Comparison of deep learning segmentation and multigrader-annotated mandibular canals of multicenter CBCT scans

Autor: Jorma Järnstedt, Jaakko Sahlsten, Joel Jaskari, Kimmo Kaski, Helena Mehtonen, Ziyuan Lin, Ari Hietanen, Osku Sundqvist, Vesa Varjonen, Vesa Mattila, Sangsom Prapayasotok, Sakarat Nalampang
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Scientific Reports, Vol 12, Iss 1, Pp 1-11 (2022)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-022-20605-w
Popis: Abstract Deep learning approach has been demonstrated to automatically segment the bilateral mandibular canals from CBCT scans, yet systematic studies of its clinical and technical validation are scarce. To validate the mandibular canal localization accuracy of a deep learning system (DLS) we trained it with 982 CBCT scans and evaluated using 150 scans of five scanners from clinical workflow patients of European and Southeast Asian Institutes, annotated by four radiologists. The interobserver variability was compared to the variability between the DLS and the radiologists. In addition, the generalisation of DLS to CBCT scans from scanners not used in the training data was examined to evaluate its out-of-distribution performance. The DLS had a statistically significant difference (p
Databáze: Directory of Open Access Journals
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