{"doi":"10.1002/oby.22743","title":"High‐Resolution Three‐Dimensional Photonic Scan‐Derived Equations Improve Body Surface Area Prediction in Diverse Populations","abstract":"Objective Equations for predicting body surface area (BSA) produce flawed estimates, especially for individuals with obesity. This study aimed to compare BSA measured by a three‐dimensional photonic scanner (3DPS) with BSA predicted by six commonly cited prediction equations and to develop new prediction equations if warranted. Methods The 3DPS was validated against manual measurements by breadth caliper for body thicknesses measured at three anatomical sites on a mannequin. BSA was derived from 3DPS whole‐body scans of 67 males and 201 females, aged 18 to 83 years, with BMI between 17.8 and 77.8 kg/m 2 and varied races/ethnicities. Results Width and depth measurements by 3DPS and caliper were within 1%, except for hip, with an error of 1.8%. BSA 3DPS differed from BSA predicted by each equation ( P < 0.05), except for males by DuBois and DuBois ( P = 0.60), Tikuisis ( P = 0.27), and Yu ( P = 0.45) and for females by Tikuisis ( P = 0.70). The combined and sex‐specific equations obtained by regressing ln(BSA) on ln(weight in kilograms [W]) and ln(height in meters [H]) are as follows ( R 2 and SEE correspond to ln[BSA]): combined, BSA 3DPS = 0.03216 × W 0.4904 × H 0.3769 , R 2 = 0.982, SEE = 0.021; males, BSA 3DPS = 0.01624 × W 0.4725 × H 0.5231 ; and females, BSA 3DPS = 0.01522 × W 0.4921 × H 0.5231 , R 2 = 0.986, SEE = 0.019. Conclusions New height and weight BSA equations improve BSA estimation in individuals with BMI ≥ 40 and in African Americans, Hispanic Americans, and Asian Americans.","journal":"Obesity","year":2020,"id":87303,"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":11,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9499,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":443937,"name":"Ying Ji","orcid":"0000-0001-5691-1303","position":1,"is_corresponding":false},{"id":443938,"name":"Jack Wang","orcid":"0000-0002-5392-0076","position":2,"is_corresponding":false},{"id":443939,"name":"Wen Yu","orcid":"0000-0002-6923-3158","position":3,"is_corresponding":false},{"id":444765,"name":"John C. Thornton","orcid":null,"position":4,"is_corresponding":false},{"id":444766,"name":"Carla Wolper","orcid":null,"position":5,"is_corresponding":false},{"id":444767,"name":"Richard Weil","orcid":null,"position":6,"is_corresponding":false},{"id":443940,"name":"Earle C. Chambers","orcid":"0000-0002-1323-0556","position":7,"is_corresponding":false},{"id":107066,"name":"Blandine Laferrère","orcid":"0000-0001-8255-3175","position":8,"is_corresponding":false},{"id":400005,"name":"F. Xavier Pi‐Sunyer","orcid":"0000-0003-2207-2008","position":9,"is_corresponding":false},{"id":12087,"name":"Dympna Gallagher","orcid":"0000-0003-1769-9754","position":10,"is_corresponding":false},{"id":443936,"name":"Maxine Ashby‐Thompson","orcid":"0000-0001-9752-2208","position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-18T21:59:31.247492Z","pmid":"32100449","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":[]}