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

• Main Authors: Jimmy Londono (Author), Shohreh Ghasemi (Author), Altaf Hussain Shah (Author), Amir Fahimipour (Author), Niloofar Ghadimi (Author), Sara Hashemi (Author), Zohaib Khurshid (Author), Mahmood Dashti (Author)
• Format: Book
• Published: Elsevier, 2023-07-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1013-9052; 10.1016/j.sdentj.2023.05.014
• Keywords: Machine learning; Convolutional neural network; Artificial intelligence; Lateral cephalometry; Orthodontics; Accuracy; Medicine; R; Dentistry; RK1-715
• Source: Saudi Dental Journal, Vol 35, Iss 5, Pp 487-497 (2023)

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.