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Lerch O, Ferreira D, Stomrud E, van Westen D, Tideman P, Palmqvist S, Mattsson-Carlgren N, Hort J, Hansson O, Westman E. Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns. Alzheimers Res Ther 2024; 16:153. [PMID: 38970077 PMCID: PMC11225196 DOI: 10.1186/s13195-024-01517-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 06/24/2024] [Indexed: 07/07/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on β-amyloid (Aβ) positive patients with amnestic mild cognitive impairment (aMCI). METHODS We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aβ-negative = 220; SCD, Aβ positive and negative = 139; aMCI, Aβ-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aβ positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. RESULTS In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). CONCLUSION When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.
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Affiliation(s)
- Ondrej Lerch
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, 15006, Czech Republic.
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, 14183, Sweden.
| | - Daniel Ferreira
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, 14183, Sweden
- Department of Radiology, Mayo Clinic, Rochester, MN, 55902, USA
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Danielle van Westen
- Diagnostic Radiology, Institution for Clinical Sciences Lund, Lund University, Lund, 22184, Sweden
| | - Pontus Tideman
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Jakub Hort
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, 15006, Czech Republic
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, 20502, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, 21428, Sweden
| | - Eric Westman
- Division of Clinical Geriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences, and Society, Karolinska Institutet, Stockholm, 14183, Sweden
- Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE58AF, UK
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García-Gutiérrez F, Hernández-Lorenzo L, Cabrera-Martín MN, Matias-Guiu JA, Ayala JL. Predicting changes in brain metabolism and progression from mild cognitive impairment to dementia using multitask Deep Learning models and explainable AI. Neuroimage 2024; 297:120695. [PMID: 38942101 DOI: 10.1016/j.neuroimage.2024.120695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 06/18/2024] [Indexed: 06/30/2024] Open
Abstract
BACKGROUND The prediction of Alzheimer's disease (AD) progression from its early stages is a research priority. In this context, the use of Artificial Intelligence (AI) in AD has experienced a notable surge in recent years. However, existing investigations predominantly concentrate on distinguishing clinical phenotypes through cross-sectional approaches. This study aims to investigate the potential of modeling additional dimensions of the disease, such as variations in brain metabolism assessed via [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET), and utilize this information to identify patients with mild cognitive impairment (MCI) who will progress to dementia (pMCI). METHODS We analyzed data from 1,617 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had undergone at least one FDG-PET scan. We identified the brain regions with the most significant hypometabolism in AD and used Deep Learning (DL) models to predict future changes in brain metabolism. The best-performing model was then adapted under a multi-task learning framework to identify pMCI individuals. Finally, this model underwent further analysis using eXplainable AI (XAI) techniques. RESULTS Our results confirm a strong association between hypometabolism, disease progression, and cognitive decline. Furthermore, we demonstrated that integrating data on changes in brain metabolism during training enhanced the models' ability to detect pMCI individuals (sensitivity=88.4%, specificity=86.9%). Lastly, the application of XAI techniques enabled us to delve into the brain regions with the most significant impact on model predictions, highlighting the importance of the hippocampus, cingulate cortex, and some subcortical structures. CONCLUSION This study introduces a novel dimension to predictive modeling in AD, emphasizing the importance of projecting variations in brain metabolism under a multi-task learning paradigm.
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Affiliation(s)
| | | | - María Nieves Cabrera-Martín
- Department of Nuclear Medicine, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain.
| | - Jordi A Matias-Guiu
- Department of Neurology, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain.
| | - José L Ayala
- Department of Computer Architecture and Automation, Universidad Complutense, Madrid, Spain.
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Aucoin A, Lin KK, Gothard KM. Detection of latent brain states from baseline neural activity in the amygdala. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.14.598974. [PMID: 38915563 PMCID: PMC11195171 DOI: 10.1101/2024.06.14.598974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The amygdala responds to a large variety of socially and emotionally salient environmental and interoceptive stimuli. The context in which these stimuli occur determines their social and emotional significance. In canonical neurophysiological studies, the fast-paced succession of stimuli and events induce phasic changes in neural activity. During inter-trial intervals neural activity is expected to return to a stable and relatively featureless baseline. Context, such as the presence of a social partner, or the similarity of trials in a blocked design, induces brain states that can transcend the fast-paced succession of stimuli and can be recovered from the baseline firing rate of neurons. Indeed, the baseline firing rates of neurons in the amygdala change between blocks of trials of gentle grooming touch, delivered by a trusted social partner, and non-social airflow stimuli, delivered by a computer-controlled air valve. In this experimental paradigm, the presence of the groomer alone was sufficient to induce small but significant changes in baseline firing rates. Here, we examine local field potentials (LFP) recorded during these baseline periods to determine whether context was encoded by network dynamics that emerge in the local field potentials from the activity of large ensembles of neurons. We found that machine learning techniques can reliably decode social vs. non-social context from spectrograms of baseline local field potentials. Notably, decoding accuracy improved significantly with access to broad-band information. No significant differences were detected between the nuclei of the amygdala that receive direct or indirect inputs from areas of the prefrontal cortex known to coordinate flexible, context-dependent behaviors. The lack of nuclear specificity suggests that context-related synaptic inputs arise from a shared source, possibly interoceptive inputs that signal the sympathetic- vs. parasympathetic-dominated states characterizing non-social and social blocks, respectively.
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Affiliation(s)
- Alexa Aucoin
- Program in Applied Mathematics, University of Arizona
| | - Kevin K Lin
- Program in Applied Mathematics, University of Arizona
- Department of Mathematics, University of Arizona
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Lee MW, Kim HW, Choe YS, Yang HS, Lee J, Lee H, Yong JH, Kim D, Lee M, Kang DW, Jeon SY, Son SJ, Lee YM, Kim HG, Kim REY, Lim HK. A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease. Sci Rep 2024; 14:12276. [PMID: 38806509 PMCID: PMC11133319 DOI: 10.1038/s41598-024-60134-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 04/19/2024] [Indexed: 05/30/2024] Open
Abstract
Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.
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Affiliation(s)
- Min-Woo Lee
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Hye Weon Kim
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Yeong Sim Choe
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Hyeon Sik Yang
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Jiyeon Lee
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Hyunji Lee
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Jung Hyeon Yong
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Donghyeon Kim
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Minho Lee
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
| | - So Yeon Jeon
- Department of Psychiatry, Chungnam National University Hospital, Daejeon, 35015, Republic of Korea
- Department of Psychiatry, College of Medicine, Chungnam National University, Daejeon, 35015, Republic of Korea
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, 16499, Republic of Korea
| | - Young-Min Lee
- Department of Psychiatry, Pusan National University School of Medicine, Pusan National University, Busan, 49241, Republic of Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University School of Medicine, Seoul, 02447, Republic of Korea
| | - Regina E Y Kim
- Research Institute, Neurophet Inc., Seoul, 06234, Republic of Korea.
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 10 63-ro, Yeongdeungpo-gu, Seoul, 07345, Korea.
- CMC Institute for Basic Medical Science, the Catholic Medical Center of The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea.
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Xu J, Yang Y, Gao Z, Song T, Ma Y, Yu X, Shi C. Distinguishing EGFR mutation molecular subtypes based on MRI radiomics features of lung adenocarcinoma brain metastases. Clin Neurol Neurosurg 2024; 240:108258. [PMID: 38552362 DOI: 10.1016/j.clineuro.2024.108258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 03/23/2024] [Indexed: 04/29/2024]
Abstract
OBJECTIVE To explore the feasibility of identifying epidermal growth factor receptor (EGFR) mutation molecular subtypes in primary lesions based on the radiomics features of lung adenocarcinoma brain metastases using magnetic resonance imaging (MRI). METHODS We retrospectively analyzed clinical, imaging, and genetic testing data of patients with lung adenocarcinoma with EGFR gene mutations who had brain metastases. Three-dimensional radiomics features were extracted from contrast-enhanced T1-weighted images. The volume of interest was delineated and normalized using Z-score, dimensionality reduction was performed using principal component analysis, feature selection using Relief, and radiomics model construction using adaptive boosting as a classifier. Data were randomly divided into training and testing datasets at an 8:2 ratio. Five-fold cross-validation was conducted in the training set to select the optimal radiomics features and establish a predictive model for distinguishing between exon 19 deletion (19Del) and exon 21 L858R point mutation (21L858R), the two most common EGFR gene mutations. The testing set was used for external validation of the models. Model performance was evaluated using receiver operating characteristic curve and decision curve analyses. RESULTS Overall, 86 patients with 228 brain metastases were included. Patient age was identified as an independent predictor for distinguishing between 19Del and 21L858R. The area under the curve (AUC) values of the radiomics model in the training and testing datasets were 0.895 (95% confidence interval [CI]: 0.850-0.939) and 0.759 (95% CI: 0.0.614-0.903), respectively. The AUC for diagnosis of all cases using a combined model of age and radiomics was 0.888 (95% CI: 0.846-0.930), slightly higher than that of the radiomics model alone (0.866, 95% CI: 0.820-0.913), but without statistical significance (p=0.1626). In the decision curve analysis, both models demonstrated clinical net benefits. CONCLUSIONS The radiomics model based on MRI of lung adenocarcinoma brain metastases could distinguish between EGFR 19Del and 21L858R mutations in the primary lesion.
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Affiliation(s)
- Jiali Xu
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233004, China; Department of Medical Imaging Diagnosis, School of Medical Imaging, Bengbu Medical University, Bengbu, Anhui, China.
| | - Yuqiong Yang
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233004, China; School of Graduate, Bengbu Medical University, Bengbu, Anhui 233030,China
| | - Zhizhen Gao
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233004, China
| | - Tao Song
- Vascular Surgery Department, the First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233004, China
| | - Yichuan Ma
- Department of Radiology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui 233004, China
| | - Xiaojun Yu
- Department of Medical Imaging Center, the First Affiliated Hospital, Jinan University, Guangzhou 510630, China
| | - Changzheng Shi
- Department of Medical Imaging Center, the First Affiliated Hospital, Jinan University, Guangzhou 510630, China
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6
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Wang Y, Gao R, Wei T, Johnston L, Yuan X, Zhang Y, Yu Z. Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects. J Transl Med 2024; 22:265. [PMID: 38468358 PMCID: PMC10926590 DOI: 10.1186/s12967-024-05025-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/24/2024] [Indexed: 03/13/2024] Open
Abstract
BACKGROUND Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression. METHODS This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. RESULTS On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers. CONCLUSIONS The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.
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Affiliation(s)
- Yifan Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitian Gao
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Luke Johnston
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Xin Yuan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai, 200240, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China.
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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7
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Lamadé A, Beekmann D, Eickhoff S, Grefkes C, Tscherpel C, Meyding-Lamadé U, Bassa B. [Quality indicators artificial intelligence]. DER NERVENARZT 2024; 95:242-246. [PMID: 38085285 DOI: 10.1007/s00115-023-01573-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/30/2023] [Indexed: 03/06/2024]
Abstract
The ability of some artificial intelligence (AI) systems to autonomously evolve and the sometimes very limited possibilities to comprehend their decision-making processes present new challenges to our legal system. At a European level this has led to reform efforts, of which the proposal for a European AI regulation promises to close regulatory gaps in existing product safety law through cross-sectoral AI-specific safety requirements. A prerequisite, however, would be that the EU legislator does not only avoid duplications and contradictions with existing safety requirements but also refrains from imposing exaggerated and unattainable demands. If this were to be taken into consideration, the new safety requirements could also be used to specify the undefined standard of care in liability law. Nevertheless, challenges in the context of provability continue to remain unresolved, posing a risk of rendering the legal protection efforts of the aggrieved party ineffective. It remains to be seen whether the EU legislator will address this need for reform with the recently proposed reform of product liability law by the Commission.
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Affiliation(s)
| | - Dustin Beekmann
- Philipps-Universität Marburg, Marburg, Deutschland
- Watson Farley & Williams LLP, Hamburg, Deutschland
| | - Simon Eickhoff
- Institut für Neurowissenschaften und Medizin, Jülich Forschungszentrum, Jülich, Deutschland
| | - Christian Grefkes
- Zentrum der Neurologie und Neurochirurgie, Universitätsklinikum Frankfurt, Frankfurt, Deutschland
| | - Caroline Tscherpel
- Zentrum der Neurologie und Neurochirurgie, Universitätsklinikum Frankfurt, Frankfurt, Deutschland
| | - Uta Meyding-Lamadé
- Klinik für Neurologie, Nordwestkrankenhaus in Frankfurt am Main, Frankfurt am Main, Deutschland
| | - Burc Bassa
- Klinik für Neurologie, Nordwestkrankenhaus in Frankfurt am Main, Frankfurt am Main, Deutschland
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Zhang X, Gao L, Wang Z, Yu Y, Zhang Y, Hong J. Improved neural network with multi-task learning for Alzheimer's disease classification. Heliyon 2024; 10:e26405. [PMID: 38434063 PMCID: PMC10906290 DOI: 10.1016/j.heliyon.2024.e26405] [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: 09/27/2023] [Revised: 01/13/2024] [Accepted: 02/13/2024] [Indexed: 03/05/2024] Open
Abstract
Alzheimer's disease(AD) poses a significant challenge due to its widespread prevalence and the lack of effective treatments, highlighting the urgent need for early detection. This research introduces an enhanced neural network, named ADnet, which is based on the VGG16 model, to detect Alzheimer's disease using two-dimensional MRI slices. ADNet incorporates several key improvements: it replaces traditional convolution with depthwise separable convolution to reduce model parameters, replaces the ReLU activation function with ELU to address potential issues with exploding gradients, and integrates the SE(Squeeze-and-Excitation) module to enhance feature extraction efficiency. In addition to the primary task of MRI feature extraction, ADnet is simultaneously trained on two auxiliary tasks: clinical dementia score regression and mental state score regression. Experimental results demonstrate that compared to the baseline VGG16, ADNet achieves a 4.18% accuracy improvement for AD vs. CN classification and a 6% improvement for MCI vs. CN classification. These findings highlight the effectiveness of ADnet in classifying Alzheimer's disease, providing crucial support for early diagnosis and intervention by medical professionals. The proposed enhancements represent advancements in neural network architecture and training strategies for improved AD classification.
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Affiliation(s)
- Xin Zhang
- School of Electronic and Information Engineering, Wuyi University, Jiangmen, 529000, China
| | - Le Gao
- School of Electronic and Information Engineering, Wuyi University, Jiangmen, 529000, China
| | - Zhimin Wang
- School of Electronic and Information Engineering, Wuyi University, Jiangmen, 529000, China
| | - Yong Yu
- School of Computer Science, Shaanxi Normal University, Xi'an, 710062, China
| | - Yudong Zhang
- School of Computing and Mathematic Sciences, University of Leicester, Leicester, LE17RH, UK
| | - Jin Hong
- School of Information Engineering, Nanchang University, Nanchang, 330031, China
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Moradi E, Prakash M, Hall A, Solomon A, Strange B, Tohka J. Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals. Alzheimers Res Ther 2024; 16:46. [PMID: 38414035 PMCID: PMC10900722 DOI: 10.1186/s13195-024-01415-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 02/11/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND The pathophysiology of Alzheimer's disease (AD) involves β -amyloid (A β ) accumulation. Early identification of individuals with abnormal β -amyloid levels is crucial, but A β quantification with positron emission tomography (PET) and cerebrospinal fluid (CSF) is invasive and expensive. METHODS We propose a machine learning framework using standard non-invasive (MRI, demographics, APOE, neuropsychology) measures to predict future A β -positivity in A β -negative individuals. We separately study A β -positivity defined by PET and CSF. RESULTS Cross-validated AUC for 4-year A β conversion prediction was 0.78 for the CSF-based and 0.68 for the PET-based A β definitions. Although not trained for the clinical status-change prediction, the CSF-based model excelled in predicting future mild cognitive impairment (MCI)/dementia conversion in cognitively normal/MCI individuals (AUCs, respectively, 0.76 and 0.89 with a separate dataset). CONCLUSION Standard measures have potential in detecting future A β -positivity and assessing conversion risk, even in cognitively normal individuals. The CSF-based definition led to better predictions than the PET-based definition.
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Affiliation(s)
- Elaheh Moradi
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland.
| | - Mithilesh Prakash
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland
| | - Anette Hall
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
| | - Alina Solomon
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
- Division of Clinical Geriatrics, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College London, London, UK
| | - Bryan Strange
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, Madrid, Spain
- Reina Sofia Centre for Alzheimer's Research, Madrid, Spain
| | - Jussi Tohka
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70150, Finland
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Kim S, Adams JN, Chappel-Farley MG, Keator D, Janecek J, Taylor L, Mikhail A, Hollearn M, McMillan L, Rapp P, Yassa MA. Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden. Neuropsychologia 2023; 191:108727. [PMID: 37939874 PMCID: PMC10764118 DOI: 10.1016/j.neuropsychologia.2023.108727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/20/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023]
Abstract
Alzheimer's disease (AD) is the most common type of dementia, characterized by early memory impairments and gradual worsening of daily functions. AD-related pathology, such as amyloid-beta (Aβ) plaques, begins to accumulate many years before the onset of clinical symptoms. Predicting risk for AD via related pathology is critical as the preclinical stage could serve as a therapeutic time window, allowing for early management of the disease and reducing health and economic costs. Current methods for detecting AD pathology, however, are often expensive and invasive, limiting wide and easy access to a clinical setting. A non-invasive, cost-efficient platform, such as computerized cognitive tests, could be potentially useful to identify at-risk individuals as early as possible. In this study, we examined the diagnostic value of an episodic memory task, the mnemonic discrimination task (MDT), for predicting risk of cognitive impairment or Aβ burden. We constructed a random forest classification algorithm, utilizing MDT performance metrics and various neuropsychological test scores as input features, and assessed model performance using area under the curve (AUC). Models based on MDT performance metrics achieved classification results with an AUC of 0.83 for cognitive status and an AUC of 0.64 for Aβ status. Our findings suggest that mnemonic discrimination function may be a useful predictor of progression to prodromal AD or increased risk of Aβ load, which could be a cost-efficient, noninvasive cognitive testing solution for potentially wide-scale assessment of AD pathological and cognitive risk.
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Affiliation(s)
- Soyun Kim
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA.
| | - Jenna N Adams
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Miranda G Chappel-Farley
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - David Keator
- Department of Psychiatry and Behavioral Sciences, University of California, Irvine, CA, USA
| | - John Janecek
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Lisa Taylor
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Abanoub Mikhail
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Martina Hollearn
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Liv McMillan
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA
| | - Paul Rapp
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA; Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, USA
| | - Michael A Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA; Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, Irvine, CA, USA.
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11
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Lü W, Zhang M, Yu W, Kuang W, Chen L, Zhang W, Yu J, Lü Y. Differentiating Alzheimer's disease from mild cognitive impairment: a quick screening tool based on machine learning. BMJ Open 2023; 13:e073011. [PMID: 38070931 PMCID: PMC10729043 DOI: 10.1136/bmjopen-2023-073011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder characterised by cognitive decline, behavioural and psychological symptoms of dementia (BPSD) and impairment of activities of daily living (ADL). Early differentiation of AD from mild cognitive impairment (MCI) is necessary. METHODS A total of 458 patients newly diagnosed with AD and MCI were included. Eleven batteries were used to evaluate ADL, BPSD and cognitive function (ABC). Machine learning approaches including XGboost, classification and regression tree, Bayes, support vector machines and logical regression were used to build and verify the new tool. RESULTS The Alzheimer's Disease Assessment Scale (ADAS-cog) word recognition task showed the best importance in judging AD and MCI, followed by correct numbers of auditory verbal learning test delay recall and ADAS-cog orientation. We also provided a selected ABC-Scale that covered ADL, BPSD and cognitive function with an estimated completion time of 18 min. The sensitivity was improved in the four models. CONCLUSION The quick screen ABC-Scale covers three dimensions of ADL, BPSD and cognitive function with good efficiency in differentiating AD from MCI.
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Affiliation(s)
- Wenqi Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
| | - Meiwei Zhang
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Weihua Yu
- Institutes of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital, Sichuan University, Chengdu, China
| | - Lihua Chen
- Institutes of Neuroscience, Chongqing Medical University, Chongqing, China
| | - Wenbo Zhang
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Juan Yu
- College of Electrical Engineering, Chongqing University, Chongqing, China
| | - Yang Lü
- Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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12
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Borchert RJ, Azevedo T, Badhwar A, Bernal J, Betts M, Bruffaerts R, Burkhart MC, Dewachter I, Gellersen HM, Low A, Lourida I, Machado L, Madan CR, Malpetti M, Mejia J, Michopoulou S, Muñoz-Neira C, Pepys J, Peres M, Phillips V, Ramanan S, Tamburin S, Tantiangco HM, Thakur L, Tomassini A, Vipin A, Tang E, Newby D, Ranson JM, Llewellyn DJ, Veldsman M, Rittman T. Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review. Alzheimers Dement 2023; 19:5885-5904. [PMID: 37563912 DOI: 10.1002/alz.13412] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 05/18/2023] [Accepted: 06/02/2023] [Indexed: 08/12/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. METHODS We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. RESULTS A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. DISCUSSION The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. HIGHLIGHTS There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias.
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Affiliation(s)
- Robin J Borchert
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Tiago Azevedo
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
| | - AmanPreet Badhwar
- Department of Pharmacology and Physiology, University of Montreal, Montreal, Canada
- Centre de recherche de l'Institut Universitaire de Gériatrie (CRIUGM), Montreal, Canada
| | - Jose Bernal
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Matthew Betts
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Center for Behavioral Brain Sciences, University of Magdeburg, Magdeburg, Germany
| | - Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit, Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | | | - Ilse Dewachter
- Biomedical Research Institute, Hasselt University, Diepenbeek, Belgium
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Audrey Low
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - Luiza Machado
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Jhony Mejia
- Department of Biomedical Engineering, Universidad de Los Andes, Bogotá, Colombia
| | - Sofia Michopoulou
- Imaging Physics, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Carlos Muñoz-Neira
- Research into Memory, Brain sciences and dementia Group (ReMemBr Group), Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Artificial Intelligence & Computational Neuroscience Group (AICN Group), Sheffield Institute for Translational Neuroscience (SITraN), Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Jack Pepys
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Italy
| | - Marion Peres
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Siddharth Ramanan
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, UK
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alessandro Tomassini
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Eugene Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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13
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Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia research methods optimization. Alzheimers Dement 2023; 19:5934-5951. [PMID: 37639369 DOI: 10.1002/alz.13441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/31/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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14
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Lamontagne-Caron R, Desrosiers P, Potvin O, Doyon N, Duchesne S. Predicting cognitive decline in a low-dimensional representation of brain morphology. Sci Rep 2023; 13:16793. [PMID: 37798311 PMCID: PMC10556003 DOI: 10.1038/s41598-023-43063-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 09/19/2023] [Indexed: 10/07/2023] Open
Abstract
Identifying early signs of neurodegeneration due to Alzheimer's disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We trained two embeddings, one on cortical thickness measurements of 6237 cognitively healthy participants aged 18-100 years old and the other on 233 mild cognitively impaired (MCI) and AD participants from the longitudinal database, the Alzheimer's Disease Neuroimaging Initiative database (ADNI). Each participant had multiple visits ([Formula: see text]), one year apart. The first embedding's principal axis was shown to be positively associated ([Formula: see text]) with participants' age. Data from ADNI is projected into these 2D spaces. After clustering the data, average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their trajectory in a 2D space with an AUC of 0.80 with 10-fold cross-validation.
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Affiliation(s)
- Rémi Lamontagne-Caron
- Département de médecine, Université Laval, Quebec, QC, G1V 0A6, Canada.
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada.
| | - Patrick Desrosiers
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de physique, de génie physique et d'optique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | | | - Nicolas Doyon
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de mathématiques et de statistique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | - Simon Duchesne
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Département de radiologie et médecine nucléaire, Université Laval, Quebec, QC, G1V 0A6, Canada
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15
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Ge X, Cui K, Qin Y, Chen D, Han H, Yu H. Screening strategies and dynamic risk prediction models for Alzheimer's disease. J Psychiatr Res 2023; 166:92-99. [PMID: 37757706 DOI: 10.1016/j.jpsychires.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/16/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Characterizing the progression from Mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early AD prevention and targeted intervention. Our goal was to construct precise screening schemes for individuals with different risk of AD and to establish prognosis models for them. METHODS We constructed a retrospective cohort by reviewing individuals with baseline diagnosis of MCI and at least one follow-up visits between November 2005 and May 2021. They were stratified into high-risk and low-risk groups with longitudinal cognitive trajectory. Then, we established a screening framework and obtained optimal screening strategies for two risk groups. Cox and random survival forest (RSF) models were developed for dynamic prognosis prediction. RESULTS In terms of screening strategies, the combination of Clinical Dementia Rating Sum of Boxes (CDRSB) and hippocampus volume was recommended for the high-risk MCI group, while the combination of Alzheimer's Disease Assessment Scale Cognitive 13 items (ADAS13) and FAQ was recommended for low-risk MCI group. The concordance index (C-index) of the Cox model for the high-risk group was 0.844 (95% CI: 0.815-0.873) and adjustments for demographic information and APOE ε4. The RSF model incorporating longitudinal ADAS13, FAQ, and demographic information and APOE ε4 performed for the low-risk group. CONCLUSION This precise screening scheme will optimize allocation of medical resources and reduce the economic burden on individuals with low risk of MCI. Moreover, dynamic prognosis models may be helpful for early identification of individuals at risk and clinical decisions, which will promote the secondary prevention of AD.
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Affiliation(s)
- Xiaoyan Ge
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China.
| | - Kai Cui
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China.
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
| | - Durong Chen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
| | - Hongjuan Han
- Department of Mathematics, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China; Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, 56 XinJian South Road, Taiyuan, China.
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16
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Kesler SR, Henneghan AM, Prinsloo S, Palesh O, Wintermark M. Neuroimaging based biotypes for precision diagnosis and prognosis in cancer-related cognitive impairment. Front Med (Lausanne) 2023; 10:1199605. [PMID: 37720513 PMCID: PMC10499624 DOI: 10.3389/fmed.2023.1199605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/15/2023] [Indexed: 09/19/2023] Open
Abstract
Cancer related cognitive impairment (CRCI) is commonly associated with cancer and its treatments, yet the present binary diagnostic approach fails to capture the full spectrum of this syndrome. Cognitive function is highly complex and exists on a continuum that is poorly characterized by dichotomous categories. Advanced statistical methodologies applied to symptom assessments have demonstrated that there are multiple subclasses of CRCI. However, studies suggest that relying on symptom assessments alone may fail to account for significant differences in the neural mechanisms that underlie a specific cognitive phenotype. Treatment plans that address the specific physiologic mechanisms involved in an individual patient's condition is the heart of precision medicine. In this narrative review, we discuss how biotyping, a precision medicine framework being utilized in other mental disorders, could be applied to CRCI. Specifically, we discuss how neuroimaging can be used to determine biotypes of CRCI, which allow for increased precision in prediction and diagnosis of CRCI via biologic mechanistic data. Biotypes may also provide more precise clinical endpoints for intervention trials. Biotyping could be made more feasible with proxy imaging technologies or liquid biomarkers. Large cross-sectional phenotyping studies are needed in addition to evaluation of longitudinal trajectories, and data sharing/pooling is highly feasible with currently available digital infrastructures.
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Affiliation(s)
- Shelli R. Kesler
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States
- Department of Diagnostic Medicine, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
| | - Ashley M. Henneghan
- Division of Adult Health, School of Nursing, The University of Texas at Austin, Austin, TX, United States
- Department of Oncology, Dell School of Medicine, The University of Texas at Austin, Austin, TX, United States
| | - Sarah Prinsloo
- Department of Neurosurgery, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Oxana Palesh
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Max Wintermark
- Department of Neuroradiology, The University of Texas MD Anderson Cancer, Houston, TX, United States
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17
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Felsky D, Cannitelli A, Pipitone J. Whole Person Modeling: a transdisciplinary approach to mental health research. DISCOVER MENTAL HEALTH 2023; 3:16. [PMID: 37638348 PMCID: PMC10449734 DOI: 10.1007/s44192-023-00041-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
Abstract
The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.
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Affiliation(s)
- Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
- Rotman Research Institute, Baycrest Hospital, Toronto, ON Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Alyssa Cannitelli
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Jon Pipitone
- Department of Psychiatry, Queen’s University, Kingston, ON Canada
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18
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Saleh H, Elrashidy N, Elaziz MA, Aseeri AO, El-sappagh S. Genetic algorithms based optimized hybrid deep learning model for explainable Alzheimer's prediction based on temporal multimodal cognitive data.. [DOI: 10.21203/rs.3.rs-3250006/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Abstract
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease. Its early detection is crucial to stop disease progression at an early stage. Most deep learning (DL) literature focused on neuroimage analysis. However, there is no noticed effect of these studies in the real environment. Model's robustness, cost, and interpretability are considered the main reasons for these limitations. The medical intuition of physicians is to evaluate the clinical biomarkers of patients then test their neuroimages. Cognitive scores provide an medically acceptable and cost-effective alternative for the neuroimages to predict AD progression. Each score is calculated from a collection of sub-scores which provide a deeper insight about patient conditions. No study in the literature have explored the role of these multimodal time series sub-scores to predict AD progression.
We propose a hybrid CNN-LSTM DL model for predicting AD progression based on the fusion of four longitudinal cognitive sub-scores modalities. Bayesian optimizer has been used to select the best DL architecture. A genetic algorithms based feature selection optimization step has been added to the pipeline to select the best features from extracted deep representations of CNN-LSTM. The SoftMax classifier has been replaced by a robust and optimized random forest classifier. Extensive experiments using the ADNI dataset investigated the role of each optimization step, and the proposed model achieved the best results compared to other DL and classical machine learning models. The resulting model is robust, but it is a black box and it is difficult to understand the logic behind its decisions. Trustworthy AI models must be robust and explainable. We used SHAP and LIME to provide explainability features for the proposed model. The resulting trustworthy model has a great potential to be used to provide decision support in the real environments.
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Affiliation(s)
- Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada, Egypt
| | - Nora ElRashidy
- Machine Learning and Information Retrieval Department, Faculty of Artificial Intelligence, Kafrelsheiksh University, Kafrelsheiksh, 13518, Egypt
| | - Mohamed Abd Elaziz
- Faculty of Computer Science and Engineerings, Galala University, Suez, 435611, Egypt, Egypt
| | - Ahmad O. Aseeri
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineerings, Galala University, Suez, 435611, Egypt, Egypt
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19
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Lv J, Roy S, Xie M, Yang X, Guo B. Contrast Agents of Magnetic Resonance Imaging and Future Perspective. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2003. [PMID: 37446520 DOI: 10.3390/nano13132003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 07/01/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
In recent times, magnetic resonance imaging (MRI) has emerged as a highly promising modality for diagnosing severe diseases. Its exceptional spatiotemporal resolution and ease of use have established it as an indispensable clinical diagnostic tool. Nevertheless, there are instances where MRI encounters challenges related to low contrast, necessitating the use of contrast agents (CAs). Significant efforts have been made by scientists to enhance the precision of observing diseased body parts by leveraging the synergistic potential of MRI in conjunction with other imaging techniques and thereby modifying the CAs. In this work, our focus is on elucidating the rational designing approach of CAs and optimizing their compatibility for multimodal imaging and other intelligent applications. Additionally, we emphasize the importance of incorporating various artificial intelligence tools, such as machine learning and deep learning, to explore the future prospects of disease diagnosis using MRI. We also address the limitations associated with these techniques and propose reasonable remedies, with the aim of advancing MRI as a cutting-edge diagnostic tool for the future.
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Affiliation(s)
- Jie Lv
- School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
| | - Shubham Roy
- Shenzhen Key Laboratory of Flexible Printed Electronics Technology, School of Science, Harbin Institute of Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, School of Science, Harbin Institute of Technology, Shenzhen 518055, China
| | - Miao Xie
- School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
| | - Xiulan Yang
- School of Computer Science and Engineering, Yulin Normal University, Yulin 537000, China
| | - Bing Guo
- Shenzhen Key Laboratory of Flexible Printed Electronics Technology, School of Science, Harbin Institute of Technology, Shenzhen 518055, China
- Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, School of Science, Harbin Institute of Technology, Shenzhen 518055, China
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20
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Gao Y, Lewis N, Calhoun VD, Miller RL. Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment. Comput Biol Med 2023; 161:107005. [PMID: 37211004 PMCID: PMC10365638 DOI: 10.1016/j.compbiomed.2023.107005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 04/09/2023] [Accepted: 05/02/2023] [Indexed: 05/23/2023]
Abstract
Alzheimer's Disease (AZD) is a neurodegenerative disease for which there is now no known effective treatment. Mild cognitive impairment (MCI) is considered a precursor to AZD and affects cognitive abilities. Patients with MCI have the potential to recover cognitive health, can remain mildly cognitively impaired indefinitely or eventually progress to AZD. Identifying imaging-based predictive biomarkers for disease progression in patients presenting with evidence of very mild/questionable MCI (qMCI) can play an important role in triggering early dementia intervention. Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly studied in brain disorder diseases. In this work, employing a recent developed a time-attention long short-term memory (TA-LSTM) network to classify multivariate time series data. A gradient-based interpretation framework, transiently-realized event classifier activation map (TEAM) is introduced to localize the group-defining "activated" time intervals over the full time series and generate the class difference map. To test the trustworthiness of TEAM, we did a simulation study to validate the model interpretative power of TEAM. We then applied this simulation-validated framework to a well-trained TA-LSTM model which predicts the progression or recovery from questionable/mild cognitive impairment (qMCI) subjects after three years from windowless wavelet-based dFNC (WWdFNC). The FNC class difference map points to potentially important predictive dynamic biomarkers. Moreover, the more highly time-solved dFNC (WWdFNC) achieves better performance in both TA-LSTM and a multivariate CNN model than dFNC based on windowed correlations between timeseries, suggesting that better temporally resolved measures can enhance the model's performance.
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Affiliation(s)
- Yutong Gao
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; Department of Computer Science, Georgia State University, Atlanta, GA, USA.
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Robyn L Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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21
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van der Haar D, Moustafa A, Warren SL, Alashwal H, van Zyl T. An Alzheimer's disease category progression sub-grouping analysis using manifold learning on ADNI. Sci Rep 2023; 13:10483. [PMID: 37380746 DOI: 10.1038/s41598-023-37569-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 06/23/2023] [Indexed: 06/30/2023] Open
Abstract
Many current statistical and machine learning methods have been used to explore Alzheimer's disease (AD) and its associated patterns that contribute to the disease. However, there has been limited success in understanding the relationship between cognitive tests, biomarker data, and patient AD category progressions. In this work, we perform exploratory data analysis of AD health record data by analyzing various learned lower dimensional manifolds to separate early-stage AD categories further. Specifically, we used Spectral embedding, Multidimensional scaling, Isomap, t-Distributed Stochastic Neighbour Embedding, Uniform Manifold Approximation and Projection, and sparse denoising autoencoder based manifolds on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We then determine the clustering potential of the learned embeddings and then determine if category sub-groupings or sub-categories can be found. We then used a Kruskal-sWallis H test to determine the statistical significance of the discovered AD subcategories. Our results show that the existing AD categories do exhibit sub-groupings, especially in mild cognitive impairment transitions in many of the tested manifolds, showing there may be a need for further subcategories to describe AD progression.
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Affiliation(s)
- Dustin van der Haar
- Academy of Computer Science and Software Engineering, University of Johannesburg, Gauteng, South Africa.
| | - Ahmed Moustafa
- Department of Human Anatomy and Physiology, University of Johannesburg, Gauteng, South Africa
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD, Australia
| | - Samuel L Warren
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, QLD, Australia
| | - Hany Alashwal
- College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates
| | - Terence van Zyl
- Institute for Intelligent Systems, University of Johannesburg, Gauteng, South Africa
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22
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Al-hajjar ALN, Al-Qurabat AKM. An overview of machine learning methods in enabling IoMT-based epileptic seizure detection. THE JOURNAL OF SUPERCOMPUTING 2023; 79:1-48. [PMID: 37359338 PMCID: PMC10123593 DOI: 10.1007/s11227-023-05299-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today's world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people's lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.
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Affiliation(s)
| | - Ali Kadhum M. Al-Qurabat
- Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq
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23
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Blum D, Hepp T, Belov V, Goya-Maldonado R, la Fougère C, Reimold M. Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases. Hum Brain Mapp 2023; 44:2802-2814. [PMID: 36947555 PMCID: PMC10089107 DOI: 10.1002/hbm.26246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 01/13/2023] [Accepted: 02/11/2023] [Indexed: 03/23/2023] Open
Abstract
Quantifying pathology-related patterns in patient data with pattern expression score (PES) is a standard approach in medical image analysis. In order to estimate the PES error, we here propose to express the uncertainty contained in read-out patterns in terms of the expected squared Euclidean distance between the read-out pattern and the unknown "true" pattern (squared standard error of the read-out pattern, SE2 ). Using SE2 , we predicted and optimized the net benefit (NBe) of the recently suggested method controls-based denoising (CODE) by weighting patterns of nonpathological variance (NPV). Multi-center MRI (1192 patients with various neurodegenerative and neuropsychiatric diseases, 1832 healthy controls) were analysed with voxel-based morphometry. For each pathology, accounting for SE2 , NBe correctly predicted classification improvement and allowed to optimize NPV pattern weights. Using these weights, CODE improved classification performances in all but one analyses, for example, for prediction of conversion to Alzheimer's disease (AUC 0.81 vs. 0.75, p = .01), diagnosis of autism (AUC 0.66 vs. 0.60, p < .001), and of major depressive disorder (AUC 0.62 vs. 0.50, p = .03). We conclude that the degree of uncertainty in a read-out pattern should generally be reported in PES-based analyses and suggest using weighted CODE as a complement to PES-based analyses.
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Affiliation(s)
- Dominik Blum
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
| | - Tobias Hepp
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany
- Max Planck Institute for Intelligent Systems, Tübingen, Germany
| | - Valdimir Belov
- Laboratory of Systems Neuroscience and Imaging in Psychiatry, University Medical Center Göttingen, Göttingen, Germany
| | - Roberto Goya-Maldonado
- Laboratory of Systems Neuroscience and Imaging in Psychiatry, University Medical Center Göttingen, Göttingen, Germany
| | - Christian la Fougère
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
| | - Matthias Reimold
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tübingen, Tübingen, Germany
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24
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Liu F, Wang H, Liang SN, Jin Z, Wei S, Li X. MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer's disease prediction with structural MRI. Comput Biol Med 2023; 157:106790. [PMID: 36958239 DOI: 10.1016/j.compbiomed.2023.106790] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/13/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023]
Abstract
Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI scans are highly associated with AD. The degree of atrophy in patients' brain tissues and the distribution of lesion areas differ among patients. Therefore, a key challenge in sMRI-based AD diagnosis is identifying discriminating atrophy features. Hence, we propose a multiplane and multiscale feature-level fusion attention (MPS-FFA) model. The model has three components, (1) A feature encoder uses a multiscale feature extractor with hybrid attention layers to simultaneously capture and fuse multiple pathological features in the sagittal, coronal, and axial planes. (2) A global attention classifier combines clinical scores and two global attention layers to evaluate the feature impact scores and balance the relative contributions of different feature blocks. (3) A feature similarity discriminator minimizes the feature similarities among heterogeneous labels to enhance the ability of the network to discriminate atrophy features. The MPS-FFA model provides improved interpretability for identifying discriminating features using feature visualization. The experimental results on the baseline sMRI scans from two databases confirm the effectiveness (e.g., accuracy and generalizability) of our method in locating pathological locations. The source code is available at https://github.com/LiuFei-AHU/MPSFFA.
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Affiliation(s)
- Fei Liu
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
| | - Huabin Wang
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.
| | - Shiuan-Ni Liang
- School of Engineering, Monash University Malaysia, Kuala Lumpur, Malaysia
| | - Zhe Jin
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China
| | - Shicheng Wei
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China
| | - Xuejun Li
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
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25
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Dolcet-Negre MM, Imaz Aguayo L, de Eulate RG, Martí-Andrés G, Matarrubia MF, Domínguez P, Fernández Seara MA, Riverol M. Predicting Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer's Disease Dementia Using Ensemble Machine Learning. J Alzheimers Dis 2023; 93:125-140. [PMID: 36938735 DOI: 10.3233/jad-221002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
BACKGROUND Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge. OBJECTIVE To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD. METHODS Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model. RESULTS Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11). CONCLUSION A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.
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Affiliation(s)
| | - Laura Imaz Aguayo
- Memory Unit, Department of Neurology, Clínica Universidad de Navarra, Pamplona, Spain
| | | | - Gloria Martí-Andrés
- Memory Unit, Department of Neurology, Clínica Universidad de Navarra, Pamplona, Spain
| | | | - Pablo Domínguez
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Mará A Fernández Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain.,IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain.,Institute of Data Science and Artificial Intelligence, Universidad de Navarra, Pamplona, Spain
| | - Mario Riverol
- Memory Unit, Department of Neurology, Clínica Universidad de Navarra, Pamplona, Spain.,IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
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26
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Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders-A Scoping Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3062. [PMID: 36991773 PMCID: PMC10053494 DOI: 10.3390/s23063062] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/09/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
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Affiliation(s)
| | | | | | - Edmond Prakash
- Research Center for Creative Arts, University for the Creative Arts (UCA), Farnham GU9 7DS, UK
| | - Chaminda Hewage
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK
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27
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Hollenbenders Y, Pobiruchin M, Reichenbach A. Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions. J Alzheimers Dis 2023; 92:1399-1412. [PMID: 36911937 DOI: 10.3233/jad-221061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification. OBJECTIVE The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages. METHODS Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages. RESULTS The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments. CONCLUSION Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.
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Affiliation(s)
- Yasmin Hollenbenders
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
| | - Monika Pobiruchin
- Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,GECKO Institute for Medicine, Informatics and Economics, Heilbronn University of Applied Sciences, Germany
| | - Alexandra Reichenbach
- Medical Faculty Heidelberg, Heidelberg University, Germany.,Faculty of Computer Science, Heilbronn University of Applied Sciences, Germany.,Center for Machine Learning, Heilbronn University of Applied Sciences, Germany
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28
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Bucholc M, James C, Al Khleifat A, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial Intelligence for Dementia Research Methods Optimization. ARXIV 2023:arXiv:2303.01949v1. [PMID: 36911275 PMCID: PMC10002770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
INTRODUCTION Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. METHODS We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. RESULTS We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. DISCUSSION ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montréal, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J. Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M. Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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29
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Nath N, Chakroborty S, Vishwakarma DP, Goga G, Yadav AS, Mohan R. Recent advances in sustainable nature-based functional materials for biomedical sensor technologies. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023:10.1007/s11356-023-26135-w. [PMID: 36857000 PMCID: PMC9975880 DOI: 10.1007/s11356-023-26135-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
The lightweight, low-density, and low-cost natural polymers like cellulose, chitosan, and silk have good chemical and biodegradable properties due to their individually unique structural and functional elements. However, the mechanical properties of these polymers differ from each other. In this scenario, chitosan lacks good mechanical properties than cellulose and silk. The synthesis of nano natural polymer and reinforcement with suitable chemical compounds as the development of nanocomposite gives them promising multidisciplinary applications. Many kinds of research are already published with innovative bio-derived polymeric functional materials (Bd-PFM) applications. Most research interest is carried out on health concerns. Lots of attention has been paid to biomedical applications of Bd-PFM as biosensors. This review aims to provide a glimpse of the nanostructures Bd-PFM biosensors.
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Affiliation(s)
- Nibedita Nath
- Department of Chemistry, D.S Degree College, Laida, Sambalpur, Odisha, India
| | | | | | - Geetesh Goga
- Department of Mechanical Engineering, Bharat Group of Colleges, Sardulgarh, Punjab, 151507, India
| | - Anil Singh Yadav
- Department of Mechanical Engineering, IES College of Technology, Bhopal, Madhya Pradesh, India
| | - Ravindra Mohan
- Department of Mechanical Engineering, IES College of Technology, Bhopal, Madhya Pradesh, India
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Cruzat J, Herzog R, Prado P, Sanz-Perl Y, Gonzalez-Gomez R, Moguilner S, Kringelbach ML, Deco G, Tagliazucchi E, Ibañez A. Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer's Disease. J Neurosci 2023; 43:1643-1656. [PMID: 36732071 PMCID: PMC10008060 DOI: 10.1523/jneurosci.1312-22.2022] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/12/2022] [Accepted: 12/25/2022] [Indexed: 02/04/2023] Open
Abstract
Healthy brain dynamics can be understood as the emergence of a complex system far from thermodynamic equilibrium. Brain dynamics are temporally irreversible and thus establish a preferred direction in time (i.e., arrow of time). However, little is known about how the time-reversal symmetry of spontaneous brain activity is affected by Alzheimer's disease (AD). We hypothesized that the level of irreversibility would be compromised in AD, signaling a fundamental shift in the collective properties of brain activity toward equilibrium dynamics. We investigated the irreversibility from resting-state fMRI and EEG data in male and female human patients with AD and elderly healthy control subjects (HCs). We quantified the level of irreversibility and, thus, proximity to nonequilibrium dynamics by comparing forward and backward time series through time-shifted correlations. AD was associated with a breakdown of temporal irreversibility at the global, local, and network levels, and at multiple oscillatory frequency bands. At the local level, temporoparietal and frontal regions were affected by AD. The limbic, frontoparietal, default mode, and salience networks were the most compromised at the network level. The temporal reversibility was associated with cognitive decline in AD and gray matter volume in HCs. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from control subjects. Findings were validated using an out-of-sample cohort. Present results offer new evidence regarding pathophysiological links between the entropy generation rate of brain dynamics and the clinical presentation of AD, opening new avenues for dementia characterization at different levels.SIGNIFICANCE STATEMENT By assessing the irreversibility of large-scale dynamics across multiple brain signals, we provide a precise signature capable of distinguishing Alzheimer's disease (AD) at the global, local, and network levels and different oscillatory regimes. Irreversibility of limbic, frontoparietal, default-mode, and salience networks was the most compromised by AD compared with more sensory-motor networks. Moreover, the time-irreversibility properties associated with cognitive decline and atrophy outperformed and complemented classical neurocognitive markers of AD in predictive classification performance. Findings were generalized and replicated with an out-of-sample validation procedure. We provide novel multilevel evidence of reduced irreversibility in AD brain dynamics that has the potential to open new avenues for understating neurodegeneration in terms of the temporal asymmetry of brain dynamics.
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Affiliation(s)
- Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, 7911328, Santiago, Chile
- Fundación para el Estudio de la Conciencia Humana (ECoH), 7550000, Santiago, Chile
| | - Ruben Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, 7911328, Santiago, Chile
- Fundación para el Estudio de la Conciencia Humana (ECoH), 7550000, Santiago, Chile
| | - Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, 7911328, Santiago, Chile
- Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Yonatan Sanz-Perl
- Department of Physics, University of Buenos Aires, C1428EGA, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), C1033AAJ, Buenos Aires, Argentina
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, C116ABJ, Buenos Aires, Argentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Raul Gonzalez-Gomez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, 7911328, Santiago, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, 7911328, Santiago, Chile
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94143
- Global Brain Health Institute, Trinity College, Dublin 2, Ireland
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, 8000 Århus, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, United Kingdom
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, 08005 Barcelona, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA), 08010 Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, D-04303 Leipzig, Germany
- School of Psychological Sciences, Monash University, Melbourne 3168, Australia
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, 7911328, Santiago, Chile
- Department of Physics, University of Buenos Aires, C1428EGA, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), C1033AAJ, Buenos Aires, Argentina
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, C116ABJ, Buenos Aires, Argentina
| | - Agustín Ibañez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, 7911328, Santiago, Chile
- National Scientific and Technical Research Council (CONICET), C1033AAJ, Buenos Aires, Argentina
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, C116ABJ, Buenos Aires, Argentina
- Global Brain Health Institute, University of California, San Francisco, San Francisco, California 94143
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
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Sensi SL, Russo M, Tiraboschi P. Biomarkers of diagnosis, prognosis, pathogenesis, response to therapy: Convergence or divergence? Lessons from Alzheimer's disease and synucleinopathies. HANDBOOK OF CLINICAL NEUROLOGY 2023; 192:187-218. [PMID: 36796942 DOI: 10.1016/b978-0-323-85538-9.00015-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Alzheimer's disease (AD) is the most common disorder associated with cognitive impairment. Recent observations emphasize the pathogenic role of multiple factors inside and outside the central nervous system, supporting the notion that AD is a syndrome of many etiologies rather than a "heterogeneous" but ultimately unifying disease entity. Moreover, the defining pathology of amyloid and tau coexists with many others, such as α-synuclein, TDP-43, and others, as a rule, not an exception. Thus, an effort to shift our AD paradigm as an amyloidopathy must be reconsidered. Along with amyloid accumulation in its insoluble state, β-amyloid is becoming depleted in its soluble, normal states, as a result of biological, toxic, and infectious triggers, requiring a shift from convergence to divergence in our approach to neurodegeneration. These aspects are reflected-in vivo-by biomarkers, which have become increasingly strategic in dementia. Similarly, synucleinopathies are primarily characterized by abnormal deposition of misfolded α-synuclein in neurons and glial cells and, in the process, depleting the levels of the normal, soluble α-synuclein that the brain needs for many physiological functions. The soluble to insoluble conversion also affects other normal brain proteins, such as TDP-43 and tau, accumulating in their insoluble states in both AD and dementia with Lewy bodies (DLB). The two diseases have been distinguished by the differential burden and distribution of insoluble proteins, with neocortical phosphorylated tau deposition more typical of AD and neocortical α-synuclein deposition peculiar to DLB. We propose a reappraisal of the diagnostic approach to cognitive impairment from convergence (based on clinicopathologic criteria) to divergence (based on what differs across individuals affected) as a necessary step for the launch of precision medicine.
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Affiliation(s)
- Stefano L Sensi
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology-CAST and ITAB Institute for Advanced Biotechnology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
| | - Mirella Russo
- Department of Neuroscience, Imaging, and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Molecular Neurology Unit, Center for Advanced Studies and Technology-CAST and ITAB Institute for Advanced Biotechnology, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Pietro Tiraboschi
- Division of Neurology V-Neuropathology, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
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Zhao Z, Chuah JH, Lai KW, Chow CO, Gochoo M, Dhanalakshmi S, Wang N, Bao W, Wu X. Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review. Front Comput Neurosci 2023; 17:1038636. [PMID: 36814932 PMCID: PMC9939698 DOI: 10.3389/fncom.2023.1038636] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 01/13/2023] [Indexed: 02/08/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down. Early and accurate detection is critical for treatment. In recent years, deep-learning-based approaches have achieved great success in Alzheimer's disease diagnosis. The main objective of this paper is to review some popular conventional machine learning methods used for the classification and prediction of AD using Magnetic Resonance Imaging (MRI). The methods reviewed in this paper include support vector machine (SVM), random forest (RF), convolutional neural network (CNN), autoencoder, deep learning, and transformer. This paper also reviews pervasively used feature extractors and different types of input forms of convolutional neural network. At last, this review discusses challenges such as class imbalance and data leakage. It also discusses the trade-offs and suggestions about pre-processing techniques, deep learning, conventional machine learning methods, new techniques, and input type selection.
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Affiliation(s)
- Zhen Zhao
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Joon Huang Chuah
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,*Correspondence: Joon Huang Chuah ✉
| | - Khin Wee Lai
- Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia,Khin Wee Lai ✉
| | - Chee-Onn Chow
- Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Munkhjargal Gochoo
- Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates
| | - Samiappan Dhanalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, India
| | - Na Wang
- School of Automation, Guangdong Polytechnic Normal University, Guangzhou, China
| | - Wei Bao
- China Electronics Standardization Institute, Beijing, China,Wei Bao ✉
| | - Xiang Wu
- School of Medical Information Engineering, Xuzhou Medical University, Xuzhou, China
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Chen Z, Liu Y, Zhang Y, Li Q. Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis. Med Image Anal 2023; 84:102698. [PMID: 36462372 DOI: 10.1016/j.media.2022.102698] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 10/18/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
Abstract
Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.
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Affiliation(s)
- Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qiaoqin Li
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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Zhou K, Piao S, Liu X, Luo X, Chen H, Xiang R, Geng D. A novel cascade machine learning pipeline for Alzheimer's disease identification and prediction. Front Aging Neurosci 2023; 14:1073909. [PMID: 36726800 PMCID: PMC9884698 DOI: 10.3389/fnagi.2022.1073909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/30/2022] [Indexed: 01/19/2023] Open
Abstract
Introduction Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder early. Among all diagnostic strategies, hippocampal atrophy is considered a promising diagnostic method. In order to proactively detect patients with early Alzheimer's disease, we built an Alzheimer's segmentation and classification (AL-SCF) pipeline based on machine learning. Methods In our study, we collected coronal T1 weighted images that include 187 patients with AD and 230 normal controls (NCs). Our pipeline began with the segmentation of the hippocampus by using a modified U2-net. Subsequently, we extracted 851 radiomics features and selected 37 features most relevant to AD by the Hierarchical clustering method and Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. At last, four classifiers were implemented to distinguish AD from NCs, and the performance of the models was evaluated by accuracy, specificity, sensitivity, and area under the curve. Results Our proposed pipeline showed excellent discriminative performance of classification with AD vs NC in the training set (AUC=0.97, 95% CI: (0.96-0.98)). The model was also verified in the validation set with Dice=0.93 for segmentation and accuracy=0.95 for classification. Discussion The AL-SCF pipeline can automate the process from segmentation to classification, which may assist doctors with AD diagnosis and develop individualized medical plans for AD in clinical practice.
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Affiliation(s)
- Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Sirong Piao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Rui Xiang
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, China,Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China,*Correspondence: Daoying Geng,
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Wang X, Tang F, Chen H, Cheung CY, Heng PA. Deep semi-supervised multiple instance learning with self-correction for DME classification from OCT images. Med Image Anal 2023; 83:102673. [PMID: 36403310 DOI: 10.1016/j.media.2022.102673] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/03/2022] [Accepted: 10/20/2022] [Indexed: 11/18/2022]
Abstract
Supervised deep learning has achieved prominent success in various diabetic macular edema (DME) recognition tasks from optical coherence tomography (OCT) volumetric images. A common problematic issue that frequently occurs in this field is the shortage of labeled data due to the expensive fine-grained annotations, which increases substantial difficulty in accurate analysis by supervised learning. The morphological changes in the retina caused by DME might be distributed sparsely in B-scan images of the OCT volume, and OCT data is often coarsely labeled at the volume level. Hence, the DME identification task can be formulated as a multiple instance classification problem that could be addressed by multiple instance learning (MIL) techniques. Nevertheless, none of previous studies utilize unlabeled data simultaneously to promote the classification accuracy, which is particularly significant for a high quality of analysis at the minimum annotation cost. To this end, we present a novel deep semi-supervised multiple instance learning framework to explore the feasibility of leveraging a small amount of coarsely labeled data and a large amount of unlabeled data to tackle this problem. Specifically, we come up with several modules to further improve the performance according to the availability and granularity of their labels. To warm up the training, we propagate the bag labels to the corresponding instances as the supervision of training, and propose a self-correction strategy to handle the label noise in the positive bags. This strategy is based on confidence-based pseudo-labeling with consistency regularization. The model uses its prediction to generate the pseudo-label for each weakly augmented input only if it is highly confident about the prediction, which is subsequently used to supervise the same input in a strongly augmented version. This learning scheme is also applicable to unlabeled data. To enhance the discrimination capability of the model, we introduce the Student-Teacher architecture and impose consistency constraints between two models. For demonstration, the proposed approach was evaluated on two large-scale DME OCT image datasets. Extensive results indicate that the proposed method improves DME classification with the incorporation of unlabeled data and outperforms competing MIL methods significantly, which confirm the feasibility of deep semi-supervised multiple instance learning at a low annotation cost.
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Affiliation(s)
- Xi Wang
- Zhejiang Lab, Hangzhou, China; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Fangyao Tang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China
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Li M, Jiang Y, Li X, Yin S, Luo H. Ensemble of convolutional neural networks and multilayer perceptron for the diagnosis of mild cognitive impairment and Alzheimer's disease. Med Phys 2023; 50:209-225. [PMID: 36121183 DOI: 10.1002/mp.15985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 08/09/2022] [Accepted: 09/07/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Structural magnetic resonance imaging (sMRI) can provide morphological information about the structure and function of the brain in the same scanning process. It has been widely used in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). PURPOSE To capture the anatomical changes in the brain caused by AD/MCI, deep learning-based MRI image analysis methods have been proposed in recent years. However, it is observed that the performance of most existing methods is limited as they only construct a single type of deep network and ignore the significance of other clinical information. METHODS To make up for these defects, an ensemble framework that incorporates three types of dedicatedly-designed convolutional neural networks (CNNs) and a multilayer perceptron (MLP) network is proposed, where three CNNs with entropy-based multi-instance learning pooling layers have more reliable feature selection abilities. The dedicatedly-designed base classifiers can make use of the heterogeneous data, and empower the framework with enhanced diversity and robustness. In particular, to consider the interactions among the base classifiers, a novel multi-head self-attention voting scheme is designed. Moreover, considering the chance that MCI can be transformed to AD, the proposed framework is designed to diagnose AD and predict MCI conversion simultaneously, with the aid of the transfer learning technique. RESULTS For performance evaluation and comparison, extensive experiments are conducted on the public dataset of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results show that the proposed ensemble framework provides superior performance under most of the evaluation metrics. Especially, the proposed framework achieves state-of-the-art diagnostic accuracy (98.61% for the AD diagnosis task, and 84.49% for the MCI conversion prediction task). CONCLUSIONS These promising results demonstrate the proposed ensemble framework can accurately diagnose AD patients and predict the conversion of MCI patients, which has the potential of clinical practice for diagnosing AD and MCI.
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Affiliation(s)
- Minglei Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Yuchen Jiang
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Xiang Li
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
| | - Shen Yin
- Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway
| | - Hao Luo
- Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
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Arco JE, Ortiz A, Castillo-Barnes D, Górriz JM, Ramírez J. Ensembling shallow siamese architectures to assess functional asymmetry in Alzheimer’s disease progression. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.109991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Ciarmiello A, Giovannini E, Pastorino S, Ferrando O, Foppiano F, Mannironi A, Tartaglione A, Giovacchini G. Machine Learning Model to Predict Diagnosis of Mild Cognitive Impairment by Using Radiomic and Amyloid Brain PET. Clin Nucl Med 2023; 48:1-7. [PMID: 36240660 DOI: 10.1097/rlu.0000000000004433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
PURPOSE The study aimed to develop a deep learning model for predicting amnestic mild cognitive impairment (aMCI) diagnosis using radiomic features and amyloid brain PET. PATIENTS AND METHODS Subjects (n = 328) from the Alzheimer's Disease Neuroimaging Initiative database and the EudraCT 2015-001184-39 trial (159 males, 169 females), with a mean age of 72 ± 7.4 years, underwent PET/CT with 18 F-florbetaben. The study cohort consisted of normal controls (n = 149) and subjects with aMCI (n = 179). Thirteen gray-level run-length matrix radiomic features and amyloid loads were extracted from 27 cortical brain areas. The least absolute shrinkage and selection operator regression was used to select features with the highest predictive value. A feed-forward neural multilayer network was trained, validated, and tested on 70%, 15%, and 15% of the sample, respectively. Accuracy, precision, F1-score, and area under the curve were used to assess model performance. SUV performance in predicting the diagnosis of aMCI was also assessed and compared with that obtained from the machine learning model. RESULTS The machine learning model achieved an area under the receiver operating characteristic curve of 90% (95% confidence interval, 89.4-90.4) on the test set, with 80% and 78% for accuracy and F1-score, respectively. The deep learning model outperformed SUV performance (area under the curve, 71%; 95% confidence interval, 69.7-71.4; 57% accuracy, 48% F1-score). CONCLUSIONS Using radiomic and amyloid PET load, the machine learning model identified MCI subjects with 84% specificity at 81% sensitivity. These findings show that a deep learning algorithm based on radiomic data and amyloid load obtained from brain PET images improves the prediction of MCI diagnosis compared with SUV alone.
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Zhou B, Zhao Q, Kojima S, Ding D, Higashide S, Fukushima M, Hong Z. Early Detection of Dementia using Risk Classification in MCI: Outcomes of Shanghai Mild Cognitive Impairment Cohort Study. Curr Alzheimer Res 2023; 20:431-439. [PMID: 37711110 DOI: 10.2174/1567205020666230914161034] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/27/2023] [Accepted: 08/07/2023] [Indexed: 09/16/2023]
Abstract
INTRODUCTION The purpose of this study is to identify the risk factors and risk classification associated with the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia to facilitate early intervention and the design of clinical trials for AD. METHODS The study comprised a prospective cohort study of 400 subjects with MCI who had annual follow-ups for 3 years. RESULTS During an average follow-up period of 3.5 years, 109 subjects were diagnosed with all cause of dementia, of whom 104 subjects converted to Alzheimer's dementia and 5 subjects converted to other types of dementia. The cumulative conversion rate was 5.5% (95% CI: 3.4, 8.6), 16.3% (95% CI: 12.9, 21.1), and 31.0% (95% CI: 25.4, 36.5) in each of the first 3 follow-up years, respectively. The factors associated with a greater risk of conversion from MCI to AD included smoking status, ApoE4 carrier status, right hippocampal volume (rt. HV), left temporal lobe volume, and scores on the Revised Chinese version of the Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog-C). The risk classification of the ADAS-Cog-C or Preclinical Alzheimer Cognitive Composite (PACC) score combined with the rt. HV showed a conversion difference among the groups at every annual follow-up. CONCLUSION A simple risk classification using the rt. HV and neuropsychological test scores, including those from the ADAS-Cog-C and PACC, could be a practicable and efficient approach to indentify individuals at risk of all-cause dementia.
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Affiliation(s)
- Bin Zhou
- Foundation of Learning Health Society Institute, Nagoya, Japan
| | - Qianhua Zhao
- Institute of Neurology, Huashan Hospital Fudan University, Shanghai, China
| | - Shinsuke Kojima
- Translational Research Informatics Center, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | - Ding Ding
- Institute of Neurology, Huashan Hospital Fudan University, Shanghai, China
| | - Satoshi Higashide
- Translational Research Informatics Center, Foundation for Biomedical Research and Innovation, Kobe, Japan
| | | | - Zhen Hong
- Institute of Neurology, Huashan Hospital Fudan University, Shanghai, China
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Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through immune landscape analysis. NPJ AGING 2022; 8:15. [PMCID: PMC9636153 DOI: 10.1038/s41514-022-00096-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
Abstract
AbstractMild cognitive impairment (MCI) is a clinical precursor of Alzheimer’s disease (AD). Recent genetic studies have reported on associations between AD risk genes and immunity. Here, we obtained samples and data from 317 AD, 432 MCI, and 107 cognitively normal (CN) subjects and investigated immune-cell type composition and immune clonal diversity of T-cell receptor (TRA, TRB, TRG, and TRD) and B-cell receptor (IGH, IGK, and IGL) repertoires through bulk RNA sequencing. We found the proportions of plasma cells, γδ T cells, neutrophils, and B cells were significantly different and the diversities of IGH, IGK, and TRA were significantly small with AD progression. We then identified a differentially expressed gene, WDR37, in terms of risk of MCI-to-AD conversion. Our prognosis prediction model using the potential blood-based biomarkers for early AD diagnosis, which combined two immune repertoires (IGK and TRA), WDR37, and clinical information, successfully classified MCI patients into two groups, low and high, in terms of risk of MCI-to-AD conversion (log-rank test P = 2.57e-3). It achieved a concordance index of 0.694 in a discovery cohort and of 0.643 in an independent validation cohort. We believe that further investigation, using larger sample sizes, will lead to practical clinical use in the near future.
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Sharma S, Gupta S, Gupta D, Juneja S, Mahmoud A, El–Sappagh S, Kwak KS. Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease. Front Comput Neurosci 2022; 16:1000435. [PMID: 36387304 PMCID: PMC9664223 DOI: 10.3389/fncom.2022.1000435] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/29/2022] [Indexed: 09/29/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative ailment, which gradually deteriorates memory and weakens the cognitive functions and capacities of the body, such as recall and logic. To diagnose this disease, CT, MRI, PET, etc. are used. However, these methods are time-consuming and sometimes yield inaccurate results. Thus, deep learning models are utilized, which are less time-consuming and yield results with better accuracy, and could be used with ease. This article proposes a transfer learning-based modified inception model with pre-processing methods of normalization and data addition. The proposed model achieved an accuracy of 94.92 and a sensitivity of 94.94. It is concluded from the results that the proposed model performs better than other state-of-the-art models. For training purposes, a Kaggle dataset was used comprising 6,200 images, with 896 mild demented (M.D) images, 64 moderate demented (Mod.D) images, and 3,200 non-demented (N.D) images, and 1,966 veritably mild demented (V.M.D) images. These models could be employed for developing clinically useful results that are suitable to descry announcements in MRI images.
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Affiliation(s)
- Sarang Sharma
- Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chandigarh, Punjab, India
| | - Sheifali Gupta
- Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chandigarh, Punjab, India
| | - Deepali Gupta
- Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chandigarh, Punjab, India
| | - Sapna Juneja
- Department of Computer Science, KIET Group of Institutions, Ghaziabad, India
| | - Amena Mahmoud
- Department of Computer Science, Kafrelsheikh University, Kafr el-Sheikh, Egypt
| | - Shaker El–Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, Egypt
| | - Kyung-Sup Kwak
- Department of Information and Communication Engineering, Inha University, Incheon, South Korea
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Automatic detection of Alzheimer’s disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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A Deep Longitudinal Model for Mild Cognitive Impairment to Alzheimer’s Disease Conversion Prediction in Low-Income Countries. APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING 2022. [DOI: 10.1155/2022/1419310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive and fatal disease, due to the nonavailability of any permanent cure. Some treatments are under experimentation that can slow down and possibly pause the progression of the disease only if the disease is diagnosed earlier. The onset of AD can only be detected at the mild cognitive impairment (MCI) stage in which slight memory loss is observed but daily routine functions are intact. A small fraction of the patient progresses from MCI to AD. In this research, we have designed a cascaded deep neural network model to identify those MCI subjects who will progress to AD in the following year. The analysis and experimentation have been performed using twenty longitudinal neuropsychological measures (NMs) provided by Alzheimer’s Disease Neuroimaging Initiative (ADNI). After normalization and ranking of longitudinal data, the deep neural network regression model is trained and tuned to forecast the next in-sequence biomarker value using two previous follow-up readings for each marker. Then, the three time-domain window samples are fed into another deep neural network classifier model for the classification of MCI progressor (MCIp) and MCI stables (MCIs). Our model presented regression forecasting MAE of 0.13 and classification accuracy of 86.9% with AUC of 92.1% (Sensitivity: 67.7%, specificity: 92.3%) over 5-fold cross-validation. We conclude that time-domain measures of NM alone can deliver comparable MCI to AD conversion prediction performance without leveraging more expensive and invasive counterparts such as MR imaging, PET scans, and CSF measures. Middle and low-income countries will benefit from such cheap and effective solutions greatly.
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Chatterjee S, Byun YC. Voting Ensemble Approach for Enhancing Alzheimer's Disease Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22197661. [PMID: 36236757 PMCID: PMC9571155 DOI: 10.3390/s22197661] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/01/2022] [Accepted: 10/05/2022] [Indexed: 05/28/2023]
Abstract
Alzheimer's disease is dementia that impairs one's thinking, behavior, and memory. It starts as a moderate condition affecting areas of the brain that make it challenging to retain recently learned information, causes mood swings, and causes confusion regarding occasions, times, and locations. The most prevalent type of dementia, called Alzheimer's disease (AD), causes memory-related problems in patients. A precise medical diagnosis that correctly classifies AD patients results in better treatment. Currently, the most commonly used classification techniques extract features from longitudinal MRI data before creating a single classifier that performs classification. However, it is difficult to train a reliable classifier to achieve acceptable classification performance due to limited sample size and noise in longitudinal MRI data. Instead of creating a single classifier, we propose an ensemble voting method that generates multiple individual classifier predictions and then combines them to develop a more accurate and reliable classifier. The ensemble voting classifier model performs better in the Open Access Series of Imaging Studies (OASIS) dataset for older adults than existing methods in important assessment criteria such as accuracy, sensitivity, specificity, and AUC. For the binary classification of with dementia and no dementia, an accuracy of 96.4% and an AUC of 97.2% is attained.
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Affiliation(s)
- Subhajit Chatterjee
- Department of Computer Engineering, Jeju National University, Jeju 63243, Korea
| | - Yung-Cheol Byun
- Department of Computer Engineering, Major of Electronic Engineering, Institute of Information Science & Technology, Jeju National University, Jeju 63243, Korea
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Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants. Transl Psychiatry 2022; 12:397. [PMID: 36130921 PMCID: PMC9492670 DOI: 10.1038/s41398-022-02162-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/04/2022] [Accepted: 09/08/2022] [Indexed: 11/12/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years (p < 0.0001, Cohen's d = 0.31, 95% CI: 2.23-3.88) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant +2.09 years (p < 0.05, Cohen's d = 0.134525) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.
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Chang M, Brainerd CJ. Predicting conversion from mild cognitive impairment to Alzheimer's disease with multimodal latent factors. J Clin Exp Neuropsychol 2022; 44:316-335. [PMID: 36036715 DOI: 10.1080/13803395.2022.2115015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Abstract
INTRODUCTION We studied the ability of latent factor scores to predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) and investigated whether multimodal factor scores improve predictive power, relative to single-modal factor scores. METHOD We conducted exploratory factor analyses (EFAs) and confirmatory factor analyses (CFAs) of the baseline data of MCI subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) to generate factor scores for three data modalities: neuropsychological (NP), magnetic resonance imaging (MRI), and cerebrospinal fluid (CSF). Factor scores from single or multiple modalities were entered in logistic regression models to predict MCI to AD conversion for 160 ADNI subjects over a 2-year interval. RESULTS NP factors attained an area under the curve (AUC) of .80, with a sensitivity of .66 and a specificity of .77. MRI factors reached a comparable level of performance (AUC = .80, sensitivity = .66, specificity = .78), whereas CSF factors produced weaker prediction (AUC = .70, sensitivity = .56, specificity = .79). Combining NP factors with MRI or CSF factors produced better prediction than either MRI or CSF factors alone. Similarly, adding MRI factors to NP or CSF factors produced improvements in prediction relative to NP or CSF factors alone. However, adding CSF factors to either NP or MRI factors produced no improvement in prediction. CONCLUSIONS Latent factor scores provided good accuracy for predicting MCI to AD conversion. Adding NP or MRI factors to factors from other modalities enhanced predictive power but adding CSF factors did not.
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Affiliation(s)
- Minyu Chang
- Department of Psychology and Human Neuroscience Institute, Cornell University, Ithaca, New York, USA
| | - C J Brainerd
- Department of Psychology and Human Neuroscience Institute, Cornell University, Ithaca, New York, USA
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Inverted papilloma and nasal polyp classification using a deep convolutional network integrated with an attention mechanism. Comput Biol Med 2022; 149:105976. [DOI: 10.1016/j.compbiomed.2022.105976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 07/18/2022] [Accepted: 08/13/2022] [Indexed: 11/18/2022]
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Sharma S, Gupta S, Gupta D, Altameem A, Saudagar AKJ, Poonia RC, Nayak SR. HTLML: Hybrid AI Based Model for Detection of Alzheimer’s Disease. Diagnostics (Basel) 2022; 12:diagnostics12081833. [PMID: 36010183 PMCID: PMC9406825 DOI: 10.3390/diagnostics12081833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 07/05/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer’s disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brain’s ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Naïve base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective.
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Affiliation(s)
- Sarang Sharma
- Chitkara Institute of Engineering and Technology, Chitkara University, Punjab 140401, India; (S.S.); (S.G.); (D.G.)
| | - Sheifali Gupta
- Chitkara Institute of Engineering and Technology, Chitkara University, Punjab 140401, India; (S.S.); (S.G.); (D.G.)
| | - Deepali Gupta
- Chitkara Institute of Engineering and Technology, Chitkara University, Punjab 140401, India; (S.S.); (S.G.); (D.G.)
| | - Ayman Altameem
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, Riyadh 11533, Saudi Arabia;
| | - Abdul Khader Jilani Saudagar
- Information Systems Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
- Correspondence:
| | - Ramesh Chandra Poonia
- Department of Computer Science, CHRIST (Deemed to be University), Bangalore 560029, India;
| | - Soumya Ranjan Nayak
- Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida 201301, India;
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Tekin HO, Almisned F, Erguzel TT, Abuzaid MM, Elshami W, Ene A, Issa SAM, Zakaly HMH. Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment. Front Public Health 2022; 10:892789. [PMID: 35968466 PMCID: PMC9366721 DOI: 10.3389/fpubh.2022.892789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 07/05/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose This study aimed to evaluate Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during the abdominal CT examinations for quality assurance in a retrospective, cross-sectional study. Methods The structure of the ANN model was designed considering various input parameters, namely patient weight, patient size, body mass index, mean CTDI volume, scanning length, kVp, mAs, exposure time per rotation, and pitch factor. The aforementioned examination details of 551 abdominal CT scans were used as retrospective data. Different types of learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient were checked in terms of the accuracy of the training data. Results The R-value representing the correlation coefficient for the real system and system output is given as 0.925, 0.785, and 0.854 for the Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient algorithms, respectively. The findings showed that the Levenberg-Marquardt algorithm comprehensively detects DLP values for abdominal CT examinations. It can be a helpful approach to simplify CT quality assurance. Conclusion It can be concluded that outcomes of this novel artificial intelligence method can be used for high accuracy DLP estimations before the abdominal CT examinations, where the radiation-related risk factors are high or risk evaluation of multiple CT scans is needed for patients in terms of ALARA. Likewise, it can be concluded that artificial learning methods are powerful tools and can be used for different types of radiation-related risk assessments for quality assurance in diagnostic radiology.
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Affiliation(s)
- H. O. Tekin
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
- Computer Engineering Department, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, Turkey
- H. O. Tekin
| | - Faisal Almisned
- Department Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
| | - T. T. Erguzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Mohamed M. Abuzaid
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - W. Elshami
- Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Antoaneta Ene
- Department of Chemistry, Physics and Environment, Faculty of Sciences and Environment, INPOLDE Research Center, Dunarea de Jos University of Galati, Galati, Romania
- Antoaneta Ene
| | - Shams A. M. Issa
- Physics Department, Faculty of Science, Al-Azhar University, Assiut, Egypt
- Physics Department, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia
| | - Hesham M. H. Zakaly
- Physics Department, Faculty of Science, Al-Azhar University, Assiut, Egypt
- Experimental Physics Department, Institute of Physics and Technology, Ural Federal University, Ekaterinburg, Russia
- *Correspondence: Hesham M. H. Zakaly
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A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer's disease. COMMUNICATIONS MEDICINE 2022; 2:70. [PMID: 35759330 PMCID: PMC9209493 DOI: 10.1038/s43856-022-00133-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 05/24/2022] [Indexed: 01/12/2023] Open
Abstract
Background Alzheimer's disease, the most common cause of dementia, causes a progressive and irreversible deterioration of cognition that can sometimes be difficult to diagnose, leading to suboptimal patient care. Methods We developed a predictive model that computes multi-regional statistical morpho-functional mesoscopic traits from T1-weighted MRI scans, with or without cognitive scores. For each patient, a biomarker called "Alzheimer's Predictive Vector" (ApV) was derived using a two-stage least absolute shrinkage and selection operator (LASSO). Results The ApV reliably discriminates between people with (ADrp) and without (nADrp) Alzheimer's related pathologies (98% and 81% accuracy between ADrp - including the early form, mild cognitive impairment - and nADrp in internal and external hold-out test sets, respectively), without any a priori assumptions or need for neuroradiology reads. The new test is superior to standard hippocampal atrophy (26% accuracy) and cerebrospinal fluid beta amyloid measure (62% accuracy). A multiparametric analysis compared DTI-MRI derived fractional anisotropy, whose readout of neuronal loss agrees with ADrp phenotype, and SNPrs2075650 is significantly altered in patients with ADrp-like phenotype. Conclusions This new data analytic method demonstrates potential for increasing accuracy of Alzheimer diagnosis.
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