Head Ailment AI for Scalp Segmentation and SALT Scoring
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Solution Overview
Problem
Current diagnostic systems lack a standardized and objective method for quantifying hair loss in conditions like Alopecia Areata, leading to inconsistencies and observational biases in clinical research and treatment.
Innovation Solution
A compute system utilizing a multi-task deep learning model for scalp segmentation and hair loss heat map generation, followed by a quadrant computation to calculate the Severity of Alopecia Tool (SALT) score, providing a standardized and objective scoring system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic methods are used for hair loss assessment, then clinical evaluation can be performed, but measurement precision and objectivity are insufficient leading to observational biases
Solution Approach 1:
The patent replaces manual clinical assessment with an automated deep learning system that processes scalp images. The multi-task decoder block with U-Net architecture automatically segments the scalp and generates heat maps to quantify hair loss, eliminating human observational bias and improving both measurement precision and reliability through consistent algorithmic evaluation.
2Measurement precision
If multiple separate models are used for scalp segmentation and hair loss analysis, then task specialization is achieved, but device complexity and inference time increase
Solution Approach 1:
The patent combines scalp segmentation and hair loss quantification into a single multi-task deep learning model. The shared encoder and multi-task decoder block simultaneously perform both functions, reducing device complexity by eliminating the need for separate processing models while maintaining high measurement precision through integrated feature extraction and analysis.
Solution Approach 2:
The deep learning model is designed with multi-functionality to handle both scalp segmentation and hair loss analysis within a single architecture. The U-Net based decoder block processes images to generate both segmentation masks and heat maps, allowing one model to perform multiple diagnostic tasks that would traditionally require separate specialized models.
3Measurement precision
If comprehensive hair loss analysis across all scalp regions is performed, then measurement completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent divides the scalp analysis into four distinct quadrants using the multi-task decoder block. This segmentation approach allows the system to process and evaluate each region independently and efficiently, then aggregate results to provide comprehensive overall assessment. The quadrant-based method reduces computational complexity compared to analyzing the entire scalp as a single region while maintaining complete coverage.
Data Source
AI summary
A method of operation of a compute system includes: receiving patient images by an head ailment artificial intelligence (AI) including a multi-task decoder block; concurrently generating a scalp segmentation and a hair loss heat map from the multi-task decoder block of the head ailment AI; generating a composite hair loss image based on the hair loss heat map and a quadrant computation of the scalp segmentation; and generating a SALT score based the hair loss heat map and the composite hair loss image for displaying on a device.