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dc.contributor.authorWortman, Tyler D.
dc.contributor.authorCarlson, Jay D.
dc.contributor.authorPerez, Edward
dc.contributor.authorSlocum, Alexander H.
dc.date.accessioned2019-02-28T11:05:00Z
dc.date.available2019-02-28T11:05:00Z
dc.date.copyright3/5/2018 12:00:00 AM
dc.date.issued2018
dc.identifier.issn1932-6181
dc.identifier.othermed_012_02_021001.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4252489
dc.description.abstractCurrent techniques for diagnosing skin cancer lack specificity and sensitivity, resulting in unnecessary biopsies and missed diagnoses. Automating tissue palpation and morphology quantification will result in a repeatable, objective process. LesionAir is a low-cost skin cancer diagnostic tool that measures the full-field compliance of tissue by applying a vacuum force and measuring the precise deflection using structured light three-dimensional (3D) reconstruction. The technology was tested in a benchtop setting on phantom skin and in a small clinical study. LesionAir has been shown to measure deflection with a 0.085 mm root-mean-square (RMS) error and measured the stiffness of phantom tissue to within 20% of finite element analysis (FEA) predictions. After biopsy and analysis, a dermatopathologist confirmed the diagnosis of skin cancer in tissue that LesionAir identified as noticeably stiffer and the regions of this stiffer tissue aligned with the bounds of the lesion. A longitudinal, full-scale study is required to determine the clinical efficacy of the device. This technology shows initial promise as a low-cost tool that could rapidly identify and diagnose skin cancer.
dc.publisherThe American Society of Mechanical Engineers (ASME)
dc.titleLesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool
dc.typeJournal Paper
dc.journal.volume12
dc.journal.issue2
dc.journal.titleJournal of Medical Devices
dc.identifier.doi10.1115/1.4039209
dc.journal.fristpage21001
dc.journal.lastpage021001-6
dc.treeJournal of Medical Devices:;2018:;volume( 012 ):;issue: 002
dc.contenttypeFulltext


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