Evaluating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM)-Based Quality of Reports Using Convolutional Neural Network for Odontogenic Cyst and Tumor Detection

Applied Sciences

Van Nhat Thang Le¹  Jae-Gon Kim¹  Dae-Woo Lee¹  Yeon-Mi Yang¹ 

[1] Department of Pediatric Dentistry, Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University, Jeonju 54896, Korea;

关键词: odontogenic cyst;    odontogenic tumor;    convolutional neural network;    medical imaging;    methodological quality evaluation;   

DOI  :  10.3390/app11209688

来源: DOAJ

【 摘 要 】

This review aimed to explore whether studies employing a convolutional neural network (CNN) for odontogenic cyst and tumor detection follow the methodological reporting recommendations, the checklist for artificial intelligence in medical imaging (CLAIM). We retrieved the CNN studies using panoramic and cone-beam-computed tomographic images from inception to April 2021 in PubMed, EMBASE, Scopus, and Web of Science. The included studies were assessed according to the CLAIM. Among the 55 studies yielded, 6 CNN studies for odontogenic cyst and tumor detection were included. Following the CLAIM items, abstract, methods, results, discussion across the included studies were insufficiently described. The problem areas included item 2 in the abstract; items 6–9, 11–18, 20, 21, 23, 24, 26–31 in the methods; items 33, 34, 36, 37 in the results; item 38 in the discussion; and items 40–41 in “other information.” The CNN reports for odontogenic cyst and tumor detection were evaluated as low quality. Inadequate reporting reduces the robustness, comparability, and generalizability of a CNN study for dental radiograph diagnostics. The CLAIM is accepted as a good guideline in the study design to improve the reporting quality on artificial intelligence studies in the dental field.

【 授权许可】

Unknown