AI Systems for Skin Analysis

A system for monitoring skin health at home and providing personalized skin care advice by analyzing interim images and compressing the results into a synoptic representation of skin characteristics. The system uses machine learning techniques to analyze user-captured skin images and extract relevant information about skin health. This compressed representation is then transmitted to a dermatologist who can quickly understand the skin concerns and provide targeted advice without needing to review the full image dataset.

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Skin health visualization method, device and equipment based on big data analysis to improve the accuracy of skin health analysis and visualization by using pixel-level classification. The method involves enhancing skin images, extracting detailed features, calculating a global dependency graph, fusing detailed and semantic features, categorizing pixels based on skin health factors, and visualizing skin health using the categorized pixels. This allows pixel-level skin health assessment and visualization rather than just overall skin health.

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Diagnosing skin conditions using 2D images captured with a smartphone camera. The system involves processing the captured images to diagnose wrinkles, pores, skin tone, and pigmentation. It uses machine learning to analyze the images and derive diagnosis results. The image processing steps include resizing, brightness equalization, segmentation, feature extraction, binarization, polarization, and score calculation. The system detects oiliness using brightness differences, keratin using lack of pigmentation, and pigmentation using semantic segmentation.

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Skin diagnosis system that accurately diagnoses skin conditions using deep learning and compensated detection of multiple skin diseases. The system involves training separate deep learning models for different skin diseases. To improve diagnosis accuracy, the models learn by intersecting areas detected by each model on real images and extracting areas where the intersection exceeds a threshold. This provides valid areas for learning. By compensating for each disease's area detection, the models can better diagnose the multiple skin diseases simultaneously.

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System for early detection and classification of skin diseases using deep learning. The system involves capturing real-time images of a subject's skin using a camera, preprocessing the images, augmenting them, applying deep learning models, and using performance metrics to classify skin diseases. The preprocessing steps include resizing and training. Augmentation involves techniques like scaling, cropping, rotation, and transitions to create additional training data. The deep learning models are trained using labeled skin disease datasets. The system leverages custom layers and unique activation functions for improved accuracy. It compares captured images to public databases for diagnosis.

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Using deep learning to automatically detect and classify skin conditions from images. The method involves extracting features from images of skin lesions and combining them with textual input like symptom descriptions to generate combined vectors. These vectors are compared with stored vectors associated with known skin disorders to identify and classify the condition. The system uses a trained learning module with a repository of labeled images to classify skin disorders.

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Objectively determining the state of living tissue like hair or skin using machine learning. The method involves acquiring an image of the tissue with a demarcated region surrounded by a boundary. Features like size and shape of the region are calculated. A machine learning model is used to recognize the region and another model determines the tissue state based on the feature values. This provides objective tissue condition assessment compared to subjective visual diagnosis.

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Analyzing skin diseases using AI and machine learning techniques to improve accuracy compared to visual diagnosis by human dermatologists. The method involves using a trained neural network model to analyze skin images, considering factors like past medical history, environmental trends, and changes between multiple images. The model learns connectivity between layers and neurons in the neural network. The analysis results are outputted to provide skin disease diagnosis based on the images.

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Skin condition warning system for auxiliary follow-up consultation that enables real-time monitoring of skin conditions, dynamically adjusting treatment regimens, and improving follow-up efficiency for dermatology patients. The system uses image processing techniques to analyze skin conditions from patient-submitted images. It extracts features like acne scars and cyst size, calculates severity scores, and provides recommendations to doctors for targeted treatment. By leveraging patient-submitted images, the system allows remote monitoring and follow-up without requiring in-person visits.

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AI-based skin disease diagnosis system that uses deep learning to accurately identify skin conditions. The system acquires images from users and compares them against learned skin disease classes using CNNs. It provides users with the identified disease and allows re-learning from new images. The system uses a convolutional neural network (CNN) to classify skin diseases based on learned classes. It compares user-provided skin images against the learned classes to identify the disease. The system also allows re-learning from new images.

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AI-based skin disease diagnosis device that uses convolutional neural networks (CNNs) to learn and diagnose skin diseases. It acquires skin disease images, classifies them using CNNs, learns and stores the classes, then diagnoses new images using the learned classes to determine if they are skin cancer, skin pigment disease, infectious disease, or normal skin. The device can run on smart devices after learning. By learning and storing skin disease classes, it improves accuracy compared to just applying CNNs.

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A neural network system for accurately diagnosing skin conditions using images and metadata. The system receives images of an affected skin area along with metadata about the patient and symptoms. It processes the images using a separate neural network to classify the skin conditions. An input array is created containing the metadata and the image classification confidence values. This array is then fed into a primary neural network to diagnose the skin condition. This allows using limited images by leveraging metadata and trained image classifiers.

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Analyzing pixel data of an image of a skin area of a user for determining skin roughness using AI. The method involves training a skin roughness model with images of individuals' skin to output roughness values. When a user's skin image is analyzed, the model determines the user's skin roughness and generates recommendations to address identified rough areas.

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Determining suitability of a skin analysis image before actually analyzing the skin to provide consistent and accurate skin analysis results. The method involves checking if the image is appropriate for skin analysis by analyzing factors like orientation, blurriness, brightness, local lighting, and makeup level. If the image fails certain criteria, it indicates unsuitable conditions like poor focus, brightness, or lighting. The server instructs re-photographing for better images. This prevents analyzing inappropriate images that can lead to wrong skin analysis results.

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Method for diagnosing skin conditions using artificial intelligence that involves preprocessing skin images for diagnosis. The method involves training an AI model to recognize groups of skin images based on features like hair, patches, and backgrounds. Then, when diagnosing new skin images, the model groups them based on these features and applies appropriate preprocessing for each group. This allows more accurate diagnosis from varied skin images.

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Analyzing skin features to help people understand how others perceive their skin issues. It involves detecting skin features in images, determining how much the person interacts with each feature (e.g., looking, touching), and calculating a severity score based on that interaction. This score indicates how concerned the person is about the feature. Additionally, a social perception score is calculated for each feature based on how others typically view similar features. The severity score can then be compared to the social perception score to provide a more accurate understanding of how visible the feature is to others. This helps people better assess whether their concern about a skin issue is justified or excessive.

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Skin type determination method that allows for improvement in the accuracy and integrity of skin score data by training an artificial intelligence module using labeled learning data. The method includes receiving, by an electronic device, a first image of a user's skin, and generating a second image by pre-processing the first image.

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Determining skin type and melanin index using images to enable personalized skin care and treatment. The method involves analyzing skin images to determine skin type and melanin index based on characteristics of the pigment network structure. It uses an algorithm trained on images annotated with skin type and melanin index to analyze new images. This allows assessing skin without contact devices. By identifying skin properties like melanin distribution and pigment network density, it provides personalized skin care without invasive devices. The method involves normalizing images to eliminate color and using polarized light to improve skin depth in images.

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Using machine learning models to diagnose skin diseases. The method involves obtaining images of a patient's skin, generating embeddings for each image using a first portion of a machine-learned model, combining the embeddings into a unified representation, and using a second portion of the model to classify the patient's skin condition based on the unified representation. The model can also process additional patient metadata alongside the images to improve diagnosis accuracy.

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Detecting skin conditions in a human subject using computer vision and machine learning to provide more complete and precise skin condition detection compared to existing methods. The system extracts significant aspects like pore size, wrinkles, acne, etc. from images, generates normalized skin characteristic data, and predicts skin conditions like texture, tone, translucency. The predictions are displayed to the user. The method involves extracting object/significant aspect data, generating normalized skin characteristic data, and predicting skin conditions using machine learning models.

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Extracting abnormal skin regions from rare skin diseases using a trained region determination model. The model is trained using images of rare abnormal skin areas along with target objects. This increases sample data compared to just using rare skin images. The model has sub-models for feature extraction from abnormal skin and target objects. By analyzing features from both, it improves accuracy of extracting abnormal skin regions.

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Method for accurately detecting skin diseases using AI and computer vision techniques to improve diagnosis accuracy compared to traditional methods. The method involves analyzing skin images of objects to determine the specific skin lesions and attributes. This is done by identifying the lesion area, determining the lesion type based on features and object type, and matching against a database of lesions. The matched lesion data provides the disease diagnosis. By focusing on the lesion area and attributes, it provides more accurate disease identification compared to just analyzing the overall skin image.

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Automated system for objectively analyzing and classifying skin tones using machine learning to augment analysis, diagnosis, and treatment of skin conditions. The system receives an image of a patient's skin tone, calibrates it using a reference profile, and determines the patient's skin tone. It then selects machine learning diagnostic models based on the patient's skin tone to analyze images of skin concerns. This aims to provide more accurate and relevant diagnoses for patients with diverse skin tones compared to generic classification systems.

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A skin condition measuring system that improves the accuracy of skin diagnosis by capturing images of both the user's body and a specific area of their skin. The system uses two imaging devices, one to capture the user's body and another to capture a smaller area of their skin. By comparing the images, the system derives more detailed and accurate information about the user's skin condition compared to just imaging their body. This allows identifying areas with potential issues, comparing body and spot results, and tracking changes over time.

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A method for examining skin conditions using color space conversion to increase clinical applicability. The method involves capturing a skin image, decomposing it into an RBX image, and determining skin conditions like redness, whitening, or flecking based on parameters of the RBX color model. This conversion enables more accurate skin analysis compared to regular RGB images.

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Automated skin condition diagnosis using images and user data. The system identifies skin conditions from images and other user data using machine learning. It combines visual classification with questions that encode expert knowledge. The images are processed locally or remotely. The system trains a visual classifier on a dataset of skin images. Medical collaborators review images and assign labels with confidence. The collaborator labels are combined across experts to increase robustness. The system also collects user data like age, gender, body location. It asks questions and uses answers to refine diagnosis. The questions encode dermatologist knowledge. The system leverages online machine learning to continuously improve.

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Skin image analysis system using machine learning for improving accuracy of diagnosis by databaseizing skin images from hospitals and sharing them. The system receives skin images, analyzes them using machine learning models, generates analysis results, and provides a user interface to view the images and results. It also allows sharing of images and results between users. This enables accumulation and sharing of analysis results to improve diagnosis accuracy.

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A skin condition measurement system that improves the accuracy and usefulness of skin analysis by combining images from a handheld skin analyzer with full-body images. The system uses two devices, an imaging device like a smartphone to capture full-body images, and a skin analyzer to capture close-up images of specific areas. It extracts skin condition data from both images, associates the partial data with the full-body area, and displays the composite results to the user. This provides more detailed and accurate skin analysis than just using full-body images.

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Method and system for analyzing skin type using multiple skin images. The system involves capturing multiple skin images using a handheld device that measures the camera's inclination. The images are rotated to align the inclination angles. The rotated images are integrated to create a composite skin image. This composite is analyzed using a neural network to determine the overall skin type. This allows analyzing skin type by integrating multiple images rather than just analyzing isolated areas.

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Method and system for identifying skin conditions and determining the features that distinguish them. The method involves using a convolutional neural network to classify skin conditions, then training a second network to predict the feature importance for each class. This second network is trained on labeled data with pseudo-labels generated from the first network. The feature importance provides interpretability and insight into the classification decisions.

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An image processing method and device that analyzes user selfies to determine skin condition and provide personalized advice. The method involves capturing a user's first selfie, analyzing it to determine the user's initial skin condition score, and then comparing that score to historical scores to determine skin condition trends. Based on the trends, the device generates skin condition contrast images showing the user's score over time, along with targeted advice. This allows users to see their skin improvement/deterioration visually and get tailored recommendations.

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A method for identifying skin diseases using image processing that improves accuracy compared to traditional methods. The method involves preprocessing the skin image to enhance relevant features and simplify data. Then, a pre-trained image recognition model is used to identify the skin disease categories and sites. Screening the sites determines the target area. The categories are sorted based on probability. Finally, the categories are filtered using the target site to identify the specific skin disease.

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Evaluating skin texture using the size and shape of skin masses in an image to provide a more direct and cost-effective way to assess skin health compared to 3D imaging. The method involves determining the area and shape features of skin masses in an image, such as total area, average area, number, side length, direction, and angle. These measurements are used to evaluate skin texture based on the size and distribution of skin wrinkles.

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Automated analysis, diagnosis, and treatment planning for skin conditions using AI. The system analyzes user-provided skin images to determine skin color and characteristics. It also diagnoses skin issues based on images and user data. The AI-based diagnosis is personalized using medical records and biometric info. Treatment plans are also generated based on diagnosed conditions. The system provides tailored treatment options considering user history and progress.

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A method for objectively evaluating facial skin quality using computer vision techniques that avoids subjectivity and external condition sensitivity compared to manual evaluation. The method involves steps like face detection, brightness normalization, acne and wrinkle detection using local binary pattern features, and histogram equalization. It aims to provide consistent and objective skin quality assessment using machine vision instead of manual methods.

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Estimating skin loss and moisture levels using image AI to enable objective, device-based skin analysis without specialized equipment. The method involves capturing skin images with a device camera, normalizing them, determining whether to analyze the whole image or a specific region, applying visual AI analysis using convolutional neural networks to estimate skin loss and moisture levels, and outputting the results. The AI analyzes texture, color, etc. to assess skin health.

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A skin disease classification system that uses convolutional neural networks (CNNs) to accurately diagnose skin conditions from images. The system involves a device that learns to classify skin diseases like cancer, pigmentation, infection, and normal skin using a CNN. It stores this learned information. When a user provides a new skin image, the device determines the user's skin condition using the CNN and provides the diagnosis to the user. It also sends the new image to the learning module to further train the CNN. This iterative training improves the diagnosis accuracy over time.

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Accurately classifying skin diseases using artificial intelligence that overcomes the limitations of prior methods that only classify severe diseases. The method involves extracting skin disease image features, matching to location info, learning the combined data, and using it to classify new images. This allows faster, more accurate classification compared to just learning lesion images. Specific skin diseases with similar lesions are professionally learned with dermatologist input to improve accuracy.

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Determining changes in skin conditions over time using images to aid diagnosis and treatment. Multiple images of an area are captured at different times. Changes in size, shape, color, uniformity of objects in the images are determined. This is used to diagnose skin conditions like acne and determine severity, number of lesions, progression, etc.

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Using image analysis from smartphones to diagnose skin conditions and monitor wounds without specialized equipment. The method involves capturing images of skin features over time with colorized surfaces nearby. The images are analyzed to track changes in skin segment colors. This allows diagnosing skin conditions and recommending treatment based on time-lapse analysis of skin segment colors.

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Detecting skin roughness using a mobile device camera to provide a low-cost, portable skin analysis tool. The method involves extracting texture features from grayscale images of skin areas to determine roughness. Features like texture depth, width, density are extracted from image blocks. Connected domain analysis is used to find the texture regions. This provides a more accurate and stable roughness assessment than simple thresholding.

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Accurate classification of skin abnormalities using artificial intelligence. The method involves using both single-point and area-level detection models to classify skin abnormalities. A skin image is first processed using single-point and area detection models to identify the abnormal regions. Then, the single-point model classifies the abnormal region and the area model classifies the entire abnormal region. The results are combined to determine the overall skin abnormality category. This two-stage detection improves accuracy compared to just using single-point or area models.

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Method and program for evaluating skin condition using AI to determine skin attributes and grades from images. The method involves generating feature vectors from skin images, checking attribute feature vectors against trained recognizers, and mapping those to skin grades using another recognizer. This allows assessing skin health without dermatologist visits or specialized devices.

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Intelligent method and system for identifying dry skin states based on facial partitions that avoids the issues with existing methods like relying on specialized equipment and accuracy issues due to individual differences. The method involves partitioning facial images into regions, extracting relative feature parameters from each region, and analyzing the feature parameter differences between regions to accurately predict dry skin states. By leveraging regional differences in moisture content rather than overall texture, it avoids issues with variations in skin texture due to factors like race, age, gender, and genetics.

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Portable skin detection system using Bluetooth Low Energy (BLE) technology to enable compact, low-power, wireless skin analysis without the limitations of bulky, expensive dedicated devices. The system has separate collection, analysis, and storage modules. Images are compressed, encrypted, and wirelessly transmitted via BLE from the collection module to the analysis module. The analysis module decrypts and decompresses the images, uses a skin analysis algorithm, and returns results. The storage module can save the images and analysis data. This allows portable skin analysis using a mobile device with BLE capability instead of dedicated equipment.

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Method to evaluate skin conditions using image analysis and learned skin recognizers. It involves determining attribute values like skin color, wrinkles, and flatness from images, generating feature vectors, and using a trained skin recognizer to determine skin grades. The recognizer correlates feature vectors to grades based on labeled training images. The method allows assessing skin conditions from images without specialized equipment.

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Smart skin disease identification platform using AI deep learning to diagnose skin diseases from images. The platform learns skin diseases from images and continuously improves diagnosis accuracy as new images are provided. It uses convolutional neural networks (CNNs) to extract features from skin images and classify them into skin cancer, pigmentation, infection, or normal skin. User authentication allows personalized diagnosis. The platform provides real-time skin disease determination and re-learns from new images. It aims to improve skin disease diagnosis using AI and provide a service platform for skin disease diagnosis apps.

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Using artificial intelligence to identify novel targets and treatment strategies for skin conditions like acne. The AI analyzes gene expression data and molecular networks to predict effective targets and compounds for treating acne. It also identifies natural products that could be used in skin care. The AI-based approach aims to provide new and more effective ways to develop treatments for skin conditions like acne.

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Analyzing user's facial images before and after skin treatments to provide a visual representation of skin improvement. The method involves extracting skin analysis objects from the images using reference areas like eyes, nose, mouth, and forehead. The skin analysis objects are then analyzed to generate images showing skin changes like wrinkles or blemishes. These images can be overlaid on the original images to visually demonstrate skin improvement.

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Automatic image-based skin diagnostics using deep learning to decode skin conditions from images without human input. A convolutional neural network (CNN) trained on skin sign data is used to classify pixels of an input image and determine diagnoses for multiple skin signs. The CNN is trained using skin sign data for each sign. The system can accurately diagnose signs like wrinkles, sagging, pigmentation, and vascular disorders from images without human intervention. It outperforms expert dermatologists in diagnosing facial signs from unconstrained images. The CNN can also monitor skin treatment progress over time and recommend products based on skin sign diagnoses.

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