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research-article

LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool

[+] Author and Article Information
Tyler D Wortman

ASME Student Member, Massachusetts Institute of Technology, Department of Mechanical Engineering, 77 Massachusetts Ave., Cambridge, MA 02139
wortman@mit.edu

Jay Carlson

University of Nebraska-Lincoln, Department of Electrical Engineering, 209N SEC, 844 N. 16th St., Lincoln, NE 68588
jcarlson@unl.edu

Edward Perez

Dermatology Laser Center, 1605 Redwood Rd, San Marcos, TX 78666
epperez_md@yahoo.com

Alexander Slocum

ASME Fellow, Massachusetts Institute of Technology, Department of Mechanical Engineering, 77 Massachusetts Ave., Cambridge, MA 02139
slocum@mit.edu

1Corresponding author.

ASME doi:10.1115/1.4039209 History: Received March 15, 2017; Revised January 19, 2018

Abstract

Current 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 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 RMS error and measured the stiffness of phantom tissue to within 20% of 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. LesionAir is capable of replicating the palpation of a lesion through an automated, repeatable process that increases the spatial resolution and sensitivity over a physician's finger. A longitudinal study is required to further validate the technology. This technology shows initial promise as a low-cost tool to rapidly identify and diagnose skin cancer.

Copyright (c) 2018 by ASME
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