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Csorba A, Imre L, Szalai I, Lukáts O, Fodor E, Szabó A, Nagy ZZ. Presentation of Meibomian Acini Compared to Dermal Papillae of the Eyelid Margin, Using Confocal Laser Scanning Microscopy and Corresponding Histology. Klin Monbl Augenheilkd 2024. [PMID: 38802075 DOI: 10.1055/a-2302-7526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
BACKGROUND Numerous studies have investigated the eyelid margin using confocal laser scanning microscopy (CLSM) and have presented morphological alterations of the examined structures, which were presumed to be Meibomian acini. However, recent data confirm that these structures are the cross-sections of dermal papillae of the dermoepidermal junction. This study aims to present the morphological appearance of Meibomian acini examined by confocal laser scanning microscopy in comparison to dermal papillae, and to reveal the corresponding patterns with specific histological sections. METHODS AND MATERIAL Twenty healthy patients were examined with a CLSM device in vivo at the marginal edge of the eyelid. Twenty-two samples of full-thickness eyelid wedges from 22 patients treated surgically with ectropion were collected, of which 11 freshly excised samples were imaged on the incision surface with CLSM ex vivo and 11 eyelids underwent conventional histological preparation. The represented structures on CLSM images were compared to Meibomian acini on histological sections in terms of area, longest and shortest diameter, as well as depth and density. RESULTS On in vivo CLSM images, Meibomian orifices, epidermal cells, and dermal connective tissue could be identified, the latter in a cross-sectional view of the dermal papillae surrounded by basal cells of the epidermis, forming reflective ring-like structures. All morphological parameters of these structures differed from Meibomian acini measured on histological sections. In contrast, the CLSM images of the incision surface showed acinar units with the same morphology as the Meibomian acini seen in the histological images and no statistically significant difference was found between the corresponding parameters. CONCLUSION The morphological appearance of Meibomian acini differs from the structures that were previously presumed as Meibomian glands on CLSM images. In vivo imaging of Meibomian glands by commonly used in vivo CLSM cannot be performed.
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Affiliation(s)
- A Csorba
- Department of Ophthalmology, Semmelweis University of Medicine, Budapest, Hungary
| | - L Imre
- Department of Ophthalmology, Semmelweis University of Medicine, Budapest, Hungary
- Department of Ophthalmology, Bajcsy-Zsilinszky Teaching Hospital, Budapest, Hungary
| | - I Szalai
- Department of Ophthalmology, Semmelweis University of Medicine, Budapest, Hungary
| | - O Lukáts
- Department of Ophthalmology, Semmelweis University of Medicine, Budapest, Hungary
| | - E Fodor
- Department of Ophthalmology, Semmelweis University of Medicine, Budapest, Hungary
| | - A Szabó
- Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
| | - Z Z Nagy
- Department of Ophthalmology, Semmelweis University of Medicine, Budapest, Hungary
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Fayemiwo MA, Olowookere TA, Olaniyan OO, Ojewumi TO, Oyetade IS, Freeman S, Jackson P. Immediate word recall in cognitive assessment can predict dementia using machine learning techniques. Alzheimers Res Ther 2023; 15:111. [PMID: 37322550 DOI: 10.1186/s13195-023-01250-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 05/29/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND Dementia, one of the fastest-growing public health problems, is a cognitive disorder known to increase in prevalence as age increases. Several approaches had been used to predict dementia, especially in building machine learning (ML) models. However, previous research showed that most models developed had high accuracies, and they suffered from considerably low sensitivities. The authors discovered that the nature and the scope of the data used in this study had not been explored to predict dementia based on cognitive assessment using ML techniques. Therefore, we hypothesized that using word-recall cognitive features could help develop models for the prediction of dementia through ML techniques and emphasized assessing the models' sensitivity performance. METHODS Nine distinct experiments were conducted to determine which responses from either sample person (SP)'s or proxy's responses in the "word-delay," "tell-words-you-can-recall," and "immediate-word-recall" tasks are essential in the prediction of dementia cases, and to what extent the combination of the SP's or proxy's responses can be helpful in the prediction of dementia. Four ML algorithms (K-nearest neighbors (KNN), decision tree, random forest, and artificial neural networks (ANN)) were used in all the experiments to build predictive models using data from the National Health and Aging Trends Study (NHATS). RESULTS In the first scenario of experiments using "word-delay" cognitive assessment, the highest sensitivity (0.60) was obtained from combining the responses from both SP and proxies trained KNN, random forest, and ANN models. Also, in the second scenario of experiments using the "tell-words-you-can-recall" cognitive assessment, the highest sensitivity (0.60) was obtained by combining the responses from both SP and proxies trained KNN model. From the third set of experiments performed in this study on the use of "Word-recall" cognitive assessment, it was equally discovered that the use of combined responses from both SP and proxies trained models gave the highest sensitivity of 1.00 (as obtained from all the four models). CONCLUSION It can be concluded that the combination of responses in a word recall task as obtained from the SP and proxies in the dementia study (based on the NHATS dataset) is clinically useful in predicting dementia cases. Also, the use of "word-delay" and "tell-words-you-can-recall" cannot reliably predict dementia as they resulted in poor performances in all the developed models, as shown in all the experiments. However, immediate-word recall is reliable in predicting dementia, as seen in all the experiments. This, therefore, shows the significance of immediate-word-recall cognitive assessment in predicting dementia and the efficiency of combining responses from both SP and proxies in the immediate-word-recall task.
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Affiliation(s)
- Michael Adebisi Fayemiwo
- Department of Computer Science, Redeemer's University, Ede, Osun State, Nigeria
- School of Nursing, University of Northern British Columbia, Prince George, British Columbia, Canada
- School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, Northern Ireland, UK
| | | | | | | | | | - Shannon Freeman
- School of Nursing, University of Northern British Columbia, Prince George, British Columbia, Canada
| | - Piper Jackson
- Department of Computing Science, Thompson Rivers University, Kamloops, British Columbia, Canada
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ZhuParris A, de Goede AA, Yocarini IE, Kraaij W, Groeneveld GJ, Doll RJ. Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115243. [PMID: 37299969 DOI: 10.3390/s23115243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/23/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. OBJECTIVE This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. METHODS This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. RESULTS This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. CONCLUSION mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.
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Affiliation(s)
- Ahnjili ZhuParris
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, The Netherlands
| | - Annika A de Goede
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
| | - Iris E Yocarini
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Wessel Kraaij
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
- The Netherlands Organisation for Applied Scientific Research (TNO), Anna van Buerenplein 1, 2595 DA, Den Haag, The Netherlands
| | - Geert Jan Groeneveld
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
- Leiden Institute of Advanced Computer Science (LIACS), Snellius Gebouw, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
| | - Robert Jan Doll
- Centre for Human Drug Research (CHDR), Zernikedreef 8, 2333 CL Leiden, The Netherlands
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Huang CY, Lin YC, Lu YC, Chen CI. Application of Grey Relational Analysis to Predict Dementia Tendency by Cognitive Function, Sleep Disturbances, and Health Conditions of Diabetic Patients. Brain Sci 2022; 12:brainsci12121642. [PMID: 36552102 PMCID: PMC9775556 DOI: 10.3390/brainsci12121642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 12/05/2022] Open
Abstract
Background: The number of elderly diabetic patients has been increasing recently, and these patients have a higher morbidity of dementia than those without diabetes. Diabetes is associated with an increased risk for the development of dementia in elderly individuals, which is a serious health problem. Objectives: The primary aim was to examine whether diabetes is a risk factor for dementia among elderly individuals. The secondary aim was to apply grey theory to integrate the results and how they relate to cognitive impairments in elderly diabetic patients and to predict which participants are at high risk of developing dementia. Methods: Two hundred and twenty patients aged 50 years or older who were diagnosed with diabetes mellitus were recruited. Information on demographics, disease characteristics, activities of daily living, Mini Mental State Examination, sleep quality, depressive symptoms, and health-related quality of life was collected via questionnaires. The grey relational analysis approach was applied to evaluate the relationship between the results and health outcomes. Results: A total of 13.6% of participants had cognitive disturbances, of whom 1.4% had severe cognitive dysfunction. However, with regard to sleep disorders, 56.4% had sleep disturbances of varying degrees from light to severe. Further investigation is needed to address this problem. A higher prevalence of sleep disturbances among diabetic patients translates to a higher degree of depressive symptoms and a worse physical and mental health-related quality of life. Furthermore, based on the grey relational analysis, the grey relation coefficient varies from 0.6217~0.7540. Among the subjects, Participant 101 had the highest value, suggesting a need for immediate medical care. In this study, we observed that 20% of the total participants, for whom the grey relation coefficient was 0.6730, needed further and immediate medical care.
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Affiliation(s)
- Chiung-Yu Huang
- Nursing Department, I-Shou University, Kaohsiung 82445, Taiwan
| | - Yu-Ching Lin
- Department of Family Medicine and Physical Examination, E-Da Hospital, Kaohsiung 82445, Taiwan
| | - Yung-Chuan Lu
- College of Medicine, School of Medicine for International Students, I-Shou University, Kaohsiung 82445, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, E-Da Hospital, Kaohsiung 82445, Taiwan
| | - Chun-I Chen
- Management College, I-Shou University, Kaohsiung 82445, Taiwan
- Correspondence:
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Lim J. A smart healthcare-based system for classification of dementia using deep learning. Digit Health 2022; 8:20552076221131667. [PMID: 36312848 PMCID: PMC9597480 DOI: 10.1177/20552076221131667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 09/22/2022] [Indexed: 11/07/2022] Open
Abstract
Objectives This study aims to develop a deep learning-based classification model for early detection of dementia using a wearable device that can measure the electrical conductivity of the skin, temperature, and movement as factors related to dementia, interlocking them with an application, and analyzing the collected data. Methods This study was conducted on 18 elderly individuals (5 males, 13 females) aged 65 years or older who consented to the study. The Korean Mini-Mental State Examination survey for cognitive function tests was conducted by well-trained researchers. The subjects were first grouped into high- or low-risk group for dementia based on their Korean Mini-Mental State Examination score. Data obtained by wearable devices of each subject were then used for the classification of the high- and low-risk groups of dementia through a smart healthcare-based system implementing a deep neural network with scaled principal component analysis. The correlation coefficients between the Korean Mini-Mental State Examination score and the featured data were also investigated. Results Our study showed that the proposed system using a deep neural network with scaled principal component analysis was effective in detecting individuals at high risk for dementia with up to 99% accuracy and which performance was better compared with commonly used classification algorithms. In addition, it was found that the electrical conductivity of skin had the closest correlation with the results of the Korean Mini-Mental State Examination score among data collected through wearable devices in this study. Conclusions Our proposed system can contribute to effective early detection of dementia for the elderly, using a non-invasive and easy-to-wear wearable device and classification algorithms with a simple cognitive function test. In the future, we intend to have more subjects participate in the experiment, to include more relevant variables in the wearable device, and to analyze the effectiveness of the smart healthcare-based dementia classification system over the long term.
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Affiliation(s)
- Jihye Lim
- Department of Health Care and Science, Donga University, Saha-Gu Busan, Korea,Department of Health Care and Science, Donga University, Nakdong-Daero 550 beongil 37, Saha-Gu Busan 49315, Korea.
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Jennings JL, Peraza LR, Baker M, Alter K, Taylor JP, Bauer R. Investigating the power of eyes open resting state EEG for assisting in dementia diagnosis. Alzheimers Res Ther 2022; 14:109. [PMID: 35932060 PMCID: PMC9354304 DOI: 10.1186/s13195-022-01046-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 07/13/2022] [Indexed: 11/21/2022]
Abstract
INTRODUCTION The differentiation of Lewy body dementia from other common dementia types clinically is difficult, with a considerable number of cases only being found post-mortem. Consequently, there is a clear need for inexpensive and accurate diagnostic approaches for clinical use. Electroencephalography (EEG) is one potential candidate due to its relatively low cost and non-invasive nature. Previous studies examining the use of EEG as a dementia diagnostic have focussed on the eyes closed (EC) resting state; however, eyes open (EO) EEG may also be a useful adjunct to quantitative analysis due to clinical availability. METHODS We extracted spectral properties from EEG signals recorded under research study protocols (1024 Hz sampling rate, 10:5 EEG layout). The data stems from a total of 40 dementia patients with an average age of 74.42, 75.81 and 73.88 years for Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD), respectively, and 15 healthy controls (HC) with an average age of 76.93 years. We utilised k-nearest neighbour, support vector machine and logistic regression machine learning to differentiate between groups utilising spectral data from the delta, theta, high theta, alpha and beta EEG bands. RESULTS We found that the combination of EC and EO resting state EEG data significantly increased inter-group classification accuracy compared to methods not using EO data. Secondly, we observed a distinct increase in the dominant frequency variance for HC between the EO and EC state, which was not observed within any dementia subgroup. For inter-group classification, we achieved a specificity of 0.87 and sensitivity of 0.92 for HC vs dementia classification and 0.75 specificity and 0.91 sensitivity for AD vs DLB classification, with a k-nearest neighbour machine learning model which outperformed other machine learning methods. CONCLUSIONS The findings of our study indicate that the combination of both EC and EO quantitative EEG features improves overall classification accuracy when classifying dementia types in older age adults. In addition, we demonstrate that healthy controls display a definite change in dominant frequency variance between the EC and EO state. In future, a validation cohort should be utilised to further solidify these findings.
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Affiliation(s)
- Jack L Jennings
- School of Computing, Newcastle University, Newcastle upon Tyne, UK.
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK.
| | | | - Mark Baker
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus of Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
- Department of Clinical Neurophysiology, Royal Victoria Infirmary, Queen Victoria Rd, Newcastle upon Tyne, NE1 4LP, UK
| | - Kai Alter
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, UK
- Faculty of Medical Sciences, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Campus of Ageing and Vitality, Newcastle upon Tyne, NE4 5PL, UK
| | - Roman Bauer
- Department of Computer Science, University of Surrey, Guildford, GU2 7XH, UK
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