Implementation of the 7-point checklist for melanoma detection on smart handheld devices
Open accessAbstract
In this paper we implement the 7-point checklist, a set of dermoscopic criteria widely used by clinicians for melanoma detection, on smart handheld devices, such as the Apple iPhone and iPad. The application developed is using sophisticated image processing and pattern recognition algorithms, yet it is light enough to run on a handheld device with limited memory and computational speed. When combined with a commercially available handheld dermoscope that provides proper lesion illumination, this application provides a truly self-contained handheld system for melanoma detection. Such a device can be used in a clinical setting for routine skin screening, or as an assistive diagnostic device in underserved areas and in developing countries with limited healthcare infrastructure.
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Automated smartphone apps have low sensitivity, ranging from 7% to 87% with a median of 69%.
“Regarding automated smartphone apps, the major concerns are the lack of assessment in clinical practice conditions, the lack of assessment in primary care populations, and their low sensitivity, ranging from 7% to 87% (median 69%).”
From Use of Smartphones for Early Detection of Melanoma: Systematic Review (2018) · cites this paper as [48]
Methods (Dataset; page not provided) describes selected, low-difficulty library images rather than a primary-care population, and Results (Classification Results; page not provided) reports cross-validation of one iPhone application rather than assessment in clinical practice. The paper does not report a 7%–87% sensitivity range or a 69% median across automated smartphone apps, so the numerical claim is unsupported. (LLM, conf=0.82)
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References 18 · Cited by 72
Research works printed but not linked: 1
- [3]Hicks J Ramanathan N Kim D Monibi M Selsky J And wellness: an open mobile system for activity and experience sampling Wireless health 2010, WH ’10 34 43 New York, NY, USA 2010 ACM
- Authors
- Wadhawan, T., Situ, N., Rui, H., Lancaster, K., Yuan, X., & Zouridakis, G.
- Affiliations
- University of Houston (US)
- Venue
- 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
- Publisher
- IEEE
- Published
- 2011-08
- Volume
- 2011
- Pages
- 3180-3183
- ISSN
- 2375-7477 (ISSN-L)
- PMC
- PMC3261517
- OpenAlex
- W1973570002
- Cited by
- 72
- References
- 18
- Citations by year
- 2026: 3 · 2023: 4 · 2022: 6 · 2021: 5 · 2020: 4 · 2019: 6 · 2018: 7 · 2017: 4 · 2016: 7 · 2015: 6 · 2014: 10 · 2013: 8 · 2012: 2
- FWCI
- 71.94
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R21AR057921)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- melanoma detection, dermoscopy, handheld devices, mobile application, skin lesion analysis, image processing, pattern recognition, skin cancer screening, low-resource healthcare
- MeSH terms
- Algorithms, Humans, Melanoma, Microcomputers, Cell Phone
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