{"doi":"10.1109/tbme.2025.3584076","title":"In Vivo Demonstration of Deep Learning-Based Photoacoustic Visual Servoing System","abstract":"OBJECTIVE: To develop the first known deep learning-based photoacoustic visual servoing system utilizing point source localization and hybrid position-force control to track catheter tips in three dimensions in real-time. METHODS: We integrated either object detection or instance segmentation-based localization with hybrid position-force control to create our novel system. Cardiac catheter tips were then tracked across distances of 40 mm in a plastisol phantom and 25-64 mm in an in vivo swine in real-time in nine visual servoing trials total. RESULTS: Object detection-based localization identified the cardiac catheter tip in 88.0-91.7% and 66.7-70.4% of phantom and in vivo channel data frames, respectively. Instance segmentation detection rates ranged 86.4-100.0% in vivo. These catheter tips were tracked with errors as low as 0.5 mm in phantom trials and 0.8 mm in the in vivo trials. The mean inference times were $\\geq$ 145.3 ms and $\\geq$ 516.3 ms with object detection-based and instance segmentation-based point source localization, respectively. The hybrid position-force control system enabled contact with the imaging surface during $\\geq$99.43% of each visual servoing trial. CONCLUSION: Our novel deep learning-based photoacoustic visual servoing system was successfully demonstrated. Object detection-based localization operated with inference times that are more suitable for real-time implementations while instance segmentation had lower tracking errors. SIGNIFICANCE: After implementing suggested optimization modifications, our novel system has the potential to track catheter tips, needle tips, and other surgical tool tips in real-time during surgical and interventional procedures.","journal":"IEEE Transactions on Biomedical Engineering","year":2025,"id":531560,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9563,"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":771310,"name":"Aravindan Kolandaivelu","orcid":"0000-0003-3128-765X","position":1,"is_corresponding":false},{"id":1284113,"name":"Nethra Venkatayogi","orcid":"0000-0002-9689-5341","position":2,"is_corresponding":false},{"id":1411790,"name":"Jiaxin Zhang","orcid":"0009-0005-4514-925X","position":3,"is_corresponding":false},{"id":1384919,"name":"Pankaj Warbal","orcid":"0000-0001-7800-6099","position":4,"is_corresponding":false},{"id":1412284,"name":"G.S. Keene","orcid":null,"position":5,"is_corresponding":false},{"id":362395,"name":"Mawia Khairalseed","orcid":"0000-0002-6245-461X","position":6,"is_corresponding":false},{"id":333397,"name":"Jonathan Chrispin","orcid":"0000-0002-7985-3019","position":7,"is_corresponding":false},{"id":275807,"name":"Muyinatu A. Lediju Bell","orcid":"0000-0002-8394-4482","position":8,"is_corresponding":false},{"id":1099486,"name":"Mardava R. Gubbi","orcid":"0000-0001-8222-3571","position":0,"is_corresponding":true}],"reference_count":57,"raw_metadata":null,"created_at":"2026-07-19T02:51:14.579161Z","pmid":"40577292","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":[]}