In the framework of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024), taking place in Marrakesh, Morocco, in October 2024, the EviRed consortium organizes an AI challenge on Device-Independent diAbetic Macular edema ONset preDiction (DIAMOND).
The challenge’s focus is on center-involved Diabetic Macular Edema (ci-DME), a critical form of DME responsible for significant vision impairment. The presence of DME is generally assessed through 3D optical coherence tomography (OCT) imaging. As a sub-objective of EviRed, DIAMOND seeks to revolutionize the approach to diagnosing and treating ci-DME by integrating AI and deep learning with 2D ultra-wide-field color fundus photography (UWF-CFP). More challenging than assessing the presence of ci-DME, the goal is to develop and evaluate models that can predict if a patient will develop ci-DME within a year, using UWF-CFP images alone. Success in this challenge could significantly improve early detection and treatment planning, reduce vision loss incidents, and exemplify AI’s efficacy in healthcare.
For training, the DIAMOND Challenge uses data collected in the framework of EviRed. For performance evaluation, DIAMOND will also use independent data from the LAZOUNI Ophthalmology Clinic in Tlemcen, Algeria. By leveraging diverse datasets, the challenge underscores its commitment to developing solutions that are universally applicable, transcending geographic and demographic boundaries. This generality is critical in ensuring that the predictive models developed are robust and effective across different population groups, enhancing their clinical utility on a global scale.
Participants in this challenge will not have access to the training, validation, or test datasets and, consequently, will not have the opportunity to train their models directly. Instead, they are required to submit their code to the organizing committee, who will then run it on a specialized cloud-based cluster. This methodology not only challenges participants to develop highly generalizable models but also prepares them for real-world situations where data privacy, ethical considerations, and logistical limitations are common.
More information here: https://www.codabench.org/competitions/2333/
