Abstract：
OBJECTIVE: 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. 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. 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: 864 and 164 greyscale US images of SMGs, PGs, and LGs were collected for development and validation of the model. The AUCs of DL model in the SMG, PG, and LG 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 DL model were consistent with the actual probability in both model cohort and validation cohort.
CONCLUSIONS: DL model based on greyscale US images showed diagnostic potential in the precise assessment of pSS patients in the SMG, PG, and LG, outperforming conventional radiologist evaluation.
METHODS: This was a multicentre prospective analysis. All pSS patients were diagnosed according to 2016 ACR/EULAR criteria. 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. 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: 864 and 164 greyscale US images of SMGs, PGs, and LGs were collected for development and validation of the model. The AUCs of DL model in the SMG, PG, and LG 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 DL model were consistent with the actual probability in both model cohort and validation cohort.
CONCLUSIONS: DL model based on greyscale US images showed diagnostic potential in the precise assessment of pSS patients in the SMG, PG, and LG, outperforming conventional radiologist evaluation.
摘要：
目的：本研究旨在探讨基于灰度超声（US）图像的深度学习（DL）模型对原发性干燥综合征（pSS）的精确评估和准确诊断的价值。
方法：这是一项多中心前瞻性分析。所有pSS患者均按照2016年ACR/EULAR标准进行诊断。2022年1月至2023年4月招募的72名pSS患者和72名性别和年龄匹配的健康对照，以及2023年6月至2024年2月招募的41名患者和41名健康对照用于DL模型开发和验证。分别。DL模型是基于ResNet50构建的，输入预处理了所有参与者的双侧下颌下腺（SMG），腮腺（PG），和泪腺（LGs）灰度美国图像。与两名放射科医生比较了该模型的诊断性能。通过校正曲线评价DL模型的预测精度和辨识性能。
结果：864和164张SMG灰度美国图像，PG,和LGs被收集用于模型的开发和验证。SMG中DL模型的AUC，PG,LG在模型队列中分别为0.92、0.93、0.91，在验证队列中分别为0.90、0.88、0.87，胜过两个放射科医生。校准曲线显示DL模型的预测概率与模型队列和验证队列中的实际概率一致。
结论：基于灰度US图像的DL模型在SMG中精确评估pSS患者方面显示出诊断潜力，PG,LG,优于常规放射科医生评估。
方法：这是一项多中心前瞻性分析。所有pSS患者均按照2016年ACR/EULAR标准进行诊断。2022年1月至2023年4月招募的72名pSS患者和72名性别和年龄匹配的健康对照，以及2023年6月至2024年2月招募的41名患者和41名健康对照用于DL模型开发和验证。分别。DL模型是基于ResNet50构建的，输入预处理了所有参与者的双侧下颌下腺（SMG），腮腺（PG），和泪腺（LGs）灰度美国图像。与两名放射科医生比较了该模型的诊断性能。通过校正曲线评价DL模型的预测精度和辨识性能。
结果：864和164张SMG灰度美国图像，PG,和LGs被收集用于模型的开发和验证。SMG中DL模型的AUC，PG,LG在模型队列中分别为0.92、0.93、0.91，在验证队列中分别为0.90、0.88、0.87，胜过两个放射科医生。校准曲线显示DL模型的预测概率与模型队列和验证队列中的实际概率一致。
结论：基于灰度US图像的DL模型在SMG中精确评估pSS患者方面显示出诊断潜力，PG,LG,优于常规放射科医生评估。
