{"doi":"10.1111/bju.70001","title":"A computer vision model for automated kidney stone segmentation and evaluation of its performance vs surgeons","abstract":"OBJECTIVES: To develop a computer vision model that segments stones to improve visualisation during ureteroscopy (URS) and to compare model performance to that of experts. MATERIALS AND METHODS: We collected 136 videos of URS for intrarenal kidney stone treatment. Frames were extracted at 3 frames per second (FPS) and manually annotated. The video dataset was split into training (75%), validation (5%) and testing (20%) subsets. Model performance was evaluated for stone localisation, laser ablation, and final evaluation of remaining fragments based on area under the receiver-operating curve, binary cross-entropy loss and Dice similarity coefficient (DSC). Model performance was compared to the manual annotations of five board-certified urologists through pairwise comparison of frame-by-frame segmentation accuracy. RESULTS: The final dataset consisted of 21 718 frames from 38 fibreoptic and 98 digital videos. Overall, the model showed excellent performance: DSC 0.97 (interquartile range [IQR] 0.91, 0.99) and could segment at 30 FPS. Performance was similar for both fibreoptic (0.97 [IQR 0.91, 0.99]) and digital scopes (0.97 [IQR 0.92, 0.99]). Additionally, the model demonstrated good performance during stone localisation (0.98 [IQR 0.93, 0.99]) and stone laser ablation (0.96 [IQR 0.89, 0.97]), with slightly worse performance during evaluation of residual fragments (0.91 [IQR 0.50, 0.97]). Model performance was comparable to the five expert surgeons overall. In a head-to-head comparison, the model significantly outperformed three of the five experts and performed similarly to the other two. CONCLUSION: The computer vision model demonstrates good performance for task-specific stone segmentation evaluation during URS. The segmentation performance of the model was similar to the segmentation performance of expert surgeons, demonstrating the feasibility of its real-time intra-operative utilisation.","journal":"British Journal of Urology","year":2025,"id":534820,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6767,"is_data_producer":false,"deposit_databanks":null,"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":1417947,"name":"Ekamjit S. Deol","orcid":"0000-0001-9840-9581","position":1,"is_corresponding":false},{"id":361426,"name":"Tatsuki Koyama","orcid":"0000-0002-8908-9165","position":2,"is_corresponding":false},{"id":249468,"name":"İpek Oğuz","orcid":"0000-0002-1403-2420","position":3,"is_corresponding":false},{"id":373243,"name":"Nicholas Kavoussi","orcid":"0000-0001-7202-0412","position":4,"is_corresponding":false},{"id":1159088,"name":"Daiwei Lu","orcid":"0000-0001-6693-4445","position":0,"is_corresponding":true}],"reference_count":14,"raw_metadata":null,"created_at":"2026-07-19T02:51:52.019261Z","pmid":"40994261","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":[]}