Preliminary Study on the Diagnostic Performance of a Deep Learning System for Submandibular Gland Inflammation Using Ultrasonography Images.
Preliminary Study on the Diagnostic Performance of a Deep Learning System for Submandibular Gland Inflammation Using Ultrasonography Images.
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DOI：
10.3390/jcm10194508
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发表时间：
2021-09-29
影响因子：
3.9
通讯作者：
Ariji E
中科院分区：
文献类型：
--
作者：
Kise Y;Kuwada C;Ariji Y;Naitoh M;Ariji E
本研究旨在评估深度学习系统在三种不同情况下使用下颌下腺 (SMG) 超声检查 (USG) 图像的诊断性能：阻塞性唾液腺炎、干燥综合征 (SjS) 和正常腺体。该研究包括 50 幅确诊为阻塞性唾液腺炎的 USG 图像、50 幅确诊为 SjS 的 USG 图像和 50 幅无 SMG 异常的 USG 图像。训练组包含 40 个阻塞性唾液腺炎图像、40 个 SjS 图像和 40 个对照图像，测试组包含 10 个阻塞性唾液腺炎图像、10 个 SjS 图像和 10 个用于深度学习分析的对照图像。深度学习系统的性能是在两位经验丰富的放射科医生之间进行计算和比较的。深度学习系统在阻塞性唾液腺炎组、SjS组和对照组中的敏感性分别为55.0%、83.0%和73.0%，总准确率为70.3%。两位放射科医生的敏感度分别为 64.0%、72.0% 和 86.0%，总准确度为 74.0%。这项研究表明，在两个病例组和一组健康受试者的 SMG 炎症 USG 图像中，深度学习系统比经验丰富的放射科医生诊断 SjS 更敏感。
This study was performed to evaluate the diagnostic performance of deep learning systems using ultrasonography (USG) images of the submandibular glands (SMGs) in three different conditions: obstructive sialoadenitis, Sjögren’s syndrome (SjS), and normal glands. Fifty USG images with a confirmed diagnosis of obstructive sialoadenitis, 50 USG images with a confirmed diagnosis of SjS, and 50 USG images with no SMG abnormalities were included in the study. The training group comprised 40 obstructive sialoadenitis images, 40 SjS images, and 40 control images, and the test group comprised 10 obstructive sialoadenitis images, 10 SjS images, and 10 control images for deep learning analysis. The performance of the deep learning system was calculated and compared between two experienced radiologists. The sensitivity of the deep learning system in the obstructive sialoadenitis group, SjS group, and control group was 55.0%, 83.0%, and 73.0%, respectively, and the total accuracy was 70.3%. The sensitivity of the two radiologists was 64.0%, 72.0%, and 86.0%, respectively, and the total accuracy was 74.0%. This study revealed that the deep learning system was more sensitive than experienced radiologists in diagnosing SjS in USG images of two case groups and a group of healthy subjects in inflammation of SMGs.
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