{"doi":"10.1002/jbio.202500181","title":"Deep Learning for Autonomous Surgical Guidance Using 3‐Dimensional Images From Forward‐Viewing Endoscopic Optical Coherence Tomography","abstract":"A three-dimensional convolutional neural network (3D-CNN) was developed for the analysis of volumetric optical coherence tomography (OCT) images to enhance endoscopic guidance during percutaneous nephrostomy. The model was performance-benchmarked using a 10-fold nested cross-validation procedure and achieved an average test accuracy of 90.57% across a dataset of 10 porcine kidneys. This performance significantly exceeded that of 2D-CNN models that attained average test accuracies ranging from 85.63% to 88.22% using 1, 10, or 100 radial sections extracted from the 3D OCT volumes. The 3D-CNN (~12 million parameters) was benchmarked against three state-of-the-art volumetric architectures: the 3D Vision Transformer (3D-ViT, ~45 million parameters), 3D-DenseNet121 (~12 million parameters), and the Multi-plane and Multi-slice Transformer (M3T, ~29 million parameters). While these models achieved comparable inferencing accuracy, the 3D-CNN exhibited lower inference latency (33 ms) than 3D-ViT (86 ms), 3D-DenseNet121 (58 ms), and M3T (93 ms), representing a critical advantage for real-time surgical guidance applications. These results demonstrate the 3D-CNN's capability as a powerful and practical tool for computer-aided diagnosis in OCT-guided surgical interventions.","journal":"Journal of Biophotonics","year":2025,"id":534270,"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":0.9566,"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":306438,"name":"Adrien Badré","orcid":null,"position":1,"is_corresponding":false},{"id":1417373,"name":"Parker Brandt","orcid":null,"position":2,"is_corresponding":false},{"id":514862,"name":"Chen Wang","orcid":"0000-0003-4645-3227","position":3,"is_corresponding":false},{"id":552363,"name":"Paul P. Calle","orcid":"0009-0000-1849-4481","position":4,"is_corresponding":false},{"id":1416878,"name":"Justin Reynolds","orcid":"0009-0006-7752-3063","position":5,"is_corresponding":false},{"id":1416879,"name":"Qinghao Zhang","orcid":"0009-0005-2906-6315","position":6,"is_corresponding":false},{"id":408890,"name":"Kar‐Ming Fung","orcid":"0000-0002-4623-9469","position":7,"is_corresponding":false},{"id":1416880,"name":"Haoyang Cui","orcid":"0009-0009-5818-0319","position":8,"is_corresponding":false},{"id":678688,"name":"Zhongxin Yu","orcid":"0000-0002-2302-908X","position":9,"is_corresponding":false},{"id":1313145,"name":"Sanjay G. Patel","orcid":null,"position":10,"is_corresponding":false},{"id":1091197,"name":"Yunlong Liu","orcid":"0000-0003-2601-3152","position":11,"is_corresponding":false},{"id":1417374,"name":"Nathan A. Bradley","orcid":null,"position":12,"is_corresponding":false},{"id":514860,"name":"Qinggong Tang","orcid":"0000-0001-9499-5384","position":13,"is_corresponding":false},{"id":49492,"name":"Chongle Pan","orcid":"0000-0003-2860-0334","position":14,"is_corresponding":false},{"id":1374042,"name":"S. Ly","orcid":null,"position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T02:51:47.434742Z","pmid":"40709742","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":[]}