Imaging Sci Dent.
2024 Sep;54(3):240-250. 10.5624/isd.20240009.
Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm
- Affiliations
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- 1Department of Orthodontics, College of Dentistry, Yonsei University, Seoul, Korea
- 2College of Dentistry, Seoul National University, Seoul, Korea
- 3Imagoworks Incorporated, Seoul, Korea
- 4Center for Bionics, Korea Institute of Science and Technology, Seoul, Korea
- 5Department of Oral and Maxillofacial Surgery, College of Dentistry, Chungang University Hospital, Seoul, Korea
- 6Department of Orthodontics, The Institute of Craniofacial Deformity, College of Dentistry, Yonsei University, Seoul, Korea
- KMID: 2559833
- DOI: http://doi.org/10.5624/isd.20240009
Abstract
- Purpose
This study was performed to assess the clinical validity and accuracy of a deep learning-based automatic landmarking algorithm for cone-beam computed tomography (CBCT). Three-dimensional (3D) CBCT head measurements obtained through manual and automatic landmarking were compared.
Materials and Methods
A total of 80 CBCT scans were divided into 3 groups: non-surgical (39 cases); surgical without hardware, namely surgical plates and mini-screws (9 cases); and surgical with hardware (32 cases). Each CBCT scan was analyzed to obtain 53 measurements, comprising 27 lengths, 21 angles, and 5 ratios, which were determined based on 65 landmarks identified using either a manual or a 3D automatic landmark detection method.
Results
In comparing measurement values derived from manual and artificial intelligence landmarking, 6 items displayed significant differences: R U6CP-L U6CP, R L3CP-L L3CP, S-N, Or_R-R U3CP, L1L to Me-GoL, and GoR-Gn/S-N (P<0.05). Of the 3 groups, the surgical scans without hardware exhibited the lowest error, reflecting the smallest difference in measurements between human- and artificial intelligence-based landmarking. The time required to identify 65 landmarks was approximately 40-60 minutes per CBCT volume when done manually, compared to 10.9 seconds for the artificial intelligence method (PC specifications: GeForce 2080Ti, 64GB RAM, and an Intel i7 CPU at 3.6 GHz).
Conclusion
Measurements obtained with a deep learning-based CBCT automatic landmarking algorithm were similar in accuracy to values derived from manually determined points. By decreasing the time required to calculate these measurements, the efficiency of diagnosis and treatment may be improved.
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