背景鹰综合征的特征是茎突异常伸长。这种情况通常是通过人工评估正骨图(OPG)图像来识别的,这是耗时的,并且可能具有观察者之间的可变性。近年来,人工智能(AI)在放射学中的应用越来越受到重视和兴趣。人工智能在茎突伸长检测中的应用探索较少,倡导在同一领域进行研究。目的和目的该研究旨在评估人工智能在检测数字OPG中的茎突伸长率方面的准确性,并将三种不同AI算法的性能与放射科医师的手动射线照相评估的性能进行比较。材料与方法共筛选400个数字OPG,和茎突长度的线性测量(ImageJ软件(美国国立卫生研究院,马里兰,USA))是由单个校准的观察者进行的茎突伸长的鉴定,最终包括一个经处理的图像数据集,其中包括169个伸长的茎突的图像和200个正常的茎突的图像。使用机器学习方法使用三种不同的AI模型来检测茎突伸长:逻辑回归,神经网络,和Orange软件中的朴素贝叶斯算法(卢布尔雅那大学,斯洛文尼亚)。使用准确性进行性能评估,灵敏度,特异性,精度,召回,F1得分,和AUC-ROC(接受者工作特征下面积)曲线。结果Logistic回归和神经网络算法描述了100%的最高准确率,没有假阳性或假阴性。确保所有指标的得分为1.000。然而,朴素贝叶斯模型表现出相当大的准确性,对49张假阳性图像和59张假阴性图像进行分类,AUC(曲线下面积)得分为78%。然而,它比随机猜测表现得更好。结论Logistic回归和神经网络算法可以准确检测茎突伸长,与人工射线照相评估相似。朴素贝叶斯算法没有执行准确的分类,但比随机猜测更好。AI在自动检测数字OPG中的茎突过程伸长方面具有广阔的应用前景。
Background Eagle\'s syndrome is characterized by the anomalous elongation of the styloid process. This condition is usually identified through the manual evaluation of orthopantomogram (OPG) images, which is time-consuming and can have interobserver variability. The application of Artificial intelligence (AI) in radiology is gaining importance and interest in recent years. The application of AI in detecting styloid process elongation is less explored, advocating for research in the same arena. Aim and objectives The study aimed to evaluate the accuracy of artificial intelligence in detecting styloid process elongation in digital OPGs and to compare the performance of the three different AI algorithms with that of the manual radiographic evaluation by the radiologist. Materials and methods A total of 400 digital OPGs were screened, and linear measurements of the styloid process length (ImageJ software (National Institute of Health, Maryland, USA)) were done for the identification of styloid process elongation by a single calibrated observer to finally include a processed image dataset including 169 images of the elongated styloid process and 200 images of the normal styloid process. A machine learning approach was used to detect the styloid process elongation using the three different AI models: logistic regression, neural network, and Naïve Bayes algorithms in Orange software (University of Ljubljana, Slovenia). Performance evaluation was done using the accuracy, sensitivity, specificity, precision, recall, F1 score, and AUC-ROC (area under the receiver operating characteristic) curve. Results Logistic regression and neural network algorithms depicted the highest accuracy of 100% with no false positives or false negatives, securing a score of 1.000 for all the metrics. However, the Naïve Bayes model demonstrated a fairly considerable accuracy, classifying 49 false positive images and 59 false negative images with an AUC (area under the curve) score of 78 %. Nevertheless, it performed better than random guessing. Conclusion Logistic regression and neural network algorithms accurately detected styloid process elongation similar to that of manual radiographic evaluation. The Naïve Bayes algorithm did not perform an accurate classification yet performed better than random guessing. AI holds a promising scope for its application in automatically detecting styloid process elongation in digital OPGs.