{"doi":"10.1002/ohn.257","title":"Objective Pharyngeal Phenotyping in Obstructive Sleep Apnea With High‐Resolution Manometry","abstract":"OBJECTIVE: Drug-induced sleep endoscopy (DISE) is a commonly used diagnostic tool for surgical procedural selection in obstructive sleep apnea (OSA), but it is expensive, subjective, and requires sedation. Here we present an initial investigation of high-resolution pharyngeal manometry (HRM) for upper airway phenotyping in OSA, developing a software system that reliably predicts pharyngeal sites of collapse based solely on manometric recordings. STUDY DESIGN: Prospective cross-sectional study. SETTING: An academic sleep medicine and surgery practice. METHODS: Forty participants underwent simultaneous HRM and DISE. A machine learning algorithm was constructed to estimate pharyngeal level-specific severity of collapse, as determined by an expert DISE reviewer. The primary outcome metrics for each level were model accuracy and F1-score, which balances model precision against recall. RESULTS: During model training, the average F1-score across all categories was 0.86, with an average weighted accuracy of 0.91. Using a holdout test set of 9 participants, a K-nearest neighbor model trained on 31 participants attained an average F1-score of 0.96 and an average accuracy of 0.97. The F1-score for prediction of complete concentric palatal collapse was 0.86. CONCLUSION: Our findings suggest that HRM may enable objective and dynamic mapping of the pharynx, opening new pathways toward reliable and reproducible assessment of this complex anatomy in sleep.","journal":"Otolaryngology","year":2023,"id":365143,"datarank":0.4516223814334538,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.15973585907515678,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.15973585907515678,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":6,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9626,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT03198416"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":346121,"name":"William C. Scott","orcid":null,"position":1,"is_corresponding":false},{"id":445508,"name":"Cheng Ye","orcid":"0009-0003-1113-6990","position":2,"is_corresponding":false},{"id":737395,"name":"Daniel Fabbri","orcid":"0000-0003-0530-2510","position":3,"is_corresponding":false},{"id":532254,"name":"David T. Kent","orcid":"0000-0001-8183-5269","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-19T01:14:46.760245Z","pmid":"36939475","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":[]}