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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:

Prediction of Perforated and Nonperforated Acute Appendicitis Using Machine Learning-Based Explainable Artificial Intelligence
Sami Akbulut, Fatma Hilal Yağın, İpek BALIKÇI ÇİÇEK, et al.
Diagnostics (2023) Vol. 13, Iss. 6, pp. 1173-1173
Open Access | Times Cited: 17

Showing 17 citing articles:

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models
Mahbod Issaiy, Diana Zarei, Amene Saghazadeh
World Journal of Emergency Surgery (2023) Vol. 18, Iss. 1
Open Access | Times Cited: 20
Diagnostic value of the MZXBTCH scoring system for acute complex appendicitis
Tianyi Ma, Qian Zhang, Hongwei Zhao, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty
Min Seok Kim, Heesuk Kim, Hyung Keun Lee, et al.
Investigative Ophthalmology & Visual Science (2025) Vol. 66, Iss. 1, pp. 61-61
Open Access
Diagnosis of Acute Appendicitis with Machine Learning-Based Computer Tomography: Diagnostic Reliability and Role in Clinical Management
Osman Sıbıç, Erkan Somuncu, Serhan Yılmaz, et al.
Journal of Laparoendoscopic & Advanced Surgical Techniques (2025)
Closed Access
Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review
Valentina Bianchi, Mauro Giambusso, Alessandra De Iacob, et al.
Updates in Surgery (2024) Vol. 76, Iss. 3, pp. 783-792
Open Access | Times Cited: 4
Applying an explainable machine learning model might reduce the number of negative appendectomies in pediatric patients with a high probability of acute appendicitis
Ivan Males, Zvonimir Boban, Marko Kumrić, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 4
Appendicitis Diagnosis: Ensemble Machine Learning and Explainable Artificial Intelligence-Based Comprehensive Approach
Mohammed Gollapalli, Atta Rahman, Sheriff A. Kudos, et al.
Big Data and Cognitive Computing (2024) Vol. 8, Iss. 9, pp. 108-108
Open Access | Times Cited: 3
An interpretable and transparent machine learning framework for appendicitis detection in pediatric patients
Krishnaraj Chadaga, Varada Vivek Khanna, Srikanth Prabhu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
Diagnostic performance of type I hypersensitivity-specific markers combined with CRP and IL-6 in complicated acute appendicitis in pediatric patients
Tao Zhang, Yuan Cheng, Yuliang Zhou, et al.
International Immunopharmacology (2023) Vol. 124, pp. 110977-110977
Open Access | Times Cited: 7
Usefulness of serum sodium levels as a novel marker for predicting acute appendicitis severity: a retrospective cohort study
Bruno Messias, Isabella Cubas, Caio Oliveira, et al.
BMC Surgery (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 7
A Novel Deep Learning Approach for the Automatic Diagnosis of Acute Appendicitis
Kamil Doğan, Turab Selçuk
Journal of Clinical Medicine (2024) Vol. 13, Iss. 16, pp. 4949-4949
Open Access | Times Cited: 1
Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study
Deren Xu, Weng Howe Chan, Habibollah Haron
PeerJ Computer Science (2024) Vol. 10, pp. e2217-e2217
Open Access | Times Cited: 1
Elevated total and direct bilirubin are associated with acute complicated appendicitis: a single-center based study in Saudi Arabia
Mohammed Alfehaid, Ayman M. Babiker, Abdullah Hamad Alkharraz, et al.
BMC Surgery (2023) Vol. 23, Iss. 1
Open Access | Times Cited: 3
Current use of artificial intelligence in the diagnosis and management of acute appendicitis
Micaela Cappuccio, Paolo Bianco, Marco Rotondo, et al.
Minerva Surgery (2024) Vol. 79, Iss. 3
Closed Access
Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis
Sebastian Sanduleanu, Koray Ersahin, Johannes Bremm, et al.
AI (2024) Vol. 5, Iss. 4, pp. 1942-1954
Open Access
New Genetic Biomarkers to Diagnose Pediatric Appendicitis
Maurizio Pacilli, Rishikesan Kamaleswaran
JAMA Pediatrics (2024) Vol. 178, Iss. 4, pp. 341-341
Closed Access
Interpretable Clinical Decision-Making Application for Etiological Diagnosis of Ventricular Tachycardia Based on Machine Learning
Min Wang, Hu Zhao, Ziyang Wang, et al.
Diagnostics (2024) Vol. 14, Iss. 20, pp. 2291-2291
Open Access
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