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Cao E, Ma D, Nayak S, Duong TQ. Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis. Neurobiol Dis 2023; 187:106310. [PMID: 37769746 DOI: 10.1016/j.nbd.2023.106310] [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: 03/28/2023] [Revised: 09/20/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023] Open
Abstract
INTRODUCTION This study reports a novel deep learning approach to predict mild cognitive impairment (MCI) conversion to Alzheimer's dementia (AD) within three years using whole-brain fluorodeoxyglucose (FDG) positron emission tomography (PET) and cognitive scores (CS). METHODS This analysis consisted of 150 normal controls (CN), 257 MCI, and 205 AD subjects from ADNI. FDG-PET and CS were obtained at MCI diagnosis to predict AD conversion within three years of MCI diagnosis using convolutional neural networks. RESULTS Neurocognitive scores predicted better than FDG-PET per se, but the best model was a combination of FDG-PET, age, and neurocognitive data, yielding an AUC of 0.785 ± 0.096 and a balanced accuracy of 0.733 ± 0.098. Saliency maps highlighted putamen, thalamus, inferior frontal gyrus, parietal operculum, precuneus cortices, calcarine cortices, temporal gyrus, and planum temporale to be important for prediction. DISCUSSION Deep learning accurately predicts MCI conversion to AD and provides neural correlates of brain regions associated with AD conversion.
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Affiliation(s)
- Eric Cao
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10467, United States
| | - Da Ma
- Department of Internal Medicine Section of Gerontology and Geriatric Medicine, Wake Forest, University School of Medicine, Winston-Salam, NC 27109, United States
| | - Siddharth Nayak
- Department of Radiology, Weill Cornell Medicine, New York, 10065, United States
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY 10467, United States.
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Xu X, Lin L, Sun S, Wu S. A review of the application of three-dimensional convolutional neural networks for the diagnosis of Alzheimer's disease using neuroimaging. Rev Neurosci 2023:revneuro-2022-0122. [PMID: 36729918 DOI: 10.1515/revneuro-2022-0122] [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: 10/03/2022] [Accepted: 01/02/2023] [Indexed: 02/03/2023]
Abstract
Alzheimer's disease (AD) is a degenerative disorder that leads to progressive, irreversible cognitive decline. To obtain an accurate and timely diagnosis and detect AD at an early stage, numerous approaches based on convolutional neural networks (CNNs) using neuroimaging data have been proposed. Because 3D CNNs can extract more spatial discrimination information than 2D CNNs, they have emerged as a promising research direction in the diagnosis of AD. The aim of this article is to present the current state of the art in the diagnosis of AD using 3D CNN models and neuroimaging modalities, focusing on the 3D CNN architectures and classification methods used, and to highlight potential future research topics. To give the reader a better overview of the content mentioned in this review, we briefly introduce the commonly used imaging datasets and the fundamentals of CNN architectures. Then we carefully analyzed the existing studies on AD diagnosis, which are divided into two levels according to their inputs: 3D subject-level CNNs and 3D patch-level CNNs, highlighting their contributions and significance in the field. In addition, this review discusses the key findings and challenges from the studies and highlights the lessons learned as a roadmap for future research. Finally, we summarize the paper by presenting some major findings, identifying open research challenges, and pointing out future research directions.
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Affiliation(s)
- Xinze Xu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Lan Lin
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Shen Sun
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing 100124, China
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Meysami S, Raji CA, Glatt RM, Popa ES, Ganapathi AS, Bookheimer T, Slyapich CB, Pierce KP, Richards CJ, Lampa MG, Gill JM, Rapozo MK, Hodes JF, Tongson YM, Wong CL, Kim M, Porter VR, Kaiser SA, Panos SE, Dye RV, Miller KJ, Bookheimer SY, Martin NA, Kesari S, Kelly DF, Bramen JE, Siddarth P, Merrill DA. Handgrip Strength Is Related to Hippocampal and Lobar Brain Volumes in a Cohort of Cognitively Impaired Older Adults with Confirmed Amyloid Burden. J Alzheimers Dis 2023; 91:999-1006. [PMID: 36530088 PMCID: PMC9912728 DOI: 10.3233/jad-220886] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Strength and mobility are essential for activities of daily living. With aging, weaker handgrip strength, mobility, and asymmetry predict poorer cognition. We therefore sought to quantify the relationship between handgrip metrics and volumes quantified on brain magnetic resonance imaging (MRI). OBJECTIVE To model the relationships between handgrip strength, mobility, and MRI volumetry. METHODS We selected 38 participants with Alzheimer's disease dementia: biomarker evidence of amyloidosis and impaired cognition. Handgrip strength on dominant and non-dominant hands was measured with a hand dynamometer. Handgrip asymmetry was calculated. Two-minute walk test (2MWT) mobility evaluation was combined with handgrip strength to identify non-frail versus frail persons. Brain MRI volumes were quantified with Neuroreader. Multiple regression adjusting for age, sex, education, handedness, body mass index, and head size modeled handgrip strength, asymmetry and 2MWT with brain volumes. We modeled non-frail versus frail status relationships with brain structures by analysis of covariance. RESULTS Higher non-dominant handgrip strength was associated with larger volumes in the hippocampus (p = 0.02). Dominant handgrip strength was related to higher frontal lobe volumes (p = 0.02). Higher 2MWT scores were associated with larger hippocampal (p = 0.04), frontal (p = 0.01), temporal (p = 0.03), parietal (p = 0.009), and occipital lobe (p = 0.005) volumes. Frailty was associated with reduced frontal, temporal, and parietal lobe volumes. CONCLUSION Greater handgrip strength and mobility were related to larger hippocampal and lobar brain volumes. Interventions focused on improving handgrip strength and mobility may seek to include quantified brain volumes on MR imaging as endpoints.
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Affiliation(s)
- Somayeh Meysami
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Cyrus A. Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Ryan M. Glatt
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Emily S. Popa
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Aarthi S. Ganapathi
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Tess Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Colby B. Slyapich
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Kyron P. Pierce
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Casey J. Richards
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Melanie G. Lampa
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Jaya M. Gill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - Molly K. Rapozo
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
| | - John F. Hodes
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Drexel University College of Medicine, Philadelphia, PA, USA
| | - Ynez M. Tongson
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Claudia L. Wong
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Mihae Kim
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Verna R. Porter
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Scott A. Kaiser
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Stella E. Panos
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Richelin V. Dye
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Behavioral Health Institute, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Karen J. Miller
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Susan Y. Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - Neil A. Martin
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Santosh Kesari
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Daniel F. Kelly
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Jennifer E. Bramen
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
| | - Prabha Siddarth
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
| | - David A. Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute and Foundation, Santa Monica, CA, USA
- Saint John’s Cancer Institute at Providence Saint John’s Health Center, Santa Monica, CA, USA
- Providence Saint John’s Health Center, Santa Monica, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, USA
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Post B, Badea C, Faisal A, Brett SJ. Breaking bad news in the era of artificial intelligence and algorithmic medicine: an exploration of disclosure and its ethical justification using the hedonic calculus. AI AND ETHICS 2022; 3:1-14. [PMID: 36338525 PMCID: PMC9628590 DOI: 10.1007/s43681-022-00230-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 10/12/2022] [Indexed: 11/05/2022]
Abstract
An appropriate ethical framework around the use of Artificial Intelligence (AI) in healthcare has become a key desirable with the increasingly widespread deployment of this technology. Advances in AI hold the promise of improving the precision of outcome prediction at the level of the individual. However, the addition of these technologies to patient-clinician interactions, as with any complex human interaction, has potential pitfalls. While physicians have always had to carefully consider the ethical background and implications of their actions, detailed deliberations around fast-moving technological progress may not have kept up. We use a common but key challenge in healthcare interactions, the disclosure of bad news (likely imminent death), to illustrate how the philosophical framework of the 'Felicific Calculus' developed in the eighteenth century by Jeremy Bentham, may have a timely quasi-quantitative application in the age of AI. We show how this ethical algorithm can be used to assess, across seven mutually exclusive and exhaustive domains, whether an AI-supported action can be morally justified.
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Affiliation(s)
- Benjamin Post
- Department of Bioengineering, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
- UKRI Centre in AI for Healthcare, Imperial College London, London, UK
| | - Cosmin Badea
- Department of Computing, Imperial College London, London, UK
| | - Aldo Faisal
- Department of Bioengineering, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
- UKRI Centre in AI for Healthcare, Imperial College London, London, UK
- Institute of Artificial and Human Intelligence, University of Bayreuth, Bayreuth, Germany
| | - Stephen J. Brett
- UKRI Centre in AI for Healthcare, Imperial College London, London, UK
- Department of Surgery and Cancer, Imperial College London, London, UK
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Genish T, Kavitha S, Vijayalakshmi S. A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer’s Disease. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2022. [DOI: 10.1142/s1469026822500201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer’s disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer’s disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber’s law which determines the luminance factor of the image. In the region extraction process, Chan–Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively.
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Affiliation(s)
- T. Genish
- School of Computing Science, KPR College of Arts Science and Research, Avinashi Road, Coimbatore, India
| | - S. Kavitha
- PG and Research, Department of Computer Science, Sakthi College of Arts and Science for Women, Oddanchatram, Dindigul, India
| | - S. Vijayalakshmi
- Department of Data Science, CHRIST (Deemed to be University), Pune, Lavasa Campus, India
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