autoscoRA: Deep Learning to Automate Sharp/van der Heijde Scoring of Radiographic Damage in Rheumatoid Arthritis
Deimel, T.; Weiser, P.; Urschler, M.; Payer, C.; Mandl, P.; Langs, G.; Aletaha, D.
Show abstract
ObjectiveRegular imaging by conventional radiography to assess for joint damage is a cornerstone in the management of rheumatoid arthritis (RA). Scoring systems to quantify such damage, such as the widely used Sharp/van der Heijde (SvdH) score, are limited by the requirement of time and experienced staff as well as intra- and inter-rater variability. To alleviate these problems, autoscoRA, a fully automated scoring system to assign SvdH scores to radiographs of the hands and feet was developed. MethodsUsing the hitherto largest dataset of adult rheumatoid arthritis patients, autoscoRA, a deep learning-based system, was trained to automatically perform joint extraction and scoring of joint space narrowing and bone erosion. ResultsThe dataset included 769 patients (155 of which in the test set) with 3437 visits (707) and 12144 radiographs (2507). The model reached excellent agreement with a human scorer for joint space narrowing, erosion, and combined scores both on the joint level and for summed total SvdH scores (ICC 0.9). On a subset of data scored by a second human reader, the model outperformed the former in terms of agreement with the first human reader. In addition, autoscoRA demonstrated good agreement with a human reader for detecting longitudinal progression of joint damage across different SvdH score cut-offs defining the presence of progression (average agreement of 70 %). ConclusionAutomated systems like autoscoRA could be used to facilitate scoring of radiographic joint damage in clinical trials, registries and observational studies, and, eventually, routine clinical care.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Incorporating computer vision on smart phone photographs into screening for inflammatory arthritis: results from an Indian patient cohort 94%
- Development of a scoring model for the Sharp/van der Heijde score using convolutional neural networks and its clinical application in predicting radiographic progression using a graph convolutional network 89%
- A new indicator to measure discordance between patient reported outcomes and traditional disease activity holds promise to advance care trajectories improve care in patients with early Rheumatoid Arthritis 89%
Similar papers in this journal
Similar papers in this journal
- The association between mineralised tissue formation and the mechanical local in vivo environment: Time-lapsed quantification of a mouse defect healing model 92%
- Integrating Multidimensional Data Analytics for Precision Diagnosis of Chronic Low Back Pain 92%
- Clinical Observation of Diminished Bone Quality and Quantity through Longitudinal HR-pQCT-derived Remodeling and Mechanoregulation 92%
Similar papers in this journal
- Real-time three-dimensional MRI for the assessment of dynamic carpal instability 92%
- pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage 91%
- Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation 90%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.