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Open Access

The Application of Artificial Intelligence in Alzheimer’s Research

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
School of Medicine, Tsinghua University, Beijing 100084, China
Department of Computer Science, Sukkur IBA University, Sukkur 65200, Pakistan
Department of Neurology, Tsinghua University Yuquan Hospital, Beijing 100040, China
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Abstract

Alzheimer’s disease (AD) is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function, but the etiology of AD is still unclear. With the explosive growth of electronic health data, the application of artificial intelligence (AI) in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches, and improving the disease’s diagnostic and prognostic outcome. This paper first briefly introduces AI technologies and applications in medicine, and then presents a comprehensive review of AI in AD. In simple, it includes etiology discovery based on genetic data, computer-aided diagnosis (CAD), computer-aided prognosis (CAP) of AD using multi-modality data (genetic, neuroimaging and linguistic data), and pharmacological or non-pharmacological approaches for treating AD. Later, some popular publicly available AD datasets are introduced, which are important for advancing AI technologies in AD analysis. Finally, core research challenges and future research directions are discussed.

Keywords

Alzheimer’s diseaseartificial intelligenceetiology discoverycomputer-aided diagnosiscomputer-aided prognosistreatment

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Tsinghua Science and Technology
Volume 29 Issue 1,
February 2024
Pages 13-33
DOI: 10.26599/TST.2023.9010037
Cite this article:
Zhao Q, Xu H, Li J, et al. The Application of Artificial Intelligence in Alzheimer’s Research. Tsinghua Science and Technology, 2024, 29(1): 13-33. https://doi.org/10.26599/TST.2023.9010037
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10.26599/TST.2023.9010037.T001Etiologies for AD revealed by AI-aid methods.

LiteratureModelGene factor discovery
Zakeri et al.[54]Gene co-expression networkMBOAT1, LCOR, ARMC7, CREBRF, HNRNPUL1, PLAFL2, MRI1, LAMTOR1, miR-4722-5p, miR-4768-3p, miR-1872, miR-940, miR30b-3p
Sharma et al.[59]Co-expression networkBACE1, LRP1, CASP7, MME, GAPDH, GSK3B, PSEN1, PSEN2, MAPT, CASP3, APP, SNCA, TNF, LPL, APBB1, APOE
Wang et al.[60]RF, regularized regressionCORO1C, PDYN, SLC25A46, CRLF3, RAE1 ANKIBI
Chen et al.[61]Non-parametric kernel approachUQCRB-NDUFV2, NDUFV3-ATP5PB, NDUFB8-PSENEN, NDUFC2-NDUFA5, NDUFV2-NDUFA10, NDUFS8-IL1B, CAPN2-NDUFAB1, ATP5MC2-NDUFB5, PSEN1-NDUFAB1, PPP3CC-NDUFB5
Huang et al.[62]SVMWhole-genome spectrum

10.26599/TST.2023.9010037.T002Related studies with different imaging features for AI-aid diagnosis of AD.

LiteratureFeature name
Tripoliti et al.[68]Demographics, head motion, behavioral, volumetric measures, activation patterns, and hemodynamics
Wu et al.[69]Volume, voxel intensities, and texture
Er and Goularas[71]Longitudinal time sequence features, gender, age, and total intracranial volume
Lei et al.[72]Global and local ROI features
Hong et al.[73]Longitudinal sequence features, thickness standard deviation, cortical thickness average, volume of WM parcellation, surface area, and the volume of cortical Parcellation and ROI features
Jo et al.[76]five heatmaps, the top ten regions
An et al.[77]medical history, hachinski ischemic score, cerebrovascular disease, unified Parkinson’s disease rating scale, neuropsychiatric inventory questionnaire, geriatric depression scale, and functional activities questionnaire
Lei et al.[78]Intensity homogeneity, brain segmentation, and ROI features

10.26599/TST.2023.9010037.T003Related studies with different linguistic features for AI-aid diagnosis of AD.

LiteratureCategoryFeature name
Zhu and Razavian[16]Text-basedWord embedding
Alkenani Ahmed et al.[79]Text-basedCharacter n-gram, Type-Token Ratio (TTR), Type-token count (TTC), content density (CD), idea density, word, character, stop word, and sentence count
Orimaye et al.[80]Text-basedLexical features
Fraser et al.[81]Text-basedPart-of-speech, word, and character n-gram
Fraser et al.[82]Text-based, acoustic featuresPart-of-speech, syntactic complexity, grammatical constituents, psycholinguistics, TTR, information content, repetitiveness, MFCCs
Luz et al.[83]Acoustic featuresComParE, emobase, eGeMAPS, MRCG, minimal
Edwards et al.[84]Text-based, acoustic featuresWord and sentence embedding, phonetic representation, GeMPAS, eGEMAPS, emobase, emobase2010, emolarge, ComParE2016, MRCG
Sadeghian et al.[85]Text-based, acoustic featuresRace, speech rate, content density, the Linguistic Inquiry Word Counts (LIWC), total number of pause, idea density, fraction of pause less than 0.5 s or 1 s
Alireza et al.[86]Text-basedWord embedding

10.26599/TST.2023.9010037.T004Summary of CAD system for AD detection using neuroimaging, linguistic, and gene data.

LiteratureModalityDatasetTaskResult
Tripoliti et al.[68]fMRIPrivateClassification: NC (young and elderly), AD (mild and very mild)94% for NC (elderly)/AD, 97% for NC (elderly)/AD (mild and very mild), 98.78% for NC (young and elderly)/AD (mild and very mild) ((ACC)
Wu et al.[69]PETADNIClassification: NC, MCI, AD,84.3% for AD, 75.2% for MCI, 88.6% for CN (ACC)
Er and Goularas[71]MRIADNIClassification: MCI, AD87.2% (ACC)
Lei et al.[72]MRIADNIClassification: AD, MCI, NC97.91% for AD/NC, 94% for AD/MCI, 90.74% for MCI/NC (ACC)
Hong et al.[73]MRI, PET, DTIADNIClassification: NC, MCI, AD,77.7% (AUC)
Jo et al.[76]Tau-PETADNIClassification: AD, NC, MCI90.4% (ACC)
An et al.[77]MRINACCClassification: AD, NC76.4% (ACC)
Lei et al.[78]MRIADNIAD score prediction4.781 for M06, 5.099 for M12, 4.521 for M18, 5.736 for M24, 4.981 for M36 (MAE)
Alkenani Ahmed et al.[79]Spoken, textDementiaBank, AD Blog corpusClassification: AD, NC98.1% for DementiaBank, 99.47% for AD blog corpus (ACC)
Orimaye et al.[80]TextPitt CorpusClassification: MCI, Ad80% for MCI dataset, 83% for AD dataset (AUC)
Zhu and Razavian[16]TextPrivateClassification: AD, NC80.2% (AUROC)
Fraser et al.[81]TextGothenburg (private), Karolinska, DementiaBankClassification: MCI, NC63% in English, 51% in Swedish (ACC)
Fraser et al.[82]Audio, textDementiaBankClassification: AD, NC81.92% (ACC)
Luz et al.[83]Audio, textADreSS ChallengeClassification: AD, NC62.5% (ACC)
Edwards et al.[84]Audio, textADreSS ChallengeClassification: AD, NC79.17% (ACC)
Sadeghian et al.[85]Audio, textPrivateClassification, AD, NC94% (ACC)
Alireza et al.[86]TextDementiaBankClassification: AD, NC88.08% (ACC)
Wei et al.[100]GeneNACCClassification: LOAD, NC72% ( AUC)
Xu et al.[101]GeneADNIClassification: AD, NC85.7% (ACC)
Javier et al.[102]GeneADNIClassification: LOAD, NC71.9% (AUC)
Golovanevsky et al.[37]MRI, clinical data, geneADNIClassification: AD, NC, MCI96.88% (ACC)
Qiu et al.[107]MRI, clinical information, functional and neuropsychological assessmentsLBDSU, NACC, ADNI, OASIS, AIBL, FHS, PPMI, NIFDClassification: AD, NC, MCI, nADD94.5% for COG GN, 97.1% for COG DE, 73.3% for ADD on NACC (AUC)
Lin et al.[108]MRI, PET, CSF, geneADNIClassification: AD, NC, MCI66.7% (ACC)
Chen et al.[109]MRI, text report, structured reportPrivateClassification: AD, MCI, NC79.6% (ACC)

10.26599/TST.2023.9010037.T005Summary of CAP system for AD detection using neuroimaging, linguistic, and gene data.

LiteratureModalityDatasetTaskConversion period (months)Result
Ritter et al.[113]MRI, FDG-PET, CSFADNIPrediction: sMCI, pMCI0–3673% (ACC)
Zhang et al.[114]FDG-PET, MRI, CSFADNIPrediction: sMCI, pMCI0–1876.4% (ACC)
Cheng et al.[115]MRI, PET, CSFADNIPrediction: sMCI, pMCI0–3679.4% (ACC)
Yuan et al.[116]MRI, SNPADNIPrediction: sMCI, pMCI0–3685.5% (ACC)
Lin et al.[117]MRIADNIPrediction: sMCI, pMCI0–3679.9% (ACC)
Tabarestani et al.[118]CSF, MRI, PETADNIPrediction: AD, MCI, NC0–12, 0–24, 0–3688% for 0–12 months, 87% for 0–24 months, 88% for 0–36 months (ACC)
Beheshti et al.[119]MRIADNIClassification: NC, AD Prediction: sMCI, pMCI0–3693.01% for NC/AD 75% for sMCI/pMCI (ACC)
Zheng et al.[120]fMRI, MRI, PET, CSFADNI, ABIDEPrediction: sMCI, pMCI Classification: AD, ASD, sMCI, NC0–3692.31% for AD/sMCI/NV, 92.3% for sMCI/pMCI, 89.77% for NC/ASD (ACC)
Yubraj et al.[121]sMRI, CSF, FDG-PET, geneADNIClassification: AD, NC, sMCI, pMCIPrediction: sMCI, pMCI0–2498.33% for AD/NC, 93.59% for sMCI/pMCI, 96.83% for AD/sMCI, 94.64% for AD/pMCI, 96.43% for NC/pMCI, 95.24% for NC/sMCI (ACC)