Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
권호 표지
DOI QR코드

DOI QR Code

Brain MRI-Based Artificial Intelligence Software in Patients with Neurodegenerative Diseases: Current Status

퇴행성 뇌질환에서 뇌 자기공명영상 기반 인공지능 소프트웨어 활용의 현재

  • So Yeong Jeong (Department of Radiology, Hanyang University Medical Center, Hanyang University Medical College) ;
  • Chong Hyun Suh (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Ho Young Park (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Hwon Heo (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Woo Hyun Shim (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine) ;
  • Sang Joon Kim (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • 정소영 (한양대학교 의과대학 한양대학교병원 영상의학과) ;
  • 서종현 (울산대학교 의과대학 서울아산병원 영상의학과) ;
  • 박호영 (울산대학교 의과대학 서울아산병원 영상의학과) ;
  • 허훤 (울산대학교 의과대학 서울아산병원 영상의학과) ;
  • 심우현 (울산대학교 의과대학 서울아산병원 영상의학과) ;
  • 김상준 (울산대학교 의과대학 서울아산병원 영상의학과)
  • Received : 2022.04.08
  • Accepted : 2022.05.15
  • Published : 2022.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

The incidence of neurodegenerative diseases in the older population has increased in recent years. A considerable number of studies have been performed to characterize these diseases. Imaging analysis is an important biomarker for the diagnosis of neurodegenerative disease. Objective and reliable assessment and precise detection are important for the early diagnosis of neurodegenerative diseases. Artificial intelligence (AI) using brain MRI applied to the study of neurodegenerative diseases could promote early diagnosis and optimal decisions for treatment plans. MRI-based AI software have been developed and studied worldwide. Representatively, there are MRI-based volumetry and segmentation software. In this review, we present the development process of brain volumetry analysis software in neurodegenerative diseases, currently used and developed AI software for neurodegenerative disease in the Republic of Korea, probable uses of AI in the future, and AI software limitations.

현대사회가 점차 고령화 사회가 됨에 따라 퇴행성 뇌질환의 발병률이 증가하고 있으며, 이러한 퇴행성 뇌질환에 관한 많은 연구들이 이루어지고 있다. 퇴행성 뇌질환의 진단에서 영상분석은 영상표지자로서 중요한 역할을 하고 있다. 영상분석에서 객관적이고 일관성 있는 평가는 퇴행성 뇌질환의 조기 진단 및 정확한 진단에 중요하다. 이에 다양한 퇴행성 뇌질환과 관련한 영상연구에 자기공명영상(이하 MRI)을 이용한 인공지능이 조기 진단과 최적의 치료 방향 계획 및 결정에 도움이 될 가능성을 보여주었다. 특히 MRI 기반의 뇌용적 측정과 분획화 및 특성을 포착하는 인공지능 소프트웨어들이 개발되고 연구되기 시작했다. 본 고찰에서는 우리나라에서 퇴행성 뇌질환과 관련하여 사용되고 있는 인공지능 소프트웨어의 현재 상황과 향후 인공지능 소프트웨어의 퇴행성 뇌질환 연구에의 활용, 그리고 인공지능 소프트웨어의 한계에 대해서 다루고자 한다.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF-2021R1C1C1014413).

References

  1. Jack CR Jr. Alzheimer disease: new concepts on its neurobiology and the clinical role imaging will play. Radiology 2012;263:344-361
  2. 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;14:535-562
  3. Park M, Moon WJ. Structural MR imaging in the diagnosis of Alzheimer's disease and other neurodegenerative dementia: current imaging approach and future perspectives. Korean J Radiol 2016;17:827-845
  4. Lee JY, Park JE, Chung MS, Oh SW, Moon WJ. Expert opinions and recommendations for the clinical use of quantitative analysis software for MRI-based brain volumetry. J Korean Soc Radiol 2021;82:1124-1139
  5. Brewer JB, Magda S, Airriess C, Smith ME. Fully-automated quantification of regional brain volumes for improved detection of focal atrophy in Alzheimer disease. AJNR Am J Neuroradiol 2009;30:578-580
  6. Tanpitukpongse TP, Mazurowski MA, Ikhena J, Petrella JR; Alzheimer's Disease Neuroimaging Initiative. Predictive utility of marketed volumetric software tools in subjects at risk for Alzheimer disease: do regions outside the hippocampus matter? AJNR Am J Neuroradiol 2017;38:546-552
  7. Wang C, Beadnall HN, Hatton SN, Bader G, Tomic D, Silva DG, et al. Automated brain volumetrics in multiple sclerosis: a step closer to clinical application. J Neurol Neurosurg Psychiatry 2016;87:754-757
  8. Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P, et al. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 1992;55:967-972
  9. Park HY, Park CR, Suh CH, Shim WH, Kim SJ. Diagnostic performance of the medial temporal lobe atrophy scale in patients with Alzheimer's disease: a systematic review and meta-analysis. Eur Radiol 2021;31:9060-9072
  10. Cavallin L, Loken K, Engedal K, Oksengard AR, Wahlund LO, Bronge L, et al. Overtime reliability of medial temporal lobe atrophy rating in a clinical setting. Acta Radiol 2012;53:318-323
  11. Park HY, Suh CH, Heo H, Shim WH, Kim SJ. Diagnostic performance of hippocampal volumetry in Alzheimer's disease or mild cognitive impairment: a meta-analysis. Eur Radiol 2022 May 4 [Epub]. https://doi.org/10.1007/s00330-022-08838-9
  12. Enkirch SJ, Traschutz A, Muller A, Widmann CN, Gielen GH, Heneka MT, et al. The ERICA score: an MR imaging-based visual scoring system for the assessment of entorhinal cortex atrophy in Alzheimer disease. Radiology 2018;288:226-333
  13. Ochs AL, Ross DE, Zannoni MD, Abildskov TJ, Bigler ED; Alzheimer's Disease Neuroimaging Initiative. Comparison of automated brain volume measures obtained with NeuroQuant® and FreeSurfer. J Neuroimaging 2015;25:721-727
  14. Persson K, Bohbot VD, Bogdanovic N, Selbaek G, Braekhus A, Engedal K. Finding of increased caudate nucleus in patients with Alzheimer's disease. Acta Neurol Scand 2018;137:224-232
  15. Lee JY, Oh SW, Chung MS, Park JE, Moon Y, Jeon HJ, et al. Clinically available software for automatic brain volumetry: comparisons of volume measurements and validation of intermethod reliability. Korean J Radiol 2021;22:405-414
  16. Kim M, Kim SJ, Park JE, Yun J, Shim WH, Oh JS, et al. Combination of automated brain volumetry on MRI and quantitative tau deposition on THK-5351 PET to support diagnosis of Alzheimer's disease. Sci Rep 2021;11:10343
  17. Min J, Moon WJ, Jeon JY, Choi JW, Moon YS, Han SH. Diagnostic efficacy of structural MRI in patients with mild-to-moderate Alzheimer disease: automated volumetric assessment versus visual assessment. AJR Am J Roentgenol 2017;208:617-623
  18. Noor MBT, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia. Brain Inform 2020;7:11
  19. Korea Health Industry Development Institute. A study on the investigation of new medical device classification and management system in major countries. Cheongju: KHIDI 2021
  20. Suh CH, Shim WH, Kim SJ, Roh JH, Lee JH, Kim MJ, et al. 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 2020;41:2227-2234
  21. Bae JB, Lee S, Jung W, Park S, Kim W, Oh H, et al. Identification of Alzheimer's disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging. Sci Rep 2020;10:22252
  22. Lee M, Kim J, Kim R EY, Kim HG, Oh SW, Lee MK, et al. Split-attention U-Net: a fully convolutional network for robust multi-label segmentation from brain MRI. Brain Sci 2020;10:974
  23. Kim REY, Lee M, Kang DW, Wang SM, Kim NY, Lee MK, et al. Deep learning-based segmentation to establish East Asian normative volumes using multisite structural MRI. Diagnostics (Basel) 2020;11:13
  24. Song M, Jung H, Lee S, Kim D, Ahn M. Diagnostic classification and biomarker identification of Alzheimer's disease with random forest algorithm. Brain Sci 2021;11:453
  25. Kim YJ, Han JW, So YS, Seo JY, Kim KY, Kim KW. Prevalence and trends of dementia in Korea: a systematic review and meta-analysis. J Korean Med Sci 2014;29:903-912
  26. Erkinjuntti T. Clinical criteria for vascular dementia: the NINDS-AIREN criteria. Dementia 1994;5:189-192
  27. Fazekas F, Kleinert R, Offenbacher H, Schmidt R, Kleinert G, Payer F, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993;43:1683-1689
  28. Noh Y, Lee Y, Seo SW, Jeong JH, Choi SH, Back JH, et al. A new classification system for ischemia using a combination of deep and periventricular white matter hyperintensities. J Stroke Cerebrovasc Dis 2014;23:636-642
  29. Mitchell T, Lehericy S, Chiu SY, Strafella AP, Stoessl AJ, Vaillancourt DE. Emerging neuroimaging biomarkers across disease stage in Parkinson disease: a review. JAMA Neurol 2021;78:1262-1272
  30. Pyatigorskaya N, Magnin B, Mongin M, Yahia-Cherif L, Valabregue R, Arnaldi D, et al. Comparative study of MRI biomarkers in the substantia nigra to discriminate idiopathic Parkinson disease. AJNR Am J Neuroradiol 2018;39:1460-1467
  31. Frosini D, Cosottini M, Volterrani D, Ceravolo R. Neuroimaging in Parkinson's disease: focus on substantia nigra and nigro-striatal projection. Curr Opin Neurol 2017;30:416-426
  32. Kim EY, Sung YH, Lee J. Nigrosome 1 imaging: technical considerations and clinical applications. Br J Radiol 2019;92:20180842
  33. Kim PH, Lee DH, Suh CH, Kim M, Shim WH, Kim SJ. Diagnostic performance of loss of nigral hyperintensity on susceptibility-weighted imaging in parkinsonism: an updated meta-analysis. Eur Radiol 2021;31:6342-6352
  34. Nam Y, Gho SM, Kim DH, Kim EY, Lee J. Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map-weighted imaging (SMWI). J Magn Reson Imaging 2017;46:528-536
  35. Shin DH, Heo H, Song S, Shin NY, Nam Y, Yoo SW, et al. 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 2021;85:84-90
  36. Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 2002;125(Pt 4):861-870
  37. Hoglinger GU, Respondek G, Stamelou M, Kurz C, Josephs KA, Lang AE, et al. Clinical diagnosis of progressive supranuclear palsy: the movement disorder society criteria. Mov Disord 2017;32:853-864
  38. Gilman S, Wenning GK, Low PA, Brooks DJ, Mathias CJ, Trojanowski JQ, et al. Second consensus statement on the diagnosis of multiple system atrophy. Neurology 2008;71:670-676
  39. Quattrone A, Nicoletti G, Messina D, Fera F, Condino F, Pugliese P, et al. MR imaging index for differentiation of progressive supranuclear palsy from Parkinson disease and the Parkinson variant of multiple system atrophy. Radiology 2008;246:214-221
  40. Chougar L, Faouzi J, Pyatigorskaya N, Yahia-Cherif L, Gaurav R, Biondetti E, et al. Automated categorization of parkinsonian syndromes using magnetic resonance imaging in a clinical setting. Mov Disord 2021;36:460-470
  41. Martin-Laez R, Caballero-Arzapalo H, Lopez-Menendez LA, Arango-Lasprilla JC, Vazquez-Barquero A. Epidemiology of idiopathic normal pressure hydrocephalus: a systematic review of the literature. World Neurosurg 2015;84:2002-2009
  42. Andersson J, Rosell M, Kockum K, Lilja-Lund O, Soderstrom L, Laurell K. Prevalence of idiopathic normal pressure hydrocephalus: a prospective, population-based study. PLoS One 2019;14:e0217705
  43. Relkin N, Marmarou A, Klinge P, Bergsneider M, Black PM. Diagnosing idiopathic normal-pressure hydrocephalus. Neurosurgery 2005;57(3 Suppl):S4-S16
  44. Williams MA, Malm J. Diagnosis and treatment of idiopathic normal pressure hydrocephalus. Continuum (Minneap Minn) 2016;22:579-599
  45. Mori E, Ishikawa M, Kato T, Kazui H, Miyake H, Miyajima M, et al. Guidelines for management of idiopathic normal pressure hydrocephalus: second edition. Neurol Med Chir (Tokyo) 2012;52:775-809
  46. Park HY, Kim M, Suh CH, Lee DH, Shim WH, Kim SJ. 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 2021;31:5300-5311
  47. Park HY, Park CR, Suh CH, Kim MJ, Shim WH, Kim SJ. 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 2021;42:1429-1436
  48. Takagi K, Watahiki R, Machida T, Onouchi K, Kato K, Oshima M. Reliability and interobserver variability of evans' index and disproportionately enlarged subarachnoid space hydrocephalus as diagnostic criteria for idiopathic normal pressure hydrocephalus. Asian J Neurosurg 2020;15:107-112
  49. Duan W, Zhang J, Zhang L, Lin Z, Chen Y, Hao X, et al. Evaluation of an artificial intelligent hydrocephalus diagnosis model based on transfer learning. Medicine (Baltimore) 2020;99:e21229
  50. Gunter NB, Schwarz CG, Graff-Radford J, Gunter JL, Jones DT, Graff-Radford NR, et al. Automated detection of imaging features of disproportionately enlarged subarachnoid space hydrocephalus using machine learning methods. Neuroimage Clin 2019;21:101605
  51. Kim DW, Jang HY, Kim KW, Shin Y, Park SH. Design characteristics of studies reporting the performance of artificial intelligence algorithms for diagnostic analysis of medical images: results from recently published papers. Korean J Radiol 2019;20:405-410
  52. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018;286:800-809
  53. Lee J, Lee JY, Oh SW, Chung MS, Park JE, Moon Y, et al. Evaluation of reproducibility of brain volumetry between commercial software, inbrain and established research purpose method, FreeSurfer. J Clin Neurol 2021;17:307-316