*Result*: Identifying key gait features in stroke patients using wearable inertial sensors and supervised and unsupervised machine learning.
Title:
Identifying key gait features in stroke patients using wearable inertial sensors and supervised and unsupervised machine learning.
Authors:
Brasiliano P; Department of Motor, Human and Health Sciences, University of Rome 'Foro Italico', Rome, 00135, Italy. paolobrasilianores@gmail.com.; Department of Management, Information and Production Engineering, University of Bergamo, Bologna, 24044, Italy. paolobrasilianores@gmail.com., Orejel-Bustos AS; Department of Motor, Human and Health Sciences, University of Rome 'Foro Italico', Rome, 00135, Italy.; IRCCS Santa Lucia Foundation, Rome, 00179, Italy., Belluscio V; Department of Motor, Human and Health Sciences, University of Rome 'Foro Italico', Rome, 00135, Italy., Cereatti A; Department of Electronics and Telecommunications, Politecnico di Torino, Turin, 10129, Italy., Della Croce U; Department of Engineering, University of Sassari, Sassari, 07100, Italy., Trabassi D; Department of Medico - Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, 04100, Italy., Salis F; Department of Engineering, University of Sassari, Sassari, 07100, Italy., Tramontano M; Department of Biomedical and Neuromotor Sciences, Alma Mater University of Bologna, Bologna, 40138, Italy.; Unit of Occupational Medicine, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, 40138, Italy., Buzzi MG; IRCCS Santa Lucia Foundation, Rome, 00179, Italy., Vannozzi G; Department of Motor, Human and Health Sciences, University of Rome 'Foro Italico', Rome, 00135, Italy., Bergamini E; Department of Management, Information and Production Engineering, University of Bergamo, Bologna, 24044, Italy.
Source:
Scientific reports [Sci Rep] 2026 Mar 09; Vol. 16 (1). Date of Electronic Publication: 2026 Mar 09.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:
Stroke*/physiopathology , Gait*/physiology , Gait Analysis*/methods , Gait Analysis*/instrumentation , Wearable Electronic Devices* , Unsupervised Machine Learning* , Supervised Machine Learning*, Humans ; Male ; Female ; Middle Aged ; Aged ; Support Vector Machine ; Adult ; Stroke Rehabilitation ; Machine Learning
References:
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Wang, W. et al. Prevalence, incidence, and mortality of stroke in China. Circulation https://doi.org/10.1161/CIRCULATIONAHA.116.025250 (2017). (PMID: 10.1161/CIRCULATIONAHA.116.025250292589919856244)
Kim, Y. W. Update on stroke rehabilitation in motor impairment. Brain Neurorehabil. 15, e12 (2022). (PMID: 36743199983347210.12786/bn.2022.15.e12)
Selves, C., Stoquart, G. & Lejeune, T. Gait rehabilitation after stroke: Review of the evidence of predictors, clinical outcomes and timing for interventions. Acta Neurol. Belg. 120, 783790 (2020). (PMID: 10.1007/s13760-020-01320-7)
Kinoshita, S., Abo, M., Okamoto, T. & Tanaka, N. Utility of the revised version of the ability for basic movement scale in predicting ambulation during rehabilitation in poststroke patients. J. Stroke Cerebrovasc. Dis. 26, 1663–1669 (2017). (PMID: 2857902110.1016/j.jstrokecerebrovasdis.2017.02.021)
Hutabarat, Y., Owaki, D. & Hayashibe, M. Recent advances in quantitative gait analysis using wearable sensors: A review. IEEE Sens. J. 21, 26470–26487 (2021). (PMID: 10.1109/JSEN.2021.3119658)
Mohan, D. M. et al. Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis. Frontiers Neurology 12, 650024 (2021).
Kim, G. J., Parnandi, A., Eva, S. & Schambra, H. The use of wearable sensors to assess and treat the upper extremity after stroke: A scoping review. Disabil. Rehabil. 44, 6119–6138 (2022). (PMID: 3432880310.1080/09638288.2021.1957027)
Picerno, P. et al. Wearable inertial sensors for human movement analysis: a five-year update. Expert Rev. Med. Devices. 18, 79–94 (2021). (PMID: 3460199510.1080/17434440.2021.1988849)
Jiao, Y., Hart, R., Reading, S. & Zhang, Y. Systematic review of automatic post-stroke gait classification systems. Gait Posture. 109, 259–270 (2024). (PMID: 3836745710.1016/j.gaitpost.2024.02.011)
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Altilio, R., Paoloni, M. & Panella, M. Selection of clinical features for pattern recognition applied to gait analysis. Med. Biol. Eng. Comput. 55, 685–695 (2017). (PMID: 2743506810.1007/s11517-016-1546-1)
Iosa, M., Picerno, P., Paolucci, S. & Morone, G. Wearable inertial sensors for human movement analysis. Expert Rev. Med. Devices. 13, 641–659 (2016). (PMID: 2730949010.1080/17434440.2016.1198694)
Wang, F.-C. et al. Detection and classification of stroke gaits by deep neural networks employing inertial measurement units. Sensors (Basel) 21, 1864 (2021). (PMID: 33800061796212810.3390/s21051864)
Mathur, D. & Bhatia, D. Gait classification of stroke survivors - An analytical study. J. Interdiscip. Math. 25, 163–181 (2022). (PMID: 10.1080/09720502.2021.2006332)
Sung, J. et al. Classification of stroke severity using clinically relevant symmetric gait features based on recursive feature elimination with cross-validation. IEEE Access 10, 119437–119447 (2022). (PMID: 10.1109/ACCESS.2022.3218118)
Altilio, R., Liparulo, L., Proietti, A., Paoloni, M. & Panella, M. A genetic algorithm for feature selection in gait analysis. in IEEE Congress on Evolutionary Computation (CEC) 4584–4591 (2016). 4584–4591 (2016). (2016). https://doi.org/10.1109/CEC.2016.7744374.
Altilio, R., Rossetti, A., Fang, Q., Gu, X. & Panella, M. A comparison of machine learning classifiers for smartphone-based gait analysis. Med. Biol. Eng. Comput. 59, 535–546 (2021). (PMID: 33548017792550610.1007/s11517-020-02295-6)
Iosa, M. et al. Artificial neural network analyzing wearable device gait data for identifying patients with stroke unable to return to work. Front. Neurol. 12, 650542 (2021). (PMID: 34093396817031010.3389/fneur.2021.650542)
Holden, M. K., Gill, K. M., Magliozzi, M. R., Nathan, J. & Piehl-Baker, L. Clinical gait assessment in the neurologically impaired. Reliability and meaningfulness. Phys. Ther. 64, 35–40 (1984). (PMID: 669105210.1093/ptj/64.1.35)
Folstein, M. F., Folstein, S. E. & McHugh, P. R. ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975). (PMID: 120220410.1016/0022-3956(75)90026-6)
Bergamini, E. et al. Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks. Sens. (Basel). 14, 18625–18649 (2014). (PMID: 10.3390/s141018625)
Kavanagh, J. J. & Menz, H. B. Accelerometry: A technique for quantifying movement patterns during walking. Gait Posture 28, 1–15 (2008). (PMID: 1817843610.1016/j.gaitpost.2007.10.010)
Madgwick, S. O. H., Harrison, A. J. L. & Vaidyanathan, A. Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE Int Conf Rehabil Robot 5975346 (2011). (2011).
Bertoli, M. et al. Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: Multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults. BioMed. Eng. OnLine 17, 58 (2018). (PMID: 29739456594159410.1186/s12938-018-0488-2)
Menz, H. B., Lord, S. R. & Fitzpatrick, R. C. Acceleration patterns of the head and pelvis when walking on level and irregular surfaces. Gait Posture 18, 35–46 (2003). (PMID: 1285529910.1016/S0966-6362(02)00159-5)
Buckley, C., Galna, B., Rochester, L. Mazzà, C. Attenuation of upper body accelerations during gait: Piloting an innovative assessment tool for Parkinson’s Disease. BioMed. Res. Int. 2015, 865873 (2015). (PMID: 26539532461987410.1155/2015/865873)
Pasciuto, I., Bergamini, E., Iosa, M., Vannozzi, G. & Cappozzo, A. Overcoming the limitations of the harmonic ratio for the reliable assessment of gait symmetry. J. Biomech. 53, 84–89 (2017). (PMID: 2810424610.1016/j.jbiomech.2017.01.005)
Melendez-Calderon, A., Shirota, C. & Balasubramanian, S. Estimating movement smoothness from inertial measurement units. Front. Bioeng. Biotechnol. 8, 558771 (2020). (PMID: 3352094910.3389/fbioe.2020.558771)
Zifchock, R. A., Davis, I., Higginson, J. & Royer, T. The symmetry angle: A novel, robust method of quantifying asymmetry. Gait Posture 27, 622–627 (2008). (PMID: 1791349910.1016/j.gaitpost.2007.08.006)
Trabassi, D. et al. Machine learning approach to support the detection of Parkinson’s Disease in IMU-based gait analysis. Sensors 22, 3700 (2022). (PMID: 35632109914813310.3390/s22103700)
Pavan, K. K., Rao, A. A., Rao, A. V. D. & Sridhar, G. R. Single pass seed selection algorithm for k-Means. JCS 6, 60–66 (2010).
McCrum, C., van Beek, J., Schumacher, C., Janssen, S. & Van Hooren, B. Sample size justifications in gait & posture. Gait & Posture 92, 333–337 (2022). (PMID: 10.1016/j.gaitpost.2021.12.010)
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Tramontano, M. et al. Dynamic stability, symmetry, and smoothness of gait in people with neurological health conditions. Sensors 24, 2451 (2024). (PMID: 386760681105388210.3390/s24082451)
Bergamini, E. et al. Multi-sensor assessment of dynamic balance during gait in patients with subacute stroke. J Biomech 61, 208–215 (2017). (PMID: 2882346810.1016/j.jbiomech.2017.07.034)
Vabalas, A., Gowen, E., Poliakoff, E. & Casson, A. J. Machine learning algorithm validation with a limited sample size. PLOS ONE 14, e0224365 (2019). (PMID: 31697686683744210.1371/journal.pone.0224365)
Chaibub Neto, E. et al. Detecting the impact of subject characteristics on machine learning-based diagnostic applications. npj Digit. Med. 2, 1–6 (2019). (PMID: 10.1038/s41746-019-0178-x)
Little, V. L., Perry, L. A., Mercado, M. W., Kautz, S. A. & Patten, C. Gait asymmetry pattern following stroke determines acute response to locomotor task. Gait Posture. 77, 300–307 (2020). (PMID: 32126493788789410.1016/j.gaitpost.2020.02.016)
Patterson, K. K., Gage, W. H., Brooks, D., Black, S. E. & McIlroy, W. E. Evaluation of gait symmetry after stroke: A comparison of current methods and recommendations for standardization. Gait Posture. 31, 241–246 (2010). (PMID: 1993262110.1016/j.gaitpost.2009.10.014)
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Wang, W. et al. Prevalence, incidence, and mortality of stroke in China. Circulation https://doi.org/10.1161/CIRCULATIONAHA.116.025250 (2017). (PMID: 10.1161/CIRCULATIONAHA.116.025250292589919856244)
Kim, Y. W. Update on stroke rehabilitation in motor impairment. Brain Neurorehabil. 15, e12 (2022). (PMID: 36743199983347210.12786/bn.2022.15.e12)
Selves, C., Stoquart, G. & Lejeune, T. Gait rehabilitation after stroke: Review of the evidence of predictors, clinical outcomes and timing for interventions. Acta Neurol. Belg. 120, 783790 (2020). (PMID: 10.1007/s13760-020-01320-7)
Kinoshita, S., Abo, M., Okamoto, T. & Tanaka, N. Utility of the revised version of the ability for basic movement scale in predicting ambulation during rehabilitation in poststroke patients. J. Stroke Cerebrovasc. Dis. 26, 1663–1669 (2017). (PMID: 2857902110.1016/j.jstrokecerebrovasdis.2017.02.021)
Hutabarat, Y., Owaki, D. & Hayashibe, M. Recent advances in quantitative gait analysis using wearable sensors: A review. IEEE Sens. J. 21, 26470–26487 (2021). (PMID: 10.1109/JSEN.2021.3119658)
Mohan, D. M. et al. Assessment Methods of Post-stroke Gait: A Scoping Review of Technology-Driven Approaches to Gait Characterization and Analysis. Frontiers Neurology 12, 650024 (2021).
Kim, G. J., Parnandi, A., Eva, S. & Schambra, H. The use of wearable sensors to assess and treat the upper extremity after stroke: A scoping review. Disabil. Rehabil. 44, 6119–6138 (2022). (PMID: 3432880310.1080/09638288.2021.1957027)
Picerno, P. et al. Wearable inertial sensors for human movement analysis: a five-year update. Expert Rev. Med. Devices. 18, 79–94 (2021). (PMID: 3460199510.1080/17434440.2021.1988849)
Jiao, Y., Hart, R., Reading, S. & Zhang, Y. Systematic review of automatic post-stroke gait classification systems. Gait Posture. 109, 259–270 (2024). (PMID: 3836745710.1016/j.gaitpost.2024.02.011)
Boukhennoufa, I., Zhai, X., Utti, V., Jackson, J. & McDonald-Maier, K. D. Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review. Biomed. Signal Process. Control. 71, 103197 (2022). (PMID: 10.1016/j.bspc.2021.103197)
Lee, J., Park, S. & Shin, H. Detection of hemiplegic walking using a wearable inertia sensing device. Sens. (Basel) 18, 1736 (2018). (PMID: 10.3390/s18061736)
Hsu, W.-C. et al. Can trunk acceleration differentiate stroke patient gait patterns using time- and frequency-domain features?. Applied Sciences 11, 1541 (2021). (PMID: 10.3390/app11041541)
Mannini, A., Trojaniello, D., Cereatti, A. & Sabatini, A. M. A machine learning framework for gait classification using inertial sensors: Application to elderly, post-stroke and Huntington’s disease patients. Sensors (Basel) 16, 134 (2016). (PMID: 26805847473216710.3390/s16010134)
Scheffer, C. & Cloete, T. Inertial motion capture in conjunction with an artificial neural network can differentiate the gait patterns of hemiparetic stroke patients compared with able-bodied counterparts. Comput. Methods Biomech. Biomed. Engin. 15, 285–294 (2012). (PMID: 2146900110.1080/10255842.2010.527836)
Wang, L., Sun, Y., Li, Q., Liu, T. & Yi, J. Two shank-mounted IMUs-based gait analysis and classification for neurological disease patients. IEEE Rob. Autom. Lett. 5, 1970–1976 (2020). (PMID: 10.1109/LRA.2020.2970656)
Altilio, R., Paoloni, M. & Panella, M. Selection of clinical features for pattern recognition applied to gait analysis. Med. Biol. Eng. Comput. 55, 685–695 (2017). (PMID: 2743506810.1007/s11517-016-1546-1)
Iosa, M., Picerno, P., Paolucci, S. & Morone, G. Wearable inertial sensors for human movement analysis. Expert Rev. Med. Devices. 13, 641–659 (2016). (PMID: 2730949010.1080/17434440.2016.1198694)
Wang, F.-C. et al. Detection and classification of stroke gaits by deep neural networks employing inertial measurement units. Sensors (Basel) 21, 1864 (2021). (PMID: 33800061796212810.3390/s21051864)
Mathur, D. & Bhatia, D. Gait classification of stroke survivors - An analytical study. J. Interdiscip. Math. 25, 163–181 (2022). (PMID: 10.1080/09720502.2021.2006332)
Sung, J. et al. Classification of stroke severity using clinically relevant symmetric gait features based on recursive feature elimination with cross-validation. IEEE Access 10, 119437–119447 (2022). (PMID: 10.1109/ACCESS.2022.3218118)
Altilio, R., Liparulo, L., Proietti, A., Paoloni, M. & Panella, M. A genetic algorithm for feature selection in gait analysis. in IEEE Congress on Evolutionary Computation (CEC) 4584–4591 (2016). 4584–4591 (2016). (2016). https://doi.org/10.1109/CEC.2016.7744374.
Altilio, R., Rossetti, A., Fang, Q., Gu, X. & Panella, M. A comparison of machine learning classifiers for smartphone-based gait analysis. Med. Biol. Eng. Comput. 59, 535–546 (2021). (PMID: 33548017792550610.1007/s11517-020-02295-6)
Iosa, M. et al. Artificial neural network analyzing wearable device gait data for identifying patients with stroke unable to return to work. Front. Neurol. 12, 650542 (2021). (PMID: 34093396817031010.3389/fneur.2021.650542)
Holden, M. K., Gill, K. M., Magliozzi, M. R., Nathan, J. & Piehl-Baker, L. Clinical gait assessment in the neurologically impaired. Reliability and meaningfulness. Phys. Ther. 64, 35–40 (1984). (PMID: 669105210.1093/ptj/64.1.35)
Folstein, M. F., Folstein, S. E. & McHugh, P. R. ‘Mini-mental state’. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12, 189–198 (1975). (PMID: 120220410.1016/0022-3956(75)90026-6)
Bergamini, E. et al. Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks. Sens. (Basel). 14, 18625–18649 (2014). (PMID: 10.3390/s141018625)
Kavanagh, J. J. & Menz, H. B. Accelerometry: A technique for quantifying movement patterns during walking. Gait Posture 28, 1–15 (2008). (PMID: 1817843610.1016/j.gaitpost.2007.10.010)
Madgwick, S. O. H., Harrison, A. J. L. & Vaidyanathan, A. Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE Int Conf Rehabil Robot 5975346 (2011). (2011).
Bertoli, M. et al. Estimation of spatio-temporal parameters of gait from magneto-inertial measurement units: Multicenter validation among Parkinson, mildly cognitively impaired and healthy older adults. BioMed. Eng. OnLine 17, 58 (2018). (PMID: 29739456594159410.1186/s12938-018-0488-2)
Menz, H. B., Lord, S. R. & Fitzpatrick, R. C. Acceleration patterns of the head and pelvis when walking on level and irregular surfaces. Gait Posture 18, 35–46 (2003). (PMID: 1285529910.1016/S0966-6362(02)00159-5)
Buckley, C., Galna, B., Rochester, L. Mazzà, C. Attenuation of upper body accelerations during gait: Piloting an innovative assessment tool for Parkinson’s Disease. BioMed. Res. Int. 2015, 865873 (2015). (PMID: 26539532461987410.1155/2015/865873)
Pasciuto, I., Bergamini, E., Iosa, M., Vannozzi, G. & Cappozzo, A. Overcoming the limitations of the harmonic ratio for the reliable assessment of gait symmetry. J. Biomech. 53, 84–89 (2017). (PMID: 2810424610.1016/j.jbiomech.2017.01.005)
Melendez-Calderon, A., Shirota, C. & Balasubramanian, S. Estimating movement smoothness from inertial measurement units. Front. Bioeng. Biotechnol. 8, 558771 (2020). (PMID: 3352094910.3389/fbioe.2020.558771)
Zifchock, R. A., Davis, I., Higginson, J. & Royer, T. The symmetry angle: A novel, robust method of quantifying asymmetry. Gait Posture 27, 622–627 (2008). (PMID: 1791349910.1016/j.gaitpost.2007.08.006)
Trabassi, D. et al. Machine learning approach to support the detection of Parkinson’s Disease in IMU-based gait analysis. Sensors 22, 3700 (2022). (PMID: 35632109914813310.3390/s22103700)
Pavan, K. K., Rao, A. A., Rao, A. V. D. & Sridhar, G. R. Single pass seed selection algorithm for k-Means. JCS 6, 60–66 (2010).
McCrum, C., van Beek, J., Schumacher, C., Janssen, S. & Van Hooren, B. Sample size justifications in gait & posture. Gait & Posture 92, 333–337 (2022). (PMID: 10.1016/j.gaitpost.2021.12.010)
Lakens, D. Sample size justification. Collabra: Psychology 8, 33267 (2022). (PMID: 10.1525/collabra.33267)
Trabassi, D. et al. Optimizing rare disease gait classification through data balancing and generative AI: Insights from Hereditary Cerebellar Ataxia. Sensors 24, 3613 (2024). (PMID: 388944041117524010.3390/s24113613)
Tramontano, M. et al. Dynamic stability, symmetry, and smoothness of gait in people with neurological health conditions. Sensors 24, 2451 (2024). (PMID: 386760681105388210.3390/s24082451)
Bergamini, E. et al. Multi-sensor assessment of dynamic balance during gait in patients with subacute stroke. J Biomech 61, 208–215 (2017). (PMID: 2882346810.1016/j.jbiomech.2017.07.034)
Vabalas, A., Gowen, E., Poliakoff, E. & Casson, A. J. Machine learning algorithm validation with a limited sample size. PLOS ONE 14, e0224365 (2019). (PMID: 31697686683744210.1371/journal.pone.0224365)
Chaibub Neto, E. et al. Detecting the impact of subject characteristics on machine learning-based diagnostic applications. npj Digit. Med. 2, 1–6 (2019). (PMID: 10.1038/s41746-019-0178-x)
Little, V. L., Perry, L. A., Mercado, M. W., Kautz, S. A. & Patten, C. Gait asymmetry pattern following stroke determines acute response to locomotor task. Gait Posture. 77, 300–307 (2020). (PMID: 32126493788789410.1016/j.gaitpost.2020.02.016)
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Grant Information:
GR-2019-12370757 Ministero della Salute
Entry Date(s):
Date Created: 20260309 Date Completed: 20260314 Latest Revision: 20260316
Update Code:
20260316
PubMed Central ID:
PMC12987975
DOI:
10.1038/s41598-026-43666-7
PMID:
41796232
Database:
MEDLINE
*Further Information*
*Stroke is a major cause of motor disability, degrading walking and quality of life. Wearable gait analysis with magneto-inertial measurement units (MIMUs) can quantify post-stroke impairments. We used machine learning to identify discriminative gait features in stroke, coupling supervised feature selection with unsupervised clustering to improve interpretability and generalizability. Eighty-five stroke patients and 97 healthy controls completed 10-Meter Walk Tests while wearing five MIMUs. Feature selection spanned spatiotemporal, symmetry, stability, and smoothness metrics. K-nearest neighbors (KNN), support vector machines (SVM), and decision trees (TREE) were trained, validated, and tested iteratively across data splits; clustering then verified discriminative ability. Sequential backward feature selection retained nine features, yielding accuracies (healthy vs. patient) of 94.1% (KNN), 96.7% (SVM), and 89.1% (TREE). SVM generalized best. Unsupervised k-medoids with cosine distance confirmed discrimination, reaching 90% accuracy with only three features: stride speed, stance-phase coefficient of variation, and medio-lateral harmonic ratio. Results indicate that gait variability, trunk smoothness, and upper-body stability robustly characterize post-stroke dysfunctions. Notably, head-movement smoothness emerged as a novel, discriminative feature. This integrated framework shows how wearable sensors plus machine learning can support clinical gait analysis and rehabilitation planning. Future work should enable real-time deployment and broaden datasets to cover more clinical scenarios.
(© 2026. The Author(s).)*
*Declarations. Competing interests: The authors declare no competing interests.*