Explainable AI Points to White Matter Hyperintensities for Alzheimer's Disease Identification: a Preliminary Study

Bordin, Valentina; Coluzzi, Davide; Rivolta, Massimo W.; Baselli, Giuseppe

doi:10.1109/embc48229.2022.9871306

Cited by 7 publications

(16 citation statements)

References 10 publications

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“…74 This approach was utilized by Bordin et al to identify relationships between white matter hyperintensities and the anatomical areas that are most important for the categorization of AD. 74 In conclusion, these XAI approaches present a potentially important addition that may eventually boost radiologists' confidence in the use of AI models.…”

Section: Explainable Aimentioning

confidence: 99%

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

et al. 2023

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Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer‐reviewed publications. However, the rate of deployment in clinical settings is low. Therefore, this review attempts to bring together the ideas from data collection to deployment in the clinic, building on the guidelines and principles that accreditation agencies have espoused. We introduce the need for and the role of DL to deliver accessible MRI. This is followed by a brief review of DL examples in the context of neuropathologies. Based on these studies and others, we collate the prerequisites to develop and deploy DL models for brain MRI. We then delve into the guiding principles to develop good machine learning practices in the context of neuroimaging, with a focus on explainability. A checklist based on the United States Food and Drug Administration's good machine learning practices is provided as a summary of these guidelines. Finally, we review the current challenges and future opportunities in DL for brain MRI.

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Section: Explainable Aimentioning

confidence: 99%

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

et al. 2023

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“…Valentina et al [158] created heat maps using the occlusion sensitivity method by occluding a section of the input image with a black patch. The model's brain regions contributing to the classification decision were easily discernible from fluctuations in the output probability predictions.…”

Section: Visualmentioning

confidence: 99%

“…Fig. 20 Valentina et al [158] in the paper contribute the use of the Occlusion Sensitivity method to reveal the relevant measure of white matter hyperintensities lesion with healthy lesions. Understanding which elements of a picture are most crucial for a deep network's classification can be done simply using occlusion sensitivity analysis.…”

Section: Global Posthoc Model Agnosticmentioning

confidence: 99%

“…Hence, most of the studies in the literature ended up with subjective claims but exhibited subpar performance due to generalisability issues when tested on a different dataset [166]. Another closely related issue with the dataset is the issue of class imbalance [158,116]. The ML or DL learning algorithms predict dominant classes more accurately than classes with inadequate samples.…”

Section: Xai Researchers Often Resort To Self-intuition To De-mentioning

confidence: 99%

“…The ML or DL learning algorithms predict dominant classes more accurately than classes with inadequate samples. Most studies had limited AD samples compared to HC or MCI cohorts [158,116,118,144,150,154,126]. Only a balanced dataset can draw meaningful insights.…”

Section: Xai Researchers Often Resort To Self-intuition To De-mentioning

confidence: 99%

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Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

Vimbi¹,

Shaffi²,

Mahmud

et al. 2023

Preprint

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Context: Artificial Intelligence (AI) in the medical domain has achieved remarkable results on various metrics primarily due to recent advancements in computational capabilities. However, AI models used for Computer Aided Diagnosis (CAD) have limited acceptance and trust due to their typical {Blackbox} nature. Consequently, explainable AI (XAI) is needed to justify the trustworthiness of the machine's predictions. Approach: In this work, we undertake a systematic review based on PRISMA guidelines on research articles investigating Alzheimer's Disease (AD) prediction with XAI for the past decade. The review was driven by carefully formulated research questions (RQ) which helped categorize AI models into different XAI methods(Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, etc) and frameworks (LIME, SHAP, GradCAM, LRP, etc.). Summary: This categorisation provides broad coverage of the interpretation spectrum from intrinsic (model-specific, ante-hoc models) to complex patterns (model-agnostic, post-hoc models) and by taking local explanations to a global scope. Additionally, the study categorizes merit in terms of different forms of interpretation to provide in-depth insight into the factors that support the clinical diagnosis of AD. Finally, limitations and open research challenges are outlined, and possible prospects are presented.

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Spatial–temporal patterns of brain disconnectome in Alzheimer's disease

Zhou

et al. 2023

Human Brain Mapping

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Mounting evidences have shown that progression of white matter hyperintensities (WMHs) with vascular origin might cause cognitive dysfunction symptoms through their effects on brain networks. However, the vulnerability of specific neural connection related to WMHs in Alzheimer's disease (AD) still remains unclear. In this study, we established an atlas‐guided computational framework based on brain disconnectome to assess the spatial–temporal patterns of WMH‐related structural disconnectivity within a longitudinal investigation. Alzheimer's Disease Neuroimaging Initiative (ADNI) database was adopted with 91, 90 and 44 subjects including in cognitive normal aging, stable and progressive mild cognitive impairment (MCI), respectively. The parcel‐wise disconnectome was computed by indirect mapping of individual WMHs onto population‐averaged tractography atlas. By performing chi‐square test, we discovered a spatial–temporal pattern of brain disconnectome along AD evolution. When applied such pattern as predictor, our models achieved highest mean accuracy of 0.82, mean sensitivity of 0.86, mean specificity of 0.82 and mean area under the receiver operating characteristic curve (AUC) of 0.91 for predicting conversion from MCI to dementia, which outperformed methods utilizing lesion volume as predictors. Our analysis suggests that brain WMH‐related structural disconnectome contributes to AD evolution mainly through attacking connections between: (1) parahippocampal gyrus and superior frontal gyrus, orbital gyrus, and lateral occipital cortex; and (2) hippocampus and cingulate gyrus, which are also vulnerable to Aβ and tau confirmed by other researches. All the results further indicate that a synergistic relationship exists between multiple contributors of AD as they attack similar brain connectivity at the prodromal stage of disease.

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Explainable AI Points to White Matter Hyperintensities for Alzheimer's Disease Identification: a Preliminary Study

Cited by 7 publications

References 10 publications

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

Developing and deploying deep learning models in brain magnetic resonance imaging: A review

Application of Explainable Artificial Intelligence in Alzheimer's Disease Classification: A Systematic Review

Spatial–temporal patterns of brain disconnectome in Alzheimer's disease

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