Expert Opinion Elicitation for Assisting Deep Learning based Lyme Disease Classifier with Patient Data

TL;DR

This paper enhances Lyme disease diagnosis by integrating expert opinion on patient data with deep learning image analysis, improving accuracy and robustness in early detection of erythema migrans skin lesions.

Contribution

It introduces a method to incorporate physician-elicited probability scores from patient data into deep learning models for Lyme disease diagnosis.

Findings

Elicited probability scores improve model robustness.

Gaussian mixture density estimation effectively converts expert opinions.

Decision tree validation supports the probability model.

Abstract

Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only utilizes lesion image due to the lack of a dataset of Lyme disease related images with associated patient data. Physicians rely on patient information about the background of the skin lesion to confirm their diagnosis. In order to assist the deep learning model with a probability score calculated from patient data, this study elicited opinion from fifteen doctors. For the elicitation process, a questionnaire with questions and possible answers related to EM was prepared. Doctors provided relative weights to different answers to the questions. We converted doctors evaluations to probability scores using Gaussian mixture based density estimation. For elicited probability model validation, we exploited formal concept analysis and decision tree. The elicited probability scores can be utilized to make image based deep learning Lyme disease pre-scanners robust.