Enhancing the diagnosis and prognosis of stroke through explainable artificial intelligence: Α multimodal approach
Keywords:
motor impairment rehabilitation, artificial intelligence in health, personalized medical care, innovation in healthcareAbstract
Stroke is one of the leading causes of disability and death worldwide. Accurate prediction of recovery (prognosis) and timely selection of optimal interventions for more effective motor rehabilitation are critical elements in improving patients' quality of life. This article presents novel approaches that combine Artificial Intelligence (AI) with clinical and laboratory assessments to enhance the care of individuals with stroke. We explain how AI systems can predict stroke occurrence and assess potential recovery trajectories based on biomarkers, clinical data, and biomechanical gait parameters, which serve as early indicators of motor impairment. Special emphasis is placed on the importance of "explainable" artificial intelligence, which makes the decisions of computational systems understandable to professionals, patients, and caregivers. With the proper use of these new tools, a promising outlook emerges for more personalized, and effective post-stroke care. The article is based on a series of published studies conducted by the Biomechanics Division of the Laboratory of Physical Education and Sport at the Department of Physical Education and Sport Science of Democritus University of Thrace, in collaboration with the Laboratory of Clinical Neurophysiology of the Medical School of Democritus University of Thrace.
References
Ali, S.F., Smith, E.E., Bhatt, D.L., Fonarow, G.C., & Schwamm, L.H. (2013). Paradoxical Association of Smoking with In-Hospital Mortality Among Patients Admitted with Acute Ischemic Stroke. Journal of the American Heart Association, 2, e000171.
Apostolidis, K., Kokkotis, C., Karakasis, E., Karampina, E., Moustakidis, S., Menychtas, D., Giarmatzis, G., Tsiptsios, D., Vadikolias, K., & Aggelousis, N. (2023). Innovative Visualization Approach for Biomechanical Time Series in Stroke Diagnosis Using Explainable Machine Learning Methods: A Proof-of-Concept Study. Information, 14(10), 559.
Bacchi, S., Oakden-Rayner, L., Menon, D.K., Jannes, J., Kleinig, T., & Koblar, S. (2020). Stroke prognostication for discharge planning with machine learning: A derivation study. Journal of Clinical Neuroscience, 79, 100–103.
Balaban, B. & Tok, F. (2014). Gait Disturbances in Patients with Stroke. Physical Medicine and Rehabilitation, 6, 635–642.
Buckley, C., Alcock, L., McArdle, R., Rehman, R.Z.U., Del Din, S., Mazzà, C., Yarnall, A.J., & Rochester, L. (2019). The Role of Movement Analysis in Diagnosing and Monitoring Neurodegenerative Conditions: Insights from Gait and Postural Control. Brain Sciences, 9, 34.
Chamorro, Á. (2004). Role of Inflammation in Stroke and Atherothrombosis. Cerebrovascular Diseases, 17, 1–5.
Cheng, B., Forkert, N.D., Zavaglia, M., Hilgetag, C.C., Golsari, A., Siemonsen, S., Fiehler, J., Pedraza, S., Puig, J., & Cho, T.-H. (2014). Influence of Stroke Infarct Location on Functional Outcome Measured by the Modified Rankin Scale. Stroke, 45, 1695–1702.
Elagizi, A., Kachur, S., Lavie, C.J., Carbone, S., Pandey, A., Ortega, F.B., & Milani, R.V. (2018). An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases. Prog. Cardiovascular Diseases, 61, 142–150.
Farrell, S.W., Leonard, D., Li, Q., Barlow, C.E., Shuval, K., Berry, J.D., Pavlovic, A., & DeFina, L.F. (2024). Association between baseline levels of muscular strength and risk of stroke in later life: The Cooper Center Longitudinal Study. Journal of Sport Health Science, 13(5), 642-649.
Feigin, V.L., & GBD 2019 Stroke Collaborators. (2021). Global, regional, and national burden of stroke and its risk factors, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurology, 20(10), 795-820.
Ferrari, F., Moretti, A., & Villa, R.F. (2022). Hyperglycemia in acute ischemic stroke: physiopathological and therapeutic complexity. Neural Regeneration Research, 17(2), 292-299.
Gkantzios, A., Kokkotis, C., Tsiptsios, D., Moustakidis, S., Gkartzonika, E., Avramidis, T., Tripsianis, G., Iliopoulos, I., Aggelousis, N., & Vadikolias, K. (2023). From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning. Journal of Personalized Medicine, 13(9), 1375.
Hankey, G.J. (2017). Stroke. Lancet, 389(10069), 641-654.
Jamrozik, K. (2002). Age-specific relevance of usual blood pressure to vascular mortality: A meta-analysis of individual data for one million adults in 61 prospective studies. Lancet, 360, 1903–1913.
Knarr, B.A., Reisman, D.S., Binder-Macleod, S.A., & Higginson, J.S. (2013). Understanding Compensatory Strategies for Muscle Weakness during Gait by Simulating Activation Deficits Seen Post-Stroke. Gait & Posture, 38, 270–275.
Kokkotis, C., Giarmatzis, G., Giannakou, E., Moustakidis, S., Tsatalas, T., Tsiptsios, D., Vadikolias, K., & Aggelousis, N. (2022). An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data. Diagnostics, 12(10), 2392.
Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.I. (2020). From local explanations to global understanding with explainable AI for trees. Nature Machine. Intelligence, 2, 56–67.
Mohan, D.M., Khandoker, A.H., Wasti, S.A., Ismail Ibrahim Ismail Alali, S., Jelinek, H.F., & Khalaf, K. (2021). Assessment Methods of Post-Stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis. Frontiers in Neurology, 12, 650024.
Mozaffarian, D., Benjamin, E.J., Go, A.S., Arnett, D.K., Blaha, M.J., Cushman, M., de Ferranti, S., Després, J.P., Fullerton, H.J., Howard, V.J., Huffman, M.D., Judd, S.E., Kissela, B.M., Lackland, D.T., Lichtman, J.H., Lisabeth, L.D., Liu, S., Mackey, R.H., Matchar, D.B., McGuire, D.K., Mohler, E.R. 3rd, Moy, C.S., Muntner, P., Mussolino, M.E., Nasir, K., Neumar, R.W., Nichol, G., Palaniappan, L., Pandey, D.K., Reeves, M.J., Rodriguez, C.J., Sorlie, P.D., Stein, J., Towfighi, A., Turan, T.N., Virani, S.S., Willey, J.Z., Woo, D., Yeh, R.W., Turner, M.B., & American Heart Association Statistics Committee and Stroke Statistics Subcommittee. (2015). Heart disease and stroke statistics--2015 update: a report from the American Heart Association. Circulation, 27, 131(4), e29-322. Erratum (2015) in: Circulation, 16, 131(24), e535. Erratum (2016) in: Circulation, 23, 133(8), e417.
O'Donnell, M.J., Chin, S.L., Rangarajan, S., Xavier, D., Liu, L., Zhang, H., Rao-Melacini, P., Zhang, X., Pais, P., Agapay, S., Lopez-Jaramillo, P., Damasceno, A., Langhorne, P., McQueen, M.J., Rosengren, A., Dehghan, M., Hankey, G.J., Dans, A.L., Elsayed, A., Avezum, A., Mondo, C., Diener, H.C., Ryglewicz, D., Czlonkowska, A., Pogosova, N., Weimar, C., Iqbal, R., Diaz, R., Yusoff, K., Yusufali, A., Oguz, A., Wang, X., Penaherrera, E., Lanas, F., Ogah, O.S., Ogunniyi, A., Iversen, H.K., Malaga, G., Rumboldt, Z., Oveisgharan, S., Al Hussain, F., Magazi, D., Nilanont, Y., Ferguson, J., Pare, G., Yusuf, S., & INTERSTROKE investigators. (2016). Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case-control study. Lancet, 388(10046), 761-75.
Park, H., Lee, H.W., Yoo, J., Lee, H.S., Nam, H.S., Kim, Y.D., & Heo, J.H. (2019). Body Mass Index and Prognosis in Ischemic Stroke Patients with Type 2 Diabetes Mellitus. Frontiers in Neurology, 10, 563.
Pezzini, A., Grassi, M., Del Zotto, E., Volonghi, I., Giossi, A., Costa, P., Cappellari, M., Magoni, M., & Padovani, A. (2010). Influence of acute blood pressure on short- and mid-term outcome of ischemic and hemorrhagic stroke. Journal of Neurology, 258, 634–640.
Powers, W.J., Rabinstein, A.A., Ackerson, T., Adeoye, O.M., Bambakidis, N.C., Becker, K., Biller, J., Brown, M., Demaerschalk, B.M., Hoh, B., Jauch, E.C., Kidwell, C.S., Leslie-Mazwi, T.M., Ovbiagele, B., Scott, P.A., Sheth, K.N., Southerland, A.M., Summers, D.V., Tirschwell, D.L., & American Heart Association Stroke Council. (2018). Guidelines for the Early Management of Patients with Acute Ischemic Stroke: A Guideline for Healthcare Professionals from the American Heart Association/American Stroke Association. Stroke, 49(3), e46-e110.
Robba, C., Bonatti, G., Battaglini, D., Rocco, P.R.M., & Pelosi, P. (2019). Mechanical ventilation in patients with acute ischaemic stroke: From pathophysiology to clinical practice. Critical Care, 23, 388.
Saver, J.L. (2006). Time is brain quantified. Stroke, 37(1), 263-266.
Steyerberg, E.W., Vickers, A.J., Cook, N.R., Gerds, T., Gonen, M., Obuchowski, N., Pencina, M.J., & Kattan, M.W. (2010). Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology, 21(1), 128-38.
Teasell, R., Salbach, N.M., Foley, N., Mountain, A., Cameron, J.I., Jong, A., Acerra, N.E., Bastasi, D., Carter, S.L., Fung, J., Halabi, M.L., Iruthayarajah, J., Harris, J., Kim, E., Noland, A., Pooyania, S., Rochette, A., Stack, B.D., Symcox, E., Timpson, D., Varghese, S., Verrilli, S., Gubitz, G., Casaubon, L.K., Dowlatshahi, D., & Lindsay, M.P. (2019). Canadian Stroke Best Practice Recommendations: Rehabilitation, Recovery, and Community Participation following Stroke. Part One: Rehabilitation and Recovery Following Stroke; 6th Edition Update 2019. International Journal of Stroke, 15(7), 763-788.
Topol, E.J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25, 44–56 (2019).
Vermeij, F.H., Reimer, W.J.S.O., de Man, P., van Oostenbrugge, R.J., Franke, C.L., de Jong, G., de Kort, P.L., & Dippel, D.W. (2009). Stroke-Associated Infection Is an Independent Risk Factor for Poor Outcome after Acute Ischemic Stroke: Data from the Netherlands Stroke Survey. Cerebrovascular Diseases, 27, 465–471.
Wajngarten, M., & Silva, G.S. (2019). Hypertension and Stroke: Update on Treatment. European Cardiology Reviews, 14, 111–115.
Yaghi, S. & Elkind, M.S. (2015). Lipids and Cerebrovascular Disease. Stroke, 46, 3322–3328. Yousufuddin, M. & Young, N. (2019). Aging and ischemic stroke. Aging, 11, 2542–2544.
Zheng, L., Wen, L., Lei, W., & Ning, Z. (2021). Added value of systemic inflammation markers in predicting pulmonary infection in stroke patients: A retrospective study by machine learning analysis. Medicine, 100, e28439.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
a. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
b. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
c. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
