Utility of the radiological report function of an artificial intelligence system in interpreting CBCT images: a technical report.

IF 2.9 2区 医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE Dento maxillo facial radiology Pub Date : 2025-01-20 DOI:10.1093/dmfr/twaf004
Luciano Tonetto Feltraco, Carolina Rossetto, Andy Wai Kan Yeung, Mariana Quirino Silveira Soares, Anne Caroline Oenning
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Abstract

Objectives: The aim of this technical report was to assess whether the "Radiological Report" tool within the Artificial Intelligence (AI) software Diagnocat can achieve a satisfactory level of performance comparable to that of experienced dentomaxillofacial radiologists in interpreting cone-beam CT scans.

Methods: Ten cone-beam CT scans were carefully selected and analyzed using the AI tool, and they were also evaluated by two dentomaxillofacial radiologists. Observations related to tooth numeration, alterations in dental crowns, roots, and periodontal tissues were documented and subsequently compared to the AI findings. Kappa statistics, along with their corresponding 95% confidence intervals, were calculated to ascertain the degree of agreement.

Results: The agreement between the AI tool and the radiologists ranged from substantial to nearly perfect for identifying teeth, determining the number of roots and canals, assessing crown conditions, and detecting endodontic treatments. However, for tasks such as classifying bone loss, identifying posts, evaluating the quality of fillings, and appraising the situation of periodontal spaces, the agreement was deemed slight.

Conclusions: The "radiological report" tool of the Diagnocat demonstrates satisfactory performance in reliably identifying teeth, roots, canals, assessing crown conditions, and detecting endodontic treatment. However, further investigations are needed to evaluate the tool's effectiveness in diagnosing posts, assessing the condition and quality of fillings, and determining the status of periodontal spaces.

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人工智能系统在解释CBCT图像中的放射报告功能的应用:技术报告。
目的:本技术报告的目的是评估人工智能(AI)软件诊断中的“放射报告”工具在解释锥束CT扫描时是否能达到与经验丰富的牙颌面放射科医生相当的令人满意的性能水平。方法:对10张锥形束CT扫描图进行人工智能分析,并由2名牙颌面放射科医师对其进行评价。记录了与牙齿数量、牙冠、牙根和牙周组织的变化有关的观察结果,并随后与人工智能结果进行了比较。计算Kappa统计量及其相应的95%置信区间,以确定一致性程度。结果:人工智能工具与放射科医生在识别牙齿、确定根管数量、评估牙冠状况和检测牙髓治疗方面的一致性从基本到近乎完美。然而,对于诸如骨丢失分类、定位、评估补牙质量和评估牙周间隙状况等任务,这种一致性被认为是轻微的。结论:诊断仪的“放射学报告”工具在可靠地识别牙齿、根、根管、评估冠状况和检测根管治疗方面表现出令人满意的性能。然而,需要进一步的研究来评估该工具在诊断牙柱、评估充填物的状况和质量以及确定牙周间隙状态方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
5.60
自引率
9.10%
发文量
65
审稿时长
4-8 weeks
期刊介绍: Dentomaxillofacial Radiology (DMFR) is the journal of the International Association of Dentomaxillofacial Radiology (IADMFR) and covers the closely related fields of oral radiology and head and neck imaging. Established in 1972, DMFR is a key resource keeping dentists, radiologists and clinicians and scientists with an interest in Head and Neck imaging abreast of important research and developments in oral and maxillofacial radiology. The DMFR editorial board features a panel of international experts including Editor-in-Chief Professor Ralf Schulze. Our editorial board provide their expertise and guidance in shaping the content and direction of the journal. Quick Facts: - 2015 Impact Factor - 1.919 - Receipt to first decision - average of 3 weeks - Acceptance to online publication - average of 3 weeks - Open access option - ISSN: 0250-832X - eISSN: 1476-542X
期刊最新文献
Enhancing panoramic dental imaging with AI-driven arch surface fitting: Achieving improved clarity and accuracy through an optimal reconstruction zone. Investigation of the effect of thyroid collar, radiation safety glasses and lead apron on radiation dose in cone beam computed tomography. Methods for assessing peri-implant marginal bone levels on digital periapical radiographs: a meta-research. Utility of the radiological report function of an artificial intelligence system in interpreting CBCT images: a technical report. Assessment of the Quality of Root Canal Fillings-An Ex-Vivo Comparison of CBCT Scans, Conventional Intraoral Sensors, and a Novel Photon-Counting Sensor.
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