{"doi":"10.1101/2024.12.01.625907","title":"ARBEL: A Machine Learning Tool with Light-Based Image Analysis for Automatic Classification of 3D Pain Behaviors","abstract":"A detailed analysis of pain-related behaviors in rodents is essential for exploring both the mechanisms of pain and evaluating analgesic efficacy. With the advancement of pose-estimation tools, automatic single-camera video animal behavior pipelines are growing and integrating rapidly into quantitative behavioral research. However, current existing algorithms do not consider an animal's body-part contact intensity with- and distance from- the surface, a critical nuance for measuring certain pain-related responses like paw withdrawals ('flinching') with high accuracy and interpretability. Quantifying these bouts demands a high degree of attention to body part movement and currently relies on laborious and subjective human visual assessment. Here, we introduce a supervised machine learning algorithm, ARBEL: Automated Recognition of Behavior Enhanced with Light, that utilizes a combination of pose estimation together with a novel light-based analysis of body part pressure and distance from the surface, to automatically score pain-related behaviors in freely moving mice in three dimensions. We show the utility and accuracy of this algorithm for capturing a range of pain-related behavioral bouts using a bottom-up animal behavior platform, and its application for robust drug-screening. It allows for rapid objective pain behavior scoring over extended periods with high precision. This open-source algorithm is adaptable for detecting diverse behaviors across species and experimental platforms.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":486461,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.959,"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":1330258,"name":"Biyao Zhang","orcid":"0000-0001-5466-8637","position":1,"is_corresponding":false},{"id":859300,"name":"Bruna Lenfers Turnes","orcid":"0000-0002-3989-8394","position":2,"is_corresponding":false},{"id":1330259,"name":"Maryam Arab","orcid":"0000-0002-9575-0511","position":3,"is_corresponding":false},{"id":621817,"name":"David A. Yarmolinsky","orcid":"0009-0007-7750-9154","position":4,"is_corresponding":false},{"id":669252,"name":"Zihe Zhang","orcid":"0000-0002-7768-8101","position":5,"is_corresponding":false},{"id":498348,"name":"Lee Barrett","orcid":null,"position":6,"is_corresponding":false},{"id":262675,"name":"Clifford J. Woolf","orcid":"0000-0002-6636-3897","position":7,"is_corresponding":false},{"id":1330257,"name":"Omer Barkai","orcid":"0000-0001-9912-9338","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T02:08:01.404471Z","pmid":"39677681","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":[]}