Ten years of image analysis and machine learning competitions in dementia

• URL: http://arxiv.org/abs/2112.07922v1
• Date: Wed, 15 Dec 2021 06:57:47 GMT
• Title: Ten years of image analysis and machine learning competitions in dementia
• Authors: Esther E. Bron, Stefan Klein, Annika Reinke, Janne M. Papma, Lena Maier-Hein, Daniel C. Alexander, Neil P. Oxtoby
• Abstract summary: Seven grand challenges have been organized in the last decade: MIRIAD, Alzheimer's Disease Big Data DREAM, CADDementia, Machine Learning Challenge, MCI Neuroimaging, TADPOLE, and the Predictive Analytics Competition. We analyzed how these grand challenges are complementing each other regarding research questions, datasets, validation approaches, results and impact. There was little overlap in clinical questions, tasks and performance metrics. Whereas this has the advantage of providing insight on a broad range of questions, it also limits the validation of results across challenges.
• Score: 5.072348652654899
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and assess their potential for use in clinical practice and clinical trials, seven grand challenges have been organized in the last decade: MIRIAD, Alzheimer's Disease Big Data DREAM, CADDementia, Machine Learning Challenge, MCI Neuroimaging, TADPOLE, and the Predictive Analytics Competition. Based on two challenge evaluation frameworks, we analyzed how these grand challenges are complementing each other regarding research questions, datasets, validation approaches, results and impact. The seven grand challenges addressed questions related to screening, diagnosis, prediction and monitoring in (pre-clinical) dementia. There was little overlap in clinical questions, tasks and performance metrics. Whereas this has the advantage of providing insight on a broad range of questions, it also limits the validation of results across challenges. In general, winning algorithms performed rigorous data pre-processing and combined a wide range of input features. Despite high state-of-the-art performances, most of the methods evaluated by the challenges are not clinically used. To increase impact, future challenges could pay more attention to statistical analysis of which factors (i.e., features, models) relate to higher performance, to clinical questions beyond Alzheimer's disease, and to using testing data beyond the Alzheimer's Disease Neuroimaging Initiative. Given the potential and lessons learned in the past ten years, we are excited by the prospects of grand challenges in machine learning and neuroimaging for the next ten years and beyond.

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