Accuracy and reliability of automatic three-dimensional cephalometric landmarking

Abstract

The aim of this systematic review was to assess the accuracy and reliability of automatic landmarking for cephalometric analysis of 3D craniofacial images. We searched for studies that reported results of automatic landmarking and/or measurements of human head CT or CBCT scans in MEDLINE, EMBASE and Web of Science until march 2019.Two authors independently screened articles for eligibility. Risk of bias and applicability concerns for each included study were assessed using the QUADAS-2 tool. Eleven studies with test dataset sample sizes ranging from 18 to 77 images were included. They used knowledge-, atlas-or learning-based algorithms to landmark 2 to 33 points of cephalometric interest. Ten studies measured mean localization errors between manually-and automatically-detected landmarks. Depending on the studies and the landmarks, mean errors ranged from <0.50 mm to >5 mm. The two best-performing algorithms used a deep learning method and reported mean errors <2 mm for every landmark, approximating results of operator variability in manual landmarking. Risk of bias regarding patient selection and implementation of the reference standard were found, therefore the studies might have yielded overoptimistic results. The robustness of these algorithms needs to be more thoroughly tested in challenging clinical settings. PROSPERO registration number: CRD42019119637.

Three-dimensional cranio-facial landmark detection in CT slices from a publicly available database, using multi-phased regression networks on a personal computer

“…Rebuilding 3D models from series of two-dimensional CT images became possible. In comparison with 2D cephalograms, reports on automatic landmark detection systems for 3D images are small in number[Shahidi et al, 2014] [Dot et al, 2020].…”

“…Rebuilding 3D models from series of two-dimensional CT images became possible. In comparison with 2D cephalograms, reports on automatic landmark detection systems for 3D images are small in number [Shahidi et al, 2014] [Dot et al, 2020. Some previous automated 3D landmark detectors in craniofacial area employed knowledge-based methods [Mestiri and Kamel, 2014][Gupta et al, 2015][Codari et al, 2017.…”

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

“…Automated cephalometric analysis is helpful in reducing the workload of orthodontists while achieving higher accuracy and efficiency ( Dot et al, 2020 ). In 1984, computer-aided automated skeletal landmarking was created ( Cohen, Ip & Linney, 1984 ).…”

“…Three-dimensional automated analysis is the ramification of plane cephalometrics. The main annotation methods can be classified into three categories: knowledge-based, atlas-based and learning-based methods ( Dot et al, 2020 ). Gupta et al (2015) created a knowledge-based algorithm in MATLAB that consists of preset mathematical entities.…”

Deep learning for cephalometric landmark detection: systematic review and meta-analysis

“…While the value of cephalometric analysis and the definition of landmarks remains an issue of debate [ 10 ], automating this task has been identified as useful, particularly as landmarking is laborsome, requiring the time of experienced (and expensive) experts [ 11 , 12 ]. Automated landmark detection for cephalometric analysis has been in the focus for decades, while DL has been demonstrated to possibly exceed less advanced (e.g., knowledge-based or atlas-based) systems [ 13 ]. Moreover, DL-based cephalometric software applications from different companies (e.g., CellmatIQ, Hamburg, Germany; ORCA AI, Herzliya, Israel; WebCeph, Gyeonggi-do, Korea) are by now available to orthodontists worldwide.…”