論文の公開元へ

書き出し

Refer/BibIX

RIS

BibTeX

TSV

Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation.

SYAIFULLAH Ali Haidar SHIINO Akihiko 50215935 0000-0001-6203-9339 KITAHARA Hitoshi 40402721 0000-0002-2093-2572 ITOH Ryuta 80263052 ISHIDA Manabu TANIGAKI Kenji 70362473 滋賀医科大学

2021.02.05

概要

Background:
With the growing momentum for the adoption of machine learning (ML) in medical field, it is likely that reliance on ML for imaging will become routine over the next few years. We have developed a software named BAAD, which uses ML algorithms for the diagnosis of Alzheimer's disease (AD) and prediction of mild cognitive impairment (MCI) progression.
Methods:
We constructed an algorithm by combining a support vector machine (SVM) to classify and a voxel-based morphometry (VBM) to reduce concerned variables. We grouped progressive MCI and AD as an AD spectrum and trained SVM according to this classification. We randomly selected half from the total 1,314 subjects of AD neuroimaging Initiative (ADNI) from North America for SVM training, and the remaining half were used for validation to fine-tune the model hyperparameters. We created two types of SVMs, one based solely on the brain structure (SVMst), and the other based on both the brain structure and Mini-Mental State Examination score (SVMcog). We compared the model performance with two expert neuroradiologists, and further evaluated it in test datasets involving 519, 592, 69, and 128 subjects from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL), Japanese ADNI, the Minimal Interval Resonance Imaging in AD (MIDIAD) and the Open Access Series of Imaging Studies (OASIS), respectively.
Results:
BAAD's SVMs outperformed radiologists for AD diagnosis in a structural magnetic resonance imaging review. The accuracy of the two radiologists was 57.5 and 70.0%, respectively, whereas, that of the SVMst was 90.5%. The diagnostic accuracy of the SVMst and SVMcog in the test datasets ranged from 88.0 to 97.1% and 92.5 to 100%, respectively. The prediction accuracy for MCI progression was 83.0% in SVMst and 85.0% in SVMcog. In the AD spectrum classified by SVMst, 87.1% of the subjects were Aβ positive according to an AV-45 positron emission tomography. Similarly, among MCI patients classified for the AD spectrum, 89.5% of the subjects progressed to AD.
Conclusion:
Our ML has shown high performance in AD diagnosis and prediction of MCI progression. It outperformed expert radiologists, and is expected to provide support in clinical practice.

論文の公開元へ

この論文で使われている画像

参考文献

algebra improves the diagnosis of probable Alzheimer Disease. AJNR Am J

Neuroradiol. (2012) 33:1109–14. doi: 10.3174/ajnr.A2935

4. Shimoda K, Kimura M, Yokota M, Okubo Y. Comparison of regional

gray matter volume abnormalities in Alzheimers disease and late

life depression with hippocampal atrophy using VSRAD analysis:

a voxel-based morphometry study. Psychiatry Res. (2015) 232:71–5.

doi: 10.1016/j.pscychresns.2015.01.018

5. Ashburner

Friston

KJ.

Voxel-based

morphometry–the

methods. Neuroimage. (2000) 11:805–21. doi: 10.1006/nimg.20

00.0582

6. Kurth F, Gaser C, Luders E. A 12-step user guide for analyzing voxel-wise

gray matter asymmetries in statistical parametric mapping (SPM). Nat Protoc.

(2015) 10:293–304. doi: 10.1038/nprot.2015.014

1. Clerx L, van Rossum IA, Burns L, Knol DL, Scheltens P, Verhey F, et al.

Measurements of medial temporal lobe atrophy for prediction of Alzheimer’s

disease in subjects with mild cognitive impairment. Neurobiol Aging. (2013)

34:2003–13. doi: 10.1016/j.neurobiolaging.2013.02.002

2. Khlif MS, Egorova N, Werden E, Redolfi A, Boccardi M, DeCarli CS, et al.

A comparison of automated segmentation and manual tracing in estimating

hippocampal volume in ischemic stroke and healthy control participants.

Neuroimage Clin. (2019) 21:101581. doi: 10.1016/j.nicl.2018.10.019

3. Matsuda H, Mizumura S, Nemoto K, Yamashita F, Imabayashi E, Sato

N, et al. Automatic voxel-based morphometry of structural MRI by

SPM8 plus diffeomorphic anatomic registration through exponentiated lie

Frontiers in Neurology | www.frontiersin.org

February 2021 | Volume 11 | Article 576029

Syaifullah et al.

BAAD for Early Diagnosis of AD

20. Matthews BW. Comparison of the predicted and observed secondary

structure of T4 phage lysozyme. Biochim Biophys Acta. (1975) 405:442–51.

doi: 10.1016/0005-2795(75)90109-9

21. Yeo JM, Lim X, Khan Z, Pal S. Systematic review of the diagnostic utility

of SPECT imaging in dementia. Eur Arch Psychiatry Clin Neurosci. (2013)

263:539–52. doi: 10.1007/s00406-013-0426-z

22. Valotassiou V, Malamitsi J, Papatriantafyllou J, Dardiotis E, Tsougos I,

Psimadas D, et al. SPECT and PET imaging in Alzheimer’s disease. Ann Nucl

Med. (2018) 32:583–93. doi: 10.1007/s12149-018-1292-6

23. Whitwell JL, Graff-Radford J, Tosakulwong N, Weigand SD, Machulda MM,

Senjem ML, et al. Imaging correlations of tau, amyloid, metabolism, and

atrophy in typical and atypical Alzheimer’s disease. Alzheimers Dement. (2018)

14:1005–14. doi: 10.1016/j.jalz.2018.02.020

24. Hoenig MC, Bischof GN, Seemiller J, Hammes J, Kukolja J, Onur OA, et al.

Networks of tau distribution in Alzheimer’s disease. Brain. (2018) 141:568–81.

doi: 10.1093/brain/awx353

25. Jack CR Jr., Wiste HJ, Schwarz CG, Lowe VJ, Senjem ML, Vemuri P, et

al. Longitudinal tau PET in ageing and Alzheimer’s disease. Brain. (2018)

141:1517–28. doi: 10.1093/brain/awy059

26. Mak E, Bethlehem RAI, Romero-Garcia R, Cervenka S, Rittman T,

Gabel S, et al. In vivo coupling of tau pathology and cortical thinning

in Alzheimer’s disease. Alzheimers Dement (Amst). (2018) 10:678–87.

doi: 10.1016/j.dadm.2018.08.005

27. Jack CR Jr., Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et

al. NIA-AA research framework: toward a biological definition of Alzheimer’s

disease. Alzheimers Dement. (2018) 14:535–62. doi: 10.1016/j.jalz.2018.02.018

28. Lee JC, Kim SJ, Hong S, Kim Y. Diagnosis of Alzheimer’s disease utilizing

amyloid and tau as fluid biomarkers. Exp Mol Med. (2019) 51:1–10.

doi: 10.1038/s12276-019-0250-2

7. Rajapakse JC, Giedd JN, Rapoport JL. Statistical approach to segmentation

of single-channel cerebral MR images. IEEE Trans Med Imaging. (1997)

16:176–86. doi: 10.1109/42.563663

8. Manjon JV, Coupe P, Marti-Bonmati L, Collins DL, Robles M. Adaptive

non-local means denoising of MR images with spatially varying noise levels. J

Magn Reson Imaging. (2010) 31:192–203. doi: 10.1002/jmri.22003

9. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage.

(2007) 38:95–113. doi: 10.1016/j.neuroimage.2007.07.007

10. Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on

neuroimaging-based classification studies and associated feature extraction

methods for Alzheimer’s disease and its prodromal stages. Neuroimage. (2017)

155:530–48. doi: 10.1016/j.neuroimage.2017.03.057

11. Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL. Open

Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young,

middle aged, nondemented, and demented older adults. J Cogn Neurosci.

(2007) 19:1498–507. doi: 10.1162/jocn.2007.19.9.1498

12. Jack CR Jr., Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et

al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods.

J Magn Reson Imaging. (2008) 27:685–91. doi: 10.1002/jmri.21049

13. Jack CR Jr., Barnes J, Bernstein MA, Borowski BJ, Brewer J, Clegg S, et al.

Magnetic resonance imaging in Alzheimer’s Disease Neuroimaging Initiative

2. Alzheimers Dement. (2015) 11:740–56. doi: 10.1016/j.jalz.2015.05.002

14. Malone IB, Cash D, Ridgway GR, MacManus DG, Ourselin S, Fox NC, et al.

MIRIAD–Public release of a multiple time point Alzheimer’s MR imaging

dataset. Neuroimage. (2013) 70:33–6. doi: 10.1016/j.neuroimage.2012.

12.044

15. Flemming KD, Jones LK, Mayo Clinic. Mayo Clinic Neurology Board Review:

Basic Sciences and Psychiatry for Initial Certification. Oxford, NY: Oxford

University Press (2015). doi: 10.1093/med/9780190244927.001.0001

16. Ward A, Tardiff S, Dye C, Arrighi HM. Rate of conversion from

prodromal Alzheimer’s disease to Alzheimer’s dementia: a systematic

review of the literature. Dement Geriatr Cogn Dis Extra. (2013) 3:320–32.

doi: 10.1159/000354370

17. Vos SJ, Verhey F, Frolich L, Kornhuber J, Wiltfang J, Maier W, et al. Prevalence

and prognosis of Alzheimer’s disease at the mild cognitive impairment stage.

Brain. (2015) 138:1327–38. doi: 10.1093/brain/awv029

18. Landau SM, Mintun MA, Joshi AD, Koeppe RA, Petersen RC, Aisen PS, et

al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline.

Ann Neurol. (2012) 72:578–86. doi: 10.1002/ana.23650

19. Fawcett T. An introduction to ROC analysis. Pattern Recog Lett. (2006)

27:861–74. doi: 10.1016/j.patrec.2005.10.010

Frontiers in Neurology | www.frontiersin.org

Conflict of Interest: The authors declare that the research was conducted in the

absence of any commercial or financial relationships that could be construed as a

potential conflict of interest.

open-access article distributed under the terms of the Creative Commons Attribution

License (CC BY). The use, distribution or reproduction in other forums is permitted,

provided the original author(s) and the copyright owner(s) are credited and that the

original publication in this journal is cited, in accordance with accepted academic

practice. No use, distribution or reproduction is permitted which does not comply

with these terms.

February 2021 | Volume 11 | Article 576029

...

参考文献をもっと見る

分野

大学

学位論文種類・取得年

言語

Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation.

概要

この論文で使われている画像

関連論文

Utilizing machine learning for detecting patients with IgA nephropathy from computerized medical bill database

Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability

Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer

Evaluation of Kidney Histological Images Using Unsupervised Deep Learning

Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system

参考文献

分野

大学

学位論文種類・取得年

言語

コピーが完了しました

URLをコピーしました

Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation.

概要

この論文で使われている画像

関連論文

Utilizing machine learning for detecting patients with IgA nephropathy from computerized medical bill database

Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability

Label Distribution Learning for Automatic Cancer Grading of Histopathological Images of Prostate Cancer

Evaluation of Kidney Histological Images Using Unsupervised Deep Learning

Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system

参考文献