Deep Learning Convolutional Neural Network for Lyme Disease Diagnosis
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Solution Overview
Problem
Diagnosing early Lyme disease is challenging due to the variability of its associated rash, Erythema migrans, which often lacks the classic bull's-eye appearance, leading to misdiagnosis and delayed treatment, as both the public and physicians have low accuracy in identifying it.
Innovation Solution
A deep learning convolutional neural network is trained using a dataset of digital images, including Erythema migrans and other skin lesions, to provide an accurate diagnosis from digital photos, potentially implemented as a smartphone app for pre-screening, utilizing publicly available internet images without patient consent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning convolutional neural network is trained using publicly available internet images, then diagnostic accuracy is improved to 93.04%, but ethical concerns arise regarding patient consent and data privacy
Solution Approach 1:
The patent uses publicly available internet images as copies or representations of skin lesions for training the deep learning model. These images serve as surrogate data that replicate the visual characteristics of actual patient lesions without requiring direct access to private medical records or patient identifiers, thereby achieving high diagnostic accuracy while mitigating privacy concerns
Solution Approach 2:
The patent introduces an intermediary layer of public domain images that mediate between the need for training data and patient privacy requirements. By using images already available in the public domain, the system creates a buffer that allows model training without direct patient involvement or consent processes
2Loss of time
If deep learning model is deployed as smartphone app for pre-screening, then early diagnosis capability is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-training the deep learning convolutional neural network offline using extensive datasets before deployment. The model is prepared in advance with learned features and patterns, allowing it to perform rapid inference when deployed on the smartphone app without requiring complex real-time processing or large on-device datasets
Solution Approach 2:
The patent replaces complex mechanical or manual diagnostic processes with an automated deep learning system. The convolutional neural network substitutes for manual image analysis by physicians or complex image processing algorithms, providing automated diagnosis through learned patterns rather than rule-based or mechanical analysis methods
3Ease of operation
If traditional blood testing is used for early Lyme disease diagnosis, then diagnostic simplicity is maintained, but measurement precision deteriorates with high false negative rate
Solution Approach 1:
The patent creates a visual copy or representation of the diagnostic process by using image analysis instead of blood testing. The deep learning model analyzes visual patterns in skin lesion photographs, creating an alternative diagnostic pathway that captures diagnostic information from the visual presentation rather than from serological markers in blood
Solution Approach 2:
The patent changes the diagnostic parameter from serological markers (blood test results) to visual characteristics (image features). By transitioning from measuring antibody presence in blood to analyzing visual patterns of skin lesions, the system achieves both simplicity and high precision through a different measurement modality
Data Source
AI summary
Techniques for diagnosing Lyme disease are presented. The techniques may include obtaining a digital photo of a skin lesion, providing the digital photo to a deep learning convolutional neural network, such that an output diagnosis is provided. The deep learning convolutional neural network may be trained using a training corpus including a plurality of digital training images annotated according to one of a plurality of training image diagnoses, where the plurality of training image diagnoses include at least one Lyme disease type, normal skin, and at least one non-Lyme skin lesion type, and where the plurality of digital training images include multiple digital photographs publicly available on the internet. The techniques can include outputting the output diagnosis.