Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

Yang, Dalin; Hong, Keum‐Shik

doi:10.3233/jad-201163

Cited by 33 publications

(32 citation statements)

References 86 publications

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“…Around 53% of studies attempted to offer a model that can classify MCI and normal controls from AD. Classification of AD, MCI, and normal controls was also studied extensively (Khan et al, 2019 ; Puranik et al, 2019 ; Li Y et al, 2021 ; Yang and Hong, 2021 ). Detection (Choi et al, 2020 ), Classification (Cheng et al, 2017 ), and autoencoder (Oh et al, 2019 ) methods were utilized to differentiate MCI from AD and/or normal controls.…”

Section: Resultsmentioning

confidence: 99%

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Ardalan

Subbian

2022

Front. Artif. Intell.

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Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.

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Section: Resultsmentioning

confidence: 99%

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Ardalan

Subbian

2022

Front. Artif. Intell.

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“…Using a convolutional neural network (CNN)-based AI algorithm, the researchers achieved 90%

using the verbal fluency test, and achieved the best

of 98.61% using the N-back test. Another study by D. Yang et al attempted to use resting-state fNIRS signals acquired using the same commercial fNIRS system for the classification of MCI patients and healthy controls [ 69 ]. A connectivity map of

and

features extracted using various pre-trained CNN models (e.g., variations of VGG, Densenet, Alexnet, and Resnet networks) for transfer learning, time-series of

, and

were used as input features for ML classifiers, including linear discriminant analysis, support vector machine, and K-nearest neighbor.…”

Section: Discussionmentioning

confidence: 99%

Use of Hypoxic Respiratory Challenge for Differentiating Alzheimer’s Disease and Wild-Type Mice Non-Invasively: A Diffuse Optical Spectroscopy Study

Seong

Park

et al. 2022

Biosensors

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Alzheimer’s disease is one of the most critical brain diseases. The prevalence of the disease keeps rising due to increasing life spans. This study aims to examine the use of hemodynamic signals during hypoxic respiratory challenge for the differentiation of Alzheimer’s disease (AD) and wild-type (WT) mice. Diffuse optical spectroscopy, an optical system that can non-invasively monitor transient changes in deoxygenated () and oxygenated () hemoglobin concentrations, was used to monitor hemodynamic reactivity during hypoxic respiratory challenges in an animal model. From the acquired signals, 13 hemodynamic features were extracted from each of and (26 features total) for more in-depth analyses of the differences between AD and WT. The hemodynamic features were statistically analyzed and tested to explore the possibility of using machine learning (ML) to differentiate AD and WT. Among the twenty-six features, two features of and one feature of showed statistically significant differences between AD and WT. Among ML techniques, a naive Bayes algorithm achieved the best of 84.3% when whole hemodynamic features were used for differentiation. While further works are required to improve the approach, the suggested approach has the potential to be an alternative method for the differentiation of AD and WT.

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“…E.g. models developed in the ImageNet dataset (>1 million images) were used even in smaller clinical studies with <100 patients [24,32,33], where the use of machine learning models would otherwise not be recommended or feasible. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review)…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, transfer learning was used for prediction of glucose levels [26,27], estimation of Parkinson's disease severity [71], detection of cognitive impairment [33] and schizophrenia [52], and forecasting of infectious disease trends [28] and outbreaks [29], among other applications [72][73][74][75][76][77][78][79][80][81][82].…”

Section: Time Seriesmentioning

confidence: 99%

Transfer learning for non-image data in clinical research: a scoping review

Ebbehøj

Thunbo

Andersen

et al. 2021

Preprint

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Background Transfer learning is a form of machine learning where a pre-trained model trained on a specific task is reused as a starting point and tailored to another task in a different dataset. While transfer learning has garnered considerable attention in medical image analysis, its use for clinical non-image data is not well studied. Therefore, the objective of this scoping review was to explore the use of transfer learning for non-image data in the clinical literature. Methods and Findings We systematically searched medical databases (PubMed, EMBASE, CINAHL) for peer-reviewed clinical studies that used transfer learning on human non-image data. We included 83 studies in the review. More than half of the studies (63%) were published within 12 months of the search. Transfer learning was most often applied to time series data (61%), followed by tabular data (18%), audio (12%) and text (8%). Thirty-three (40%) studies applied an image-based model to non-image data after transforming data into images (e.g. spectrograms). Twenty-nine (35%) studies did not have any authors with a health-related affiliation. Many studies used publicly available datasets (66%) and models (49%), but fewer shared their code (27%). Conclusions In this scoping review, we have described current trends in the use of transfer learning for non-image data in the clinical literature. We found that the use of transfer learning has grown rapidly within the last few years. We have identified studies and demonstrated the potential of transfer learning in clinical research in a wide range of medical specialties. More interdisciplinary collaborations and the wider adaption of reproducible research principles are needed to increase the impact of transfer learning in clinical research.

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Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

Cited by 33 publications

References 86 publications

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Use of Hypoxic Respiratory Challenge for Differentiating Alzheimer’s Disease and Wild-Type Mice Non-Invasively: A Diffuse Optical Spectroscopy Study

Transfer learning for non-image data in clinical research: a scoping review

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