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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Smart dandruff analysis system using deep learning to automatically determine the severity of a person's dandruff by analyzing images of their scalp. The system uses a convolutional neural network (CNN) to extract features from the scalp image and a transformer network to further process those features. The transformed features are then used to classify the dandruff severity. This allows automated, low-cost analysis compared to manual dandruff assessment by professionals.

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Using microscopic imaging of hair strands to provide personalized hair care assessments and recommendations. The method involves capturing images of hair strands using a microscope attached to a smartphone or standalone device. Analysis of the images determines hair characteristics like texture and condition. This data, along with user inputs, is used to recommend hair care products, regimens, and styling techniques tailored to the user's hair needs. The system leverages machine learning and artificial intelligence to accurately categorize hair attributes from the images and make product matches.

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Analyzing hair images using a trained model obtained by machine learning to determine hair attributes and conditions for each small section of hair. The method involves extracting patches of hair from evaluation and teacher images, normalizing pixel values, and feeding them into a learned model to obtain attribute/condition indexes for each patch. This allows analysis of hair characteristics across images without being affected by variations in lighting, camera angle, etc. The model is trained using teacher hair images with known attributes and patch groups.

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Portable device and method for accurately detecting and identifying scalp oil levels using an image acquisition device, wireless communication, and image processing. The device has a housing with an optical assembly and camera. It acquires scalp images using LED lighting and sends them to a smart terminal. The images are processed to convert from RGB to HSV color space, median filter the saturation and brightness, and analyze features to identify oil levels.

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A scalp diagnosis system using AI to accurately diagnose scalp conditions and recommend improvements based on scalp image analysis. The system involves transmitting scalp questionnaire data and images to a server. An AI processor analyzes the images and questionnaire to diagnose scalp types like dry, oily, sensitive, etc. A main processor checks the results in real time. This shared image analysis reduces load compared to uploading images separately. The system recommends products based on diagnosed scalp types.

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Method for selecting the best scalp image from multiple captured images to improve scalp analysis accuracy and speed. The method involves comparing quantitative pore area metrics from two images and selecting the one with more pores as the analysis target. This helps avoid issues with low quality images and provides a more representative scalp image for diagnosis. The quantitative pore area is calculated by initial pore detection followed by correction using location verification.

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Analyzing user-specific skin or hair data using AI to predict skin or hair conditions and provide personalized treatments. The method involves training an AI model with scalp/hair data from multiple users to predict features like sebum levels, dryness, oiliness, etc. Users input their own scalp/hair data, which the AI uses to generate personalized treatments addressing the predicted features.

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Method for analyzing scalp health using images. The method involves acquiring an image of a user's scalp, identifying a region with visible hair and scalp, counting the number of hairs in that region, calculating an average hair thickness based on pixel counts, and outputting the hair data. This provides a simple way to analyze scalp hair health using just an image.

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Hair recognition method, device, equipment and storage medium based on dermoscopy images that improves the accuracy of hair detection in dermoscopy images. The method involves binarizing the dermoscopy image and using a hair region search algorithm on the binarized image to preliminarily determine hair areas. Then, the hair region search algorithm is applied again to the binarized image to refine the hair region detection. This two-step process avoids missed detection of fuzzy hair regions in the original dermoscopy image.

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Image processing device and method for analyzing body hair images to extract practical information about hair growth and orientation. The device acquires an image of body hair and analyzes it to identify the orientation of individual hairs. This allows determining hair growth direction, which can provide insights into hair removal techniques like shaving or waxing. The orientation analysis can also help optimize hair styling tools like razors and trimmers.

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Method and apparatus for AI-based hair loss prediction and personalized scalp care. The system involves analyzing scalp images using AI to diagnose scalp conditions and predict hair loss. It provides customized scalp care recommendations based on the analysis. The device also allows controlled application of scalp treatments to specific areas using a multi-button remote. This allows targeted application of different ampoule amounts and needle lengths to match the diagnosed scalp conditions.

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A method for accurately recognizing and segmenting hair in images using difference images to improve recognition stability. The method involves acquiring hair images with different properties like lighting conditions, then calculating the difference between those images. The resulting difference image is used for hair segmentation and recognition since background areas have different absorption for the different properties. This improves segmentation accuracy compared to using a single image.

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Artificial intelligence-based hair loss management system that objectively and accurately diagnoses hair loss severity for personalized treatment recommendations. The system uses AI to analyze images of a user's scalp to segment hair and scalp regions. It quantifies hair loss based on the segmentation results and provides customized hair loss management solutions tailored to the user's level of hair loss. The AI model is periodically trained and optimized.

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Scalp image analysis system that can automatically select an optimal image for calculating hair loss diagnosis auxiliary information. The system uses a neural network model to analyze scalp images and extract pore area information. It then compares the pore density in different images to select the one with more pores as the target for hair loss analysis. This improves accuracy by using a representative image instead of relying solely on the operator's perspective.

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Extracting image features for intelligent hair loss diagnosis and prognosis prediction using machine learning. The method involves acquiring measurement images of hair from a device, generating characteristic images using machine learning techniques, verifying the characteristic images, and repeatedly learning the verified images to create standard data for intelligent hair loss diagnosis and prognosis prediction. The measurement images include both wide-angle and close-up views of the scalp. By classifying and segmenting the close-up images to extract hair density and thickness, subjective factors in hair loss treatment can be minimized.

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A system for accurately determining the severity of hair loss using image analysis that reduces subjectivity and provides objective data for hair loss diagnosis and treatment. The system involves generating standard data for hair loss diagnosis and prognosis prediction using gross and magnified images. This standard data is used to label hair loss severity. The labeled data is then re-labeled using decision trees to improve accuracy. The re-labeled data is used to update the standard data. This iterative process improves the accuracy of hair loss severity determination over time.

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Detection algorithm for scalp health that doesn't require interaction or large data sets compared to existing methods. The algorithm accurately segments hair in macro images and calculates scalp health dimensions. It preprocesses images with noise removal, color conversion, and illumination compensation. Segmentation uses iterative structure from motion to find hair lines and match them against a hair line model.

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Accurately segmenting the area for hair transplantation and then providing intelligent guidance for the procedure based on analyzing images of the hair follicles. The method involves capturing multiple angles of the target area, preprocessing the images to enhance features, recognizing hair follicle boundaries, identifying follicle density, and using a model to evaluate follicle health. This data is used to plan the transplant.

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Method for analyzing hair conditions using scalp images to provide hair diagnosis information like number of hair roots, number of hairs, hair density, and hair thickness. The method involves using machine learning techniques to detect hair roots, classify the number of roots, visualize root patterns, calculate hair count and density based on root count, and automatically measure hair thickness using the root detector.

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An AI scalp diagnosis system that accurately diagnoses scalp conditions using images and questionnaires, and recommends suitable products. The system involves receiving questionnaire data and scalp images through APIs, transmitting images to an AI processor for diagnosis, using accumulated image data to classify diagnosis items, and diagnosing with deep learning algorithms. This allows real-time, accurate scalp diagnosis using AI analysis of images. Based on the diagnosis, products are recommended for the diagnosed scalp conditions.

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Artificial intelligence-based hair and scalp diagnostic system that uses IoT devices and AI to diagnose hair and scalp conditions and recommend treatments. The system collects hair and scalp data from IoT devices, processes it using AI to identify problems and recommend treatments, and displays the results to users. It involves capturing hair and scalp data using IoT devices, identifying characteristic information and determining identifiers, and processing the data using AI models and logic to analyze hair and scalp conditions and recommend treatments.

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Digital imaging method to analyze user body images to determine hair density and provide personalized grooming recommendations. The method involves training a hair density model using images of users with known hair densities. The model analyzes new user images to determine their hair density. Based on the density, it recommends products, behaviors, and styles tailored to the user's hair.

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Digital imaging system that analyzes user images to predict trapped hair before hair removal and recommend products to reduce trapped hair. The system trains a trapped hair model using images of people before hair removal to associate trapped hair levels with pixel data. It then analyzes a user's image to determine their trapped hair value. Based on that, it generates recommendations like using specific products or techniques to address the trapped hair issue. The recommendations are displayed to the user along with annotated images.

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Hair analyzing device worn on hair to continuously monitor hair conditions like moisture, sebum, temperature, and humidity. The device has sensors to detect these parameters and an electronics module to process the data. The device can transmit the sensor values to a display device to provide recommendations for hair care products, styling techniques, and treatments based on the analyzed hair conditions.

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Scalp management system using panoptic segmentation technology to provide personalized scalp analysis and care services. The system involves capturing an image of a user's scalp, dividing it into classes for analysis, performing panoptic segmentation that combines instance segmentation and semantic segmentation, deep learning to analyze the results, and using that to provide customized scalp care. The panoptic segmentation allows identifying and analyzing the location of each individual hair, keratin, redness, and oil to provide tailored scalp care.

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Monitoring scalp health using a smart hair dryer with integrated camera to capture head images while drying hair. The images are analyzed to identify scalp regions, extract relevant parameters like redness or hair density, and compare with historical data to diagnose scalp conditions. This allows proactive scalp care and prevention by identifying issues before they become severe.

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Self-scalp diagnosis system that guides consistent image acquisition and quantitative analysis to objectively diagnose scalp conditions. The system uses a separate scalp camera and front camera on a device to distinguish face and hair areas. It sets consistent inspection regions on the scalp by having the user part their hair. This provides accurate and comparable scalp analysis over time.

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Hair analysis system that uses AI to accurately assess hair conditions for a wide range of hair types and styles. It captures an image of a user's hair, analyzes it using a trained neural network, and provides insights like hair prediction, product recommendations, and styling suggestions based on the analyzed hair condition. The neural network is trained on images with micro hair features to generalize to real-life hair conditions.

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Automatic hair follicle separation device that screens for defective follicles during hair transplantation. The device uses image analysis to determine if follicles are normal or defective. It captures images of extracted follicles, extracts outlines, compares to reference patterns, counts pixels, calculates percentages, and sets thresholds to classify as normal or defective. This allows automated sorting of follicles for transplantation to improve graft success by removing damaged ones.

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A method for quickly and accurately analyzing hair condition using artificial intelligence and computer vision. The method involves capturing a user's hair image, preprocessing it to extract basic hair analysis data like cuticle region counts, interval and size standard deviations, and average values. This data is then fed into an AI neural network model to output a hair damage classification. By analyzing hair features like cuticle spacing and size, the AI can accurately determine hair health.

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