Evidence map›Paper›PMID 37520606›Full record

ReviewThe Saudi dental journal2023

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

Jimmy Londono, Shohreh Ghasemi, Altaf Hussain Shah, Amir Fahimipour, Niloofar Ghadimi, Sara Hashemi, Zohaib Khurshid, Mahmood Dashti

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The Saudi dental journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jimmy LondonoFACP, Professor and Director of the Prosthodontics Residency Program and the Ronald Goldstein Center for Esthetics and Implant Dentistry, Dental College of Georgia at Augusta University, Augusta, GA, United States.
Shohreh GhasemiDepartment of Oral and Maxillofacial Surgery, The Dental College of Georgia at Augusta University, Augusta, GA, United States.
Altaf Hussain ShahSpecial Care Dentistry Clinics, University Dental Hospital, King Saud University Medical City, Riyadh, Saudi Arabia.
Amir FahimipourSchool of Dentistry, Faculty of Medicine and Health, Westmead Centre for Oral Health, The University of Sydney, NSW 2145, Australia.
Niloofar GhadimiDepartment of Oral and Maxillofacial Radiology, Dental School, Islamic Azad University of Medical Sciences, Tehran, Iran.
Sara HashemiSchool of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
Zohaib KhurshidDepartment of Prosthodontics and Dental Implantology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.
Mahmood DashtiSchool of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cephalometry is the study of skull measurements for clinical evaluation, diagnosis, and surgical planning. Machine learning (ML) algorithms have been used to accurately identify cephalometric landmarks and detect irregularities related to orthodontics and dentistry. ML-based cephalometric imaging reduces errors, improves accuracy, and saves time. Method: In this study, we conducted a Result: Summary measurements included the mean departure from the 1-4-mm threshold or the percentage of landmarks identified within this threshold with a 95% confidence interval (CI). This Conclusion: In conclusion, ML has shown promise for landmark detection in 2D cephalometric imagery, although the accuracy has varied among studies and clinicians. Consequently, more research is required to determine its effectiveness and reliability in clinical settings.

Indexed as

AccuracyArtificial intelligenceConvolutional neural networkLateral cephalometryMachine learningOrthodontics

Identifiers

PMID37520606
PMCPMC10373073

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.