Clinical validity and precision of deep learning-based cone-beam computed tomography automatic landmarking algorithm

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. A total of 80 CBCT s...

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Veröffentlicht in:Imaging science in dentistry 2024, 54(3), , pp.240-250
Hauptverfasser: Park, Jungeun, Yoon, Seongwon, Kim, Hannah, Kim, Youngjun, Lee, Uilyong, Yu, Hyungseog
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Sprache:eng
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Zusammenfassung: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. 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. 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 (
ISSN:2233-7822
2233-7830
DOI:10.5624/isd.20240009