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.

Related papers

• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs. Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder. Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Deep Learning for Early Alzheimer Disease Detection with MRI Scans [1.9806397201363817]
  Alzheimer's disease requires diagnosis by a detailed assessment of MRI scans and neuropsychological tests of the patients. This project compares existing deep learning models in the pursuit of enhancing the accuracy and efficiency of AD diagnosis. We perform rigorous evaluation to determine strengths and weaknesses for each model by considering sensitivity, specificity, and computational efficiency.
  arXiv  Detail & Related papers  (2025-01-17T07:30:16Z)
• Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Review [30.91867799522664]
  We present a comprehensive review of deep learning applications in histopathology. We focus on the challenges posed by data scarcity over the past decade. We identify underexplored research opportunities.
  arXiv  Detail & Related papers  (2024-10-18T07:29:48Z)
• Addressing the Gaps in Early Dementia Detection: A Path Towards Enhanced Diagnostic Models through Machine Learning [0.0]
  The rapid global aging trend has led to an increase in dementia cases, including Alzheimer's disease. Traditional diagnostic techniques, such as cognitive tests, neuroimaging, and biomarker analysis, face significant limitations in sensitivity, accessibility, and cost. This study explores the potential of machine learning (ML) as a transformative approach to enhance early dementia detection.
  arXiv  Detail & Related papers  (2024-09-05T00:52:59Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [57.067409211231244]
  This paper presents meticulously curated AIready datasets covering multi-modal data (e.g., drug molecule, disease code, text, categorical/numerical features) and 8 crucial prediction challenges in clinical trial design. We provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning [24.467566885575998]
  This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. It aims to explore early detection and disease progression in Alzheimer's disease (AD)
  arXiv  Detail & Related papers  (2024-02-13T15:43:30Z)
• Incomplete Multimodal Learning for Complex Brain Disorders Prediction [65.95783479249745]
  We propose a new incomplete multimodal data integration approach that employs transformers and generative adversarial networks. We apply our new method to predict cognitive degeneration and disease outcomes using the multimodal imaging genetic data from Alzheimer's Disease Neuroimaging Initiative cohort.
  arXiv  Detail & Related papers  (2023-05-25T16:29:16Z)
• A Survey on Computer Vision based Human Analysis in the COVID-19 Era [58.79053747159797]
  The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication
  arXiv  Detail & Related papers  (2022-11-07T17:20:39Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• Application of Machine Learning to Predict the Risk of Alzheimer's Disease: An Accurate and Practical Solution for Early Diagnostics [1.1470070927586016]
  Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests. Our model is trained and validated using demographic, biomarker and cognitive test data from two prominent research studies.
  arXiv  Detail & Related papers  (2020-06-02T14:52:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.