{"doi":"10.1117/1.jbo.29.4.046001","title":"Deep-learning-based image super-resolution of an end-expandable optical fiber probe for application in esophageal cancer diagnostics","abstract":"Significance: ) significantly reduces the ability to survey large areas efficiently in EC screening. Aim: To improve the efficiency of endoscopic screening, we propose a novel concept of end-expandable endoscopic optical fiber probe for larger field of visualization and for the first time evaluate a deep-learning-based image super-resolution (DL-SR) method to overcome the issue of limited sampling capability. Approach: To demonstrate feasibility of the end-expandable optical fiber probe, DL-SR was applied on simulated low-resolution microendoscopic images to generate super-resolved (SR) ones. Varying the degradation model of image data acquisition, we identified the optimal parameters for optical fiber probe prototyping. The proposed screening method was validated with a human pathology reading study. Results: For various degradation parameters considered, the DL-SR method demonstrated different levels of improvement of traditional measures of image quality. The endoscopists' interpretations of the SR images were comparable to those performed on the high-resolution ones. Conclusions: This work suggests avenues for development of DL-SR-enabled sparse image reconstruction to improve high-yield EC screening and similar clinical applications.","journal":"Journal of Biomedical Optics","year":2024,"id":448496,"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":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9565,"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":287874,"name":"Mimi C. Tan","orcid":"0000-0001-6113-4780","position":1,"is_corresponding":false},{"id":1193460,"name":"Mansour Nabil","orcid":null,"position":2,"is_corresponding":false},{"id":775403,"name":"Richa Shukla","orcid":null,"position":3,"is_corresponding":false},{"id":1193197,"name":"Shaleen Vasavada","orcid":"0009-0001-6456-0862","position":4,"is_corresponding":false},{"id":307109,"name":"Sharmila Anandasabapathy","orcid":"0000-0001-5876-831X","position":5,"is_corresponding":false},{"id":298431,"name":"Mark A. Anastasio","orcid":"0000-0002-3192-4172","position":6,"is_corresponding":false},{"id":1193462,"name":"Е В Петрова","orcid":null,"position":7,"is_corresponding":false},{"id":655609,"name":"Xiaohui Zhang","orcid":"0000-0002-7990-1594","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T02:02:12.215092Z","pmid":"38585417","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":[]}