Comparison of RCF Scoring System to Clinical Decision for the Rey Complex Figure Using Machine-Learning Algorithm

Simfukwe, Chanda; An, Seong Soo A.; Youn, Young Chul

doi:10.12779/dnd.2021.20.4.70

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…The “Rey Complex Figure” scale comprises drawing a difficult set of figures, whether with a tablet or pen and paper. This test was used by Simfukwe et al [ 121 ] as the input for their DL solution. As two different types of classification problems were dealt with, two datasets were built: 1,296 CN and 913 non-CN subjects for the first study, whereas 1,649 CN, 453 MCI, and 107 AD patients for the second one.…”

Section: Resultsmentioning

confidence: 99%

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Cabrera-León,

Báez,

Fernández-López

et al. 2024

Journal of Alzheimer’s Disease

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Background: The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer’s disease (AD), as the most common type of dementia, has become more frequent too. Background: Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future. Methods: Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected. Results: The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field. Conclusions: The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

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Section: Resultsmentioning

confidence: 99%

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Cabrera-León,

Báez,

Fernández-López

et al. 2024

Journal of Alzheimer’s Disease

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“…The high sensitivity of the tool is clinically useful for disease screening, because it can avoid missing actual pathological conditions. 15 Nuclear medicine experts can easily apply the developed model to pre-determine whether amyloid pathology is present.…”

Section: Discussionmentioning

confidence: 99%

Classification of Aβ State From Brain Amyloid PET Images Using Machine Learning Algorithm

Simfukwe

Lee

Youn

2023

Dement Neurocogn Disord

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Background and Purpose Analyzing brain amyloid positron emission tomography (PET) images to access the occurrence of β-amyloid (Aβ) deposition in Alzheimer’s patients requires much time and effort from physicians, while the variation of each interpreter may differ. For these reasons, a machine learning model was developed using a convolutional neural network (CNN) as an objective decision to classify the Aβ positive and Aβ negative status from brain amyloid PET images. Methods A total of 7,344 PET images of 144 subjects were used in this study. The 18F-florbetaben PET was administered to all participants, and the criteria for differentiating Aβ positive and Aβ negative state was based on brain amyloid plaque load score (BAPL) that depended on the visual assessment of PET images by the physicians. We applied the CNN algorithm trained in batches of 51 PET images per subject directory from 2 classes: Aβ positive and Aβ negative states, based on the BAPL scores. Results The binary classification of the model average performance matrices was evaluated after 40 epochs of three trials based on test datasets. The model accuracy for classifying Aβ positivity and Aβ negativity was (95.00±0.02) in the test dataset. The sensitivity and specificity were (96.00±0.02) and (94.00±0.02), respectively, with an area under the curve of (87.00±0.03). Conclusions Based on this study, the designed CNN model has the potential to be used clinically to screen amyloid PET images.

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“…A systematic review about this topic reported that convolutional neural networks achieved the best results (weighted average accuracy 89%), but other approaches as Logistic Regression or Support Vector Machines also obtained high performances [17]. Other many applications of deep learning models include the classification of electroencephalographic signals for brain-computer interfaces [18]; the staging of neuropathological changes on digitized brain tissue slides [19]; the quantification of amyloid protein deposition in positron emission tomography images [20]; the scoring of the Rey Complex Figure copy, a test to evaluate visuospatial skills [21]; or the analysis of voice recordings to detect speech abnormalities [23] or dementia [22]. However, machine learning has not been explored for apraxia evaluation before.…”

Section: Related Workmentioning

confidence: 99%

Automatic apraxia detection using Deep Convolutional Neural Networks and similarity methods

Bringas

Nieto-Reyes

Vicedo

et al. 2023

Preprint

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Dementia represents one of the great problems to be solved in medicine for a society that is becoming increasingly long-lived. One of the main causes of dementia is Alzheimer’s disease, which accounts for 80% of cases. There is currently no cure for this disease, although there are treatments to try to alleviate its effects, which is why detecting Alzheimer’s disease in its early stages is crucial to slow down its evolution and thus help sufferers. One of the symptoms of the disease that manifests in its early stages is apraxia, difficulties in carrying out voluntary movements. In the clinical setting, apraxia is typically assessed by asking the patient to imitate hand gestures that are performed by the examiner. To automate this test, this paper proposes a system that, based on a video of the patient making the gesture, evaluates its execution. This evaluation is done in two steps, first extracting the skeleton the skeleton of the hands and then using a similarity function to obtain an objective score Automatic apraxia detection using Deep CNNs and similarity methods of the execution of the gesture. The results obtained in an experiment with several patients performing different gestures are shown, showing the effectiveness of the proposed method. The system is intended to serve as a diagnostic tool, enabling medical experts to detect possible mobility impairments in patients that may be signs of Alzheimer’s disease.

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Comparison of RCF Scoring System to Clinical Decision for the Rey Complex Figure Using Machine-Learning Algorithm

Cited by 10 publications

References 22 publications

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer’s Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Classification of Aβ State From Brain Amyloid PET Images Using Machine Learning Algorithm

Automatic apraxia detection using Deep Convolutional Neural Networks and similarity methods

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