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Current Updates and Unmet Needs of Brain MRI-Based Artificial Intelligence Software for Patients With Neurodegenerative Diseases in the Republic of Korea

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주제분류방사선과학

초록

In aging societies, incidences of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease are increasing. Neurodegenerative diseases are bringing main challenges to the healthcare system in today’s world. Analyzing characteristic imaging patterns of patients with neurodegenerative diseases is important. Since objective and reliable imaging assessments and precise analyses can lead to early diagnosis of neurodegenerative diseases, imaging patterns are being increasingly investigated. Artificial intelligence (AI) analyzing brain MRI has been applied to neurodegenerative diseases, providing added value in early diagnosis. MRI-based AI software has been developed and studied worldwide, with some AI-based software already being used in actual clinical care. Currently, there are MRI-based volumetry and segmentation software available. There is also an unmet demand for the application of AI in neurodegenerative diseases. Here, we review current status and unmet needs for application of AI in neurodegenerative diseases. We also discuss current limitations of AI, suggestion for AI-based software, and how it can be clinically applied in the future.

Abstract

In aging societies, incidences of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease are increasing. Neurodegenerative diseases are bringing main challenges to the healthcare system in today’s world. Analyzing characteristic imaging patterns of patients with neurodegenerative diseases is important. Since objective and reliable imaging assessments and precise analyses can lead to early diagnosis of neurodegenerative diseases, imaging patterns are being increasingly investigated. Artificial intelligence (AI) analyzing brain MRI has been applied to neurodegenerative diseases, providing added value in early diagnosis. MRI-based AI software has been developed and studied worldwide, with some AI-based software already being used in actual clinical care. Currently, there are MRI-based volumetry and segmentation software available. There is also an unmet demand for the application of AI in neurodegenerative diseases. Here, we review current status and unmet needs for application of AI in neurodegenerative diseases. We also discuss current limitations of AI, suggestion for AI-based software, and how it can be clinically applied in the future.

참고문헌 (62)

  1. Jack CR Jr, "Alzheimer disease : new concepts on its neurobiology and the clinical role imaging will play", Radiology, 263, pp.344~361 (2012)
  2. Jack CR Jr, "NIA-AA research framework : toward a biological definition of Alzheimer’s disease", Alzheimers Dement, 14, pp.535~562 (2018)
  3. 박미나, "Structural MR Imaging in the Diagnosis of Alzheimer’s Disease and Other Neurodegenerative Dementia: Current Imaging Approach and Future Perspectives", Korean Journal of Radiology, 17(6), pp.827~845 (2016), 대한영상의학회[DOI]
  4. Lee Ji Young, "Clinically Available Software for Automatic Brain Volumetry: Comparisons of Volume Measurements and Validation of Intermethod Reliability", Korean Journal of Radiology, 22(3), pp.405~414 (2021), 대한영상의학회[DOI]
  5. Brewer JB, "Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease", AJNR Am J Neuroradiol, 30, pp.578~580 (2009)
  6. Tanpitukpongse TP, "Predictive utility of marketed volumetric software tools in subjects at risk for Alzheimer disease : do regions outside the hippocampus matter?", AJNR Am J Neuroradiol, 38, pp.546~552 (2017)
  7. Wang C, "Automated brain volumetrics in multiple sclerosis : a step closer to clinical application", J Neurol Neurosurg Psychiatry, 87, pp.754~757 (2016)
  8. Scheltens P, "Atrophy of medial temporal lobes on MRI in"probable"Alzheimer’s disease and normal ageing : diagnostic value and neuropsychological correlates", J Neurol Neurosurg Psychiatry, 55, pp.967~972 (1992)
  9. Cavallin L, "Overtime reliability of medial temporal lobe atrophy rating in a clinical setting", Acta Radiol, 53, pp.318~323 (2012)
  10. Park HY, "Diagnostic performance of the medial temporal lobe atrophy scale in patients with Alzheimer’s disease : a systematic review and meta-analysis", Eur Radiol, 31, pp.9060~9072 (2021)
  11. Park HY, "Diagnostic performance of hippocampal volumetry in Alzheimer’s disease or mild cognitive impairment : a meta-analysis", Eur Radiol, 32, pp.6979~6991 (2022)
  12. Persson K, "Finding of increased caudate nucleus in patients with Alzheimer’s disease", Acta Neurol Scand, 137, pp.224~232 (2018)
  13. Ochs AL, "Comparison of automated brain volume measures obtained with NeuroQuant®and FreeSurfer", J Neuroimaging, 25, pp.721~727 (2015)
  14. Suh CH, "Development and validation of a deep learning–based automatic brain segmentation and classification algorithm for Alzheimer disease using 3D T1-weighted volumetric images", AJNR Am J Neuroradiol, 41, pp.2227~2234 (2020)
  15. Kim JS, "Deep learning-based diagnosis of Alzheimer’s disease using brain magnetic resonance images : an empirical study", Sci Rep, 12, pp.18007~ (2022)
  16. Kim REY, "Deep learning-based segmentation to establish East Asian normative volumes using multisite structural MRI", Diagnostics(Basel), 11, pp.13~ (2020)
  17. Bae JB, "Identification of Alzheimer’s disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging", Sci Rep, 10, pp.22252~ (2020)
  18. Song M, "Diagnostic classification and biomarker identification of Alzheimer’s disease with random forest algorithm", Brain Sci, 11, pp.453~ (2021)
  19. You Joung Kim, "Prevalence and Trends of Dementia in Korea: A Systematic Review and Meta-Analysis", Journal of Korean Medical Science, 29(7), pp.903~912 (2014), 대한의학회
  20. Erkinjuntti T, "Clinical criteria for vascular dementia : the NINDSAIREN criteria", Dementia, 5, pp.189~192 (1994)
  21. Fazekas F, "Pathologic correlates of incidental MRI white matter signal hyperintensities", Neurology, 43, pp.1683~1689 (1993)
  22. Noh Y, "A new classification system for ischemia using a combination of deep and periventricular white matter hyperintensities", J Stroke Cerebrovasc Dis, 23, pp.636~642 (2014)
  23. Brune S, "Lesionquant for assessment of MRI in multiple sclerosis—a promising supplement to the visual scan inspection", Front Neurol, 11, pp.546744~ (2020)
  24. Joo L, "Diagnostic performance of deep learning-based automatic white matter hyperintensity segmentation for classification of the Fazekas scale and differentiation of subcortical vascular dementia", PLoS One, 17, pp.e0274562~ (2022)
  25. Björklund A, "Dopamine neuron systems in the brain : an update", Trends Neurosci, 30, pp.194~202 (2007)
  26. Fearnley JM, "Ageing and Parkinson’s disease: substantia nigra regional selectivity", Brain, 114(Pt 5), pp.2283~2301 (1991)
  27. Kwon DH, "Seven-tesla magnetic resonance images of the substantia nigra in Parkinson disease", Ann Neurol, 71, pp.267~277 (2012)
  28. Mitchell T, "Emerging neuroimaging biomarkers across disease stage in Parkinson disease : a review", JAMA Neurol, 78, pp.1262~1272 (2021)
  29. Pyatigorskaya N, "Comparative study of MRI biomarkers in the substantia nigra to discriminate idiopathic Parkinson disease", AJNR Am J Neuroradiol, 39, pp.1460~1467 (2018)
  30. Frosini D, "Neuroimaging in Parkinson’s disease : focus on substantia nigra and nigro-striatal projection", Curr Opin Neurol, 30, pp.416~426 (2017)
  31. Kim EY, "Nigrosome 1 imaging : technical considerations and clinical applications", Br J Radiol, 92, pp.20180842~ (2019)
  32. Kim PH, "Diagnostic performance of loss of nigral hyperintensity on susceptibilityweighted imaging in parkinsonism : an updated meta-analysis", Eur Radiol, 31, pp.6342~6352 (2021)
  33. Nam Y, "Imaging of nigrosome 1in substantia nigra at 3T using multiecho susceptibility mapweighted imaging(SMWI)", J Magn Reson Imaging, 46, pp.528~536 (2017)
  34. Shin DH, "Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson’s disease", Parkinsonism Relat Disord, 85, pp.84~90 (2021)
  35. Hughes AJ, "The accuracy of diagnosis of Parkinsonian syndromes in a specialist movement disorder service", Brain, 125(Pt 4), pp.861~870 (2002)
  36. Höglinger GU, "Clinical diagnosis of progressive supranuclear palsy : the movement disorder society criteria", Mov Disord, 32, pp.853~864 (2017)
  37. Gilman S, "Second consensus statement on the diagnosis of multiple system atrophy", Neurology, 71, pp.670~676 (2008)
  38. Quattrone A, "MR imaging index for differentiation of progressive supranuclear palsy from Parkinson disease and the Parkinson variant of multiple system atrophy", Radiology, 246, pp.214~221 (2008)
  39. Kim S, "Diagnostic performance of the magnetic resonance parkinsonism index in differentiating progressive supranuclear palsy from Parkinson’s disease: an updated systematic review and meta-analysis", Diagnostics(Basel), 12, pp.12~ (2021)
  40. Chougar L, "Automated categorization of Parkinsonian syndromes using magnetic resonance imaging in a clinical setting", Mov Disord, 36, pp.460~470 (2021)
  41. Andersson J, "Idiopathic normal pressure hydrocephalus: epidemiology and diagnostics", pp.2021
  42. Williams MA, "Diagnosis and treatment of idiopathic normal pressure hydrocephalus", Continuum(Minneap Minn), 22, pp.579~599 (2016)
  43. Mori E, "Guidelines for management of idiopathic normal pressure hydrocephalus: second edition", Neurol Med Chir (Tokyo), 52, pp.775~809 (2012)
  44. Relkin N, "Diagnosing idiopathic normal-pressure hydrocephalus", Neurosurgery, 57(3 Suppl), pp.S4~ (2005)
  45. Park HY, "Diagnostic performance and interobserver agreement of the callosal angle and Evans' index in idiopathic normal pressure hydrocephalus : a systematic review and meta-analysis", Eur Radiol, 31, pp.5300~5311 (2021)
  46. Park HY, "Prognostic utility of disproportionately enlarged subarachnoid space hydrocephalus in idiopathic normal pressure hydrocephalus treated with ventriculoperitoneal shunt surgery : a systematic review and meta-analysis", AJNR Am J Neuroradiol, 42, pp.1429~1436 (2021)
  47. Takagi K, "Reliability and interobserver variability of Evans' index and disproportionately enlarged subarachnoid space hydrocephalus as diagnostic criteria for idiopathic normal pressure hydrocephalus", Asian J Neurosurg, 15, pp.107~112 (2020)
  48. Duan W, "Evaluation of an artificial intelligent hydrocephalus diagnosis model based on transfer learning", Medicine(Baltimore), 99, pp.e21229~ (2020)
  49. Gunter NB, "Automated detection of imaging features of disproportionately enlarged subarachnoid space hydrocephalus using machine learning methods", Neuroimage Clin, 21, pp.101605~ (2019)
  50. Greenberg SM, "Cerebral microbleeds : a guide to detection and interpretation", Lancet Neurol, 8, pp.165~174 (2009)
  51. Yates PA, "Cerebral microbleeds: a review of clinical, genetic, and neuroimaging associations", Front Neurol, 4, pp.205~ (2014)
  52. Poels MM, "Cerebral microbleeds are associated with worse cognitive function : the Rotterdam scan study", Neurology, 78, pp.326~333 (2012)
  53. Poels MM, "Prevalence and risk factors of cerebral microbleeds: an update of the Rotterdam scan study", Stroke, 41(10 Suppl), pp.S103~S106 (2010)
  54. Jeong SY, "Incidence of amyloid-related imaging abnormalities in patients with Alzheimer disease treated with anti-β-amyloid immunotherapy : a meta-analysis", Neurology, 99, pp.e2092~e2101 (2022)
  55. Fan P, "Cerebral microbleed automatic detection system based on the"deep learning"", Front Med(Lausanne), 9, pp.807443~ (2022)
  56. Serot JM, "Morphological alterations of the choroid plexus in late-onset Alzheimer’s disease", Acta Neuropathol, 99, pp.105~108 (2000)
  57. Wen GY, "Biondi ring tangles in the choroid plexus of Alzheimer’s disease and normal aging brains : a quantitative study", Brain Res, 832, pp.40~46 (1999)
  58. Choi JD, "Choroid plexus volume and permeability at brain MRI within the Alzheimer disease clinical spectrum", Radiology, 304, pp.635~645 (2022)
  59. Lee Ji Young, "뇌 자기공명영상 뇌용적 분석 소프트웨어의 임상적 적용에 대한 전문가 의견과 권고안", 대한영상의학회지, 82(5), pp.1124~1139 (2021), 대한영상의학회[DOI]
  60. 김동욱, "Design Characteristics of Studies Reporting the Performance of Artificial Intelligence Algorithms for Diagnostic Analysis of Medical Images: Results from Recently Published Papers", Korean Journal of Radiology, 20(3), pp.405~410 (2019), 대한영상의학회[DOI]
  61. Park SH, "Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction", Radiology, 286, pp.800~809 (2018)
  62. 이정빈, "Evaluation of Reproducibility of Brain Volumetry between Commercial Software, Inbrain and Established Research Purpose Method, FreeSurfer", Journal of Clinical Neurology, 17(2), pp.307~316 (2021), 대한신경과학회

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