
==== Front
J Periodontal Implant Sci
J Periodontal Implant Sci
JPIS
Journal of Periodontal & Implant Science
2093-2278
2093-2286
Korean Academy of Periodontology

39228090
10.5051/jpis.245404edi01
Editorial
Current advances in artificial intelligence in radiographic interpretation and reporting
https://orcid.org/0000-0002-6833-6577
Kim Hak-Sun 1
https://orcid.org/0000-0002-2075-3136
Kim Gyu-Tae 2
1 Department of Oral and Maxillofacial Radiology, Kyung Hee University Dental Hospital, Seoul, Korea.
2 Department of Oral and Maxillofacial Radiology, College of Dentistry, Kyung Hee University, Seoul, Korea.
Correspondence: Gyu-Tae Kim. Department of Oral and Maxillofacial Radiology, College of Dentistry, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea. latinum.omfr@khu.ac.kr, Tel: +82-2-958-9406, Fax: +82-2-965-1256
8 2024
26 8 2024
54 4 207208
08 8 2024
15 8 2024
Copyright © 2024. Korean Academy of Periodontology
2024
Korean Academy of Periodontology
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/).
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pmcRadiographic imaging reflects changes in the density of hard tissues by utilizing the properties of X-rays to capture images. Teeth are situated within the alveolar bone, and conditions such as periodontal disease and periapical lesions can alter the density of the surrounding bone. These changes affect how much radiation is absorbed, depending on the volume of surrounding tissue, particularly bone tissue. Therefore, the number of X-ray photons that reach the radiographic film or sensor varies, leading to differences in radiographic density. The human eye visually detects these images, and the brain processes this information to recognize and interpret the radiographic images.

Dentists interpret conventional radiographs, such as periapical or panoramic radiographs, through a series of processes. For dental implant placement or the diagnosis of cysts, benign tumors, and malignant tumors, computed tomography (CT) or cone beam CT is commonly used [1]. Unlike conventional radiographs, which consist of a single image, CT comprises multiple image layers. These images can be viewed collectively, and specific areas can be reconstructed using reconstruction software [2]. In the dentist’s mind, these areas are reconstructed as a 3-dimensional structure, allowing for the analysis of bone changes or lesion characteristics. Therefore, this process is somewhat more complex than interpreting a conventional radiograph.

Interpreting radiographic images and preparing reports are routine tasks for dentists in their daily practice. However, these activities not only demand considerable time but also require that the reports be meticulously maintained as medical records. Furthermore, if a dentist is not specialized in oral and maxillofacial radiology, the detection of lesions may vary based on their experience. Additionally, the outcomes of these interpretations play a crucial role in planning patient treatment.

Among deep learning models, convolutional neural networks (CNNs) are well-suited for image processing. In deep learning, convolution involves applying a filter to an image, which allows for the extraction of features and facilitates image analysis. Different filters can be used to extract various features from the image. The model learns by using multiple training sets and adjusting the coefficient that determines each node in the neural network. Various studies are currently being conducted to validate the modules learned in this manner by using real images to detect or segment specific objects.

ChatGPT, developed by OpenAI, is a generative artificial intelligence (AI) that has significantly influenced people since its introduction in late 2022. It features advanced natural language generation capabilities. This service has elevated previously inadequate AI technologies, improving users’ experience by enabling the generation of human-like sentences. When a question is posed to ChatGPT, it utilizes a vast dataset to produce responses that closely mimic human writing. OpenAI continues to refine this technology, offering services that generate increasingly sophisticated sentences based on improvements to the initial version of ChatGPT.

AI has traditionally been developed for specific tasks; however, current research is focusing on multimodal AI. This type of AI is anticipated to facilitate complex tasks, such as employing the CNN module to generate interpretations of radiographic images alongside ChatGPT, or alerting dentists to significant changes in these images [3]. Specifically, when a patient’s radiographic image is input into a multimodal AI system, the CNN module can analyze the image to identify dental caries, periodontitis, cystic lesions, benign tumors, or malignant tumors [45]. Subsequently, a generative AI module like ChatGPT can produce a radiological report based on these findings [6]. The use of this technology is expected to significantly reduce the time traditionally required for radiographic interpretation and report generation.

Conflict of Interest: No potential conflict of interest relevant to this article was reported.
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1 Suomalainen A Pakbaznejad Esmaeili E Robinson S Dentomaxillofacial imaging with panoramic views and cone beam CT Insights Imaging 2015 6 1 16
2 Mao WY Lei J Lim LZ Gao Y Tyndall DA Fu K Comparison of radiographical characteristics and diagnostic accuracy of intraosseous jaw lesions on panoramic radiographs and CBCT Dentomaxillofac Radiol 2021 50 20200165 32941743
3 López-Úbeda P Martín-Noguerol T Paulano-Godino F Luna A Comparative evaluation of image-based vs. text-based vs. multimodal AI approaches for automatic breast density assessment in mammograms Comput Methods Programs Biomed 2024 255 108334 39053353
4 Widyaningrum R Candradewi I Aji NRAS Aulianisa R Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis Imaging Sci Dent 2022 52 383 391 36605859
5 Chen H Zhang K Lyu P Li H Zhang L Wu J A deep learning approach to automatic teeth detection and numbering based on object detection in dental periapical films Sci Rep 2019 9 3840 30846758
6 Sacoransky E Kwan BY Soboleski D ChatGPT and assistive AI in structured radiology reporting: a systematic review Curr Probl Diagn Radiol 2024 Forthcoming
