64 research outputs found

    Human surface anatomy terminology for dermatology: a Delphi consensus from the International Skin Imaging Collaboration

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    BackgroundThere is no internationally vetted set of anatomic terms to describe human surface anatomy.ObjectiveTo establish expert consensus on a standardized set of terms that describe clinically relevant human surface anatomy.MethodsWe conducted a Delphi consensus on surface anatomy terminology between July 2017 and July 2019. The initial survey included 385 anatomic terms, organized in seven levels of hierarchy. If agreement exceeded the 75% established threshold, the term was considered - accepted- and included in the final list. Terms added by the participants were passed on to the next round of consensus. Terms with <75% agreement were included in subsequent surveys along with alternative terms proposed by participants until agreement was reached on all terms.ResultsThe Delphi included 21 participants. We found consensus (- „75% agreement) on 361/385 (93.8%) terms and eliminated one term in the first round. Of 49 new terms suggested by participants, 45 were added via consensus. To adjust for a recently published International Classification of Diseases- Surface Topography list of terms, a third survey including 111 discrepant terms was sent to participants. Finally, a total of 513 terms reached agreement via the Delphi method.ConclusionsWe have established a set of 513 clinically relevant terms for denoting human surface anatomy, towards the use of standardized terminology in dermatologic documentation.Linked Commentary: R.J.G. Chalmers. J Eur Acad Dermatol Venereol 2020; 34: 2456- 2457. https://doi.org/10.1111/jdv.16978.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163915/1/jdv16855_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163915/2/jdv16855-sup-0001-FigS1-S3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163915/3/jdv16855.pd

    Human-computer collaboration for skin cancer recognition

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    The rapid increase in telemedicine coupled with recent advances in diagnostic artificial intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI-based support into new paradigms of care. Here we build on recent achievements in the accuracy of image-based AI for skin cancer diagnosis to address the effects of varied representations of AI-based support across different levels of clinical expertise and multiple clinical workflows. We find that good quality AI-based support of clinical decision-making improves diagnostic accuracy over that of either AI or physicians alone, and that the least experienced clinicians gain the most from AI-based support. We further find that AI-based multiclass probabilities outperformed content-based image retrieval (CBIR) representations of AI in the mobile technology environment, and AI-based support had utility in simulations of second opinions and of telemedicine triage. In addition to demonstrating the potential benefits associated with good quality AI in the hands of non-expert clinicians, we find that faulty AI can mislead the entire spectrum of clinicians, including experts. Lastly, we show that insights derived from AI class-activation maps can inform improvements in human diagnosis. Together, our approach and findings offer a framework for future studies across the spectrum of image-based diagnostics to improve human-computer collaboration in clinical practice

    Ultraviolet-induced fluorescent dermoscopy for biopsy site identification prior to dermatologic surgery: A retrospective study.

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    Supplementary material for JAAD-D-23-0028

    Supplementary Table for Lentigo maligna melanoma mapping using reflectance confocal microscopy correlates with staged excision: A prospective study

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    Supplementary Table 1. Sensitivity analysis of results. Agreement statistics for the overall sample and for the 4 lesion quadrants and lesion center. In this analysis, a random sample of ~25% of the lesion quadrants were shifted to counterclockwise to the quadrant prior and the agreement statistics were recalculated

    Contemporary management of actinic keratosis

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