Effect of data size on tooth numbering performance via artificial intelligence using panoramic radiographs.

IF 1.6 3区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Oral Radiology Pub Date : 2023-10-01 Epub Date: 2023-07-05 DOI:10.1007/s11282-023-00689-4
Semih Gülüm, Seçilay Kutal, Kader Cesur Aydin, Gazi Akgün, Aleyna Akdağ
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引用次数: 0

Abstract

Objective: This study aims to investigate the effect of number of data on model performance, for the detection of tooth numbering problem on dental panoramic radiographs, with the help of image processing and deep learning algorithms.

Study design: The data set consists of 3000 anonymous dental panoramic X-rays of adult individuals. Panoramic X-rays were labeled on the basis of 32 classes in line with the FDI tooth numbering system. In order to examine the relationship between the number of data used in image processing algorithms and model performance, four different datasets which include 1000, 1500, 2000 and 2500 panoramic X-rays, were used. The training of the models was carried out with the YOLOv4 algorithm and trained models were tested on a fixed test dataset with 500 data and compared based on F1 score, mAP, sensitivity, precision and recall metrics.

Results: The performance of the model increased as the number of data used during the training of the model increased. Therefore, the last model trained with 2500 data showed the highest success among all the trained models.

Conclusion: Dataset size is important for dental enumeration, and large samples should be considered as more reliable.

Abstract Image

通过使用全景射线照片的人工智能,数据大小对牙齿编号性能的影响。
目的:本研究旨在借助图像处理和深度学习算法,研究数据数量对模型性能的影响,以检测牙齿全景照片中的牙齿编号问题。研究设计:数据集由3000个匿名的成人牙科全景X光片组成。根据符合FDI牙齿编号系统的32个类别对全景X射线进行标记。为了检验图像处理算法中使用的数据数量与模型性能之间的关系,使用了四个不同的数据集,包括1000、1500、2000和2500个全景X射线。使用YOLOv4算法对模型进行训练,并在具有500个数据的固定测试数据集上对训练后的模型进行测试,并基于F1得分、mAP、灵敏度、精确度和召回指标进行比较。结果:模型的性能随着模型训练过程中使用的数据数量的增加而增加。因此,用2500个数据训练的最后一个模型在所有训练的模型中显示出最高的成功率。结论:数据集大小对牙齿计数很重要,大样本应被认为更可靠。
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来源期刊
Oral Radiology
Oral Radiology DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
4.20
自引率
13.60%
发文量
87
审稿时长
>12 weeks
期刊介绍: As the official English-language journal of the Japanese Society for Oral and Maxillofacial Radiology and the Asian Academy of Oral and Maxillofacial Radiology, Oral Radiology is intended to be a forum for international collaboration in head and neck diagnostic imaging and all related fields. Oral Radiology features cutting-edge research papers, review articles, case reports, and technical notes from both the clinical and experimental fields. As membership in the Society is not a prerequisite, contributions are welcome from researchers and clinicians worldwide.
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