深度学习在颞骨 CT 诊断耳蜗畸形中的应用。
Utility of deep learning for the diagnosis of cochlear malformation on temporal bone CT.
机构信息
Department of Otorhinolaryngology-Head and Neck Surgery, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, People's Republic of China.
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, People's Republic of China.
出版信息
Jpn J Radiol. 2024 Mar;42(3):261-267. doi: 10.1007/s11604-023-01494-z. Epub 2023 Oct 9.
OBJECTIVE
Diagnosis of cochlear malformation on temporal bone CT images is often difficult. Our aim was to assess the utility of deep learning analysis in diagnosing cochlear malformation on temporal bone CT images.
METHODS
A total of 654 images from 165 temporal bone CTs were divided into the training set (n = 534) and the testing set (n = 120). A target region that includes the area of the cochlear was extracted to create a diagnostic model. 4 models were used: ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The testing data set was subsequently analyzed using these models and by 4 doctors.
RESULTS
The areas under the curve was 0.91, 0.94, 0.93, and 0.73 in ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The accuracy of ResNet10, ResNet50, and SE-ResNet50 is better than chief physician.
CONCLUSIONS
Deep learning technique implied a promising prospect for clinical application of artificial intelligence in the diagnosis of cochlear malformation based on CT images.
目的
颞骨 CT 图像上的耳蜗畸形诊断常常较为困难。本研究旨在评估深度学习分析在颞骨 CT 图像上诊断耳蜗畸形的效用。
方法
共纳入 165 例颞骨 CT 的 654 幅图像，分为训练集（n=534）和测试集（n=120）。提取包括耳蜗区域的目标区域，以创建诊断模型。共使用 4 种模型：ResNet10、ResNet50、SE-ResNet50 和 DenseNet121。使用这些模型和 4 位医生对测试数据集进行分析。
结果
ResNet10、ResNet50、SE-ResNet50 和 DenseNet121 的曲线下面积分别为 0.91、0.94、0.93 和 0.73。ResNet10、ResNet50 和 SE-ResNet50 的准确率优于副主任医师。
结论
深度学习技术为基于 CT 图像的人工智能在耳蜗畸形诊断中的临床应用提供了有前景的思路。
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