Transfer Learning for Alzheimer’s Disease through Neuroimaging Biomarkers: A Systematic Review

Alzheimer’s disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.

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Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

International Journal of Molecular Sciences ◽

10.3390/ijms22157911 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7911

Author(s):

Eugene Lin ◽

Chieh-Hsin Lin ◽

Hsien-Yuan Lane

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Future Research ◽

Learning Approaches ◽

Learning Models ◽

Learning Techniques ◽

Neuroimaging Data ◽

Similarities And Differences ◽

Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

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Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia

Brain Informatics ◽

10.1186/s40708-020-00112-2 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 6

Author(s):

Manan Binth Taj Noor ◽

Nusrat Zerin Zenia ◽

M Shamim Kaiser ◽

Shamim Al Mamun ◽

Mufti Mahmud

Keyword(s):

Alzheimer’S Disease ◽

Parkinson’S Disease ◽

Alzheimer's Disease ◽

Parkinson's Disease ◽

Deep Learning ◽

Magnetic Resonance ◽

Neurological Disorders ◽

Magnetic Resonance Images ◽

Future Research ◽

Comparative Performance

Abstract Neuroimaging, in particular magnetic resonance imaging (MRI), has been playing an important role in understanding brain functionalities and its disorders during the last couple of decades. These cutting-edge MRI scans, supported by high-performance computational tools and novel ML techniques, have opened up possibilities to unprecedentedly identify neurological disorders. However, similarities in disease phenotypes make it very difficult to detect such disorders accurately from the acquired neuroimaging data. This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer’s disease, Parkinson’s disease and schizophrenia—from MRI data acquired using different modalities including functional and structural MRI. The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting neurological disorders. Towards the end, a number of current research challenges are indicated and some possible future research directions are provided.

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Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

Scientific Reports ◽

10.1038/s41598-021-95249-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shelly Soffer ◽

Eyal Klang ◽

Orit Shimon ◽

Yiftach Barash ◽

Noa Cahan ◽

...

Keyword(s):

Systematic Review ◽

Pulmonary Embolism ◽

Deep Learning ◽

Sensitivity And Specificity ◽

Meta Analysis ◽

Risk Of Bias ◽

Future Research ◽

Learning Models ◽

Summary Receiver Operating Characteristic ◽

Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

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A Systematic Review and Aggregated Analysis on the Impact of Amyloid PET Brain Imaging on the Diagnosis, Diagnostic Confidence, and Management of Patients being Evaluated for Alzheimer’s Disease

Journal of Alzheimer s Disease ◽

10.3233/jad-171093 ◽

2018 ◽

Vol 63 (2) ◽

pp. 783-796 ◽

Cited By ~ 20

Author(s):

Enrico R. Fantoni ◽

Anastasia Chalkidou ◽

John T. O’ Brien ◽

Gill Farrar ◽

Alexander Hammers

Keyword(s):

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Brain Imaging ◽

Amyloid Pet ◽

Diagnostic Confidence ◽

The Impact

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SYSTEMATIC REVIEW AND GAP ANALYSIS ON ALZHEIMER’S DISEASE IN ASIAN AMERICANS, NATIVE HAWAIIANS, AND PACIFIC ISLANDERS

Innovation in Aging ◽

10.1093/geroni/igz038.431 ◽

2019 ◽

Vol 3 (Supplement_1) ◽

pp. S117-S117

Author(s):

Sahnah Lim ◽

Timothy Roberts ◽

Jazmine Wong ◽

Sadia Mohaimin ◽

Young-Jin Sohn ◽

...

Keyword(s):

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Asian American ◽

Gap Analysis ◽

Research Policy ◽

Native Hawaiians ◽

Future Research ◽

Publication Date ◽

Study Objective

Abstract Background: The Asian American, Native Hawaiian, and Pacific Islander (AANHPI) aging population is rapidly growing and the burden of Alzheimer’s disease and its related dementias (AD/ADRD) will likely mirror this demographic growth. AANHPIs face significant barriers in obtaining timely AD/ADRD diagnosis and services; yet little is known about AD/ADRD in this population. The study objective is to conduct a systematic review on the published literature on AD/ADRD among AANHPIs to identify gaps and priorities to inform future research and action plans. Methods: The systematic review was conducted following the PRISMA Protocol for Systematic Reviews. Co-author (TR), an experienced Medical Librarian, searched PubMed, EMBASE, PsycINFO, Cochrane Central of Clinical Trials, Ageline and Web of Science for peer-reviewed articles describing AD/ADRD among AANHPIs. The search was not limited by language or publication date. Each citation was reviewed by two trained independent reviewers. Conflicts were resolved through consensus. Results: The title/abstract and full texts of 1,447 unique articles were screened for inclusion, yielding 310 articles for analysis. Major research topics included prevalence, risk factors, comorbidities, interventions and outreach, knowledge/perceptions/attitudes, caregiving, and detection tools. A limited number of studies reported on national data, on NHPI communities generally, and on efficacy of interventions targeting AANHPI communities. Conclusion: To our knowledge, this is the first systematic review on AD/ADRD among AANHPI populations. Our review provides a first step in mapping the extant literature on AD/ADRD among this underserved and under-researched population and will serve as a guide for future research, policy and intervention.

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Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review

Journal of Alzheimer s Disease ◽

10.3233/jad-200888 ◽

2020 ◽

Vol 78 (4) ◽

pp. 1547-1574

Author(s):

Sofia de la Fuente Garcia ◽

Craig W. Ritchie ◽

Saturnino Luz

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Language Processing ◽

Future Research ◽

Original Research ◽

Speech And Language ◽

Eligibility Criteria ◽

Speech And Language Processing

Background: Language is a valuable source of clinical information in Alzheimer’s disease, as it declines concurrently with neurodegeneration. Consequently, speech and language data have been extensively studied in connection with its diagnosis. Objective: Firstly, to summarize the existing findings on the use of artificial intelligence, speech, and language processing to predict cognitive decline in the context of Alzheimer’s disease. Secondly, to detail current research procedures, highlight their limitations, and suggest strategies to address them. Methods: Systematic review of original research between 2000 and 2019, registered in PROSPERO (reference CRD42018116606). An interdisciplinary search covered six databases on engineering (ACM and IEEE), psychology (PsycINFO), medicine (PubMed and Embase), and Web of Science. Bibliographies of relevant papers were screened until December 2019. Results: From 3,654 search results, 51 articles were selected against the eligibility criteria. Four tables summarize their findings: study details (aim, population, interventions, comparisons, methods, and outcomes), data details (size, type, modalities, annotation, balance, availability, and language of study), methodology (pre-processing, feature generation, machine learning, evaluation, and results), and clinical applicability (research implications, clinical potential, risk of bias, and strengths/limitations). Conclusion: Promising results are reported across nearly all 51 studies, but very few have been implemented in clinical research or practice. The main limitations of the field are poor standardization, limited comparability of results, and a degree of disconnect between study aims and clinical applications. Active attempts to close these gaps will support translation of future research into clinical practice.

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Dance for People with Alzheimer’s Disease: A Systematic Review

Current Alzheimer Research ◽

10.2174/1567205016666190725151614 ◽

2019 ◽

Vol 16 (10) ◽

pp. 919-933 ◽

Cited By ~ 1

Author(s):

Alicia Ruiz-Muelle ◽

María Mar López-Rodríguez

Keyword(s):

Quality Of Life ◽

Systematic Review ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Clinical Trials ◽

Cognitive Function ◽

Dance Therapy ◽

Randomized Clinical Trials ◽

Future Research

Background: In recent years, several reviews have addressed the effectiveness of dance therapy in dementia, healthy older adults, or the elderly in general. However, reviews regarding the effect of this therapy exclusively on patients diagnosed with Alzheimer’s disease have not been found. Objective: The purpose of this study is to review the available literature describing clinical trials which explore the effects of dancing on psychological and physical outcomes, functionality, cognitive function, and quality of life in patients diagnosed with Alzheimer’s disease. In addition, this review aims to assess the quality of studies that perform dance therapy interventions in these patients. Methods: This study is a systematic review of randomized and non-randomized clinical trials regarding the effect of intervention including a dancing activity in people diagnosed with Alzheimer's disease. Results: In total, the evidence for this review rests on 12 studies with a total of 349 participants. The findings of this mini-review confirm the positive effect of dance therapy on physical and cognitive function, functionality, psychological outcomes, and quality of life in people with Alzheimer's disease. Conclusion: Most of the studies implementing dance as part of the therapeutic treatment has shown to improve or slow the worsening in the quality of life of patients with Alzheimer's disease and their caregivers. Future research focused on these patients should use a more exhaustive methodology and make a more detailed description of these kind of interventions.

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