Dermascope Imaging Device Qualification Using Confidence Similarity
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Solution Overview
Problem
Qualifying dermascope imaging devices for use with image classification algorithms is costly and time-consuming, especially with the rapid release of new devices and software updates, making traditional clinical studies impractical.
Innovation Solution
A method using a similarity metric, such as mean confidence distance (MCD), is applied to compare confidence values from unqualified and qualified dermascope imaging devices, determining suitability through artificial distortions and transformations of images, eliminating the need for extensive empirical data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional clinical studies are used to qualify dermascope imaging devices, then reliability of device qualification is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates a virtual copy of the qualification process by using simulated distorted images instead of real clinical data. The classification algorithm is tested on artificially generated images that mimic various device characteristics, providing a time-efficient proxy for actual clinical studies while maintaining qualification reliability.
Solution Approach 2:
The patent performs preliminary qualification testing using simulated data before actual clinical deployment. By pre-testing the classification algorithm on distorted images that represent potential device variations, the system can identify unsuitable devices early without requiring extensive post-deployment validation.
2Measurement precision
If traditional clinical studies are used to qualify dermascope imaging devices, then qualification accuracy is improved, but cost increases significantly
Solution Approach 1:
The patent replaces expensive real-world clinical study resources with computational simulations. By generating distorted images through algorithmic transformations of existing qualified device images, the system achieves qualification accuracy without the financial burden of conducting actual clinical trials with multiple devices and experts.
Solution Approach 2:
The patent uses disposable simulated images generated from existing data rather than investing in expensive, long-term clinical studies. The artificial distortions create one-time-use test cases that are computationally inexpensive to generate but sufficient for thorough device qualification.
3Adaptability or versatility
If new image capture devices are released rapidly, then product innovation is improved, but qualification capability keeps up
Solution Approach 1:
The patent replaces the mechanical process of physical clinical studies with computational simulations. Instead of manually capturing and evaluating images from new devices through clinical protocols, the system automatically generates distorted versions of existing images to test new devices, dramatically increasing qualification throughput.
Solution Approach 2:
The patent performs preliminary qualification testing immediately upon device release using simulated data, before the device enters widespread clinical use. This allows rapid identification of unsuitable devices and quick updates to the qualification database, keeping pace with fast device releases.
Data Source
AI summary
Qualifying an unqualified dermascope imaging device for use with an image classification algorithm is described. A qualification data set comprising a plurality of pairs of images of skin lesions is accessed, wherein each pair comprises an image of a skin lesion captured by an unqualified dermascope imaging device and an image of the skin lesion captured by a qualified dermascope imaging device. Using the image classification algorithm, a confidence value of classification of each image is computed. A similarity metric is measured between the unqualified and qualified dermascope imaging device using differences in the confidence values between images of each pair. Qualifying the unqualified dermascope imaging device for use with the image classification algorithm is done in response a comparison between the similarity metric and a similarity threshold.