2. Temperature sensors (thermistors, thermocouples, RTDs) to measure foot and ambient temperature3. Optical sensors for body composition analysis and foot imaging4. Pressure sensors to measure weight distribution for assessing balance and posture5. Accelerometers to detect motion and stepping on / off the scale6. Humidity sensors to measure foot moisture content, particularly important for diabetic patients7. Biometric sensors for user identification8. Heart rate sensors for fitness tracking and health monitoring

[0054] The Non Contact Sensor Array 106 integrates into devices such as foot scanners, smart mats, or wearable systems to enable remote, non-invasive monitoring of foot and lower limb health in both clinical and home settings. These contactless sensors include:1. Infrared thermal imaging cameras to detect temperature variations and identify inflammation areas2. Infrared thermometers for quick surface temperature measurements3. Doppler ultrasound sensors to assess blood flow and detect vascular issues4. Pulse oximetry sensors to measure blood oxygen saturation5. Laser Doppler flowmeters to assess microcirculatory blood flow6. Multispcctral imaging systems to evaluate tissue oxygenation and wound healing7. Hyperspectral imaging cameras for detailed spectral information on tissue damage8. Terahertz imaging systems to assess subsurface tissue9. Bio-impedance sensors to measure tissue electrical impedance10. 3D scanning systems to monitor changes in foot shape and volume11. Capacitive pressure sensors to detect pressure distribution without direct contact

[0055] During the initial setup, the patient's demographic information and medical history are entered into the patient profile database. The sensor array then collects data from the patient's foot and lower limbs, which is processed through Sensor Quality Processing 108. The data acquisition function of the4 Cloud Computing Instance 122 receives and digitizes this raw sensor data.

[0056] Neural Network 110 analyzes the data for usability and quality using assessment algorithms. If data quality is insufficient, the system generates user feedback for better measurements and requests re-measurement via the User Feedback Generator 124. Once quality is satisfactory, the CPU processes the digitized data using machine learning algorithms, comparing it against Stored Diagnostic Parameters Data 112 and the patient's historical data from Unique Patient Parameters Data 114 or directly from EHR Databases 116.

[0057] The personalized learning algorithms analyze current data in context with historical patterns, identifying deviations or anomalies. An adaptive threshold generator adjusts expectedparameters based on personalized data for precise anomaly detection. If data falls outside patient-specific parameters, the professional alert system notifies healthcare professionals for evaluation or intervention.

[0058] The Correlation and Display Computing Module 118 generates reports containing diagnostic parameters, considering personalized data and trends. A patient-specific recommendation engine creates tailored care plans based on unique data patterns and medical history. The user interface displays these reports and recommendations to healthcare professionals for informed decision-making.

[0059] The system features an API to 3rd Pty systems 120 for external platform integration, with processing supported by a Cloud Computing Instance (122). The system stores all patient data, results, feedback, alerts, and recommendations in the patient profile database, continuously updating their historical record.

[0060] FIG. 3 is a diagrammatic representation of a Networked Computing Environment 302 in which some examples of the present solution may be implemented or deployed.

[0061] One or more application servers 306 provide server-side functionality via a network 304 to a networked user device, in the form of a client device 308 that is accessed by a user 330. A web client 312 (e.g., a browser) and a programmatic client 310(e.g., an "app") are hosted and execute on the web client 312.

[0062] An Application Program Interface (API) server 320 and a web server 322 provide respective programmatic and web interfaces to application servers 306. A specific application server 318 hosts an Algorithm Processor 324, which further comprises components, modules and / or applications related to the function of variants of the present solution.

[0063] The web client 312 communicates with the Algorithm Processor 324 via the web interface supported by the web server 322. Similarly, the programmatic client 310 communicates with the Algorithm Processor 324 via the programmatic interface provided by the Application Program Interface (API) server 320 The third-party application 316 may, for example, be a Clinical or Consumer third party system providing diagnostics or supporting treatments to a patient.

[0064] The application server 318 is shown to be communicatively coupled to database servers 326 that facilitates access to an information storage repository or databases 328. In an example of the solution, the databases 328 includes storage devices that store information to be published and / or processed by the Algorithm Processor 324 in a variant of the solution.

[0065] Additionally, a third-party application 316 executing on a third-party server 314, is shown as having programmatic access to the Cloud Computing Instance 122 and the application servers 306 via the programmatic interface provided by the Application Program Interface (API) server 320. For example, the third-party application 316, using information retrieved from the application server 318, may support one or more features or functions on a website hosted by the third party to continue to process or respond to data collected the current solution.

[0066] FIG. 4 illustrates training and use of a Machine-Learning Program 402 by the present solution according to some example of the solutions. In some example of the solutions, machine-learning programs (MLPs), also referred to as machine-learning algorithms or tools, are used to perform operations associated with health care diagnostics, including injury sensing.USE OF MACHINE LEARNING ALGORITHMS TO IMPROVE LOWER EXTREMITY DETECTED RESULTS

[0067] By training the solution on a number of collected data sets, the solution improves its recommendations for patients that fall into defined cohorts. An example of the solution implementing a variety of MLPs is described below. Training of the solution selects combinations of training data comprising the following elements: historical data of previous podiatric treatments, treatment variables, demographics of the patients, including cohort data, demographic of the patients' parents, the demographics of the prescribers, the treatments the success of various podiatric treatments, the frequency and limitations on which podiatric treatments are paid for by insurance, data quality parameters reflecting the confidence and completeness of the data gathered, among other items listed in previous lists. The training of the solution improves the recommendations or inherent knowledge to be presented for the prescriber that are tailored to the patient's particular conditions. In another example of the solution, the MLPs are used to capture and train the solution in real time interaction with the patient, the patient's parents or care takers, and the prescribing clinician and any support staff. Depending on the MLPs incorporated on the mobile devices that may be used for data capture, a parent of the patient may use the functions of the mobile device MLP programs to capture audible, visual or other physical evidence that would be added to the podiatric treatment record related to the patient. Suitable privacy and de-identification filters would be applied to any datafrom any particular client or session to allow ongoing contribution of training data to the training corpus of historical data.

[0068] Machine learning is a field of study that gives computers the ability to learn without being explicitly programmed. Machine learning explores the study and construction of algorithms, also referred to herein as tools that may learn from existing data and make predictions about new data. Such machine-learning tools operate by building a model from example training data 406 in order to make data-driven predictions or decisions expressed as outputs or assessments (e.g., Ulcer assessment). Although example of the solutions are presented with respect to a few machine-learning tools, the principles presented herein may be applied to other machine-learning tools.

[0069] In some example of the solutions, different machine-learning tools may be used. For example, Logistic Regression (LR), Naive-Bayes, Random Forest (RF), neural networks (NN), matrix factorization, and Support Vector Machines (SVM) tools may be used for classifying or scoring clients and their cohorts. This solution is not limited by this list of machine learning tools or variants that are newly developed, for example a Generative Pre-trained Transformer (GPT.)

[0070] Two common types of problems in machine learning are classification problems and regression problems. Classification problems, also referred to as categorization problems, aim at classifying items into one of several category values (for example, is this object an apple or an orange?). Regression algorithms aim at quantifying some items (for example, by providing a value that is a real number).

[0071] The machine-learning algorithms use features 404 for analyzing the data to generate a Detected Condition 414. Each of the features 404 is an individual measurable property of a phenomenon being observed. The concept of a feature is related to that of an explanatory variable used in statistical techniques such as linear regression. Choosing informative, discriminating, and independent features is important for the effective operation of the MLP in pattern recognition, classification, and regression. Features may be of different types, such as numeric features, strings, and graphs.

[0072] In one example of the solution, the features 404 may be of different types and may include one or more of content 416, concepts 418, attributes 420, historical data 422 and / or user data 424, merely for example. The solution may also layer metadata on top of these featurethat are specific to a diagnostic type, a user classification, a treatment or other therapeutic classifications for the specific patient or treatment.

[0073] The machine-learning algorithms use the training data 406 to find correlations among the identified features 404 that affect the outcome or identify a Detected Condition 414. In some example of the solutions, the training data 406 includes labeled data, which is known data for one or more identified features 404 and one or more outcomes, such as detecting communication patterns, detecting the meaning of the message, generating a summary of a message, detecting action items in messages detecting urgency in the message, detecting a relationship of the user to the sender, calculating score attributes, calculating message scores, etc.

[0074] With the training data 406 and the identified features 404, the machine-learning tool is trained at machine-learning program training 408. The machine-learning tool appraises the value of the features 404 as they correlate to the training data 406. The result of the training is the trained machine-learning program 412.

[0075] When the trained machine-learning program 412 is used to perform an assessment, new data 410 is provided as an input to the trained machine-learning program 412, and the trained machine-learning program 412 generates the Detected Condition 414 as output.

[0076] FIG. 5 is another example of a platform variant of the present solution. The system comprises a Data Source 502, a Human Modified Data 504, a SQL Service 506, a Data Lake / Blob 508, a Data Platform 510, an Analytics and Machine Learning platform 512, a Reporting Platform 514, and a Machine Data Sources 516.

[0077] Data Source 502: The primary origin of raw data, the Data Source 502 can be any measured set of sensors that generates or collects information. This could range from web applications, sensors, user interactions, to traditional databases. This raw data, in its unprocessed form, is the foundational building block for all subsequent operations and interactions.

[0078] Human Modified Data 504: Once data is collected from the source, there might be instances where human intervention is needed for rectifications, additions, or modifications. The Human Modified Data 504 represents this manually altered data. It's crucial that this component interacts flawlessly with the primary data source to ensure that changes made reflect accurately and maintain data integrity.

[0079] SQL Service 506: A pivotal player in the data ecosystem, the SQL Service 506 offers structured querying capabilities to retrieve, manipulate, and manage data. Whether it's fetching data from the original source or accessing human-modified data, the SQL service ensures that data can be accessed in a structured, efficient, and reliable manner. Its interaction with the Human Modified Data 504 also guarantees that any manual modifications are queryable and integrated into the overall data flow.

[0080] Data Lake I Blob 508: With the explosion of data in both volume and variety, there arose a need for flexible, scalable, and diverse storage solutions. The Data Lake / Blob 508 is that solution, providing a repository for storing vast amounts of raw data in its native format, be it structured, semi- structured, or unstructured. By directly interacting with the SQL Service 506, it ensures that data, irrespective of its source, can be stored and accessed without constraints.

[0081] Data Platform 510: The Data Platform 510 acts as the central hub that orchestrates the movement, transformation, and storage of data. It interacts with the Data Lake / Blob 508 to fetch data, utilizes the SQL Service 506 to query and transform the data, and ensures that the Human Modified Data 504 is seamlessly integrated. The platform embodies the infrastructure and tools required to handle, process, and route data to various other components. An example: Azure Synapse Analytics, formerly known as Azure SQL Data Warehouse, is an integrated analytics service provided by Microsoft Azure.

[0082] Analytics and Machine Learning platform 512: With data at its fingertips, this platform is where advanced computations and predictive analytics happen. Extracting data from the Data Platform 510, the Analytics and Machine Learning platform 512 applies algorithms, statistical models, and machine learning techniques to draw insights, make predictions, or even automate decision-making processes. Its interaction with Machine Data Sources 516 ensures that machine-generated data can also be used for analytical purposes, enriching the overall analysis. An example of this platform would be Databricks is a cloud-based platform for big data analytics and machine learning.

[0083] Reporting Platform 514: After analyzing data, the insights drawn need to be presented in a comprehensible manner for stakeholders. The Reporting Platform 514 does just that. By sourcing data from the Analytics and Machine Learning platform 512, it generates visualizations, dashboards, and reports that condense vast amounts of information intodigestible formats. Its tight integration ensures that insights are timely, accurate, and actionable.

[0084] Machine Data Sources 516: In today's era of loT and automation, machines generate a staggering amount of data. The Machine Data Sources 516 represent this data, encompassing logs, sensor readings, and automation outputs. This data, when funneled into the Analytics and Machine Learning platform 512, offers unparalleled insights into machine operations, efficiency, and predictive maintenance.

[0085] FIG. 6 is a diagrammatic representation of the Solution Machine 602 implementing the current solution within which instructions 612 (e.g., software, a program, an application, an applet, an app, or other executable code) for causing the Solution Machine 602 and its Processors Processor 610 through Processor 614 to perform any one or more of the methodologies discussed herein may be executed. For example, the instructions 612 may cause the Solution Machine 602 to execute any one or more of the methods described herein. The instructions 612 transform the general, non-programmed Solution Machine 602 into a particular Solution Machine 602 programmed to carry out the described and illustrated functions in the manner described. The Solution Machine 602 may operate as a standalone device or may be coupled (e.g., networked) to other machines. In a networked deployment, the Solution Machine 602 may operate in the capacity of a server machine or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The Solution Machine 602 may comprise, but not be limited to, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a PDA, a cellular telephone, a smart phone, a mobile device, a wearable device, other smart devices, a web appliance, a network router, a network switch, a network bridge, or any machine capable of executing the instructions 612, sequentially or otherwise, that specify actions to be taken by the Solution Machine 602. Further, while only a single Solution Machine 602 is illustrated, the term "machine" shall also be taken to include a collection of machines that individually or jointly execute the instructions 612 to perform any one or more of the methodologies of this solution as discussed herein.

[0086] The Solution Machine 602 may include processors 606, memory 608, and I / O components 604, which may be configured to communicate with each other via a bus 642. In an example of the solution, the processors 606 (e.g., a Central Processing Unit (CPU), a Reduced Instruction Set Computing (RISC) Processor, a Complex Instruction Set Computing (CISC)Processor, a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), an ASIC, a Radio-Frequency Integrated Circuit (RFIC), another Processor, or any suitable combination thereof) may include, for example, a Processor 610 and a Processor 614 that execute the instructions 612. The term "Processor" is intended to include multi-core processors that may comprise two or more independent processors (sometimes referred to as "cores") that may execute instructions contemporaneously. Although FIG. 6 shows processors 606, the Solution Machine 602 may include a single Processor with a single core, a single Processor with multiple cores (e.g., a multi-core Processor), multiple processors with a single core, multiple processors with multiples cores, or any combination thereof.

[0087] The memory 608 includes a main memory 616, a static memory 618, and a storage unit 620, both accessible to the processors 606 via the bus 642. The main memory 616, the static memory 618, and storage unit 620 store the instructions 612 embodying any one or more of the methodologies or functions described herein for the various solution variants. The instructions 612 may also reside, completely or partially, within the main memory 616, within the static memory 618, within machine-readable medium 622 within the storage unit 620 within at least one of the processors 606 (e.g., within the Processor's cache memory), or any suitable combination thereof, during execution thereof by the Solution Machine 602.

[0088] The I / O components 604 may include a wide variety of components to receive input, provide output, produce output, transmit information, exchange information, capture measurements, and so on. The I / O components 604 that are included in a particular Solution Machine 602 will depend on the type of machine. For example, portable machines such as mobile phones may include a touch input device or other such input mechanisms, while a headless server machine will likely not include such a touch input device. It will be appreciated that the I / O components 604 may include many other components that are not shown in FIG. 6. In various example of the solutions, the I / O components 604 may include output components 628 and input components 630. The output components 628 may include visual components (e.g., a display such as a plasma display panel (PDP), a light emitting diode (LED) display, a liquid crystal display (LCD), a projector, or a cathode ray tube (CRT)), acoustic components (e.g., speakers), haptic components (e.g., a vibratory motor, resistance mechanisms), other signal generators, and so forth. These components may also form part of the solution shown in FIG. 1 The input components 630 may include alphanumeric input components (e.g., a keyboard, a touch screen configured to receive alphanumeric input, a photo-optical keyboard,or other alphanumeric input components), point-based input components (e.g., a mouse, a touchpad, a trackball, a joystick, a motion sensor, or another pointing instrument), tactile input components (e.g., a physical button, a touch screen that provides location and / or force of touches or touch gestures, or other tactile input components), audio input components (e.g., a microphone), and the like.

[0089] In further example of the solutions, the I / O components 604 may include biometric components 632, motion components 634, environmental components 636, or position components 638, among a wide array of other components. For example, the advanced variants of the biometric components 632 of this solution include components to detect visual expressions related distress or trauma (e.g., hand expressions, facial expressions, vocal expressions, body gestures, or eye-tracking), measure bio-signals indicative of (e.g., blood pressure, heart rate, body temperature, perspiration, levels of carbon dioxide / other chemicals in blood work, or brain waves), identify a personal individual characteristics related to injury (e.g., voice identification, retinal identification, facial identification, fingerprint identification, or electroencephalogram-based identification), and the like.

[0090] Communication may be implemented using a wide variety of technologies. The I / O components 604 further include communication components 640 operable to couple the Solution Machine 602 to a network 624 or devices 626 via respective coupling or connections. For example, the communication components 640 may include a network interface, component or another suitable device to interface with the network 624. In further examples, the communication components 640 may include wired communication components, wireless communication components, cellular communication components, Near Field Communication (NFC) components, Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and other communication components to provide communication via other modalities. The devices 626 may be another machine or any of a wide variety of peripheral devices (e.g., a peripheral device coupled via a USB).

[0091] Moreover, the communication components 640 may detect identifiers or include components operable to detect identifiers. For example, the communication components 640 may include Radio Frequency Identification (RFID) tag reader components, NFC smart tag detection components, optical reader components (e.g., an optical sensor to detect onedimensional bar codes such as Universal Product Code (UPC) bar code, multi-dimensional bar codes such as Quick Response (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode,PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), or acoustic detection components (e.g., microphones to identify tagged audio signals). In addition, a variety of information may be derived via the communication components 640, such as location via Internet Protocol (IP) geolocation, location via Wi-Fi® signal triangulation, location via detecting an NFC beacon signal that may indicate a particular location, and so forth.

[0092] The various memories (e.g., main memory 616, static memory 618, and / or memory of the processors 606) and / or storage unit 620 may store one or more sets of instructions and data structures (e.g., software) embodying or used by any one or more of the methodologies or functions described herein. These instructions (e.g., the instructions 612), when executed by processors 606, cause various operations to implement the disclosed examples of the solutions.

[0093] The instructions 612 may be transmitted or received over the network 624, using a transmission medium, via a network interface device (e.g., a network interface component included in the communication components 640 or position components 638) and using any one of several well-known transfer protocols (e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions 612 may be transmitted or received using a transmission medium via a coupling (e.g., a peer-to-peer coupling) to the devices 626.

[0094] As previously mentioned, this solution utilizes machine learning methodology to create a training solution for the data processed by each user and each client processed using this solution. By continuously measuring results of assessment outcomes and comparing it to the scoring algorithm, the present solution improves the predicted clinical and consumer diagnosis.

[0095] Turning now to FIG. 7, a diagrammatic representation of a Networked Computing Environment 302 of the present solution is shown, which includes the Processor 706, the Processor 702, and a Processor 708 (e.g., a GPU, CPU or combination thereof).

[0096] The Processor 702 is shown to be coupled to a power source 704, and to include (either permanently configured or temporarily instantiated) modules, namely a Clinical execution component 710, a Consumer execution component 712 and a Sensor Management Component 714 Sensor Management Component 714 operationally controls Sensor Quality Processing 108 and manages the Contact Sensor Array 104 and the Non Contact Sensor Array 106 sensing parameters on the Patient 102, the Consumer execution component 712 operationally manages Consumer side data for the benefit of the Patient 102, and the Clinical execution component 710 operationally manages data reporting to 3rd party systems like EHR Databases 1 16 and other Clinical Platforms. As illustrated, the Processor 702 is communicatively coupled to boththe Processor 706 and Processor 708, and receives commands from the Processor 706, as well as commands from the Processor 708.

[0097] The following example is a non-limiting manner of enabling this solution. The solution also contemplates using other analogous hardware or software implementation than those explicitly mentioned.TECHNOLOGY AND INFRASTRUCTURE

[0098] This example solution is delivered over the Internet using virtual machines (VMs) for web and database servers, (e.g., AZURE web apps to host web applications, and AZURE blob storage for content storage. The solution deploys dynamically generated application features as well as derived or published content via various servers. The web applications are developed with any modern app development environment (e.g., Customer-facing solutions are developed in .NET Core, MVC Framework, C#, Angular, and SQL formats using Web 2.0 functionality standards.DATA CENTERS

[0099] An example of the data center implementation uses 3rd party data centers like those provided by GOOGLE, AMAZON, MICROSOFT vendors that comply with stringent data privacy requirement from the jurisdiction served by the platform.SERVERS AND CONTENT HOSTING TECHNOLOGY

[0100] The solution hosts on INTEL or AMD processors using cloud-hosted server hardware on using various software formats for virtual machines, (e.g., Windows Server 2019.) Content, especially Video and other dynamic content is hosted by various media services (e.g. AZURE MEDIA SERVICE and BRIGHTCOVE). All servers are set up in a high-availability fashion to ensure ultimate up-time. An active server takes in all the traffic and a warm backup server is on stand-by with continuous synchronization. This allows the solution to react to a server going down at any one point in time, the second server automatically kicks in without any manual intervention. Disaster recovery is also in place to ensure automatic failover in case of a data center outage.SOLUTION LOAD BALANCING FOR THE APPLICATIONS AND DATA

[0101] In this example of the solution, use of a 3rd party (e.g., AZURE) hardware-based load balancing distributes end-user connections across a plurality of distributed servers. This enablesgreater resiliency, a balanced server load, with enhanced fault tolerance on the various applications deployed by this solution.CONTENT DELIVERY NETWORK

[0102] Content is held in a resilient blob storage that automatically replicates data to help guard against unexpected hardware failures. Content storage is triple-redundant with an option of geo-constrained redundant storage by jurisdictions that limit transfer of privacy related data.Content used in this context refers to content of all types and data of all types related to medical treatment (e.g., educational literature, patient data and records, clinician data and records)DATA BACK-UP AND SECURITY

[0103] The solution uses multiple levels of backups to ensure data resiliencies. Full backups of data are created daily with transactional hourly backups. Copies of the backups are also physically or virtually sent to off-site facilities to ensure the highest level of protection and resilience for customer data.

[0104] FIG. 8 is a solution block diagram 802 illustrating a machine 806 representative of the current solution, which can be installed on any one or more of the devices described herein.The machine 806 is supported by hardware such as a machine 806 that includes processors 822, memory 828, and I / O components 840. In this example, the machine 806 can be conceptualized as a stack of layers, where each layer provides a particular functionality. The machine 806 includes layers such as a kernel 816, libraries 812, frameworks 810, and applications 808. Operationally, the applications 808 invoke API libraries 826 through the software stack and receive messaging in response to the API calls.

[0105] The operating system 814 manages hardware resources and provides common services. The operating system 814 includes, for example, a kernel 816, services 818, and drivers 824. The operating system 814 acts as an abstraction layer between the hardware and the other software layers. For example, the kernel 816 provides memory management, Processor management (e.g., scheduling), component management, networking, and security settings, among other functionality. The services 818 can provide other common services for the other software layers. The drivers 824 are responsible for controlling or interfacing with the underlying hardware. For instance, the drivers 824 can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audio drivers, power management drivers, and so forth.

[0106] The libraries 812 provide a low-level common infrastructure used by the applications 808. The libraries 812 can include system libraries 820(e.g., C standard library) that provide functions such as memory allocation functions, string manipulation functions, mathematic functions, and the like. In addition, the libraries 812 can include API libraries 826 such as media libraries (e.g., libraries to support presentation and manipulation of various media formats such as Moving Picture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC), Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC), Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group (JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries (e.g., an OpenGL framework used to render in two dimensions (2D) and three dimensions (3D) in a graphic content on a display), database libraries (e.g., SQLite to provide various relational database functions), web libraries (e.g., WebKit to provide web browsing functionality), and the like. The libraries 812 can also include a wide variety of other libraries 830 to provide many other APIs to the applications 808.

[0107] The frameworks 810 provide a high-level common infrastructure that is used by the applications 808. For example, the frameworks 810 provide various graphical user interface (GUI) functions, high-level resource management, and high-level location services. The frameworks 810 can provide a broad spectrum of other APIs that can be used by the applications 808, some of which may be specific to a particular operating system or platform.

[0108] In an example of the solution, the applications 808 comprise a Measuring App 836, a contacts application 832, a browser application 834 for recommended patient treatment or educational content, a location application 844 to capture where treatment is occurring, a media application 846, a messaging application 848, and a broad assortment of other applications such as a third-party application 842. The applications 808 are programs that execute functions defined in the programs. Various programming languages can be employed to create one or more of the applications 808, structured in a variety of manners, such as object-oriented programming languages (e.g., Objective-C, Java, or C++) or procedural programming languages (e.g., C or assembly language). In a specific example, the third-party application 842 (e.g., an application developed using the ANDROID™ or IOS™ software development kit (SDK) by an entity other than the vendor of the particular platform) may be mobile softwarerunning on a mobile operating system such as IOS™, ANDROID™, WINDOWS® Phone, or another mobile operating system. In this example, the third-party application 842 can invoke the API provided by the operating system 814 to facilitate functionality described herein.

[0109] FIG. 9 shows a temperature profile of a Vascular Obstruction 902. A sudden drop in temperature at a specific point or area on one foot compared to the same point or area on the previous day may suggest a vascular obstruction. Reduced blood flow to an area can cause a decrease in temperature, as the affected tissue receives less warmth from the circulating blood. Vascular obstruction, which refers to the blockage or narrowing of blood vessels, can lead to reduced blood flow and oxygen supply to the affected area. This can cause a drop in temperature in the affected limb or region.DETECTION METHODOLOGY

[0110] By monitoring temperature changes and incorporating other contactless sensors, it is possible to detect and assess the severity of vascular obstruction. This can be done with temperature scanning or in conjunction with other sensors. The solution utilizes a multi-modal approach to accurately detect and characterize vascular obstructions:Primary Temperature Analysis

[0111] Infrared thermography or thermal imaging cameras can be used to measure and map the surface temperature of the skin. A significant temperature drop in a specific area compared to the surrounding skin or the contralateral limb may indicate reduced blood flow due to vascular obstruction. The system analyzes these temperature differentials using pattern recognition algorithms that can identify anomalous thermal signatures consistent with compromised vascular function.

[0112] When a vascular obstruction occurs, the temperature profile displays characteristic patterns that can be quantified through specialized algorithms. The solution's neural network analyzes these patterns against established baselines for the individual patient, taking into account normal daily temperature variations, ambient conditions, and patient-specific vascular anatomy.Supplementary Measurement Technologies

[0113] In conjunction with thermal imaging, the solution employs multiple complementary technologies to provide comprehensive vascular assessment:1. Contactless Pulse Oximetry: This technology can be leveraged to show lower oxygen saturation levels in the affected area. By measuring the absorption of light at different wavelengths, the system can determine oxygen saturation without direct contact with the skin. Regions affected by vascular obstruction typically show reduced oxygen saturation values, which correlate with the thermal data to provide a more accurate diagnosis.2. Laser Doppler Flowmetry (LDF): This technique uses laser light to measure microvascular blood flow in superficial tissues. LDF systems have been developed that can assess blood flow from a distance, providing a non-invasive means of detecting vascular obstruction. The solution integrates LDF data to quantify blood flow rates in specific regions and detect anomalies that may indicate vascular compromise.3. Photoacoustic Imaging: This advanced technology combines laser pulses and ultrasound detection to visualize blood vessels and can provide high-resolution images of vascular structures and detect changes in blood flow velocity and volume. The photoacoustic system generates acoustic waves through the absorption of pulsed laser energy, creating detailed maps of vascular networks and highlighting areas of restricted flow or complete blockage.4. Hyperspectral Imaging: This method captures images across multiple wavelengths of light, providing spectral information about the tissue. Using different wavelengths of light, this sensor can penetrate the skin to different depths, enabling the assessment of superficial and deeper blood vessels to reveal changes in blood oxygenation, perfusion, and the presence of vascular abnormalities. The solution's machine learning algorithms analyze these spectral signatures to identify patterns consistent with various types of vascular pathology.5. Infrared Vein Imaging: This sensor uses near-infrared light to visualize superficial veins beneath the skin to reveal a Vascular obstruction 902. By enhancing the contrast between veins and surrounding tissue, the system can identify structural abnormalities in the vascular system, including thrombosis, stenosis, or other forms of obstruction.CLINICAL INTEGRATION AND DIAGNOSTIC WORKFLOW

[0114] The solution integrates data from all these modalities to create a comprehensive vascular health profile. When a potential vascular obstruction is detected, the system:1 . Generates a detailed visualization of the affected area, highlighting temperature differentials and blood flow patterns2. Compares current measurements with the patient's historical data to identify acute changes3. Categorizes the severity of the obstruction based on established clinical criteria4. Provides recommendations for further clinical assessment or intervention based on the severity and characteristics of the obstruction

[0115] This multimodal approach significantly enhances the sensitivity and specificity of non- invasive vascular obstruction detection, potentially enabling earlier intervention and reducing complications associated with peripheral vascular disease.

[0116] FIG. 10 shows a profile of a Trauma detection 1002. A sudden spike in temperature at a specific point on one foot compared to the same point on the previous day may indicate acute trauma or injury to that area. Trauma can cause localized inflammation and increased blood flow, resulting in a higher temperature reading. Especially for patients who do not process pain, early trauma detection is important to the treatment of the injury.

[0117] To detect an injury or trauma in a contactless manner using a temperature spike across a foot, especially in patients with impaired pain sensation (such as those with diabetic neuropathy), a combination of temperature scanning and other contactless sensors can be utilized.TEMPERATURE SCANNING METHODOLOGY

[0118] Use a high-resolution infrared thermal imaging camera to capture detailed temperature maps of the foot by looking for localized temperature spikes or hot spots that deviate significantly from the surrounding areas or the contralateral foot. Daily scans that show sudden temperature increases in specific regions may indicate increased blood flow and inflammation associated with acute injury or trauma. Compare temperature patterns over time to identify any persistent or evolving temperature anomalies.COMPLEMENTARY DETECTION TECHNOLOGIESSwelling Detection

[0119] This may be paired with swelling Detection using 3D foot scanner. By utilizing 3D imaging techniques, such as structured light scanning or time-of-flight cameras, to capture the volumetric shape of the foot. You can compare the 3D foot scans to a baseline or the contralateral foot to detect any localized swelling or changes in foot shape.Tissue Oxygenation Assessment

[0120] Another technique to combine with temperature sensing is Tissue Oxygenation: Use hyperspectral imaging to assess the oxygenation levels of the foot tissue. Injuries or trauma can disrupt blood flow and oxygenation in the affected area. Analyze the spectral data to identify regions with reduced oxygenation, which may indicate compromised tissue health due to trauma.INTEGRATED ANALYSIS APPROACH

[0121] By combining these contactless sensing modalities, a comprehensive assessment of foot health can be performed, enabling the detection of injury or trauma even in patients with impaired pain sensation. The data collected from these sensors can be integrated and analyzed using machine learning algorithms to identify patterns and abnormalities indicative of foot trauma.

[0122] FIG. 11 shows a temperature trending for a foot ulcer 1104. By regularly monitoring foot temperatures and other parameters, healthcare providers and individuals at risk can detect early warning signs of potential foot ulcers or other complications. Early detection allows for timely intervention, which may include offloading, improved foot care practices, or medical treatment to prevent the progression of the condition and promote healing.DETECTING POTENTIAL ULCER

[0123] If there is a temperature difference of 4 degrees or more between a specific point on one foot compared to the corresponding point on the other foot, and this difference persists for three consecutive days, it may indicate the development of a potential ulcer. Consistently elevated temperatures in a localized area can be a sign of inflammation and increased metabolic activity, which are precursors to ulcer formation.ENHANCED MONITORING TECHNOLOGIES

[0124] Several additional sensors and parameters could be incorporated alongside temperature monitoring. Here are some sensors and measurements that could enhance the system:1) Infrared Imaging

[0125] Adding full infrared cameras can provide a visual map of temperature distribution across the foot to help identify localized hot spots or areas of inflammation that may not be detected by single-point temperature measurements.2) Skin Integrity Assessment

[0126] This same imaging can detect the presence of moisture or breaks in the skin area images as well to assist in diagnosis by scanning both feet at the same time. Infrared imaging can also reveal asymmetries in temperature patterns between the two feet, which may indicate developing issues.

[0127] 3) Use of Portable Devices as Sensor Arrays

[0128] All of the various sensor arrays may be packaged in a hand held sensor device where clinicians or consumers can readily generate thermal or other sensor mappings of the foot in two or three dimensional maps that could be color coded by risk level or by thermal levels or the use of icons on the map to assist the view of the clinician.

[0129] The above description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific examples of the healthcare solutions in which the solution can be practiced. These variants of the solutions are also referred to herein as "examples." Such examples can include elements in addition to those shown or described. However, the present inventors also contemplate examples in which only those elements shown or described are provided. Moreover, the present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

[0130] In the event of inconsistent usages between this document and any documents so incorporated by reference, the usage in this document controls.

[0131] In this document, the terms "a" or "an" are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of "at least one" or "one or more." In this document, the term "or" is used to refer to a nonexclusive or, such that "A or B" includes "A but not B," "B but not A," and "A and B," unless otherwise indicated. In this document, the terms "including" and "in which" are used as the plain-English equivalents of the respective terms "comprising" and "wherein." Also, in the following claims, the terms"including" and "comprising" are open-ended, that is, a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such a term in a claim are still deemed to fall within the scope of that claim. Moreover, in the following claims, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.MEDICAL MEASUREMENTS & HEALTHCARE PARAMETERS

[0132] Geometric terms, such as "parallel", "perpendicular", "round", or "square", are not intended to require absolute mathematical precision, unless the context indicates otherwise. Instead, such geometric terms allow for variations due to manufacturing or equivalent functions. For example, if an element is described as "round" or "generally round," a component that is not precisely circular (e.g., one that is slightly oblong or is a many-sided polygon) is still encompassed by this description.

[0133] Similarly, clinical measurements and healthcare parameters specified in this solution, such as temperature differentials, tissue oxygenation levels, or dimensional measurements of anatomical features, should be understood to include appropriate clinical tolerances and variations. These variations may arise from differences in measurement devices, environmental conditions, individual patient physiology, or other factors common in healthcare settings.IMPLEMENTATION IN HEALTHCARE SYSTEMS

[0134] Method examples described herein can be machine or computer-implemented at least in part. Some examples can include a Computer-Readable Medium or machine-readable medium encoded with instructions operable to configure an electronic device to perform methods as described in the above examples. An implementation of such methods can include code, such as microcode, assembly language code, a higher-level language code, or the like. Such code can include computer readable instructions for performing various methods. The code may form portions of computer program products. Further, in an example, the code can be tangibly stored on one or more volatile, non-transitory, or non-volatile tangible computer- readable media, such as during execution or at other times. Examples of these tangible computer-readable media can include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memories (RAMs), read only memories (ROMs), and the like.

[0135] In healthcare applications, these implementations may be integrated with existing electronic health record (EHR) systems, clinical decision support systems, remote patient monitoring platforms, and other healthcare IT infrastructure. The solution is designed to maintain compliance with relevant healthcare data security and privacy regulations while providing seamless integration with established clinical workflows.SCOPE OF THE HEALTHCARE SOLUTION

[0136] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other variants of the solutions can be used, such as by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed variant of the solution. Thus, the following claims are hereby incorporated into the Detailed Description as examples or variants of the solutions, with each claim standing on its own as a separate variant of the solution, and it is contemplated that such variant of the solutions can be combined with each other in various combinations or permutations. The scope of the solution should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

CLAIMSWhat is claimed is:

1. A system for detecting lower extremity injury in patients with neuropathy, the system comprising: a sensor apparatus comprising a plurality of sensors configured to measure physiological parameters at known locations on a patient's lower extremity; a computing entity in communication with the sensor apparatus, wherein the computing entity is configured to: receive, from the sensor apparatus, sensor data indicating the measured physiological parameters; analyze the sensor data to detect signs of lower extremity injury; determine a likelihood of injury based on the analysis; and generate a user interface comprising display data indicating the likelihood of injury for display via a display device.

2. The system of claim 1 wherein the plurality of sensors comprises at least one of: temperature sensors, pressure sensors, moisture sensors, optical sensors, or hyperspectral imaging sensors.

3. The system of claim 1 wherein analyzing the sensor data to detect signs of injury further comprises comparing the measured physiological parameters to predetermined thresholds or reference values indicative of injury.

4. The system of claim 1, wherein the computing entity is further configured to: receive patient input data identifying subjective sensations experienced by the patient; and consider the patient input data together with the sensor data when determining the likelihood of injury.

5. The system of claim 1, wherein the sensor apparatus is a wearable device contoured to fit the patient's foot and / or lower leg.

6. The system of claim 1, wherein the computing entity is further configured to: track changes in the measured physiological parameters over time; and analyze trends in the sensor data to detect progressive signs of injury.

7. The system of claim 1 The system of claim 1, wherein the user interface further comprises recommendations for preventive or remedial actions based on the likelihood of injury.

8. A method for detecting lower extremity injury in patients with neuropathy, the method comprising:receiving, from a sensor apparatus comprising a plurality of sensors, sensor data indicating physiological parameters measured at known locations on a patient's lower extremity; analyzing the sensor data to detect signs of lower extremity injury; determining a likelihood of injury based on the analysis; and generating a user interface comprising display data indicating the likelihood of injury for display via a display device.

9. The method of claim 8, wherein the plurality of sensors comprises at least one of: temperature sensors, pressure sensors, moisture sensors, optical sensors, or hyperspectral imaging sensors.

10. The method of claim 8, wherein analyzing the sensor data further comprises comparing the measured physiological parameters to predetermined thresholds or reference values indicative of injury.

11. The method of claim 8, further comprising: receiving patient input data identifying subjective sensations experienced by the patient; and considering the patient input data together with the sensor data when determining the likelihood of injury.

12. The method of claim 8, further comprising: tracking changes in the measured physiological parameters over time; and analyzing trends in the sensor data to detect progressive signs of injury.

13. The method of claim 8, wherein the user interface further comprises recommendations for preventive or remedial actions based on the likelihood of injury.

14. The method of claim 8, wherein analyzing the sensor data further comprises: detecting an abnormal rise in temperature at one or more specific locations on the patient's foot relative to surrounding tissue; comparing the detected temperature rise against established temperature differential thresholds; and flagging any location where the temperature rise exceeds the established temperature differential thresholds as a potential injury site.

15. The method of claim 8, wherein analyzing the sensor data further comprises: detecting an abnormal temperature drop at one or more specific locations on the patient's foot relative tocontralateral locations on the patient's other foot; determining whether the temperature drop exceeds a predetermined threshold indicative of compromised circulation; and calculating an increased risk factor for ulceration at locations with detected temperature drops.

16. A portable diagnostic device for early detection of foot ulcers in diabetic patients, the device comprising: a measurement platform incorporating a sensor array configured to capture physiological data from a patient's foot; a processor operatively connected to the sensor array; a memory storing reference data including normative values for the physiological data; and a display; wherein the processor is configured to: activate the sensor array to collect a set of physiological measurements from the patient's foot; compare the collected physiological measurements against the reference data to identify anomalies; quantify a risk level for foot ulcer development based on identified anomalies; and display a visual representation of the risk level on the display.

17. The portable diagnostic device of claim 16, wherein the sensor array comprises temperature gradient sensors configured to detect localized temperature differences across regions of the patient's foot.

18. The portable diagnostic device of claim 16, wherein the processor is further configured to generate a three-dimensional thermal map of the patient's foot and highlight regions with abnormal temperature patterns indicative of inflammation.

19. The portable diagnostic device of claim 16, further comprising wireless communication capabilities configured to transmit the collected physiological measurements and risk assessment to a healthcare provider's system.

20. The portable diagnostic device of claim 16, wherein the visual representation of the risk level comprises a color-coded foot map indicating specific regions of concern using different colors corresponding to different risk levels.

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