Algorithmic Methods for Trichological Analysis

AI-powered diagnostic system for scalp and hair loss conditions that provides personalized diagnostic reports based on scalp images. The system uses two trained machine learning models. The first model classifies scalp conditions and hair loss based on image features. The second model generates diagnostic scripts using the classification results. The system collects labeled scalp images and expert reports, trains the models on them, and uses them to analyze new images and output personalized diagnostic reports.

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Scalp and hair detection device and system using image recognition to objectively and accurately detect scalp and hair conditions. The system collects images of the scalp and hair using a device, recognizes features using a model, and inputs the recognized features into a scalp/hair state recognition model to output results. This improves on manual interpretation by using image analysis to obtain scalp/hair state data.

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Using smartphone attachments with AI to accurately assess hair and scalp health by analyzing user-captured head images. The attachments capture images, send them to AI for feature analysis, apply rules, and provide health scores. It provides an accessible, portable, and cost-effective way to determine hair and scalp health compared to bulky, expensive equipment.

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Scalp and hair detection method, system, and device that uses AI to objectively and accurately analyze scalp and hair health by leveraging a custom deep learning model called MobileHairNet. The method involves acquiring different scalp and hair images, passing them through the MobileHairNet model to identify scalp and hair attributes, and then mapping the identified attributes to scores using a constructed score mapping function to provide intuitive feedback on scalp and hair conditions. The custom MobileHairNet model uses a modified MobileNet architecture with skip connections and adaptive sampling to enhance feature fusion.

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Digital imaging and learning system that analyzes scalp images to generate user-specific scalp classifications. The system uses a trained scalp-based learning model accessed by an imaging app. The model is trained on images of scalp regions from multiple individuals. When a user provides an image, the app analyzes it through the model to classify features like white residue, blockages, or sores. This user-specific classification helps address scalp issues identified in the image.

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System and method for classifying hair follicles and estimating hair loss severity using deep learning. The system extracts circular regions representing hair follicles from magnified scalp images. It then simultaneously determines the hair follicle condition and estimates hair loss severity for each extracted region using a multitasking deep learning model. This allows accurate and efficient hair follicle classification and scalp-level hair loss estimation. The estimated hair loss is visualized and quantified for the entire scalp.

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Counting hair follicles using deep learning instead of extracting direct features like shape and color. The method involves using a deep neural network to generate a heatmap that indicates the probability of hair follicles at each pixel in an image. This heatmap can be used to accurately count hair follicles without relying on specific hair characteristics that can vary significantly between individuals. The heatmap is generated by processing the raw image through a series of convolutional layers, normalization layers, and activation layers in the neural network.

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Objective and automated method for determining the condition of biological tissue like hair or skin by analyzing images. The method involves identifying a region surrounded by a boundary line in the tissue image, calculating features like size and shape, and using a machine learning model to determine the tissue condition based on those features.

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Scalp detection system using AI to accurately identify various scalp conditions. The system involves a handheld scalp imaging device, a server with scalp recognition modules, a display, and a storage platform. The device captures scalp images which are sent to the server for identification using multiple AI modules for symptoms like gray hair, dandruff, fungus, etc. The results are displayed and stored for monitoring scalp health over time.

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Deep learning-based scalp health detection method and devices to improve accuracy of scalp analysis using refined strategies for different user types. The method involves segmenting user's scalp into specific regions based on their type, encoding images of those regions, classifying encoded images, fusing 3D point clouds, detecting overall scalp abnormality, and analyzing per-region abnormalities. This customized approach leverages deep learning models for scalp analysis to better match users' needs.

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A hair and scalp diagnosis system using a scanner with multiple magnification levels to accurately diagnose hair and scalp conditions. The system scans the patient's hair and scalp at different magnifications in stages, compares the scanned output to a database, identifies the closest match to determine the condition, and recommends a treatment based on the diagnosis. The multiple magnification levels and staged scanning improves diagnosis accuracy compared to traditional single magnification scans. The system can also divide the head into zones and average the scanned output to further enhance efficiency.

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A system for managing hair loss and recommending hair products using scalp images. The system analyzes scalp images to determine scores for factors like keratin ratio, hair density, scalp sensitivity, oiliness, and moisture. It learns hair analysis models using these factors and bioresistance measurements. When a user provides a scalp image and resistance value, the system applies the models to calculate scores. Based on the scores, it recommends hair products like dry shampoo, oily shampoo, or tonic to address the user's hair loss condition.

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A hair image processing method and system that accurately extracts hair endpoints from images for applications like robotic hair removal. The method involves using a hair data processing model to analyze binary hair images. It first extracts the hair contours, filters noise, calculates average nearest distances, and identifies hair endpoints. An adaptive threshold based on nearest distance classification distinguishes single vs multiple hairs. This enables precise endpoint extraction without the limitations of thinning-based skeletonization.

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Generating learning data for scalp analysis models using machine learning to efficiently analyze user's scalp conditions. The method involves acquiring images of specific scalp regions, associating them with locations, processing those images, and using the processed data to generate learning data for scalp analysis models. This allows customizing scalp analysis models to match specific scalp issues.

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Portable device and method for accurately measuring hair density using polarized light. The device acquires scalp images with a polarizing lens to enhance contrast between hair roots and follicles. This allows more accurate hair density estimation compared to visual inspection. The images are analyzed to count hair follicles and roots, aiding in decision-making for hair care products, treatments, and nutrition.

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Automated hair follicle detection and counting from scalp images using image recognition to accurately and efficiently determine the number of hair follicles without manual counting. The method involves preprocessing scalp images, running a hair follicle detection model to find follicle centers, comparing scores to a threshold to keep or discard follicles, calculating distances between remaining follicles, merging adjacent ones, and counting merged follicles to get the total.

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Image-based scalp analysis and prescription method using a scalp scanner, machine learning, and big data to enable users to easily analyze and treat their scalp conditions. The method involves acquiring a scalp image, analyzing it to determine scalp health indicators, and recommending products or treatments based on the analysis. The scalp scanner captures images, which are sent to a server with big data and machine learning models to analyze hair count, thickness, oil, inflammation, etc. This data is used to diagnose scalp conditions and recommend products.

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A method to enable users to easily and accurately analyze their scalp health using scalp images. The method involves acquiring a user's scalp image using a scalp scanner, transmitting it to a server, checking and analyzing the image using machine learning, calculating scalp condition indices from the image, and transmitting the analysis results back to the user. The server uses big data and machine learning engines to learn scalp features and accurately determine health indicators.

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Analyzing scalp condition using a machine learning model that combines scalp images, sensing data, and medical examination data. The method involves: (1) Using a machine learning model trained on scalp images to generate first analysis data. (2) Referencing sensing data and medical examination data to generate second analysis data. (3) Combining the first and second analysis data to generate third analysis data. This provides a more accurate and comprehensive assessment of scalp condition by leveraging multiple sources of information.

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Portable device for accurately measuring hair thickness and identifying hair thinning using polarized light imaging. The device has a handheld unit with a camera and polarizing optics to capture images of hair under polarized light. An image processing center converts the images to grayscale, then identifies hair and scalp areas based on grayscale thresholds. This allows quantitative measurement of hair thickness and detection of thinning.

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