SYSTEM AND METHOD FOR ANALYZING SKIN ABSORPTION AND PENETRATION PATTERNS

Abstract

An AI-enhanced patch system for analysing ointment absorption (101) comprises a sensor array (102) to collect real-time data on ointment application and skin interaction, an AI module (103) with machine learning modules (105) for data analysis, a user interface (104) to present insights, and a microprocessor (106) for managing data flow. It includes a database (107) for storing historical data and a calibration module (108) to maintain accuracy across various ointments and skin types. The patch system provides personalized, real-time analysis of ointment efficacy based on patient characteristics and environmental factors. A method for analysing ointment absorption (200) involves applying a sensor array (201), collecting and processing data (202-204), and generating personalized recommendations (205). The system offers tools such as efficacy prediction, comparative analysis, and alerts, ensuring accurate and customized results while storing data for future reference.

Specification

Description:The following is a step-by-step description of the invention, detailing the components, and their functionalities mentioned below:

The AI-enhanced transdermal patch system for analysis of absorption and penetration profiles (101) with ointment has several interrelated elements that work together to offer the whole view of topical drug delivery. Each of these elements is designed specifically to address the challenges seen in pharmaceutical technology and dermatology. Below is a detailed description of the structure, function, interaction of the components, their novelties, and advantages.

Sensor Array (102): The sensor array is an integral part of the invention, designed to gather comprehensive real-time data on ointment application and its interaction with the skin. It includes various sensors tailored to monitor specific parameters:

• Hydration Sensors: Utilizing electrical impedance technology, these sensors accurately measure moisture levels in the skin's outermost layer (stratum corneum), capturing changes before, during, and after ointment application.
• Temperature Sensors: High-precision thermistors track skin surface temperature, offering insights into variations in blood flow and metabolic activity related to ointment absorption.
• pH Sensors: Compact electrochemical sensors monitor skin pH, a critical factor in drug absorption and potential changes caused by the ointment.
• Spectroscopic Sensors: Using near-infrared spectroscopy, these sensors detect chemical markers and metabolites of the ointment to assess its effectiveness.
• Pressure Sensors: Thin-film pressure sensors record the force exerted during application, providing data on application technique and uniformity.

Designed as a flexible, adhesive patch, the sensor array is comfortable for long-term wear, hypoallergenic, and waterproof, ensuring it doesn't disrupt daily activities.

The AI module (103) serves as the core analytical engine of the system, using advanced machine learning modules (105) to interpret data from the sensor array during ointment application. Convolutional Neural Networks (CNNs) are utilized for pattern recognition in spectroscopic data, identifying unique chemical signatures of the ointment's components and their metabolites as they interact with the skin. Recurrent Neural Networks (RNNs) process time-series data from hydration, temperature, and pH sensors, enabling the system to track changes in skin parameters over time and predict future absorption trends. A Random Forest module classifies skin types and conditions, tailoring the analysis to individual characteristics and enhancing personalized treatment. Gradient Boosting Machines combine outputs from several models to predict the overall efficacy of the ointment, offering insights into performance and areas for improvement. The AI module continuously learns from new data, refining its ability to detect absorption anomalies, recommend optimal application techniques, and suggest ointment formulation modifications for enhanced results.

The user interface (UI) (104) provides an intuitive platform for users to engage with the system, transforming complex sensor data into actionable insights. Key features include real-time absorption graphs that visually track ointment penetration, allowing for immediate monitoring of treatment progress. Detailed skin parameter dashboards offer real-time and historical data on hydration, temperature, and pH, giving users a complete view of the skin's response to the treatment. Efficacy prediction tools, powered by AI, forecast expected outcomes based on current data, while comparative analysis features enable users to compare results with past treatments or population benchmarks. An alert system notifies users of significant changes in absorption patterns, allowing timely adjustments. Designed to meet the needs of both researchers and clinicians, the interface is customizable, user-friendly, and simplifies complex data for practical application.

The microprocessor (106) serves as the central coordinator, managing data flow and system operations across the device. It controls the timing and frequency of sensor readings, ensuring synchronized and consistent data capture from the sensor array. The microprocessor also handles signal processing, refining raw data from sensors into formats suitable for AI analysis, such as spectroscopic signals and normalized temperature readings. Additionally, it performs system diagnostics, continuously monitoring components for optimal performance and quickly troubleshooting issues. In wearable configurations, the microprocessor dynamically optimizes power consumption to extend battery life. It also ensures secure data transmission by encrypting and safeguarding data as it flows to the AI module and user interface, protecting sensitive user information.

The database (107) serves as a comprehensive repository for storing all relevant historical data related to ointment performance. It contains anonymized patient data, capturing records of skin parameters, absorption patterns, and treatment outcomes across a diverse population. This data helps refine treatment protocols and improve efficacy for different skin types and conditions. The database also holds detailed ointment formulation data, tracking how various compositions perform under different circumstances. In addition, it stores environmental data, including temperature, humidity, and other factors that could influence the efficacy of the ointment. Machine learning models used by the system are also archived in the database, enabling rapid deployment and facilitating comparisons with past versions. To ensure patient privacy and data security, the database is protected with advanced encryption and strict access control protocols.

Calibration Module (108): The calibration module (108) plays a vital role in ensuring that the system remains accurate and reliable across a wide range of ointment types and skin conditions. It features automated sensor calibration, which regularly checks and adjusts the sensitivity and accuracy of the sensors to maintain optimal performance. For commonly used ointments, the module includes pre-set calibration profiles tailored to specific formulations, allowing for quick setup and precise readings. The module also compensates for environmental factors, such as temperature and humidity fluctuations, that could affect sensor accuracy. Additionally, users can initiate manual calibration to account for new or unique ointment formulations or skin conditions, ensuring the system remains adaptable and highly effective in diverse clinical scenarios.

A method for analysing ointment absorption (200) using an AI-enhanced transdermal patch system involves several key steps.

Applying a Sensor Array to a Target Skin Area (201): The process begins by affixing a flexible sensor array to the designated skin area. The array is equipped with various sensors that monitor skin parameters in real-time. This ensures that data collection starts before the ointment application, capturing the baseline conditions of the skin.

Collecting Real-Time Data on Skin Parameters (202): Once the sensor array is in place, it continuously collects data during and after the ointment application. Sensors track skin hydration, temperature, pH levels, and pressure applied during the ointment application. This data provides a comprehensive view of the skin's response to the ointment over time.

Processing the Collected Data Using Machine Learning modules (203): The raw data collected by the sensor array is sent to the system's AI module, where machine learning modules like CNNs and RNNs process the data. These modules recognize patterns, detect anomalies, and classify skin conditions, refining the data for further analysis.

Analysing the Processed Data to Determine Ointment Absorption Patterns (204): After processing, the AI module analyses the data to determine how the ointment penetrates the skin. It maps absorption patterns, identifies the efficacy of the ointment, and tracks changes in skin hydration and other parameters to provide a clear understanding of how well the ointment is performing.

Generating Personalized Insights and Recommendations (205): Based on the analysed data, the system generates personalized insights such as optimal application methods, recommended ointment dosage, or changes in the formulation for better efficacy. These insights are tailored to the individual's specific skin type and environmental conditions.

Displaying the Results Through a User Interface (206): The analysed data and recommendations are displayed in real-time through a user-friendly interface. The interface includes features like absorption graphs, skin parameter dashboards, and predictive tools that help users easily interpret the results and take necessary actions.

Storing the Data and Analysis Results in a Database (207): Finally, the collected and processed data, along with the analysis results, are securely stored in a database. This allows for future reference, longitudinal studies, and comparative analysis across different patients or treatment cycles. The stored data is also useful for improving machine learning models over time.

Method of Performing an Invention:

The system incorporates several novel embodiments that set it apart from existing technologies:

An AI-driven predictive model that combines real-time sensor data with historical patient information to forecast ointment efficacy with unprecedented accuracy.

A self-learning calibration system that automatically adjusts to new ointment formulations, expanding the system's applicability without manual intervention.

A multi-modal sensor fusion module that integrates data from various sensor types to provide a holistic view of ointment behaviour on and within the skin.

An adaptive sampling rate mechanism that adjusts sensor reading frequency based on detected changes in absorption patterns, optimizing data collection and power usage.

A personalized ointment formulation recommendation engine that suggests optimal ingredient combinations based on individual patient data and treatment goals.

A virtual skin model generator that creates digital twins of patient skin, allowing for simulated testing of ointment efficacy before physical application.

An augmented reality (AR) interface that overlays absorption data onto real-time images of the patient's skin, providing an intuitive visualization of ointment behaviour.

A blockchain-based data sharing protocol that allows secure, anonymous sharing of ointment performance data across research institutions and pharmaceutical companies.

The optimal method for utilizing this AI-enhanced system for analysing ointment absorption and penetration patterns involves the following steps:

• Patient Preparation: Clean the target skin area and allow it to equilibrate to room temperature.

• Sensor Array Application: Apply the flexible sensor array to the skin area where the ointment will be used. Ensure proper adhesion and sensor contact.

• System Initialization: Power on the system and allow it to perform initial calibration and skin parameter baseline measurements.

• Ointment Application: Apply the ointment as prescribed, ensuring even coverage over the sensor array area.

• Data Collection: The system automatically begins collecting data from the sensor array at pre-set intervals.

• Real-time Monitoring: Healthcare professionals can monitor the absorption and penetration patterns in real-time through the user interface.

• AI Analysis: The AI module continuously analyses incoming data, providing predictions and insights on ointment efficacy.

• Adjustments and Optimization: Based on AI recommendations, clinicians can adjust ointment application methods or formulations to optimize treatment outcomes.

• Data Storage and Comparison: All collected data is securely stored in the database for future reference and comparative analysis.

• Long-term Analysis: Over multiple treatment sessions, the system builds a comprehensive profile of ointment performance for each patient, enabling increasingly personalized and effective treatments.

This method ensures the most accurate and insightful analysis of ointment absorption and penetration patterns, leveraging the full capabilities of the AI-enhanced system to improve patient outcomes and advance pharmaceutical research.
, Claims:1. An AI-enhanced patch system (101) for analysing ointment absorption and penetration patterns, system comprising:
a) a sensor array (102) configured to collect real-time data on ointment application and skin interaction;
b) an AI module (103) utilizing machine learning module (105) to process and analyse data from the sensor array;
c) a user interface (104) for displaying analysed data and insights;
d) a microprocessor (106) for coordinating system components and data flow;
e) a database (107) for storing historical ointment performance data; and
f) a calibration module (108) for maintaining system accuracy across different ointment types and skin conditions;
wherein the system provides real-time, personalized analysis of ointment efficacy based on individual patient characteristics and environmental factors.

2. A method for analysing ointment absorption (200) and penetration patterns using an AI-enhanced patch system, comprising the steps of:
a) applying a sensor array to a target skin area (201);
b) collecting real-time data on skin parameters before, during, and after ointment application (202);
c) processing the collected data using machine learning modules (203);
d) analysing the processed data to determine ointment absorption and penetration patterns (204);
e) generating personalized insights and recommendations based on the analysis (205);
f) displaying the results through a user interface (206); and
g) storing the data and analysis results in a database for future reference and comparative studies (206).

3. The AI-enhanced patch system for analysing ointment absorption as claimed in claim 1, wherein the sensor array (102) comprises hydration sensors, temperature sensors, pH sensors, spectroscopic sensors, and pressure sensors.

4. The AI-enhanced patch system for analysing ointment absorption as claimed in claim 1, wherein the AI module (103) employs convolutional neural networks, recurrent neural networks, random forest modules, and gradient boosting machines for data analysis and prediction.

5. The AI-enhanced patch system for analysing ointment absorption as claimed in claim 1, wherein the user interface (104) includes real-time absorption graphs, skin parameter dashboards, efficacy prediction tools, comparative analysis features, and an alert system.

6. The AI-enhanced patch system for analysing ointment absorption as claimed in claim 1, wherein the calibration module (108) includes automated sensor calibration, ointment-specific calibration profiles, environmental compensation, and user-initiated calibration options.

7. The AI-enhanced patch system for analysing ointment absorption as claimed in claim 1, further comprising a virtual skin model generator that creates digital twins of patient skin for simulated testing of ointment efficacy.

8. The AI-enhanced patch system for analysing ointment absorption as claimed in claim 1, further comprising an augmented reality interface that overlays absorption data onto real-time images of the patients skin.

9. The method for analysing ointment absorption patterns as claimed in claim 2, further comprising the step of adjusting ointment application methods or formulations based on AI-generated recommendations to optimize treatment outcomes.

10. The method for analysing ointment absorption patterns as claimed in claim 2, further comprising the step of securely sharing anonymized ointment performance data across research institutions and pharmaceutical companies using a blockchain-based protocol.

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