Abstract
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.
Highlights
Obstructive sialoadenitis is mainly due to sialoliths, most of which arise in the submandibular gland (SMG) or Wharton’s duct [2]
The sensitivity of the two radiologists in the obstructive sialoadenitis group, Sjögren’s syndrome (SjS) group, and control group was 64.0%, 72.0%, and 86.0%, respectively, and the total accuracy was 74.0%
We previously evaluated the diagnostic performance of a deep learning system for the detection of SjS in USG images and found that the accuracy, sensitivity, and specificity of the deep learning system for the SMGs were 84.0%, 81.0%, and 87.0%, respectively [17]
Summary
Among the various types of salivary gland lesions, inflammation is most common and has various underlying pathogeneses [1], including salivary flow obstruction, viral or bacterial infection, and autoimmune diseases such as Sjögren’s syndrome (SjS) Some of these diseases cause characteristic changes in the salivary gland parenchyma and are clearly visualized by ultrasonography (USG) [2,3,4,5,6,7,8,9]. USG findings are characterized by multiple small anechoic areas that frequently contain small hyperechoic spots within the inhomogeneous salivary parenchyma [7,8,9] This appearance can be observed in both the SMG and parotid gland. Achieving a correct diagnosis is often difficult, especially for inexperienced observers
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