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Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis

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Abstract

Background

The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS.

Methods

We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI).

Results

After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%).

Conclusion

Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.

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Data availability

The datasets analyzed during the current study are available upon request with no restriction.

References

  1. Pirahesh K, Motamedi-Jahromi A (2022) Acute relapse of multiple sclerosis (MS) in an adolescent patient after Tuberculin skin test (TST): a case report. Neurol Letters 1(1):34–36. https://doi.org/10.52547/nl.1.1.34

    Article  Google Scholar 

  2. Nabizadeh F, Pirahesh K, Rafiei N, Afrashteh F, Ahmadabad MA, Zabeti A, Mirmosayyeb O (2022) Autologous hematopoietic stem-cell transplantation in multiple sclerosis: a systematic review and meta-analysis. Neurol Therapy. https://doi.org/10.1007/s40120-022-00389-x

    Article  Google Scholar 

  3. Nabizadeh F, Masrouri S, Ramezannezhad E, Ghaderi A, Sharafi AM, Soraneh S et al (2022) Artificial intelligence in the diagnosis of multiple sclerosis: a systematic review. Mult Scler Relat Disord 59:103673

  4. Eshaghi A, Wottschel V, Cortese R, Calabrese M, Sahraian MA, Thompson AJ, Alexander DC, Ciccarelli O (2016) Gray matter MRI differentiates neuromyelitis optica from multiple sclerosis using random forest. Neurology 87(23):2463–2470

    Article  Google Scholar 

  5. Garcia-Martin E, Ortiz M, Boquete L, Sánchez-Morla EM, Barea R, Cavaliere C, Vilades E, Orduna E, Rodrigo MJ (2021) Early diagnosis of multiple sclerosis by OCT analysis using Cohen’s d method and a neural network as classifier. Comput Biol Med 129:104165. https://doi.org/10.1016/j.compbiomed.2020.104165

    Article  CAS  Google Scholar 

  6. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, Correale J, Fazekas F, Filippi M, Freedman MS (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. The Lancet Neurol 17(2):162–173

    Article  Google Scholar 

  7. Dias R, Torkamani A (2019) Artificial intelligence in clinical and genomic diagnostics. Genome Med 11(1):70. https://doi.org/10.1186/s13073-019-0689-8

    Article  CAS  Google Scholar 

  8. Bonacchi R, Filippi M, Rocca MA (2022) Role of artificial intelligence in MS clinical practice. NeuroImage Clin 35:103065. https://doi.org/10.1016/j.nicl.2022.103065

    Article  Google Scholar 

  9. Wang S-H, Cheng H, Phillips P, Zhang Y-D (2018) Multiple sclerosis identification based on fractional Fourier entropy and a modified Jaya algorithm. Entropy 20(4):254

    Article  Google Scholar 

  10. Azarmi F, Miri Ashtiani SN, Shalbaf A, Behnam H, Daliri MR (2019) Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI. Comput Biol Med 115:103495. https://doi.org/10.1016/j.compbiomed.2019.103495

    Article  Google Scholar 

  11. Eitel F, Soehler E, Bellmann-Strobl J, Brandt A, Ruprecht K, Giess R, Kuchling J, Asseyer S, Weygandt M, Haynes J-D, Scheel M, Paul F, Ritter K (2019) Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

  12. Zhou Q, Shen X (2018) Multiple sclerosis identification by grey-level cooccurrence matrix and biogeography-based optimization. In: 2018 IEEE 23rd international conference on digital signal processing (DSP), 19(21):1–5. https://doi.org/10.1109/ICDSP.2018.8631873

  13. Garcia-Martin E, Pablo LE, Herrero R, Ara JR, Martin J, Larrosa JM, Polo V, Garcia-Feijoo J, Fernandez J (2013) Neural networks to identify multiple sclerosis with optical coherence tomography. Acta Ophthalmol 91(8):e628-634. https://doi.org/10.1111/aos.12156

    Article  Google Scholar 

  14. Mezzaroba L, Simão ANC, Oliveira SR, Flauzino T, Alfieri DF, de Carvalho Jennings Pereira WL, Kallaur AP, Lozovoy MAB, Kaimen-Maciel DR, Maes M, Reiche EMV (2020) Antioxidant and anti-inflammatory diagnostic biomarkers in multiple sclerosis: a machine learning study. Mol Neurobiol 57(5):2167–2178. https://doi.org/10.1007/s12035-019-01856-7

    Article  CAS  Google Scholar 

  15. Sharifmousavi SS, Borhani MS (2020) Support vectors machine-based model for diagnosis of multiple sclerosis using the plasma levels of selenium, vitamin B12, and vitamin D3. Inform Med Unlocked 20:100382. https://doi.org/10.1016/j.imu.2020.100382

    Article  Google Scholar 

  16. Acquaviva M, Menon R, Di Dario M, Dalla Costa G, Romeo M, Sangalli F, Colombo B, Moiola L, Martinelli V, Comi G (2020) Inferring multiple sclerosis stages from the blood transcriptome via machine learning. Cell Reports Medicine 1(4):100053

    Article  CAS  Google Scholar 

  17. Nabizadeh F, Masrouri S, Ramezannezhad E, Ghaderi A, Sharafi AM, Soraneh S, Naser Moghadasi A (2022) Artificial intelligence in the diagnosis of multiple sclerosis: a systematic review. Mult Scler Relat Disord 59:103673. https://doi.org/10.1016/j.msard.2022.103673

    Article  Google Scholar 

  18. Moher D, Liberati A, Tetzlaff J, Altman DG, The PG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 6(7):e1000097. https://doi.org/10.1371/journal.pmed.1000097

    Article  Google Scholar 

  19. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, Leeflang MM, Sterne JA, Bossuyt PM (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536. https://doi.org/10.7326/0003-4819-155-8-201110180-00009

    Article  Google Scholar 

  20. Casalino G, Castellano G, Consiglio A, Nuzziello N, Vessio G (2021) MicroRNA expression classification for pediatric multiple sclerosis identification. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03091-2

    Article  Google Scholar 

  21. Cavaliere C, Vilades E, Alonso-Rodríguez MC, Rodrigo MJ, Pablo LE, Miguel JM, López-Guillén E, Morla EMS, Boquete L, Garcia-Martin E (2019) Computer-aided diagnosis of multiple sclerosis using a support vector machine and optical coherence tomography features. Sensors (Basel) 19:23. https://doi.org/10.3390/s19235323

  22. Ettema R, Lenders M, Vliegen J, Slettenaar A, Tjepkema-Cloostermans MC, de Vos C (2020) Detecting multiple sclerosis via breath analysis using an eNose, a pilot study. J Breath Res. https://doi.org/10.1088/1752-7163/abd080

    Article  Google Scholar 

  23. Fooladi M, Sharini H, Masjoodi S, Khodamoradi E (2018) A novel classification method using effective neural network and quantitative magnetization transfer imaging of brain white matter in relapsing remitting multiple sclerosis. J Biomed Phys Eng 8(4):409–422

    CAS  Google Scholar 

  24. Garcia-Martin E, Herrero R, Bambo MP, Ara JR, Martin J, Polo V, Larrosa JM, Garcia-Feijoo J, Pablo LE (2015) Artificial neural network techniques to improve the ability of optical coherence tomography to detect optic neuritis. Semin Ophthalmol 30(1):11–19. https://doi.org/10.3109/08820538.2013.810277

    Article  Google Scholar 

  25. Goyal M, Khanna D, Rana PS, Khaibullin T, Martynova E, Rizvanov AA, Khaiboullina SF, Baranwal M (2019) Computational intelligence technique for prediction of multiple sclerosis based on serum cytokines. Frontiers in Neurology 10:781. https://doi.org/10.3389/fneur.2019.00781

  26. Guterman H, Nehmadi Y, Chistyakov A, Soustiel JF, Feinsod M (1996) A comparison of neural network and Bayes recognition approaches in the evaluation of the brainstem trigeminal evoked potentials in multiple sclerosis. Int J Biomed Comput 43(3):203–213. https://doi.org/10.1016/s0020-7101(96)01211-1

    Article  CAS  Google Scholar 

  27. Han J, Hou S-M (2019) Multiple sclerosis detection via wavelet entropy and feedforward neural network trained by adaptive genetic algorithm. In: Rojas I, Joya G, Catala A (eds) Advances in Computational Intelligence Cham, Springer International Publishing. pp 87–97

    Google Scholar 

  28. Iswisi AFA, Karan O, Rahebi J (2021) Diagnosis of multiple sclerosis disease in brain magnetic resonance imaging based on the Harris Hawks optimization algorithm. Biomed Res Int 2021:3248834. https://doi.org/10.1155/2021/3248834

    Article  Google Scholar 

  29. Kaur R, Chen Z, Motl R, Hernandez ME, Sowers R (2021) Predicting multiple sclerosis from gait dynamics using an instrumented treadmill: a machine learning approach. IEEE Trans Biomed Eng 68(9):2666–2677. https://doi.org/10.1109/tbme.2020.3048142

    Article  Google Scholar 

  30. Linder R, Mörschner D, Pöppl SJ, Moser A (2009) Computer-aided diagnosis of multiple sclerosis. Comput Math Methods Med 10(1):39–47. https://doi.org/10.1080/17486700802070724

    Article  Google Scholar 

  31. Liu J, Yin L, Dong H, Xu E, Zhang L, Qiao Y, Liu Y, Li L, Jia J (2012) Decreased serum levels of nucleolin protein fragment, as analyzed by bead-based proteomic technology, in multiple sclerosis patients compared to controls. J Neuroimmunol 250(1–2):71–76. https://doi.org/10.1016/j.jneuroim.2012.05.002

    Article  CAS  Google Scholar 

  32. Lopatina A, Ropele S, Sibgatulin R, Reichenbach JR, Güllmar D (2020) Investigation of deep-learning-driven identification of multiple sclerosis patients based on susceptibility-weighted images using relevance analysis. Front Neurosci 14:609468–609468. https://doi.org/10.3389/fnins.2020.609468

    Article  Google Scholar 

  33. López-Dorado A, Ortiz M, Satue M, Rodrigo MJ, Barea R, Sánchez-Morla EM, Cavaliere C, Rodríguez-Ascariz JM, Orduna-Hospital E, Boquete L, Garcia-Martin E (2021) Early diagnosis of multiple sclerosis using swept-source optical coherence tomography and convolutional neural networks trained with data augmentation. Sensors (Basel) 22:1. https://doi.org/10.3390/s22010167

  34. Lötsch J, Schiffmann S, Schmitz K, Brunkhorst R, Lerch F, Ferreiros N, Wicker S, Tegeder I, Geisslinger G, Ultsch A (2018) Machine-learning based lipid mediator serum concentration patterns allow identification of multiple sclerosis patients with high accuracy. Sci Rep 8(1):14884. https://doi.org/10.1038/s41598-018-33077-8

    Article  CAS  Google Scholar 

  35. Marzullo A, Kocevar G, Stamile C, Durand-Dubief F, Terracina G, Calimeri F, Sappey-Marinier D (2019) Classification of multiple sclerosis clinical profiles via graph convolutional neural networks. Front Neurosci 13. https://doi.org/10.3389/fnins.2019.00594

  36. Montolío A, Martín-Gallego A, Cegoñino J, Orduna E, Vilades E, Garcia-Martin E, Palomar APd (2021) Machine learning in diagnosis and disability prediction of multiple sclerosis using optical coherence tomography. Comput Biol Med 133:104416. https://doi.org/10.1016/j.compbiomed.2021.104416

    Article  Google Scholar 

  37. Neeb H, Schenk J (2019) Multivariate prediction of multiple sclerosis using robust quantitative MR-based image metrics. Z Med Phys 29(3):262–271. https://doi.org/10.1016/j.zemedi.2018.10.004

    Article  Google Scholar 

  38. Nelson CA, Bove R, Butte AJ, Baranzini SE (2022) Embedding electronic health records onto a knowledge network recognizes prodromal features of multiple sclerosis and predicts diagnosis. J Am Med Inform Assoc 29(3):424–434. https://doi.org/10.1093/jamia/ocab270

    Article  Google Scholar 

  39. Pérez Del Palomar A, Cegoñino J, Montolío A, Orduna E, Vilades E, Sebastián B, Pablo LE, Garcia-Martin E (2019) Swept source optical coherence tomography to early detect multiple sclerosis disease. The use of machine learning techniques. PLoS One 14(5):e0216410. https://doi.org/10.1371/journal.pone.0216410

    Article  Google Scholar 

  40. Rocca MA, Anzalone N, Storelli L, Del Poggio A, Cacciaguerra L, Manfredi AA, Meani A, Filippi M (2021) Deep learning on conventional magnetic resonance imaging improves the diagnosis of multiple sclerosis mimics. Invest Radiol 56(4):252–260. https://doi.org/10.1097/rli.0000000000000735

    Article  Google Scholar 

  41. Sarbaz Y, Pourakbari H, Vojudi M, Ghanbari A (2017) Introducing a decision support system for multiple sclerosis based on postural tremor: a hope for separation of people who might be affected by multiple sclerosis in the future. Biomed Eng 29:1750046. https://doi.org/10.4015/S1016237217500466

    Article  Google Scholar 

  42. Schwab P, Karlen W (2021) A deep learning approach to diagnosing multiple sclerosis from smartphone data. IEEE J Biomed Health Inform 25(4):1284–1291. https://doi.org/10.1109/JBHI.2020.3021143

    Article  Google Scholar 

  43. Siar H, Teshnehlab M (2019) Diagnosing and classification tumors and MS simultaneous of magnetic resonance images using convolution neural network. In: 2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), 29(31):1–4. https://doi.org/10.1109/CFIS.2019.8692148

  44. Soltani A, Nasri S (2020) Improved algorithm for multiple sclerosis diagnosis in MRI using convolutional neural network. IET Image Proc 14(17):4507–4512. https://doi.org/10.1049/iet-ipr.2019.0366

    Article  Google Scholar 

  45. Vatian A, Gusarova N, Dobrenko N, Klochkov A, Nigmatullin N, Lobantsev A, Shalyto A (2019) Fusing of medical images and reports in diagnostics of brain diseases. Paper presented at the Proceedings of the 2019 the International Conference on Pattern Recognition and Artificial Intelligence, Wenzhou China

  46. Wang S, Zhang Y-D (2020) DenseNet-201-based deep neural network with composite learning factor and precomputation for multiple sclerosis classification. ACM Trans Multimed Comput Commun Appl 16:1–19. https://doi.org/10.1145/3341095

    Article  CAS  Google Scholar 

  47. Wang S-H, Tang C, Sun J, Yang J, Huang C, Phillips P, Zhang Y-D (2018) Multiple sclerosis identification by 14-layer convolutional neural network with batch normalization, dropout, and stochastic pooling. Front Neurosci 12:818. https://doi.org/10.3389/fnins.2018.00818

  48. Wang SH, Zhan TM, Chen Y, Zhang Y, Yang M, Lu HM, Wang HN, Liu B, Phillips P (2016) Multiple sclerosis detection based on biorthogonal wavelet transform, RBF Kernel Principal Component Analysis, and Logistic Regression. IEEE Access 4:7567–7576. https://doi.org/10.1109/ACCESS.2016.2620996

    Article  Google Scholar 

  49. Wu X, Lopez M (2017) Multiple sclerosis slice identification by haar wavelet transform and logistic regression. https://doi.org/10.2991/ammee-17.2017.10

  50. Zhang Y, Hong D, McClement D, Oladosu O, Pridham G, Slaney G (2021) Grad-CAM helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging. J Neurosci Methods 353:109098. https://doi.org/10.1016/j.jneumeth.2021.109098

    Article  Google Scholar 

  51. Zhang Y, Lu S, Zhou X, Yang M, Wu L, Liu B, Phillips P, Wang S (2016) Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. SIMULATION 92(9):861–871. https://doi.org/10.1177/0037549716666962

    Article  Google Scholar 

  52. Zhang Y-D, Pan C, Sun J, Tang C (2018) Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU. J Comp Sci 28:1–10. https://doi.org/10.1016/j.jocs.2018.07.003

    Article  CAS  Google Scholar 

  53. Zhang Y-D, Zhang Y, Phillips P, Dong Z, Wang S (2017) Synthetic minority oversampling technique and fractal dimension for identifying multiple sclerosis. Fractals 25(04):1740010. https://doi.org/10.1142/s0218348x17400102

    Article  Google Scholar 

  54. Pretorius PM, Quaghebeur G (2003) The role of MRI in the diagnosis of MS. Clin Radiol 58(6):434–448. https://doi.org/10.1016/s0009-9260(03)00089-8

    Article  CAS  Google Scholar 

  55. Sarker IH (2021) Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comp Sci 2(6):420. https://doi.org/10.1007/s42979-021-00815-1

    Article  Google Scholar 

  56. Belkin M, Hsu D, Ma S, Mandal S (2019) Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proc Natl Acad Sci 116(32):15849–15854. https://doi.org/10.1073/pnas.1903070116

    Article  CAS  Google Scholar 

  57. Afzal HMR, Luo S, Ramadan S, Lechner-Scott J (2022) The emerging role of artificial intelligence in multiple sclerosis imaging. Mult Scler 28(6):849–858. https://doi.org/10.1177/1352458520966298

    Article  Google Scholar 

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Authors and Affiliations

  1. Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran

    Fardin Nabizadeh

  2. School of Medicine, Iran University of Medical Sciences, Tehran, Iran

    Fardin Nabizadeh

  3. School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

    Elham Ramezannezhad

  4. Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Science, Tehran, Iran

    Amirhosein Kargar

  5. Student’s Scientific Research Center, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Amir Mohammad Sharafi & Ali Ghaderi

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  1. Fardin Nabizadeh
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  3. Amirhosein Kargar
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  4. Amir Mohammad Sharafi
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Contributions

FN, ER, and AK: designed the study, analyzed the data, and wrote the paper; FN, AGH, and ASH: collected data, analyzed and interpreted the data, and wrote the draft version of the manuscript. The manuscript was revised and approved by all authors.

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Correspondence to Fardin Nabizadeh.

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Nabizadeh, F., Ramezannezhad, E., Kargar, A. et al. Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis. Neurol Sci 44, 499–517 (2023). https://doi.org/10.1007/s10072-022-06460-7

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