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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis
Katrin Trentzsch, Paula Schumann, Grzegorz Śliwiński, et al.
Brain Sciences (2021) Vol. 11, Iss. 8, pp. 1049-1049
Open Access | Times Cited: 21

Showing 21 citing articles:

Role of artificial intelligence in MS clinical practice
Raffaello Bonacchi, Massimo Filippi, Maria A. Rocca
NeuroImage Clinical (2022) Vol. 35, pp. 103065-103065
Open Access | Times Cited: 48
Building a monitoring matrix for the management of multiple sclerosis
Isabel Voigt, Hernán Inojosa, Judith Wenk, et al.
Autoimmunity Reviews (2023) Vol. 22, Iss. 8, pp. 103358-103358
Closed Access | Times Cited: 17
Efficacy of Transcranial Direct Current Stimulation (tDCS) on Balance and Gait in Multiple Sclerosis Patients: A Machine Learning Approach
Nicola Marotta, Alessandro de Sire, Cinzia Marinaro, et al.
Journal of Clinical Medicine (2022) Vol. 11, Iss. 12, pp. 3505-3505
Open Access | Times Cited: 22
Machine learning based algorithms for virtual early detection and screening of neurodegenerative and neurocognitive disorders: a systematic-review
Milad Yousefi, Masih Akhbari, Zhina Mohamadi, et al.
Frontiers in Neurology (2024) Vol. 15
Open Access | Times Cited: 5
A survey of artificial intelligence in gait-based neurodegenerative disease diagnosis
Haocong Rao, Minlin Zeng, Xuejiao Zhao, et al.
Neurocomputing (2025), pp. 129533-129533
Closed Access
Consensus quality indicators for monitoring multiple sclerosis
Isabel Voigt, Stefanie Fischer, Undine Proschmann, et al.
The Lancet Regional Health - Europe (2024) Vol. 40, pp. 100891-100891
Open Access | Times Cited: 4
Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway
Wenting Hu, Owen Combden, Xianta Jiang, et al.
BioMedical Engineering OnLine (2022) Vol. 21, Iss. 1
Open Access | Times Cited: 17
Evaluation of High-quality Development of Shaanxi’s Economy Based on Digital Economy Based on Machine Learning Algorithm
Lina Wang
International Transactions on Electrical Energy Systems (2022) Vol. 2022, pp. 1-9
Open Access | Times Cited: 16
A future of AI-driven personalized care for people with multiple sclerosis
Jelle Praet, Lina Anderhalten, Cristoforo Comi, et al.
Frontiers in Immunology (2024) Vol. 15
Open Access | Times Cited: 3
NONAN GaitPrint: An IMU gait database of healthy young adults
Tyler Wiles, Madhur Mangalam, Joel Sommerfeld, et al.
Scientific Data (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 8
Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors and Machine Learning Algorithms
Rahul Soangra, Yuxin Wen, Hualin Yang, et al.
IEEE Access (2022) Vol. 10, pp. 77054-77067
Open Access | Times Cited: 11
3D Printing of Individual Running Insoles – A Case Study
Mária Danko, Jan Sekac, Eva Dzivakova, et al.
Orthopedic Research and Reviews (2023) Vol. Volume 15, pp. 105-118
Open Access | Times Cited: 5
Torsobarography: Intra-Observer Reliability Study of a Novel Posture Analysis Based on Pressure Distribution
Nico Stecher, Andreas Heinke, Arkadiusz Żurawski, et al.
Sensors (2024) Vol. 24, Iss. 3, pp. 768-768
Open Access | Times Cited: 1
Machine learning corroborates subjective ratings of walking and balance difficulty in multiple sclerosis
Wenting Hu, Owen Combden, Xianta Jiang, et al.
Frontiers in Artificial Intelligence (2022) Vol. 5
Open Access | Times Cited: 5
Automated Analysis of the Two-Minute Walk Test in Clinical Practice Using Accelerometer Data
Katrin Trentzsch, Benjamin Melzer, Heidi Stölzer-Hutsch, et al.
Brain Sciences (2021) Vol. 11, Iss. 11, pp. 1507-1507
Open Access | Times Cited: 6
A scoping review of applications of artificial intelligence in kinematics and kinetics of ankle sprains - current state-of-the-art and future prospects
Yun Xin Teoh, Jwan K. Alwan, Darshan Shah, et al.
Clinical Biomechanics (2024) Vol. 113, pp. 106188-106188
Closed Access
Data-Driven Identification of Stroke through Machine Learning Applied to Complexity Metrics in Multimodal Electromyography and Kinematics
Francesco Romano, Damiano Formenti, Daniela Cardone, et al.
Entropy (2024) Vol. 26, Iss. 7, pp. 578-578
Open Access
An exploratory study on the effect of applying various artificial neural networks to the classification of lower limb injury
RACHEL YUN, May Salama, Lamiaa A. Elrefaei
TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES (2023) Vol. 31, Iss. 2, pp. 448-461
Open Access | Times Cited: 1
Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach
Liam David Hughes, Martin Bencsik, Maria Bisele, et al.
Sensors (2023) Vol. 23, Iss. 22, pp. 9241-9241
Open Access | Times Cited: 1
Fall Risk Assessment Using Pressure Insole Sensors and Convolutional Neural Networks
Reem Brome, Jad Nasreddine, Frédéric Bonnardot, et al.
(2022), pp. 177-182
Closed Access | Times Cited: 1
A Pilot Study of Plantar Mechanics Distributions and Fatigue Profiles after Running on a Treadmill: Using a Support Vector Machine Algorithm
Qian Liu, Hairong Chen, Anand Thirupathi, et al.
Journal of Healthcare Engineering (2023) Vol. 2023, pp. 1-10
Open Access
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