人工智能与临床医生在检测下颌第三磨牙与下牙槽神经接触方面的性能比较

Amir Yari, Paniz Fasih, Atieh Nouralishahi, Meysam Mohammadikhah, Dorsa Nikeghbal
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摘要

本研究旨在评估 ResNet-50 深度学习算法在对全景图像进行分类以确定下颌第三磨牙与下牙槽神经 (IAN) 接触情况方面的性能,并将其与新毕业的牙科医生和口腔颌面外科专家的性能进行比较。ResNet-50 模型、三位新毕业的牙科医生和三位口腔颌面外科专家分别对图像进行了 "接触 "和 "非接触 "分类。颌面部放射科医生使用锥形束 CT 设定金标准。在检索到的 548 幅图像中,15% 被分配作为测试数据集,共计 82 幅图像。深度学习模型的性能指标最高,准确率为 87.80%,精确率为 78.57%,召回率为 84.61%,特异性为 89.28%,骰子系数为 81.48%。相反,新手牙医的性能指标最低(准确率:74.39% ± 2.99%;精确度:57.75% ± 4.33%;召回率:74.36% ± 1.81%;特异性:87.80% ± 2.99%;骰子系数:81.48%):74.36% ± 1.81%,特异性:74.4% ± 4.46%,骰子系数:64.87% ± 2.69%)。专家的准确率为 84.96% ± 1.52%,精确率为 72.65% ± 2.78%,召回率为 84.61% ± 3.14%,特异性为 85.12% ± 2.23%,骰子系数为 78.11% ± 1.99%。
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Performance of artificial intelligence versus clinicians on the detection of contact between mandibular third molar and inferior alveolar nerve
This study aimed to assess the performance of the ResNet‐50 deep learning algorithm in classifying panoramic images to determine contact between mandibular third molars and inferior alveolar nerve (IAN), comparing its performance with newly graduated dentists and oral and maxillofacial surgery specialists.Panoramic radiographs of mandibular third molars were retrieved from the Radiology Department, School of Dentistry, Kashan University of Medical Sciences. The images were independently classified as “contact” or “none‐contact” by the ResNet‐50 model, three newly graduated dentists, and three oral and maxillofacial surgery specialists. A maxillofacial radiologist sets the gold standard using cone beam‐CT. The accuracy, precision, recall, specificity, and dice coefficient were calculated for each group, with inter‐rater reliability assessed using Cohen's kappa value.Of the 548 images retrieved, 15% were allocated as the testing dataset, amounting to 82 images. The deep learning model showed the highest performance metrics, with an accuracy of 87.80%, precision of 78.57%, recall of 84.61%, specificity of 89.28%, and dice coefficient of 81.48%. Conversely, novice dentists had the lowest performance metrics (accuracy: 74.39% ± 2.99%, precision: 57.75% ± 4.33%, recall: 74.36% ± 1.81%, specificity: 74.4% ± 4.46%, and dice coefficient: 64.87% ± 2.69%). Specialists demonstrated accuracy of 84.96% ± 1.52%, precision of 72.65% ± 2.78%, recall of 84.61% ± 3.14%, specificity of 85.12% ± 2.23%, and dice coefficient of 78.11% ± 1.99%.Deep learning algorithms can achieve comparable outcomes with oral and maxillofacial specialists and may outperform novice clinicians in diagnosing contact between third molars and IAN.
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