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Review

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

by 1,2,3,4, 1,2,3, 1,2,3 and 1,2,3,*
1
Department of Pediatric Dentistry, Institute of Oral Bioscience, School of Dentistry, Jeonbuk National University, Jeonju 54896, Korea
2
Research Institute of Clinical Medicine, Jeonbuk National University, Jeonju 54907, Korea
3
Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju 54907, Korea
4
Faculty of Odonto-Stomatology, Hue University of Medicine and Pharmacy, Hue University, Hue 52000, Vietnam
*
Author to whom correspondence should be addressed.
Academic Editors: Seongyong Moon, Yong-Dae Kwon and Kyoobin Lee
Appl. Sci. 2021, 11(20), 9688; https://doi.org/10.3390/app11209688
Received: 30 August 2021 / Revised: 12 October 2021 / Accepted: 14 October 2021 / Published: 18 October 2021
(This article belongs to the Special Issue Computer Technologies in Oral and Maxillofacial Surgery)
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. View Full-Text
Keywords: odontogenic cyst; odontogenic tumor; convolutional neural network; medical imaging; methodological quality evaluation odontogenic cyst; odontogenic tumor; convolutional neural network; medical imaging; methodological quality evaluation
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MDPI and ACS Style

Le, V.N.T.; Kim, J.-G.; Yang, Y.-M.; Lee, D.-W. Evaluating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM)-Based Quality of Reports Using Convolutional Neural Network for Odontogenic Cyst and Tumor Detection. Appl. Sci. 2021, 11, 9688. https://doi.org/10.3390/app11209688

AMA Style

Le VNT, Kim J-G, Yang Y-M, Lee D-W. 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. 2021; 11(20):9688. https://doi.org/10.3390/app11209688

Chicago/Turabian Style

Le, Van N.T., Jae-Gon Kim, Yeon-Mi Yang, and Dae-Woo Lee. 2021. "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 11, no. 20: 9688. https://doi.org/10.3390/app11209688

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