American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2022
Automated assessment of mandibular shape asymmetry in 3-dimensions.
СтатьяНазвание и аннотация: Перевод в очереди
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Fan Yi, Zhang Yungeng, Chen Gui, He Wei, Song Guangying, Matthews Harold, Claes Peter, Pei Yuru, Zha Hongbin, Penington Anthony, Kilpatrick Nicky, Schneider Paul, Jiang Ruoping, Xu Tianmin
Аннотация
INTRODUCTION: This study aimed to develop an automatic pipeline for analyzing mandibular shape asymmetry in 3-dimensions.
METHODS: Forty patients with skeletal Class I pattern and 80 patients with skeletal Class III pattern were used. The mandible was automatically segmented from the cone-beam computed tomography images using a U-net deep learning network. A total of 17,415 uniformly sampled quasi-landmarks were automatically identified on the mandibular surface via a template mapping technique. After alignment with the robust Procrustes superimposition, the pointwise surface-to-surface distance between original and reflected mandibles was visualized in a color-coded map, indicating the location of asymmetry. The degree of overall mandibular asymmetry and the asymmetry of subskeletal units were scored using the root-mean-squared-error between the left and right sides. These asymmetry parameters were compared between the skeletal Class I and skeletal Class III groups.
RESULTS: The mandible shape was significantly more asymmetrical in patients with skeletal Class III pattern with positional asymmetry. The condyles were identified as the most asymmetric region in all groups, followed by the coronoid process and the ramus.
CONCLUSIONS: This automated approach to quantify mandibular shape asymmetry will facilitate high-throughput image processing for big data analysis. The spatially-dense landmarks allow for evaluating mandibular asymmetry over the entire surface, which overcomes the information loss inherent in conventional linear distance or angular measurements. Precise quantification of the asymmetry can provide important information for individualized diagnosis and treatment planning in orthodontics and orthognathic surgery.
METHODS: Forty patients with skeletal Class I pattern and 80 patients with skeletal Class III pattern were used. The mandible was automatically segmented from the cone-beam computed tomography images using a U-net deep learning network. A total of 17,415 uniformly sampled quasi-landmarks were automatically identified on the mandibular surface via a template mapping technique. After alignment with the robust Procrustes superimposition, the pointwise surface-to-surface distance between original and reflected mandibles was visualized in a color-coded map, indicating the location of asymmetry. The degree of overall mandibular asymmetry and the asymmetry of subskeletal units were scored using the root-mean-squared-error between the left and right sides. These asymmetry parameters were compared between the skeletal Class I and skeletal Class III groups.
RESULTS: The mandible shape was significantly more asymmetrical in patients with skeletal Class III pattern with positional asymmetry. The condyles were identified as the most asymmetric region in all groups, followed by the coronoid process and the ramus.
CONCLUSIONS: This automated approach to quantify mandibular shape asymmetry will facilitate high-throughput image processing for big data analysis. The spatially-dense landmarks allow for evaluating mandibular asymmetry over the entire surface, which overcomes the information loss inherent in conventional linear distance or angular measurements. Precise quantification of the asymmetry can provide important information for individualized diagnosis and treatment planning in orthodontics and orthognathic surgery.