{"doi":"10.1016/j.artd.2024.101398","title":"Radiographic Findings Associated With Mild Hip Dysplasia in 3869 Patients Using a Deep Learning Measurement Tool","abstract":"Background Hip dysplasia is considered one of the leading etiologies contributing to hip degeneration and the eventual need for total hip arthroplasty (THA). We validated a deep learning (DL) algorithm to measure angles relevant to hip dysplasia and applied this algorithm to determine the prevalence of dysplasia in a large population based on incremental radiographic cutoffs. Methods Patients from the Osteoarthritis Initiative with anteroposterior pelvis radiographs and without previous THAs were included. A DL algorithm automated 3 angles associated with hip dysplasia: modified lateral center-edge angle (LCEA), Tönnis angle, and modified Sharp angle. The algorithm was validated against manual measurements, and all angles were measured in a cohort of 3869 patients (61.2 ± 9.2 years, 57.1% female). The percentile distributions and prevalence of dysplastic hips were analyzed using each angle. Results The algorithm had no significant difference ( P > .05) in measurements (paired difference: 0.3°-0.7°) against readers and had excellent agreement for dysplasia classification (kappa = 0.78-0.88). In 140 minutes, 23,214 measurements were automated for 3869 patients. LCEA and Sharp angles were higher and the Tönnis angle was lower ( P < .01) in females. The dysplastic hip prevalence varied from 2.5% to 20% utilizing the following cutoffs: 17.3°-25.5° (LCEA), 9.4°-15.6° (Tönnis), and 41.3°-45.9° (Sharp). Conclusions A DL algorithm was developed to measure and classify hips with mild hip dysplasia. The reported prevalence of dysplasia in a large patient cohort was dependent on both the measurement and threshold, with 12.4% of patients having dysplasia radiographic indices indicative of higher THA risk.","journal":"Arthroplasty Today","year":2024,"id":471240,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6708,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1305795,"name":"Daniel A. Driscoll","orcid":"0000-0003-4449-8990","position":1,"is_corresponding":false},{"id":1070514,"name":"Christopher G. Anderson","orcid":"0000-0001-9399-9404","position":2,"is_corresponding":false},{"id":1305796,"name":"Ruba Sokrab","orcid":"0009-0009-5281-7737","position":3,"is_corresponding":false},{"id":1305797,"name":"Dimitrios A. Flevas","orcid":"0000-0002-9997-4846","position":4,"is_corresponding":false},{"id":739519,"name":"David J. Mayman","orcid":"0000-0002-9213-9231","position":5,"is_corresponding":false},{"id":788679,"name":"Jonathan M. Vigdorchik","orcid":"0000-0003-0308-9648","position":6,"is_corresponding":false},{"id":877110,"name":"Seth A. Jerabek","orcid":null,"position":7,"is_corresponding":false},{"id":634535,"name":"Peter K. Sculco","orcid":"0000-0001-7050-5571","position":8,"is_corresponding":false},{"id":876598,"name":"Seong J. Jang","orcid":"0000-0002-9967-9476","position":0,"is_corresponding":true}],"reference_count":44,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:05:44.736405Z","pmid":"38993836","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":[]}