DIAGNOSTIC PERFORMANCE OF DEEP LEARNING ARCHITECTURES IN THE DIAGNOSIS OF SJOGRENS SYNDROME- A SYSTEMATIC LITERTAURE REVIEW

Abstract

Background: Sjogren's Syndrome (SS) is a chronic, multifactorial autoimmune disease, characterized by clinical symptoms of dry mouth and dry eyes, due to chronic lymphocytic destruction of salivary and lacrimal glands, respectively. Proper diagnosis is a key towards better outcome. Recently introduced deep learning systems have ability to reflect the complexity of condition, with an aim to bring personalized medicine closer to the patients. Aim: The aim of this systematic review is to compile evidence-based studies pertaining to diagnostic performance of DL system and its algorithms in diagnosis of monitoring of SS. Materials and method: Computerized literature search was performed to select eligible articles from the following databases: PUBMED [MEDLINE], SCOPUS, SCIENCE DIRECT and COCHRANE DATABASE using specific keywords. The search was limited to articles published as full text in English, which were screened by two authors for eligibility. Results: Four studies satisfied our inclusion criteria, that suggested it to have high diagnostic accuracy when compared to inexperienced radiologist, but equivalent to those of experienced radiologists. Two studies found accuracy, sensitivity, and specificity of DL systems to be 89.5%, 90.0%, and 89.0%, for USG salivary gland images respectively whereas for CT images, the accuracy, sensitivity, and specificity was observed to be 96.0%,100% and 92.0%, respectively, and the diagnostic performance was higher from an inexperienced radiologist (p < 0.0001). Conclusion: DL systems have the potential to provide useful diagnostic support to inexperienced radiologists in assessment of images for the presence of characteristic features of SS. They could assist the radiologists in automated segmentation of salivary glands, and enables feature extraction in a reduced time with reduced risk of cognitive errors.