LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool
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 m...
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| 格式: | 文件 |
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ASME International
2019
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| 在线阅读: | http://hdl.handle.net/1721.1/121052 https://orcid.org/0000-0002-6803-3787 |
| _version_ | 1826206161518460928 |
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| author | Carlson, Jay D. Perez, Edward Wortman, Tyler D. Slocum Jr., Alexander H |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Carlson, Jay D. Perez, Edward Wortman, Tyler D. Slocum Jr., Alexander H |
| author_sort | Carlson, Jay D. |
| collection | MIT |
| description | 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 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 0.085mm 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. |
| first_indexed | 2024-09-23T13:25:03Z |
| format | Article |
| id | mit-1721.1/121052 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:25:03Z |
| publishDate | 2019 |
| publisher | ASME International |
| record_format | dspace |
| spelling | mit-1721.1/1210522022-10-01T15:08:37Z LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool Carlson, Jay D. Perez, Edward Wortman, Tyler D. Slocum Jr., Alexander H Massachusetts Institute of Technology. Department of Mechanical Engineering Wortman, Tyler D. Slocum Jr., Alexander H 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 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 0.085mm 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. National Science Foundation (U.S.) (Grant 1122374) 2019-03-22T14:34:57Z 2019-03-22T14:34:57Z 2018-03 2018-01 2019-01-02T19:18:26Z Article http://purl.org/eprint/type/JournalArticle 1932-6181 http://hdl.handle.net/1721.1/121052 Wortman, Tyler D., Jay D. Carlson, Edward Perez, and Alexander H. Slocum. “LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool.” Journal of Medical Devices 12, no. 2 (March 5, 2018): 021001. © 2018 by ASME https://orcid.org/0000-0002-6803-3787 http://dx.doi.org/10.1115/1.4039209 Journal of Medical Devices Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf ASME International ASME |
| spellingShingle | Carlson, Jay D. Perez, Edward Wortman, Tyler D. Slocum Jr., Alexander H LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool |
| title | LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool |
| title_full | LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool |
| title_fullStr | LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool |
| title_full_unstemmed | LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool |
| title_short | LesionAir: An Automated, Low-Cost Vision-Based Skin Cancer Diagnostic Tool |
| title_sort | lesionair an automated low cost vision based skin cancer diagnostic tool |
| url | http://hdl.handle.net/1721.1/121052 https://orcid.org/0000-0002-6803-3787 |
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