{"doi":"10.1364/boe.523831","title":"Pixel-level classification of pigmented skin cancer lesions using multispectral autofluorescence lifetime dermoscopy imaging","abstract":"There is no clinical tool available to primary care physicians or dermatologists that could provide objective identification of suspicious skin cancer lesions. Multispectral autofluorescence lifetime imaging (maFLIM) dermoscopy enables label-free biochemical and metabolic imaging of skin lesions. This study investigated the use of pixel-level maFLIM dermoscopy features for objective discrimination of malignant from visually similar benign pigmented skin lesions. Clinical maFLIM dermoscopy images were acquired from 60 pigmented skin lesions before undergoing a biopsy examination. Random forest and deep neural networks classification models were explored, as they do not require explicit feature selection. Feature pools with either spectral intensity or bi-exponential maFLIM features, and a combined feature pool, were independently evaluated with each classification model. A rigorous cross-validation strategy tailored for small-size datasets was adopted to estimate classification performance. Time-resolved bi-exponential autofluorescence features were found to be critical for accurate detection of malignant pigmented skin lesions. The deep neural network model produced the best lesion-level classification, with sensitivity and specificity of 76.84%±12.49% and 78.29%±5.50%, respectively, while the random forest classifier produced sensitivity and specificity of 74.73%±14.66% and 76.83%±9.58%, respectively. Results from this study indicate that machine-learning driven maFLIM dermoscopy has the potential to assist doctors with identifying patients in real need of biopsy examination, thus facilitating early detection while reducing the rate of unnecessary biopsies.","journal":"Biomedical Optics Express","year":2024,"id":448866,"datarank":0.30690458855676267,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.03814066817255442,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.03814066817255442,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":5,"citers_with_citation_signal":3,"citers_with_endowment":3,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9596,"is_data_producer":true,"deposit_databanks":{"figshare":["10.6084/m9.figshare.26139802"]},"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":942072,"name":"Renan Arnon Romano","orcid":"0000-0003-4408-8393","position":1,"is_corresponding":false},{"id":942630,"name":"Ramon Gabriel Teixeira Rosa","orcid":null,"position":2,"is_corresponding":false},{"id":942073,"name":"Ana Gabriela Sálvio","orcid":"0000-0003-1676-952X","position":3,"is_corresponding":false},{"id":360898,"name":"Vladislav V. Yakovlev","orcid":"0000-0002-4557-1013","position":4,"is_corresponding":false},{"id":942074,"name":"Cristina Kurachi","orcid":"0000-0001-7175-5337","position":5,"is_corresponding":false},{"id":942631,"name":"Jason M. Hirshburg","orcid":null,"position":6,"is_corresponding":false},{"id":407139,"name":"Javier A. Jo","orcid":"0000-0001-6368-2246","position":7,"is_corresponding":false},{"id":942071,"name":"Priyanka Vasanthakumari","orcid":"0000-0003-0822-5936","position":0,"is_corresponding":true}],"reference_count":70,"raw_metadata":null,"created_at":"2026-07-19T02:02:12.215092Z","pmid":"39346997","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":[]}