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
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 cephalomet...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Saudi Dental Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1013905223000962 |
| _version_ | 1797776669772087296 |
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| author | Jimmy Londono Shohreh Ghasemi Altaf Hussain Shah Amir Fahimipour Niloofar Ghadimi Sara Hashemi Zohaib Khurshid Mahmood Dashti |
| author_facet | Jimmy Londono Shohreh Ghasemi Altaf Hussain Shah Amir Fahimipour Niloofar Ghadimi Sara Hashemi Zohaib Khurshid Mahmood Dashti |
| author_sort | Jimmy Londono |
| collection | DOAJ |
| description | 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 meta-analysis and systematic review to evaluate the accuracy of ML software for detecting and predicting anatomical landmarks on two-dimensional (2D) lateral cephalometric images. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for selecting and screening research articles. The eligibility criteria were established based on the diagnostic accuracy and prediction of ML combined with 2D lateral cephalometric imagery. The search was conducted among English articles in five databases, and data were managed using Review Manager software (v. 5.0). Quality assessment was performed using the diagnostic accuracy studies (QUADAS-2) tool. 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 meta-analysis included 21 of 577 articles initially collected on the accuracy of ML algorithms for detecting and predicting anatomical landmarks. The studies were conducted in various regions of the world, and 20 of the studies employed convolutional neural networks (CNNs) for detecting cephalometric landmarks. The pooled successful detection rates for the 1-mm, 2-mm, 2.5-mm, 3-mm, and 4-mm ranges were 65%, 81%, 86%, 91%, and 96%, respectively. Heterogeneity was determined using the random effect model. 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. |
| first_indexed | 2024-03-12T22:54:09Z |
| format | Article |
| id | doaj.art-9d2dd001c202470c91c4b4b62dcab9b1 |
| institution | Directory Open Access Journal |
| issn | 1013-9052 |
| language | English |
| last_indexed | 2024-03-12T22:54:09Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Saudi Dental Journal |
| spelling | doaj.art-9d2dd001c202470c91c4b4b62dcab9b12023-07-20T04:37:38ZengElsevierSaudi Dental Journal1013-90522023-07-01355487497Evaluation 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-analysisJimmy Londono0Shohreh Ghasemi1Altaf Hussain Shah2Amir Fahimipour3Niloofar Ghadimi4Sara Hashemi5Zohaib Khurshid6Mahmood Dashti7FACP, 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 StatesDepartment of Oral and Maxillofacial Surgery, The Dental College of Georgia at Augusta University, Augusta, GA, United StatesSpecial Care Dentistry Clinics, University Dental Hospital, King Saud University Medical City, Riyadh, Saudi ArabiaSchool of Dentistry, Faculty of Medicine and Health, Westmead Centre for Oral Health, The University of Sydney, NSW 2145, AustraliaDepartment of Oral and Maxillofacial Radiology, Dental School, Islamic Azad University of Medical Sciences, Tehran, IranSchool of Dentistry, Isfahan University of Medical Sciences, Isfahan, IranDepartment of Prosthodontics and Dental Implantology, King Faisal University, Al-Ahsa 31982, Saudi Arabia; Center of Excellence for Regenerative Dentistry, Department of Anatomy, Faculty of Dentistry, Chulalongkorn University, Bangkok 10330, ThailandSchool of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Corresponding author at: School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, District 1, Daneshjou Blvd, Tehran, Tehran Province, Iran.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 meta-analysis and systematic review to evaluate the accuracy of ML software for detecting and predicting anatomical landmarks on two-dimensional (2D) lateral cephalometric images. The meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for selecting and screening research articles. The eligibility criteria were established based on the diagnostic accuracy and prediction of ML combined with 2D lateral cephalometric imagery. The search was conducted among English articles in five databases, and data were managed using Review Manager software (v. 5.0). Quality assessment was performed using the diagnostic accuracy studies (QUADAS-2) tool. 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 meta-analysis included 21 of 577 articles initially collected on the accuracy of ML algorithms for detecting and predicting anatomical landmarks. The studies were conducted in various regions of the world, and 20 of the studies employed convolutional neural networks (CNNs) for detecting cephalometric landmarks. The pooled successful detection rates for the 1-mm, 2-mm, 2.5-mm, 3-mm, and 4-mm ranges were 65%, 81%, 86%, 91%, and 96%, respectively. Heterogeneity was determined using the random effect model. 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.http://www.sciencedirect.com/science/article/pii/S1013905223000962Machine learningConvolutional neural networkArtificial intelligenceLateral cephalometryOrthodonticsAccuracy |
| spellingShingle | Jimmy Londono Shohreh Ghasemi Altaf Hussain Shah Amir Fahimipour Niloofar Ghadimi Sara Hashemi Zohaib Khurshid Mahmood Dashti 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 Saudi Dental Journal Machine learning Convolutional neural network Artificial intelligence Lateral cephalometry Orthodontics Accuracy |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Machine learning Convolutional neural network Artificial intelligence Lateral cephalometry Orthodontics Accuracy |
| url | http://www.sciencedirect.com/science/article/pii/S1013905223000962 |
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