Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning

Bermudez, Ariana; Calderon-Ramirez, Saul; Thang, Trevor; Tyrrell, Pascal; Moemeni, Armaghan; Yang, Shengxiang; Torrents-Barrena, Jordina

Abstract

Photostimulable Phosphor Plates are commonly used in digital X-ray imaging for dentistry. During its usage, these plates get damaged, influencing the diagnosis performance and confidence of the dentistry professional. We propose a deep learning based classifier to discard or extend the use of photostimulable phosphor (PSP) plates based on their physical damage. The system automatically assesses, for the first time in the literature, when dentists should discard their plates. To validate our methodology, an in-house dataset is built on 25 PSP artifact masks (Carestream, CS 7600) digitally superimposed over 100 Complementary Metal-oxide-semiconductor (CMOS) periapical images (Carestream, RVG 6200) with known radiologic interpretations. From these 2500 images, unique subsets of 100 images were evaluated by 25 dentists to find periapical inflammatory lesions on the tooth. Doctors’ opinion on whether the plates should be discarded or not was also collected. State-of-the-art deep convolutional networks were tested using fivefold cross validation, yielding classification accuracies from 87% to almost 89%. Specifically, InceptionV3 and Resnet50 obtained the best performances with statistical significance. Qualitative heat-maps showed that such models can identify and employ artifacts to decide on whether to discard the PSP plate or not. This work intends to be the base line for future works related to the automatic PSP plate assessment.

Citation

Bermudez, A., Calderon-Ramirez, S., Thang, T., Tyrrell, P., Moemeni, A., Yang, S., & Torrents-Barrena, J. (2020). Quality Assessment of Dental Photostimulable Phosphor Plates with Deep Learning. . https://doi.org/10.1109/IJCNN48605.2020.9206779

• Conference Name: 2020 International Joint Conference on Neural Networks (IJCNN)
• Conference Location: Glasgow, UK
• Start Date: Jul 19, 2020
• End Date: Jul 24, 2020
• Acceptance Date: Apr 1, 2020
• Online Publication Date: Sep 28, 2020
• Publication Date: Jul 19, 2020
• Deposit Date: Jun 24, 2020
• Publicly Available Date: Jul 19, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Series ISSN: 2161-4407
• ISBN: 978-1-7281-6927-9
• DOI: https://doi.org/10.1109/IJCNN48605.2020.9206779
• Public URL: https://nottingham-repository.worktribe.com/output/4513448
• Publisher URL: https://ieeexplore.ieee.org/document/9206779
• Related Public URLs: https://wcci2020.org/
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.