{"doi":"10.14814/phy2.70243","title":"<scp>ML</scp>‐<scp>UrineQuant</scp>: A machine learning program for identifying and quantifying mouse urine on absorbent paper","abstract":"The void spot assay has gained popularity as a way of assessing functional bladder voiding parameters in mice, but analyzing the size and distribution of urine spot patterns on filter paper with software remains problematic due to inter-laboratory differences in image contrast and resolution quality and non-void artifacts. We have developed a machine learning algorithm based on Region-based Convolutional Neural Networks (Mask-RCNN) that was trained in object recognition to detect and quantitate urine spots across a broad range of sizes-ML-UrineQuant. The model proved extremely accurate at identifying urine spots in a wide variety of illumination and contrast settings. The overwhelming advantage it offers over current algorithms will be to allow individual labs to fine-tune the model on their specific images regardless of the image characteristics. This should be a valuable tool for anyone performing lower urinary tract research using mouse models.","journal":"Physiological Reports","year":2025,"id":540679,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9553,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1271809,"name":"Bryce MacIver","orcid":null,"position":1,"is_corresponding":false},{"id":76209,"name":"Gary A. Churchill","orcid":"0000-0001-9190-9284","position":2,"is_corresponding":false},{"id":1429097,"name":"Mariana G. DeOliveira","orcid":null,"position":3,"is_corresponding":false},{"id":651819,"name":"Mark L. Zeidel","orcid":"0000-0002-5190-9770","position":4,"is_corresponding":false},{"id":235550,"name":"Marcelo Cicconet","orcid":"0000-0003-2649-1509","position":5,"is_corresponding":false},{"id":1271317,"name":"Warren G. Hill","orcid":"0000-0002-2772-7264","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-19T02:52:42.901627Z","pmid":"40102661","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":[]}