Abstract：
OBJECTIVE: Several factors such as unavailability of specialists, dental phobia, and financial difficulties may lead to a delay between receiving an oral radiology report and consulting a dentist. The primary aim of this study was to distinguish between high-risk and low-risk oral lesions according to the radiologist\'s reports of cone beam computed tomography (CBCT) images. Such a facility may be employed by dentist or his/her assistant to make the patient aware of the severity and the grade of the oral lesion and referral for immediate treatment or other follow-up care.
METHODS: A total number of 1134 CBCT radiography reports owned by Shiraz University of Medical Sciences were collected. The severity level of each sample was specified by three experts, and an annotation was carried out accordingly. After preprocessing the data, a deep learning model, referred to as CNN-LSTM, was developed, which aims to detect the degree of severity of the problem based on analysis of the radiologist\'s report. Unlike traditional models which usually use a simple collection of words, the proposed deep model uses words embedded in dense vector representations, which empowers it to effectively capture semantic similarities.
RESULTS: The results indicated that the proposed model outperformed its counterparts in terms of precision, recall, and F1 criteria. This suggests its potential as a reliable tool for early estimation of the severity of oral lesions.
CONCLUSIONS: This study shows the effectiveness of deep learning in the analysis of textual reports and accurately distinguishing between high-risk and low-risk lesions. Employing the proposed model which can Provide timely warnings about the need for follow-up and prompt treatment can shield the patient from the risks associated with delays.
CONCLUSIONS: Our collaboratively collected and expert-annotated dataset serves as a valuable resource for exploratory research. The results demonstrate the pivotal role of our deep learning model could play in assessing the severity of oral lesions in dental reports.
METHODS: A total number of 1134 CBCT radiography reports owned by Shiraz University of Medical Sciences were collected. The severity level of each sample was specified by three experts, and an annotation was carried out accordingly. After preprocessing the data, a deep learning model, referred to as CNN-LSTM, was developed, which aims to detect the degree of severity of the problem based on analysis of the radiologist\'s report. Unlike traditional models which usually use a simple collection of words, the proposed deep model uses words embedded in dense vector representations, which empowers it to effectively capture semantic similarities.
RESULTS: The results indicated that the proposed model outperformed its counterparts in terms of precision, recall, and F1 criteria. This suggests its potential as a reliable tool for early estimation of the severity of oral lesions.
CONCLUSIONS: This study shows the effectiveness of deep learning in the analysis of textual reports and accurately distinguishing between high-risk and low-risk lesions. Employing the proposed model which can Provide timely warnings about the need for follow-up and prompt treatment can shield the patient from the risks associated with delays.
CONCLUSIONS: Our collaboratively collected and expert-annotated dataset serves as a valuable resource for exploratory research. The results demonstrate the pivotal role of our deep learning model could play in assessing the severity of oral lesions in dental reports.
摘要：
目标：几个因素，如专家不到位，牙科恐惧症,经济困难可能会导致接收口腔放射学报告和咨询牙医之间的延迟。这项研究的主要目的是根据放射科医生的锥形束计算机断层扫描（CBCT）图像报告区分高风险和低风险口腔病变。这种设施可以由牙医或他/她的助手采用,以使患者了解口腔病变的严重程度和等级,并转诊以进行立即治疗或其他后续护理。
方法：收集了设拉子医科大学拥有的1134份CBCT摄影报告。每个样本的严重程度由三位专家指定，并相应地进行了注释。对数据进行预处理后,一个深度学习模型，被称为CNN-LSTM，被开发,其目的是根据对放射科医生报告的分析来检测问题的严重程度。与通常使用简单单词集合的传统模型不同，提出的深度模型使用嵌入在密集向量表示中的单词，这使得它能够有效地捕捉语义相似性。
结果：结果表明，所提出的模型在精度方面优于其对应物，召回，和F1标准。这表明其作为早期估计口腔病变严重程度的可靠工具的潜力。
结论：这项研究显示了深度学习在分析文本报告和准确区分高风险和低风险病变方面的有效性。采用所提出的模型，可以提供及时的警告，需要跟进和及时治疗，可以保护患者免受与延误相关的风险。
结论：我们合作收集和专家注释的数据集作为探索性研究的宝贵资源。结果表明，我们的深度学习模型可以在评估牙科报告中口腔病变的严重程度方面发挥关键作用。
方法：收集了设拉子医科大学拥有的1134份CBCT摄影报告。每个样本的严重程度由三位专家指定，并相应地进行了注释。对数据进行预处理后,一个深度学习模型，被称为CNN-LSTM，被开发,其目的是根据对放射科医生报告的分析来检测问题的严重程度。与通常使用简单单词集合的传统模型不同，提出的深度模型使用嵌入在密集向量表示中的单词，这使得它能够有效地捕捉语义相似性。
结果：结果表明，所提出的模型在精度方面优于其对应物，召回，和F1标准。这表明其作为早期估计口腔病变严重程度的可靠工具的潜力。
结论：这项研究显示了深度学习在分析文本报告和准确区分高风险和低风险病变方面的有效性。采用所提出的模型，可以提供及时的警告，需要跟进和及时治疗，可以保护患者免受与延误相关的风险。
结论：我们合作收集和专家注释的数据集作为探索性研究的宝贵资源。结果表明，我们的深度学习模型可以在评估牙科报告中口腔病变的严重程度方面发挥关键作用。
