Deep learning in the precise assessment of primary Sjögren’s syndrome based on ultrasound images

Abstract

Abstract Objectives This study aimed to investigate the value of a deep learning (DL) model based on greyscale ultrasound (US) images for precise assessment and accurate diagnosis of primary Sjögren’s syndrome (pSS). Methods This was a multicentre prospective analysis. All pSS patients were diagnosed according to 2016 ACR/EULAR criteria. A total of 72 pSS patients and 72 sex- and age-matched healthy controls recruited between January 2022 and April 2023, together with 41 patients and 41 healthy controls recruited from June 2023 to February 2024 were used for DL model development and validation, respectively. The DL model was constructed based on the ResNet 50 input with preprocessed all participants’ bilateral submandibular glands (SMGs), parotid glands (PGs), and lacrimal glands (LGs) greyscale US images. Diagnostic performance of the model was compared with two radiologists. The accuracy of prediction and identification performance of DL model were evaluated by calibration curve. Results A total of 864 and 164 greyscale US images of SMGs, PGs, and LGs were collected for development and validation of the model. The area under the ROC (AUCs) of DL model in the SMGs, PGs, and LGs were 0.92, 0.93, 0.91 in the model cohort, and were 0.90, 0.88, 0.87 in the validation cohort, respectively, outperforming both radiologists. Calibration curves showed the prediction probability of the DL model was consistent with the actual probability in both model cohort and validation cohort. Conclusion The DL model based on greyscale US images showed diagnostic potential in the precise assessment of pSS patients in the SMGs, PGs and LGs, outperforming conventional radiologist evaluation.