{"doi":"10.17615/mwd4-9359","title":"An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation","abstract":"Accurate assessment of fetal gestational age (GA) is critical to the clinical management of pregnancy. Industrialized countries rely upon obstetric ultrasound (US) to make this estimate. In low- and middle- income countries, automatic measurement of fetal structures using a low-cost obstetric US may assist in establishing GA without the need for skilled sonographers. In this report, we leverage a large database of obstetric US images acquired, stored and annotated by expert sonographers to train algorithms to classify, segment, and measure several fetal structures: biparietal diameter (BPD), head circumference (HC), crown rump length (CRL), abdominal circumference (AC), and femur length (FL). We present a technique for generating raw images suitable for model training by removing caliper and text annotation and describe a fully automated pipeline for image classification, segmentation, and structure measurement to estimate the GA. The resulting framework achieves an average accuracy of 93% in classification tasks, a mean Intersection over Union accuracy of 0.91 during segmentation tasks, and a mean measurement error of 1.89 centimeters, finally leading to a 1.4 day mean average error in the predicted GA compared to expert sonographer GA estimate using the Hadlock equation.","journal":"UNC Libraries","year":2024,"id":501006,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9579,"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":401971,"name":"Bellington Vwalika","orcid":"0000-0001-5548-8187","position":1,"is_corresponding":false},{"id":325196,"name":"Xiaoning Jiang","orcid":"0000-0003-3605-3801","position":2,"is_corresponding":false},{"id":248973,"name":"David M. Stamilio","orcid":"0000-0001-5873-1090","position":3,"is_corresponding":false},{"id":248972,"name":"Jeffrey S. A. Stringer","orcid":"0000-0002-9590-7216","position":4,"is_corresponding":false},{"id":1350409,"name":"Alan J. Rosenbaum","orcid":"0000-0002-5832-4863","position":5,"is_corresponding":false},{"id":266785,"name":"Juan Carlos Prieto","orcid":"0000-0002-3778-9098","position":6,"is_corresponding":false},{"id":822724,"name":"Hina Shah","orcid":"0000-0001-7050-5205","position":7,"is_corresponding":false},{"id":280009,"name":"Patrick Musonda","orcid":null,"position":8,"is_corresponding":false},{"id":401970,"name":"Joan T. Price","orcid":"0000-0003-4178-0405","position":9,"is_corresponding":false},{"id":401975,"name":"Elizabeth M. Stringer","orcid":"0000-0003-4210-381X","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:10:12.068207Z","pmid":null,"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":[]}