背景:诊断和治疗扁桃体炎对耳鼻喉科医师来说并不构成重大挑战;然而,在冠状病毒大流行期间,它会增加医疗保健专业人员的感染风险。近年来,随着人工智能(AI)的发展,其在医学成像中的应用也蓬勃发展。本研究旨在识别最佳卷积神经网络(CNN)算法,用于扁桃体炎的准确诊断和早期精确治疗。
方法:采用具有用于自我训练的伪标签的半监督学习来训练我们的CNN,使用包括UNet在内的算法,PSPNet,FPN。包括来自485名参与者的485张咽镜图像,包括健康个体(133例),普通感冒患者(295例),和扁桃体炎患者(57例)。从485张图像中提取颜色和纹理特征进行分析。
结果:UNet在自动准确分割口咽解剖结构方面优于PSPNet和FPN,平均Dice系数为97.74%,像素精度为98.12%,使其适合于增强扁桃体炎的诊断。正常的扁桃体通常具有更均匀和光滑的纹理,并具有粉红色,类似于周围的粘膜组织,而扁桃体炎,特别是需要抗生素的类型,显示白色或淡黄色的脓点或斑块,相比之下,表现出更多的颗粒状或块状纹理,表明炎症和组织结构的变化。经过485例的培训,我们的算法与UNet实现了93.75%的准确率,97.1%,在区分三个扁桃体组方面为91.67%,展示了优异的结果。
结论:我们的研究强调了使用UNet进行口咽结构的全自动语义分割的潜力,这有助于后续的特征提取,机器学习,并能够对扁桃体炎进行准确的AI诊断。这项创新显示了提高扁桃体炎评估的准确性和速度的希望。
BACKGROUND: Diagnosing and treating tonsillitis pose no significant challenge for otolaryngologists; however, it can increase the infection risk for healthcare professionals amidst the coronavirus pandemic. In recent years, with the advancement of artificial intelligence (AI), its application in medical imaging has also thrived. This research is to identify the optimal convolutional neural network (CNN) algorithm for accurate diagnosis of tonsillitis and early precision treatment.
METHODS: Semi-supervised learning with pseudo-labels used for self-training was adopted to train our CNN, with the algorithm including UNet, PSPNet, and FPN. A total of 485 pharyngoscopic images from 485 participants were included, comprising healthy individuals (133 cases), patients with the common cold (295 cases), and patients with tonsillitis (57 cases). Both color and texture features from 485 images are extracted for analysis.
RESULTS: UNet outperformed PSPNet and FPN in accurately segmenting oropharyngeal anatomy automatically, with average Dice coefficient of 97.74% and a pixel accuracy of 98.12%, making it suitable for enhancing the diagnosis of tonsillitis. The normal tonsils generally have more uniform and smooth textures and have pinkish color, similar to the surrounding mucosal tissues, while tonsillitis, particularly the antibiotic-required type, shows white or yellowish pus-filled spots or patches, and shows more granular or lumpy texture in contrast, indicating inflammation and changes in tissue structure. After training with 485 cases, our algorithm with UNet achieved accuracy rates of 93.75%, 97.1%, and 91.67% in differentiating the three tonsil groups, demonstrating excellent results.
CONCLUSIONS: Our research highlights the potential of using UNet for fully automated semantic segmentation of oropharyngeal structures, which aids in subsequent feature extraction, machine learning, and enables accurate AI diagnosis of tonsillitis. This innovation shows promise for enhancing both the accuracy and speed of tonsillitis assessments.