Ten years of image analysis and machine learning competitions in dementia

Bron, Esther E.; Klein, Stefan; Reinke, Annika; Papma, Janne M.; Maier-Hein, Lena; Alexander, Daniel C.; Oxtoby, Neil P.

doi:10.1016/j.neuroimage.2022.119083

Cited by 14 publications

(9 citation statements)

References 33 publications

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“…[31][32][33] Machine learning, including its offshoot deep learning, has emerged as a highly promising technique for disease diagnosis. [34][35][36] Numerous studies have exploited machine learning to assist imaging analysis in the classification of HC, MCI, and AD. [37][38][39] In contrast to the random forest models based on the whole brain area analysis of Huang et al 40 (AUC: 0.99 and ACC: 93.5% for AD; AUC: 0.88 and ACC: 80.8% for MCI), our hippocampal radiomics classifiers (AUC: 0.98 and ACC: 96.7% for AD; AUC: 0.86 and ACC: 80.6% for aMCI) outperformed comparably in discriminating AD and aMCI from HC.…”

Section: Discussionmentioning

confidence: 99%

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using ¹⁸F‐FDG PET/MRI Radiomics

Chen,

Bi,

Shan

et al. 2023

CNS Neurosci Ther

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PurposeThis study aimed to explore the utility of hippocampal radiomics using multiparametric simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) for early diagnosis of Alzheimer's disease (AD).MethodsA total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F‐fluorodeoxyglucose (18F‐FDG) PET, 3D arterial spin labeling (ASL), and high‐resolution T1‐weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad‐Score) of logistic regression models were evaluated from 5‐fold cross‐validation.ResultsThe hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single‐modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad‐Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests.ConclusionMultiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications.

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Section: Discussionmentioning

confidence: 99%

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using ¹⁸F‐FDG PET/MRI Radiomics

Chen,

Bi,

Shan

et al. 2023

CNS Neurosci Ther

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“…In the absence of established disease‐modifying treatments for AD and other neurodegenerative diseases, the bulk of ML prognostic studies have focused on predicting the conversion from MCI to dementia using MRI, 92 electroencephalography (EEG), 93 magnetoencephalography (MEG), 93 neuropsychological measures, 94 genetic data, 95 or combinations of modality types 96 . A recent systematic review of studies predicting MCI conversion to dementia included results of 234 experiments from 111 articles 97 .…”

Section: Goals Of the Studies Implementing Machine Learning Approache...mentioning

confidence: 99%

Artificial intelligence for dementia research methods optimization

Bucholc,

James,

Khleifat

et al. 2023

Alzheimer's & Dementia

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Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high‐dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well‐documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI‐enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co‐produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care.HIGHLIGHTS Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.

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“…For example, in the dementia field, four challenges have been organised focusing on early diagnosis [16,5,124] and predicting the natural disease course [5,124,86]. In general, algorithms winning the challenges performed rigorous data pre-processing and combined a wide range of input features [18]. In the field of brain cancer, the series of BraTS challenges has had a major impact [8,7].…”

Section: Benchmarks and Challengesmentioning

confidence: 99%

Computer-aided diagnosis and prediction in brain disorders

Venkatraghavan¹,

Voort²,

Bos³

et al. 2022

Preprint

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Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data -such as cognitive tests, imaging and genetic data -and the types of output they provide. We will focus on specific use cases for diagnosis, i.e. estimating the current 'condition' of the patient, such as early detection and diagnosis of To appear in O. Colliot (Ed.), Machine Learning for Brain Disorders, Springer

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Ten years of image analysis and machine learning competitions in dementia

Cited by 14 publications

References 33 publications

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using ¹⁸F‐FDG PET/MRI Radiomics

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using ¹⁸F‐FDG PET/MRI Radiomics

Artificial intelligence for dementia research methods optimization

Computer-aided diagnosis and prediction in brain disorders

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Ten years of image analysis and machine learning competitions in dementia

Cited by 14 publications

References 33 publications

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using 18F‐FDG PET/MRI Radiomics

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using 18F‐FDG PET/MRI Radiomics

Artificial intelligence for dementia research methods optimization

Computer-aided diagnosis and prediction in brain disorders

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Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using ¹⁸F‐FDG PET/MRI Radiomics

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using ¹⁸F‐FDG PET/MRI Radiomics