{"doi":"10.1016/j.ocarto.2025.100657","title":"Comparison between coronal FLASH and sagittal double echo steady state MRI in detecting longitudinal cartilage thickness change by fully automated segmentation – Data from the FNIH biomarker cohort","abstract":"Objective: Artificial intelligence (AI-) based automated cartilage analysis demonstrated similar sensitivity to change and only slighty inferior differentiation between radiographic progressors and non-progressors compared with manual segmentation. However, this finding was based on DESS MRI from the Osteoarthritis Initiative (OAI), whereas the vast majority of multicenter clinical trials rely on T1-weighted gradient echo (e.g. FLASH). Here we directly compare fully automated analysis of coronal FLASH vs. sagittal DESS, and vs. manually segmented DESS, in a sample with both FLASH and DESS MRI acquisitions. Design: Convolutional neural network (CNN) algorithms were trained on 86 radiographically osteoarthritic knees with manual expert segmentation of the medial and lateral femorotibial cartilages (coronal FLASH and sagittal DESS). Post-processing involved automated registration of CNN-based subchondral bone segmentation to reference areas. The models were applied to baseline and two-year follow-up MRIs of radiographic progressor and non-progressor knees in the Foundation of the NIH Biomarker sample of the OAI. Results: Of the 322 FNIH knees with both FLASH and DESS; 157 were radiographic progressors. Sensitivity to medial femorotibial cartilage thickness change (standardized response mean) in the progressor subcohort was -0.81 for FLASH (automated analysis), -0.74 for automatically, and -0.72 for manually segmented DESS. Differentiation from non-progressors (Cohen's D) was -0.82. -0.70, and -0.87, respectively. Conclusions: Fully automated, AI-based cartilage segmentation with advanced post-processing reveals that coronal FLASH is at least as discriminative between radiographic progressor vs. non-progressor knees as sagittal DESS MRI. Yet, performance of fully automated segmentation is slightly inferior to manual analysis with expert quality control. Trial id: Clinicaltrials.gov identification: NCT00080171.","journal":"Osteoarthritis and Cartilage Open","year":2025,"id":553792,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9482,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT00080171"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":301558,"name":"Akshay Chaudhari","orcid":"0000-0002-3667-6796","position":1,"is_corresponding":false},{"id":6438,"name":"David J. Hunter","orcid":"0000-0003-3197-752X","position":2,"is_corresponding":false},{"id":325184,"name":"W. Wirth","orcid":"0000-0002-2297-8283","position":3,"is_corresponding":false},{"id":325186,"name":"F. Eckstein","orcid":"0000-0002-2014-8278","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:54:45.872391Z","pmid":"40822965","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}