ThyExp: An explainable AI-assisted Decision Making Toolkit for Thyroid Nodule Diagnosis based on Ultra-sound Images

Abstract

Radiologists have an important task of diagnosing thyroid nodules present in ultra sound images. Although reporting systems exist to aid in the diagnosis process, these systems do not provide explanations about the diagnosis results. We present ThyExp -- a web based toolkit for it use by medical professionals, allowing for accurate diagnosis with explanations of thyroid nodules present in ultrasound images utilising artificial intelligence models. The proposed web-based toolkit can be easily incorporated into current medical workflows, and allows medical professionals to have the confidence of a highly accurate machine learning model with explanations to provide supplementary diagnosis data. The solution provides classification results with their probability accuracy, as well as the explanations in the form of presenting the key features or characteristics that contribute to the classification results. The experiments conducted on a real-world UK NHS hospital patient dataset demonstrate the effectiveness of the proposed approach. This toolkit can improve the trust of medical professional to understand the confidence of the model in its predictions. This toolkit can improve the trust of medical professionals in understanding the models reasoning behind its predictions.

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