Combining Automated Lesion Risk and Change Assessment Improves Melanoma Detection: A Retrospective Accuracy Study.
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| Title: | Combining Automated Lesion Risk and Change Assessment Improves Melanoma Detection: A Retrospective Accuracy Study. |
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| Authors: | Rutjes C; Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia. Electronic address: c.rutjes@uq.edu.au., Mothershaw A; Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia; Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia., D'Alessandro BM; Canfield Scientific, Parsippany, New Jersey, USA., Primiero CA; Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia., McInerney-Leo A; Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia., Soyer HP; Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia; Dermatology Department, Princess Alexandra Hospital, Brisbane, Australia., Janda M; Centre for Health Services Research, Faculty of Medicine, The University of Queensland, Brisbane, Australia., Betz-Stablein B; Frazer Institute, Dermatology Research Centre, The University of Queensland, Brisbane, Australia. |
| Source: | The Journal of investigative dermatology [J Invest Dermatol] 2025 Mar; Vol. 145 (3), pp. 703-706.e1. Date of Electronic Publication: 2024 Sep 07. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Elsevier Country of Publication: United States NLM ID: 0426720 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1523-1747 (Electronic) Linking ISSN: 0022202X NLM ISO Abbreviation: J Invest Dermatol Subsets: MEDLINE; In Process |
| Database: | MEDLINE Ultimate |
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