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Castellano G, Esposito A, Lella E, Montanaro G, Vessio G. Automated detection of Alzheimer's disease: a multi-modal approach with 3D MRI and amyloid PET. Sci Rep 2024; 14:5210. [PMID: 38433282 PMCID: PMC10909869 DOI: 10.1038/s41598-024-56001-9] [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: 11/09/2022] [Accepted: 02/28/2024] [Indexed: 03/05/2024] Open
Abstract
Recent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer's disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease's causes.
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Affiliation(s)
| | - Andrea Esposito
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Eufemia Lella
- Sirio - Research & Innovation, Sidea Group, Bari, Italy
| | | | - Gennaro Vessio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy.
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Ganji Z, Aghaee Hakak M, Zare H. Comparison of machine learning methods for the detection of focal cortical dysplasia lesions: decision tree, support vector machine and artificial neural network. Neurol Res 2022; 44:1142-1149. [PMID: 35981138 DOI: 10.1080/01616412.2022.2112381] [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: 12/13/2022]
Abstract
BACKGROUND Accurate classification of focal cortical dysplasia (FCD) has been challenging due to the problematic visual detection in magnetic resonance imaging (MRI). Hence, recently, there has been a necessity for employing new techniques to solve the problem. Among the new techniques for FCD lesion diagnosis, classification techniques can be of great help in FCD patient's detection from healthy individuals. METHODS MRI data were collected from 58 participants (30 subjects with FCD type II and 28 normal subjects). Morphological and intensity-based characteristics were calculated for each cortical level and then the performance of the three classifiers: decision tree (DT), support vector machine (SVM) and artificial neural network (ANN) was evaluated. RESULTS Metrics for evaluating classification methods, sensitivity, specificity and accuracy for the DT were 90%, 100% and 95.8%, respectively; it was 95%, 100% and 97.9% for the SVM and 96.7%, 100% and 98.6% for the ANN. CONCLUSION Comparison of the performance of the three classifications used in this study showed that all three have excellent performance in specificity, but in terms of classification sensitivity and accuracy, the artificial neural network method has worked better.
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Affiliation(s)
- Zohreh Ganji
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohsen Aghaee Hakak
- Epilepsy Monitoring Unit, Research and Education Department, Razavi Hospital, Mashhad, Iran
| | - Hoda Zare
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.,Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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EEG-Based Alzheimer's Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network. SENSORS 2022; 22:s22103696. [PMID: 35632105 PMCID: PMC9145212 DOI: 10.3390/s22103696] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 01/27/2023]
Abstract
The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer's disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved recognition accuracy, being able to manage large amounts of data. Nevertheless, machine learning methods may exhibit lower accuracies when faced with incomplete, corrupted, or otherwise missing data, so it is important do develop robust pre-processing techniques do deal with incomplete data. The aim of this paper is to develop an automatic classification method that can still work well with EEG data affected by artifacts, as can arise during the collection with, e.g., a wireless system that can lose packets. We show that a recurrent neural network (RNN) can operate successfully even in the case of significantly corrupted data, when it is pre-filtered by the robust principal component analysis (RPCA) algorithm. RPCA was selected because of its stated ability to remove outliers from the signal. To demonstrate this idea, we first develop an RNN which operates on EEG data, properly processed through traditional PCA; then, we use corrupted data as input and process them with RPCA to filter outlier components, showing that even with data corruption causing up to 20% erasures, the RPCA was able to increase the detection accuracy by about 5% with respect to the baseline PCA.
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Seo Y, Jang H, Lee H. Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer’s Disease. Life (Basel) 2022; 12:life12020275. [PMID: 35207561 PMCID: PMC8879055 DOI: 10.3390/life12020275] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/05/2022] [Accepted: 02/09/2022] [Indexed: 01/18/2023] Open
Abstract
Clinical trials for Alzheimer’s disease (AD) face multiple challenges, such as the high screen failure rate and the even allocation of heterogeneous participants. Artificial intelligence (AI), which has become a potent tool of modern science with the expansion in the volume, variety, and velocity of biological data, offers promising potential to address these issues in AD clinical trials. In this review, we introduce the current status of AD clinical trials and the topic of machine learning. Then, a comprehensive review is focused on the potential applications of AI in the steps of AD clinical trials, including the prediction of protein and MRI AD biomarkers in the prescreening process during eligibility assessment and the likelihood stratification of AD subjects into rapid and slow progressors in randomization. Finally, this review provides challenges, developments, and the future outlook on the integration of AI into AD clinical trials.
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Affiliation(s)
| | | | - Hyejoo Lee
- Correspondence: ; Tel.: +82-2-3410-1233; Fax: +82-2-3410-0052
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Jain M, Rai CS, Jain J. A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:7965677. [PMID: 34394708 PMCID: PMC8360749 DOI: 10.1155/2021/7965677] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/18/2021] [Accepted: 07/09/2021] [Indexed: 01/22/2023]
Abstract
We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.
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Affiliation(s)
- Manju Jain
- University College of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka Sector 16-C, New Delhi 110078, India
- Meerabai Institute of Technology Maharani Bagh, New Delhi 110065, India
| | - C. S. Rai
- University College of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka Sector 16-C, New Delhi 110078, India
| | - Jai Jain
- Media Agility India Ltd, New Delhi, India
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Abstract
Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients’ videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients’ recordings, demonstrating the effectiveness of the proposed solutions.
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Zhao X, Ang CKE, Acharya UR, Cheong KH. Application of Artificial Intelligence techniques for the detection of Alzheimer’s disease using structural MRI images. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Kolling ML, Furstenau LB, Sott MK, Rabaioli B, Ulmi PH, Bragazzi NL, Tedesco LPC. Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18063099. [PMID: 33802880 PMCID: PMC8002654 DOI: 10.3390/ijerph18063099] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 03/12/2021] [Accepted: 03/15/2021] [Indexed: 12/15/2022]
Abstract
In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.
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Affiliation(s)
- Maikel Luis Kolling
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
| | - Leonardo B. Furstenau
- Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil;
| | - Michele Kremer Sott
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
| | - Bruna Rabaioli
- Department of Medicine, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
| | - Pedro Henrique Ulmi
- Department of Computer Science, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
- Correspondence: (N.L.B.); (L.P.C.T.)
| | - Leonel Pablo Carvalho Tedesco
- Graduate Program of Industrial Systems and Processes, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil; (M.L.K.); (M.K.S.)
- Department of Computer Science, University of Santa Cruz do Sul, Santa Cruz do Sul 96816-501, Brazil;
- Correspondence: (N.L.B.); (L.P.C.T.)
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Herzog NJ, Magoulas GD. Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia. SENSORS 2021; 21:s21030778. [PMID: 33498908 PMCID: PMC7865614 DOI: 10.3390/s21030778] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/20/2021] [Accepted: 01/21/2021] [Indexed: 11/30/2022]
Abstract
Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve detecting structural and functional cerebral changes such as changes in the degree of asymmetry between the left and right hemispheres. Changes can be detected by computational algorithms and used for the early diagnosis of dementia and its stages (amnestic early mild cognitive impairment (EMCI), Alzheimer’s Disease (AD)), and can help to monitor the progress of the disease. In this vein, the paper proposes a data processing pipeline that can be implemented on commodity hardware. It uses features of brain asymmetries, extracted from MRI of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, for the analysis of structural changes, and machine learning classification of the pathology. The experiments provide promising results, distinguishing between subjects with normal cognition (NC) and patients with early or progressive dementia. Supervised machine learning algorithms and convolutional neural networks tested are reaching an accuracy of 92.5% and 75.0% for NC vs. EMCI, and 93.0% and 90.5% for NC vs. AD, respectively. The proposed pipeline offers a promising low-cost alternative for the classification of dementia and can be potentially useful to other brain degenerative disorders that are accompanied by changes in the brain asymmetries.
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Affiliation(s)
- Nitsa J. Herzog
- Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UK;
| | - George D. Magoulas
- Department of Computer Science, Birkbeck College, University of London, London WC1E 7HZ, UK;
- Birkbeck Knowledge Lab, University of London, London WC1E 7HZ, UK
- Correspondence:
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