Comparing the accuracy of two machine learning models in detection and classification of periapical lesions using periapical radiographs.

IF 1.6 3区 医学 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Oral Radiology Pub Date : 2024-10-01 Epub Date: 2024-06-11 DOI:10.1007/s11282-024-00759-1
Do Hoang Viet, Le Hoang Son, Do Ngoc Tuyen, Tran Manh Tuan, Nguyen Phu Thang, Vo Truong Nhu Ngoc
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引用次数: 0

Abstract

Background: Previous deep learning-based studies were mainly conducted on detecting periapical lesions; limited information in classification, such as the periapical index (PAI) scoring system, is available. The study aimed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting and classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars.

Methods: Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing. The diagnosis made by experienced dentists was used as the reference diagnosis.

Results: The Faster R-CNN and YOLOv4 models obtained great sensitivity, specificity, accuracy, and precision for detecting periapical lesions. No clear difference in the performance of both models among these three regions was found. The true prediction of Faster R-CNN was 89%, 83.01% and 91.84% for PAI 3, PAI 4 and PAI 5 lesions, respectively. The corresponding values of YOLOv4 were 68.06%, 50.94%, and 65.31%.

Conclusions: Our study demonstrated the potential of YOLOv4 and Faster R-CNN models for detecting and classifying periapical lesions based on the PAI scoring system using periapical radiographs.

Abstract Image

比较两种机器学习模型使用根尖周X光片检测和分类根尖周病变的准确性。
背景:以往基于深度学习的研究主要是针对根尖周病变的检测;在分类方面,如根尖周指数(PAI)评分系统,可用的信息有限。本研究旨在应用 Faster R-CNN 和 YOLOv4 这两种深度学习模型,利用牙弓三个不同区域(前牙、前磨牙和磨牙)的根尖周X光片(PR)中的 PAI 评分检测根尖周病变并对其进行分类:方法:从 2658 个牙根尖周片中选出 2122 个用于训练,268 个用于验证,268 个用于测试。将经验丰富的牙医做出的诊断作为参考诊断:结果:Faster R-CNN 和 YOLOv4 模型检测根尖周病变的灵敏度、特异度、准确度和精确度都很高。两个模型在这三个区域的表现没有明显差异。Faster R-CNN 对 PAI 3、PAI 4 和 PAI 5 病变的真实预测率分别为 89%、83.01% 和 91.84%。YOLOv4 的相应值分别为 68.06%、50.94% 和 65.31%:我们的研究证明了 YOLOv4 和 Faster R-CNN 模型在使用根尖周X光片根据 PAI 评分系统检测和分类根尖周病变方面的潜力。
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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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