Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.
Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir Osmangazi University, Eskisehir, Turkey.
BMC Med Imaging. 2021 Aug 13;21(1):124. doi: 10.1186/s12880-021-00656-7.
Panoramic radiography is an imaging method for displaying maxillary and mandibular teeth together with their supporting structures. Panoramic radiography is frequently used in dental imaging due to its relatively low radiation dose, short imaging time, and low burden to the patient. We verified the diagnostic performance of an artificial intelligence (AI) system based on a deep convolutional neural network method to detect and number teeth on panoramic radiographs.
The data set included 2482 anonymized panoramic radiographs from adults from the archive of Eskisehir Osmangazi University, Faculty of Dentistry, Department of Oral and Maxillofacial Radiology. A Faster R-CNN Inception v2 model was used to develop an AI algorithm (CranioCatch, Eskisehir, Turkey) to automatically detect and number teeth on panoramic radiographs. Human observation and AI methods were compared on a test data set consisting of 249 panoramic radiographs. True positive, false positive, and false negative rates were calculated for each quadrant of the jaws. The sensitivity, precision, and F-measure values were estimated using a confusion matrix.
The total numbers of true positive, false positive, and false negative results were 6940, 250, and 320 for all quadrants, respectively. Consequently, the estimated sensitivity, precision, and F-measure were 0.9559, 0.9652, and 0.9606, respectively.
The deep convolutional neural network system was successful in detecting and numbering teeth. Clinicians can use AI systems to detect and number teeth on panoramic radiographs, which may eventually replace evaluation by human observers and support decision making.
全景放射摄影是一种同时显示上颌和下颌牙齿及其支持结构的成像方法。由于其辐射剂量相对较低、成像时间短、患者负担小，全景放射摄影在牙科成像中得到了广泛应用。我们验证了一种基于深度卷积神经网络方法的人工智能（AI）系统检测和编号全景片上牙齿的诊断性能。
该数据集包括来自 Eskisehir Osmangazi 大学牙科学院口腔颌面放射学系档案中的 2482 张匿名成人全景放射照片。使用 Faster R-CNN Inception v2 模型开发了一种人工智能算法（CranioCatch，土耳其 Eskisehir），用于自动检测和编号全景放射片上的牙齿。在由 249 张全景放射片组成的测试数据集上比较了人工观察和 AI 方法。计算了每个颌骨象限的真阳性、假阳性和假阴性率。使用混淆矩阵估计了灵敏度、精度和 F 度量值。
所有象限的真阳性、假阳性和假阴性结果总数分别为 6940、250 和 320。因此，估计的灵敏度、精度和 F 度量值分别为 0.9559、0.9652 和 0.9606。
深度卷积神经网络系统成功地检测和编号了牙齿。临床医生可以使用 AI 系统来检测和编号全景放射片上的牙齿，这最终可能会取代人工观察者的评估并支持决策。
