Analyzing clinical variables indicative of uveal melanoma to determine how they affect decisions made by an artificial intelligence classifier.

Objective: The "black box" nature of many artificial intelligence (AI) models has limited their adoption in real-world ophthalmologic practices. Our lab developed an AI model for detecting the presence of a choroidal melanocytic lesion (CML) in colour fundus images. The purpose of this article is to investigate whether there are known clinical features of CMLs that are associated with false-negative (FN) classifications from the model to aid in validation and increase its interpretability.

Methods: A retrospective cohort study of CML patients was performed. A total of 388 fundus images from 194 patients with (n = 194) and without (n = 194) CMLs collected through routine clinical assessment were used to train an AI model. The model's classification (lesion present/lesion absent) of the images with CMLs, as well as CML characteristics, demographics, and risk factors for uveal melanoma (UM) were extracted. Logistic regression models were used to test for associations between the FN classifications and these characteristics.

Results: The AI model returned 150 true-positive classifications and 44 FN classifications (23%) for CML eyes. Thinner lesions were more likely to be missed by the model (p = 0.026), resulting in a FN classification. The presence of imaging risk factors for UM was not shown to have any statistically significant relationships with a FN classification.

Conclusions: The results from this study demonstrate that the FN classifications for CML fundus image classifications from our AI model are not associated with the presence of imaging risk factors for UM but are influenced by thinness of the lesion.