Evaluation of a computational model for mycetoma-causative agents identification.

Background: The therapeutic strategy for mycetoma relies heavily on the identification of the causative agents, which are either fungal or bacterial. While histopathological examination of surgical biopsies is currently the most used diagnostic tool, it requires well-trained pathologists, who are lacking in most rural areas where mycetoma is endemic. In this work we propose and evaluate a machine learning approach that semi-automatically analyses histopathological microscopic images of grains and provides a classification of the disease as eumycetoma or actinomycetoma.

Methods: The computational model is based on radiomics and partial least squares. It is assessed on a dataset that includes 890 individual grains collected from 168 patients originating from the Mycetoma Research Centre in Sudan. The dataset contained 94 eumycetoma cases and 74 actinomycetoma cases, with a distribution of the species among the two causative agents that is representative of the Sudanese distribution.

Results: The proposed model achieved identification of causative agents with an accuracy of 91.89%, which is comparable to the accuracy of experts from the domain. The method was found to be robust to a small error in the segmentation of the grain and to changes in the acquisition protocol. Among the radiomics features, the homogeneity of mycetoma grain textures was found to be the most discriminative feature for causative agent identification.

Conclusion: The results presented in this study support that this computational approach could greatly benefit rural areas with limited access to specialized clinical centres and also provide a second opinion for expert pathologists to implement the appropriate therapeutic strategy.