Artificial intelligence and machine learning in Rheumatology imaging: Are we ready?

Jun 1, 2020 | News

EULAR Artificial intelligence and machine learning in Rheumatology imaging: Are we ready?

Please find here all nine abstracts of the session “Artificial intelligence and machine learning in Rheumatology imaging: Are we ready?” at EULAR E-Congress 2020 from June 3rd:

 

AUTOSCORA: DEEP LEARNING TO AUTOMATE SCORING OF RADIOGRAPHIC PROGRESSION IN RHEUMATOID ARTHRITIS, MD Thomas Deimel, Austria
Machine learning based Berlin scoring of magnetic resonance images of the spine in patients with ankylosing spondylitis from the MEASURE 1 study, Dr. Amir Jamaludin, United Kingdom
Feasibility study on an automated quantitative system for ultrasound joint inflammation assessment in rheumatoid arthritis using deep learning, Dr. York Kiat Tan, Singapore
Predictive value of bone texture features extracted by deep learning models for the detection of osteoarthritis: data from the Osteoarthritis Initiative, Dr. Chihung Lin, Taiwan, Republic of China
Quantitative Computed Tomography predicts 10-year mortality in interstitial lung disease related to Systemic Sclerosis Dr. Alarico Ariani, Italy
Automatic scoring of arthritis disease activity on ultrasound images from Rheumatoid Arthritis patients with Cascaded Convolutional Neural Networks Mr. Anders Christensen, Denmark
Inter-rater reliability and validity of medial and lateral femoral bone marrow lesions in patients with osteoarthritis undergoing tibial osteotomy using the Knee Inflammation MRI Scoring System (KIMRISS) Ms. Jenna Schulz, Canada
Imaging-detected features of hand osteoarthritis associate with symptoms and radiographic change over time: A systematic review and meta-analysis of observational studies Mr. Abasiama Obotiba, United Kingdom
Detection of subclinical skin manifestation in patients with psoriasis and psoriatic arthritis by fluorescence optical imaging Ms. Angelique Schmidt, Germany