Retraction
Automation of Cephalometrics Using Machine Learning Methods
Paper Information
Record ID:
59092
Journal:
Publication Date:
June 21, 2022
Retraction Date:
December 13, 2023
(1.9 year years ago)
Subjects:
Institution:
Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran, Saudi ArabiaCountry:
🇸🇦 Saudi ArabiaArticle Type:
Publisher:
Hindawi
Open Access:
Yes
DOI:
PubMed ID:
Retraction PubMed ID:
Retraction Details
Citations (5)
5
Total Citations2
Post-Retraction(40.0%)
2
Pre-Retraction0
Same DayPost-Retraction Citation Analysis
1
Within 30 days
1
Within 1 year
0
After 2+ years
450
Days since retraction (latest)
Heavy Metals Removal Using Carbon Based Nanocomposites
Unknown Authors
Unknown Journal
Published: Unknown
Self-CephaloNet: a two-stage novel framework using operational neural network for cephalometric analysis
Md. Shaheenur Islam Sumon, Khandaker Reajul Islam, Md. Sakib Abrar Hossain et al. (10 authors)
Neural Computing and Applications
Open Access
Published: Mar 2025
1 citation
1 citation
450 days after retraction
Cephalometric Landmarks Identification Through an Object Detection-based Deep Learning Model
Idriss Tafala, Fatima-Ezzahraa Ben-Bouazza, Aymane Edder et al. (6 authors)
International Journal of Advanced Computer Science and Applications
Open Access
Published: Jan 2024
2 citations
2 citations
19 days after retraction
Evaluation of deep learning and convolutional neural network algorithms accuracy for detecting and predicting anatomical landmarks on 2D lateral cephalometric images: A systematic review and meta-analysis
Jimmy Londono, Shohreh Ghasemi, Altaf H Shah et al. (8 authors)
The Saudi Dental Journal
Open Access
Published: May 2023
26 citations
26 citations
204 days before retraction
Retracted: Automation of Cephalometrics Using Machine Learning Methods
Computational Intelligence and Neuroscience
Computational Intelligence and Neuroscience
Open Access
Published: Jan 2023
346 days before retraction
Quick Stats
Total Citations:
8
Years Since Retraction:
1.9 year
Open Access:
Yes
Last Checked:
Jul 24, 2025