Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability

Rovira, Àlex, and Cristina Auger. “Beyond McDonald: updated perspectives on MRI diagnosis of multiple sclerosis.” Expert Review of Neurotherapeutics 21.8 (2021): 895–911.

Article  CAS  PubMed  Google Scholar 

Wallin MT, Culpepper WJ, Campbell JD, Nelson LM, Langer-Gould A, Marrie RA, et al. The prevalence of MS in the United States. Neurology. 2019 Mar 5; 92(10): e1029–e1040.

Article  PubMed  PubMed Central  Google Scholar 

Csepany, Tunde. “Diagnosis of multiple sclerosis: A review of the 2017 revisions of the McDonald criteria.” Ideggyogyaszati szemle 71.9–10 (2018): 321–329.

Article  PubMed  Google Scholar 

Smyrke N, Dunn N, Murley C, Mason D. Standardized mortality ratios in multiple sclerosis: Systematic review with meta‐analysis. Acta Neurologica Scandinavica. 2022 Mar;145(3):360-70.

Article  PubMed  Google Scholar 

Lycklama, Geert, et al. “Spinal-cord MRI in multiple sclerosis.” The Lancet Neurology 2.9 (2003): 555–562.

Article  PubMed  Google Scholar 

McGinley, M.P., Goldschmidt, C.H. and Rae-Grant, A.D., 2021. Diagnosis and treatment of multiple sclerosis: a review. JAMA, 325(8), pp.765-779.

Article  CAS  PubMed  Google Scholar 

Aslam, Nida, et al. “Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.” Sensors 22.20 (2022): 7856.

Article  PubMed  PubMed Central  Google Scholar 

Afzal, HM Rehan, et al. “The emerging role of artificial intelligence in multiple sclerosis imaging.” Multiple Sclerosis Journal 28.6 (2022): 849–858.

Seccia, Ruggiero, et al. “Machine learning use for prognostic purposes in multiple sclerosis.” Life 11.2 (2021): 122.

Article  PubMed  PubMed Central  Google Scholar 

Eshaghi, Arman, et al. “Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data.” Nature communications 12.1 (2021): 1–12.

Google Scholar 

Shoeibi, Afshin, et al. “Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: A review.” Computers in Biology and Medicine 136 (2021): 104697.

Article  PubMed  Google Scholar 

Lukas C, Knol DL, Sombekke MH, Bellenberg B, Hahn HK, Popescu V, Weier K, Radue EW, Gass A, Kappos L, Naegelin Y. Cervical spinal cord volume loss is related to clinical disability progression in multiple sclerosis. Journal of Neurology, Neurosurgery & Psychiatry. 2015 Apr 1;86(4):410-8.

Article  Google Scholar 

Losseff NA, Webb SL, O'riordan JI, Page R, Wang L, Barker GJ, Tofts PS, McDonald WI, Miller DH, Thompson AJ. Spinal cord atrophy and disability in multiple sclerosis: a new reproducible and sensitive MRI method with potential to monitor disease progression. Brain. 1996 Jun 1;119(3):701-8.

Article  PubMed  Google Scholar 

Cohen AB, Neema M, Arora A, Dell’Oglio E, Benedict RH, Tauhid S, Goldberg‐Zimring D, Chavarro‐Nieto C, Ceccarelli A, Klein JP, Stankiewicz JM. The relationships among MRI‐defined spinal cord involvement, brain involvement, and disability in multiple sclerosis. Journal of Neuroimaging. 2012 Apr;22(2):122-8.

Article  PubMed  Google Scholar 

Hidalgo de la Cruz M, Valsasina P, Meani A, Gallo A, Gobbi C, Bisecco A, Tedeschi G, Zecca C, Rocca MA, Filippi M. Differential association of cortical, subcortical and spinal cord damage with multiple sclerosis disability milestones: a multiparametric MRI study. Multiple Sclerosis Journal. 2022 Mar;28(3):406–17.

Pravatà E, Valsasina P, Gobbi C, Zecca C, Riccitelli GC, Filippi M, Rocca MA. Influence of CNS T2-focal lesions on cervical cord atrophy and disability in multiple sclerosis. Multiple Sclerosis Journal. 2020 Oct;26(11):1402-9.

Article  PubMed  Google Scholar 

Lee LE, Vavasour IM, Dvorak A, Liu H, Abel S, Johnson P, Ristow S, Au S, Laule C, Tam R, Li DK. Cervical cord myelin abnormality is associated with clinical disability in multiple sclerosis. Multiple Sclerosis Journal. 2021 Dec;27(14):2191-8.

Article  CAS  PubMed  Google Scholar 

Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation. 1997 Nov 15;9(8):1735-80.

Article  CAS  PubMed  Google Scholar 

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. and Bengio, Y., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł. and Polosukhin, I., 2017. Attention is all you need. Advances in neural information processing systems, 30.

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S. and Uszkoreit, J., 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.

Wang, Z., Bai, Y., Zhou, Y. and Xie, C., 2022. Can cnns be more robust than transformers?. arXiv preprint arXiv:2206.03452.

Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C. and Dosovitskiy, A., 2021. Do vision transformers see like convolutional neural networks?. Advances in Neural Information Processing Systems, 34, pp.12116–12128.

Google Scholar 

Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lučić, M. and Schmid, C., 2021. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6836–6846).

Szeghalmy, S. and Fazekas, A., 2023. A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning. Sensors, 23(4), p.2333.

Article  PubMed  PubMed Central  Google Scholar 

Collins, G.S. and Moons, K.G., 2019. Reporting of artificial intelligence prediction models. The Lancet, 393(10181), pp.1577–1579.

Article  Google Scholar 

Faghani, S., Khosravi, B., Zhang, K., Moassefi, M., Jagtap, J.M., Nugen, F., Vahdati, S., Kuanar, S.P., Rassoulinejad-Mousavi, S.M., Singh, Y. and Vera Garcia, D.V., 2022. Mitigating bias in radiology machine learning: 3. Performance metrics. Radiology: Artificial Intelligence, 4(5), p.e220061.

PubMed  PubMed Central  Google Scholar 

Alpaydm E. Combined 5× 2 cv F test for comparing supervised classification learning algorithms. Neural computation. 1999 Nov 15;11(8):1885-92.

Article  Google Scholar 

Coll, L., Pareto, D., Carbonell-Mirabent, P., Cobo-Calvo, Á., Arrambide, G., Vidal-Jordana, Á., Comabella, M., Castilló, J., Rodríguez-Acevedo, B., Zabalza, A. and Galán, I., 2023. Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI. NeuroImage: Clinical, 38, p.103376.

Article  PubMed  Google Scholar 

Plati D, Tripoliti E, Zelilidou S, Vlachos K, Konitsiotis S, Fotiadis DI. Multiple Sclerosis Severity Estimation and Progression Prediction Based on Machine Learning Techniques. In2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2022 Jul 11 (pp. 1109–1112). IEEE.

Taloni A, Farrelly FA, Pontillo G, Petsas N, Giannì C, Ruggieri S, Petracca M, Brunetti A, Pozzilli C, Pantano P, Tommasin S. Evaluation of Disability Progression in Multiple Sclerosis via Magnetic-Resonance-Based Deep Learning Techniques. International Journal of Molecular Sciences. 2022 Sep 13;23(18):10651.

Article  PubMed  PubMed Central  Google Scholar 

Pontillo G, Tommasin S, Cuocolo R, Petracca M, Petsas N, Ugga L, Carotenuto A, Pozzilli C, Iodice R, Lanzillo R, Quarantelli M. A combined radiomics and machine learning approach to overcome the clinicoradiologic paradox in multiple sclerosis. American Journal of Neuroradiology. 2021 Nov 1;42(11):1927-33.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Tommasin S, Cocozza S, Taloni A, Giannì C, Petsas N, Pontillo G, Petracca M, Ruggieri S, De Giglio L, Pozzilli C, Brunetti A. Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis. Journal of Neurology. 2021 Dec;268(12):4834-45.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Roca P, Attye A, Colas L, Tucholka A, Rubini P, Cackowski S, Ding J, Budzik JF, Renard F, Doyle S, Barbier EL. Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI. Diagnostic and Interventional Imaging. 2020 Dec 1;101(12):795–802.

Article  CAS  PubMed  Google Scholar 

Zhao Y, Healy BC, Rotstein D, Guttmann CR, Bakshi R, Weiner HL, Brodley CE, Chitnis T. Exploration of machine learning techniques in predicting multiple sclerosis disease course. PloS one. 2017 Apr 5;12(4):e0174866.

Article  PubMed  PubMed Central  Google Scholar 

留言 (0)

沒有登入
gif