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Gaudin, R.* ; Vinayahalingam, S.* ; van Nistelrooij, N.* ; Ghanad, I.* ; Otto, W.* ; Kewenig, S.* ; Rendenbach, C.* ; Alevizakos, V.* ; Grün, P.* ; Kofler, F. ; Heiland, M.* ; von See, C.*

AI-powered identification of osteoporosis in dental panoramic radiographs: Addressing methodological flaws in current research.

Diagnostics 14:2298 (2024)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Background: Osteoporosis, a systemic skeletal disorder, is expected to affect 60% of women over 50. While dual-energy X-ray absorptiometry (DXA) scans are the current gold standard for diagnosis, they are typically used only after fractures occur, highlighting the need for early detection tools. Initial studies have shown panoramic radiographs (PRs) to be a potential medium, but these have methodological flaws. This study aims to address these shortcomings by developing a robust AI application for accurate osteoporosis identification in PRs. Methods: A total of 348 PRs were used for development, 58 PRs for validation, and 51 PRs for hold-out testing. Initially, the YOLOv8 object detection model was employed to predict the regions of interest. Subsequently, the predicted regions of interest were extracted from the PRs and processed by the EfficientNet classification model. Results: The model for osteoporosis detection on a PR achieved an overall sensitivity of 0.83 and an F1-score of 0.53. The area under the curve (AUC) was 0.76. The lowest detection sensitivity was for the cropped angulus region (0.66), while the highest sensitivity was for the cropped mental foramen region (0.80). Conclusion: This research presents a proof-of-concept algorithm showing the potential of deep learning to identify osteoporosis in dental radiographs. Furthermore, our thorough evaluation of existing algorithms revealed that many optimistic outcomes lack credibility when subjected to rigorous methodological scrutiny.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Ai Application ; Efficientnet Classification ; Deep Learning ; Osteoporosis Detection ; Panoramic Radiographs
ISSN (print) / ISBN 2075-4418
e-ISSN 2075-4418
Zeitschrift Diagnostics
Quellenangaben Band: 14, Heft: 20, Seiten: , Artikelnummer: 2298 Supplement: ,
Verlag MDPI
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)