Detection of Differences in Longitudinal Cartilage Thickness Loss Using a Deep‐Learning Automated Segmentation Algorithm: Data From the Foundation for the National Institutes of Health Biomarkers Study of the Osteoarthritis Initiative

Abstract

Objective. To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (OA), we evaluated the sensitivity to change in progressor knees from the Foundation for the National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and we determined whether differences in progression rates between predefined cohorts can be detected by the fully automated approach.Methods. The OA Initiative Biomarker Consortium was a nested case-control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (≥0.7 mm) and a persistent increase in Western Ontario and McMaster Universities Osteoarthritis Index pain scores (≥9 on a 0-100 scale) after 2 years from baseline (n = 194), whereas non-progressor knees did not have either of both (n = 200). Deep-learning automated algorithms trained on radiographic OA knees or knees of a healthy reference cohort (HRC) were used to automatically segment medial femorotibial compartment (MFTC) and lateral femorotibial cartilage on baseline and 2-year follow-up magnetic resonance imaging. Findings were compared with previously published manual expert segmentation.Results. The mean AE SD MFTC cartilage loss in the progressor cohort was -181 AE 245 μm by manual segmentation (standardized response mean [SRM] -0.74), -144 AE 200 μm by the radiographic OA-based model (SRM -0.72), and -69 AE 231 μm by HRC-based model segmentation (SRM -0.30). Cohen's d for rates of progression between progressor versus the non-progressor cohort was -0.84 (P < 0.001) for manual, -0.68 (P < 0.001) for the automated radiographic OA model, and -0.14 (P = 0.18) for automated HRC model segmentation. Conclusion.A fully automated deep-learning segmentation approach not only displays similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as did manual expert segmentation but also effectively differentiates longitudinal rates of loss of cartilage thickness between cohorts with different progression profiles.ClinicalTrials.gov identifier: NCT00080171. Scientific and financial support for the Foundation for the NIH (FNIH) Osteoarthritis (OA) Biomarkers Consortium and for this study has been made possible through grants as well as direct and in-kind contributions from Abb-Vie, Amgen, Inc.,

Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry

“…14,[16][17][18] However, there has been limited work demonstrating segmentation algorithm effectiveness across both healthy cohorts and cohorts with knee pathology present. 15 The OAI-DESStrained model was trained using scans of subjects with OA with KL grades 1-3, while the qDESS-trained model was trained using scans of diverse subjects that underwent routine diagnostic knee MRI. Given the heterogeneity of subjects encountered during model training, no consistent trends were evident based on the prevalence of OA and subject population for either model.…”

“…14 Prior knee DL segmentation models have primarily demonstrated high efficacy on double-echo steady-state (DESS) MRI scans obtained from the Osteoarthritis Initiative (OAI). [14][15][16] The OAI dataset contains a subset of publicly available expert-annotated segmentations, which has led to increasing use of OAI-trained DL segmentation models in musculoskeletal research. 14,17,18 However, systematic evaluation of model performance remains a challenge as different groups have used different subsets of OAI data, which makes it difficult to accurately compare methods.…”

“…Prior knee DL segmentation models have primarily demonstrated high efficacy on double‐echo steady‐state (DESS) MRI scans obtained from the Osteoarthritis Initiative (OAI) 14–16 . The OAI dataset contains a subset of publicly available expert‐annotated segmentations, which has led to increasing use of OAI‐trained DL segmentation models in musculoskeletal research 14,17,18 .…”

Osteoarthritis year in review 2021: imaging

“…AI approaches were applied most frequently in MRI and second most commonly in radiography. The applications of AI included for example tissue segmentation [49][50][51][52][53][54][55][56] , lesion detection 57,58 , and diagnosis [59][60][61][62][63][64][65][66][67][68] and prediction of OA [69][70][71][72] . Many of the AI approaches were based on deep learning which is a subfield of AI.…”

MRSaiFE: An AI-Based Approach Towards the Real-Time Prediction of Specific Absorption Rate

“…MR thermometry as an alternative approach suffers from a coarse temperature resolution [22]. The advent of artificial intelligence (AI) and machine learning in MRI [23][24][25][26][27][28][29][30][31][32][33][34][35][36] has opened up new avenues for the prediction of various imaging characteristics, among them the recent prediction of local SAR in prostate imaging [37][38][39][40], as well as the prediction of temperature rise in the brain for 33 different tissue types [41].…”