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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 and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence
Sandra Vieira, Qiyong Gong, Walter Hugo Lopez Pinaya, et al.
Schizophrenia Bulletin (2018) Vol. 46, Iss. 1, pp. 17-26
Open Access | Times Cited: 89

Showing 1-25 of 89 citing articles:

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review
Roberto Tornero-Costa, Antonio Martínez-Millana, Natasha Azzopardi‐Muscat, et al.
JMIR Mental Health (2023) Vol. 10, pp. e42045-e42045
Open Access | Times Cited: 56
Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions
Aristotle N. Voineskos, Colin Hawco, Nicholas H. Neufeld, et al.
World Psychiatry (2024) Vol. 23, Iss. 1, pp. 26-51
Open Access | Times Cited: 29
Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm
Jihoon Oh, Baek‐Lok Oh, Kyong-Uk Lee, et al.
Frontiers in Psychiatry (2020) Vol. 11
Open Access | Times Cited: 115
Identification of Children at Risk of Schizophrenia via Deep Learning and EEG Responses
David Ahmedt‐Aristizabal, Tharindu Fernando, Simon Denman, et al.
IEEE Journal of Biomedical and Health Informatics (2020) Vol. 25, Iss. 1, pp. 69-76
Open Access | Times Cited: 84
Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis
Mirjam Quaak, Laurens van de Mortel, Rajat M. Thomas, et al.
NeuroImage Clinical (2021) Vol. 30, pp. 102584-102584
Open Access | Times Cited: 74
Going deep into schizophrenia with artificial intelligence
Jose Cortes-Briones, Nicolas I. Tapia, Deepak Cyril D’Souza, et al.
Schizophrenia Research (2021) Vol. 245, pp. 122-140
Open Access | Times Cited: 70
Can we predict who will benefit from cognitive-behavioural therapy? A systematic review and meta-analysis of machine learning studies
Sandra Vieira, Xinyi Liang, Raquel Guiomar, et al.
Clinical Psychology Review (2022) Vol. 97, pp. 102193-102193
Open Access | Times Cited: 49
Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia
Du Lei, Kun Qin, Walter Hugo Lopez Pinaya, et al.
Schizophrenia Bulletin (2022) Vol. 48, Iss. 4, pp. 881-892
Open Access | Times Cited: 45
Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies—PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice
Stefania Tognin, Hendrika H. van Hell, Kate Merritt, et al.
Schizophrenia Bulletin (2019) Vol. 46, Iss. 2, pp. 432-441
Open Access | Times Cited: 70
Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk
Jessica Irving, Rashmi Patel, Dominic Oliver, et al.
Schizophrenia Bulletin (2020) Vol. 47, Iss. 2, pp. 405-414
Open Access | Times Cited: 60
Computing schizophrenia: ethical challenges for machine learning in psychiatry
Georg Starke, Eva De Clercq, Stefan Borgwardt, et al.
Psychological Medicine (2020) Vol. 51, Iss. 15, pp. 2515-2521
Open Access | Times Cited: 56
A Machine-Based Prediction Model of ADHD Using CPT Data
Ortal Slobodin, Inbal Yahav, Itai Berger
Frontiers in Human Neuroscience (2020) Vol. 14
Open Access | Times Cited: 55
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research
Fabian Eitel, Marc‐Andre Schulz, Moritz Seiler, et al.
Experimental Neurology (2021) Vol. 339, pp. 113608-113608
Open Access | Times Cited: 45
Machine learning techniques for the Schizophrenia diagnosis: a comprehensive review and future research directions
Shradha Verma, Tripti Goel, M. Tanveer, et al.
Journal of Ambient Intelligence and Humanized Computing (2023) Vol. 14, Iss. 5, pp. 4795-4807
Open Access | Times Cited: 21
Can Machine Learning help us in dealing with treatment resistant depression? A review
Alessandro Pigoni, Giuseppe Delvecchio, Domenico Madonna, et al.
Journal of Affective Disorders (2019) Vol. 259, pp. 21-26
Closed Access | Times Cited: 45
Detecting Abnormal Brain Regions in Schizophrenia Using Structural MRI via Machine Learning
Zhihong Chen, Tao Yan, Erlei Wang, et al.
Computational Intelligence and Neuroscience (2020) Vol. 2020, pp. 1-13
Open Access | Times Cited: 41
Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders
Cristina Scarpazza, Minji Ha, Lea Baecker, et al.
Translational Psychiatry (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 41
Deep learning based automatic diagnosis of first-episode psychosis, bipolar disorder and healthy controls
Zhuangzhuang Li, Wenmei Li, Wei Yan, et al.
Computerized Medical Imaging and Graphics (2021) Vol. 89, pp. 101882-101882
Closed Access | Times Cited: 38
Cerebello-Thalamo-Cortical Hyperconnectivity Classifies Patients and Predicts Long-Term Treatment Outcome in First-Episode Schizophrenia
Hengyi Cao, Wei Xia, Na Hu, et al.
Schizophrenia Bulletin (2021) Vol. 48, Iss. 2, pp. 505-513
Open Access | Times Cited: 37
Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases
Israel Elujide, Stephen Gbenga Fashoto, Bunmi Fashoto, et al.
Informatics in Medicine Unlocked (2021) Vol. 23, pp. 100545-100545
Open Access | Times Cited: 35
Magnetic resonance imaging–based machine learning classification of schizophrenia spectrum disorders: a meta‐analysis
Fabio Di Camillo, David Antonio Grimaldi, Giulia Cattarinussi, et al.
Psychiatry and Clinical Neurosciences (2024)
Open Access | Times Cited: 6
A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data
Karen S. Ambrosen, Martin W. Skjerbæk, Jonathan Foldager, et al.
Translational Psychiatry (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 38
Using machine learning to explain the heterogeneity of schizophrenia. Realizing the promise and avoiding the hype
Neeraj Tandon, Rajiv Tandon
Schizophrenia Research (2019) Vol. 214, pp. 70-75
Closed Access | Times Cited: 36
Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete
Xu Huang, Jiaqi Zhang, Jessada Sresakoolchai, et al.
Sustainability (2021) Vol. 13, Iss. 4, pp. 1691-1691
Open Access | Times Cited: 30
Consistent brain structural abnormalities and multisite individualised classification of schizophrenia using deep neural networks
Yue Cui, Chao Li, Bing Liu, et al.
The British Journal of Psychiatry (2022) Vol. 221, Iss. 6, pp. 732-739
Closed Access | Times Cited: 22
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