Literature DB >> 24075797

Skin self-examinations and visual identification of atypical nevi: comparing individual and crowdsourcing approaches.

Andy J King1, Robert W Gehl, Douglas Grossman, Jakob D Jensen.   

Abstract

PURPOSE: Skin self-examination (SSE) is one method for identifying atypical nevi among members of the general public. Unfortunately, past research has shown that SSE has low sensitivity in detecting atypical nevi. The current study investigates whether crowdsourcing (collective effort) can improve SSE identification accuracy. Collective effort is potentially useful for improving people's visual identification of atypical nevi during SSE because, even when a single person has low reliability at a task, the pattern of the group can overcome the limitations of each individual.
METHODS: Adults (N=500) were recruited from a shopping mall in the Midwest. Participants viewed educational pamphlets about SSE and then completed a mole identification task. For the task, participants were asked to circle mole images that appeared atypical. Forty nevi images were provided; nine of the images were of nevi that were later diagnosed as melanoma.
RESULTS: Consistent with past research, individual effort exhibited modest sensitivity (.58) for identifying atypical nevi in the mole identification task. As predicted, collective effort overcame the limitations of individual effort. Specifically, a 19% collective effort identification threshold exhibited superior sensitivity (.90).
CONCLUSIONS: The results of the current study suggest that limitations of SSE can be countered by collective effort, a finding that supports the pursuit of interventions promoting early melanoma detection that contain crowdsourced visual identification components.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Crowdsourcing; Detection; Melanoma; Prevention; Screening; Skin self-examination; Visual identification

Mesh:

Year:  2013        PMID: 24075797      PMCID: PMC3849386          DOI: 10.1016/j.canep.2013.09.004

Source DB:  PubMed          Journal:  Cancer Epidemiol        ISSN: 1877-7821            Impact factor:   2.984


  21 in total

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2.  Skin self-examination practices in a convenience sample of U.S. university students.

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Authors:  P F Griner; R J Mayewski; A I Mushlin; P Greenland
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6.  Comparative analysis of total body and dermatoscopic photographic monitoring of nevi in similar patient populations at risk for cutaneous melanoma.

Authors:  Agnessa Gadeliya Goodson; Scott R Florell; Mark Hyde; Glen M Bowen; Douglas Grossman
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7.  Association of increased dermatologist density with lower melanoma mortality.

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8.  Melanoma awareness and self-examination practices: results of a United States survey.

Authors:  D R Miller; A C Geller; S W Wyatt; A Halpern; J B Howell; C Cockerell; B A Reilley; B A Bewerse; D Rigel; L Rosenthal; R Amonette; T Sun; T Grossbart; R A Lew; H K Koh
Journal:  J Am Acad Dermatol       Date:  1996-06       Impact factor: 11.527

9.  Screening for skin cancer: U.S. Preventive Services Task Force recommendation statement.

Authors: 
Journal:  Ann Intern Med       Date:  2009-02-03       Impact factor: 25.391

10.  Relationship and partner moderator variables increase self-efficacy of performing skin self-examination.

Authors:  June K Robinson; Jerod Stapleton; Rob Turrisi
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  5 in total

1.  Obstacles to skin self-examination: are frontier adults inclined abstainers?

Authors:  Jakob D Jensen; Manusheela Pokharel; Andy J King; Kevin K John; Yelena P Wu; Douglas Grossman
Journal:  Psychol Health Med       Date:  2019-12-17       Impact factor: 2.423

2.  Combining independent decisions increases diagnostic accuracy of reading lumbosacral radiographs and magnetic resonance imaging.

Authors:  Ralf H J M Kurvers; Annemarie de Zoete; Shelby L Bachman; Paul R Algra; Raymond Ostelo
Journal:  PLoS One       Date:  2018-04-03       Impact factor: 3.240

3.  Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study.

Authors:  Andrea Tacchella; Silvia Romano; Michela Ferraldeschi; Marco Salvetti; Andrea Zaccaria; Andrea Crisanti; Francesca Grassi
Journal:  F1000Res       Date:  2017-12-22

Review 4.  The application of crowdsourcing approaches to cancer research: a systematic review.

Authors:  Young Ji Lee; Janet A Arida; Heidi S Donovan
Journal:  Cancer Med       Date:  2017-09-29       Impact factor: 4.452

5.  Human-machine partnership with artificial intelligence for chest radiograph diagnosis.

Authors:  Bhavik N Patel; Louis Rosenberg; Gregg Willcox; David Baltaxe; Mimi Lyons; Jeremy Irvin; Pranav Rajpurkar; Timothy Amrhein; Rajan Gupta; Safwan Halabi; Curtis Langlotz; Edward Lo; Joseph Mammarappallil; A J Mariano; Geoffrey Riley; Jayne Seekins; Luyao Shen; Evan Zucker; Matthew Lungren
Journal:  NPJ Digit Med       Date:  2019-11-18
  5 in total

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