BMC oral health · 2025
Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.
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Jiang Yan, Jiang Canyang, Shi Bin, Wu You, Xing Shuli, Liang Hao, Huang Jianping, Huang Xiaohong, Huang Li, Lin Lisong
Аннотация
BACKGROUND: Manual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft and hard tissue craniofacial landmarks on CBCT in patients with various types of malocclusion.
METHODS: A total of 498 CBCT images were collected. Following the calibration procedure, two experienced clinicians identified 43 landmarks in the x-, y-, and z-coordinate planes on the CBCT images using Checkpoint Software, creating the ground truth by averaging the landmark coordinates. To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR).
RESULTS: Each landmark prediction took approximately 4.2 s on a conventional graphics processing unit. The mean absolute error across all coordinates was 0.74 mm. The overall MRE for the 43 landmarks was 1.76 ± 1.13 mm, and the SDR was 60.16%, 91.05%, and 97.58% within 2-, 3-, and 4-mm error ranges of manual marking, respectively. The average MRE of the hard tissue landmarks (32/43) was 1.73 mm, while that for soft tissue landmarks (11/43) was 1.84 mm.
CONCLUSIONS: Our proposed algorithm demonstrates a clinically acceptable level of accuracy and robustness for automatic detection of CBCT soft- and hard-tissue landmarks across all types of malformations. The potential for artificial intelligence to assist in identifying three dimensional-CT landmarks in routine clinical practice and analysing large datasets for future research is promising.
METHODS: A total of 498 CBCT images were collected. Following the calibration procedure, two experienced clinicians identified 43 landmarks in the x-, y-, and z-coordinate planes on the CBCT images using Checkpoint Software, creating the ground truth by averaging the landmark coordinates. To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR).
RESULTS: Each landmark prediction took approximately 4.2 s on a conventional graphics processing unit. The mean absolute error across all coordinates was 0.74 mm. The overall MRE for the 43 landmarks was 1.76 ± 1.13 mm, and the SDR was 60.16%, 91.05%, and 97.58% within 2-, 3-, and 4-mm error ranges of manual marking, respectively. The average MRE of the hard tissue landmarks (32/43) was 1.73 mm, while that for soft tissue landmarks (11/43) was 1.84 mm.
CONCLUSIONS: Our proposed algorithm demonstrates a clinically acceptable level of accuracy and robustness for automatic detection of CBCT soft- and hard-tissue landmarks across all types of malformations. The potential for artificial intelligence to assist in identifying three dimensional-CT landmarks in routine clinical practice and analysing large datasets for future research is promising.