The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.

IF 2.9 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Dento maxillo facial radiology Pub Date : 2024-12-04 DOI:10.1093/dmfr/twae054

Manal Hamdan, Sergio E Uribe, Lyudmila Tuzova, Dmitry Tuzoff, Zaid Badr, André Mol, Donald A Tyndall

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Abstract

Title: The Influence of a Deep Learning Tool on the Performance of Oral and Maxillofacial Radiologists in the Detection of Apical Radiolucencies.

Objectives: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.

Methods: This study used an annotated dataset and a beta-version of a deep learning model (Denti.AI). The testing subset comprised 68 intraoral periapical radiographs confirmed with cone-beam computed tomography for presence/absence of apical radiolucencies. Four oral radiologists participated in a crossover reading scenario, analyzing the radiographs under two conditions: initially without AI assistance and later with AI predictions. The study evaluated reader performance using AFROC-AUC, sensitivity, specificity, and ROC-AUC per case. It also assessed sensitivity per lesion. Regression analysis investigated how experience, time spent on images, and specialty influenced reader performance.

Results: No statistically significant differences were found in AFROC-AUC, sensitivity, specificity, and ROC-AUC. Regression analysis identified factors influencing diagnostic outcomes: unaided reading significantly prolonged diagnostic time (Beta = 12, 95% CI [11, 13], p < 0.001), while radiologists' professional status was positively associated with diagnostic accuracy (Beta = 0.02, 95% CI [0.00, 0.04], p = 0.015). These findings underscore the impact of AI on diagnostic efficiency and the critical role of radiologists' experience in diagnostic accuracy.

Conclusion: AI did not significantly enhance radiologists' overall diagnostic accuracy. However, it showed potential to enhance efficiency, particularly advantageous for non-expert clinicians. The expertise of radiologists remains vital for accuracy, underscoring the complementary role of AI in dental diagnostics.

Advances in knowledge: AI algorithms may have more notable effects on radiologists' workflow than on the accuracy of detecting apical radiolucencies.

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深度学习工具对口腔颌面放射科医生检测根尖放射线透明的影响。

标题深度学习工具对口腔颌面放射科医生检测根尖放射状突起的影响》：本研究旨在评估深度学习模型对口腔放射科医生在根尖周X光片上检测根尖放射状突起能力的影响。次要目标是进行回归分析，评估工作经验年限、诊断时间和专业的影响：本研究使用了注释数据集和深度学习模型（Denti.AI）的测试版。测试子集包括 68 张经锥形束计算机断层扫描确认存在/不存在根尖放射状突起的口内根尖周X光片。四名口腔放射科医生参与了交叉阅读，在两种条件下分析射线照片：最初没有人工智能辅助，后来有了人工智能预测。研究使用 AFROC-AUC、灵敏度、特异性和每个病例的 ROC-AUC 评估了读片者的表现。研究还评估了每个病灶的灵敏度。回归分析研究了经验、在图像上花费的时间和专业如何影响阅读器的性能：结果：AFROC-AUC、灵敏度、特异性和 ROC-AUC 均无统计学差异。回归分析确定了影响诊断结果的因素：无辅助读片显著延长了诊断时间（Beta = 12，95% CI [11，13]，P 结论：人工智能并未显著提高放射医师的诊断能力：人工智能并未明显提高放射医师的整体诊断准确性。不过，它显示出提高效率的潜力，尤其是对非专业临床医生而言。放射医师的专业知识对准确性仍然至关重要，这突出了人工智能在牙科诊断中的补充作用：人工智能算法对放射科医生工作流程的影响可能比对根尖放射线瑕疵检测准确性的影响更显著。

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

Dento maxillo facial radiology 医学-核医学

CiteScore

5.60

自引率

9.10%

发文量

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