Details

Autor(en) / Beteiligte

• Kim, Jaerong
• Kim, Inhwan
• Kim, Yoon‐Ji
• Kim, Minji
• Cho, Jin‐Hyoung
• Hong, Mihee
• Kang, Kyung‐Hwa
• Lim, Sung‐Hoon
• Kim, Su‐Jung
• Kim, Young Ho
• Kim, Namkug
• Sung, Sang‐Jin
• Baek, Seung‐Hak

Titel

Accuracy of automated identification of lateral cephalometric landmarks using cascade convolutional neural networks on lateral cephalograms from nationwide multi‐centres

Ist Teil von

• Orthodontics & craniofacial research, 2021-12, Vol.24 (S2), p.59-67

Ort / Verlag

England: Wiley Subscription Services, Inc

Erscheinungsjahr

2021

Quelle

Wiley-Blackwell Full Collection

Beschreibungen/Notizen

• Objective To investigate the accuracy of automated identification of cephalometric landmarks using the cascade convolutional neural networks (CNN) on lateral cephalograms acquired from nationwide multi‐centres. Settings and sample population A total of 3150 lateral cephalograms were acquired from 10 university hospitals in South Korea for training. Materials and Methods We evaluated the accuracy of the developed model with independent 100 lateral cephalograms as an external validation. Two orthodontists independently identified the anatomic landmarks of the test data set using the V‐ceph software (version 8.0, Osstem, Seoul, Korea). The mean positions of the landmarks identified by two orthodontists were regarded as the gold standard. The performance of the CNN model was evaluated by calculating the mean absolute distance between the gold standard and the automatically detected positions. Factors associated with the detection accuracy for landmarks were analysed using the linear regression models. Results The mean inter‐examiner difference was 1.31 ± 1.13 mm. The overall automated detection error was 1.36 ± 0.98 mm. The mean detection error for each landmark ranged between 0.46 ± 0.37 mm (maxillary incisor crown tip) and 2.09 ± 1.91 mm (distal root tip of the mandibular first molar). A significant difference in the detection accuracy among cephalograms was noted according to hospital (P = .011), sensor type (P < .01), and cephalography machine model (P < .01). Conclusion The automated cephalometric landmark detection model may aid in preliminary screening for patient diagnosis and mid‐treatment assessment, independent of the type of the radiography machines tested.