{"doi":"10.1109/jtehm.2020.2970694","title":"Development of Low-Cost Point-of-Care Technologies for Cervical Cancer Prevention Based on a Single-Board Computer","abstract":"Cervical cancer disproportionally affects women in low- and middle-income countries, in part due to the difficulty of implementing existing cervical cancer screening and diagnostic technologies in low-resource settings. Single-board computers offer a low-cost alternative to provide computational support for automated point-of-care technologies. Here we demonstrate two new devices for cervical cancer prevention that use a single-board computer: 1) a low-cost imaging system for real-time detection of cervical precancer and 2) a low-cost reader for real-time interpretation of lateral flow-based molecular tests to detect cervical cancer biomarkers. Using a Raspberry Pi computer to provide real-time image collection and processing, we developed: 1) a low-cost, portable high-resolution microendoscope system (PiHRME); and 2) a low-cost automatic lateral flow test reader (PiReader). The PiHRME acquired high-resolution ([Formula: see text]) images of the cervix at half the cost of existing high-resolution microendoscope systems; image analysis algorithms based on convolutional neural networks were implemented to provide real-time image interpretation. The PiReader acquired and analyzed images of a point-of-care human papillomavirus (HPV) serology test with the same contrast and accuracy as a standard flatbed high-resolution scanner coupled to a laptop computer, for less than one-fifth of the cost. Raspberry Pi single-board computers provide a low-cost means to implement point-of-care tools with automatic image analysis. This work demonstrates the promise of single-board computers to develop and translate low-cost, point-of-care technologies for use in low-resource settings.","journal":"IEEE Journal of Translational Engineering in Health and Medicine","year":2020,"id":95133,"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":36,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9502,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"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":474171,"name":"Eduardo Carranza","orcid":null,"position":1,"is_corresponding":false},{"id":294094,"name":"Jackson B. Coole","orcid":"0000-0002-8821-4139","position":2,"is_corresponding":false},{"id":473135,"name":"Brady Hunt","orcid":"0000-0002-1143-7668","position":3,"is_corresponding":false},{"id":424984,"name":"Chelsey Smith","orcid":"0000-0002-9341-0580","position":4,"is_corresponding":false},{"id":473136,"name":"Pelham Keahey","orcid":"0000-0002-9504-9696","position":5,"is_corresponding":false},{"id":424988,"name":"Maurício Maza","orcid":"0000-0003-2027-4431","position":6,"is_corresponding":false},{"id":268553,"name":"Kathleen M. Schmeler","orcid":"0000-0002-9670-4189","position":7,"is_corresponding":false},{"id":294099,"name":"Rebecca Richards‐Kortum","orcid":"0000-0003-2347-9467","position":8,"is_corresponding":false},{"id":424982,"name":"Sonia Parra","orcid":"0000-0002-9285-0058","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-18T22:33:30.364261Z","pmid":"32190430","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":[]}