Dataset Information

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

ABSTRACT:

Background

There is no internationally vetted set of anatomic terms to describe human surface anatomy.

Objective

To establish expert consensus on a standardized set of terms that describe clinically relevant human surface anatomy.

Methods

We 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.

Results

The 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.

Conclusions

We have established a set of 513 clinically relevant terms for denoting human surface anatomy, towards the use of standardized terminology in dermatologic documentation.

SUBMITTER: Navarrete-Dechent C

PROVIDER: S-EPMC8236240 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

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

Navarrete-Dechent C C Liopyris K K Molenda M A MA Braun R R Curiel-Lewandrowski C C Dusza S W SW Guitera P P Hofmann-Wellenhof R R Kittler H H Lallas A A Malvehy J J Marchetti M A MA Oliviero M M Pellacani G G Puig S S Soyer H P HP Tejasvi T T Thomas L L Tschandl P P Scope A A Marghoob A A AA Halpern A C AC

Journal of the European Academy of Dermatology and Venereology : JEADV 20200902 11

<h4>Background</h4>There is no internationally vetted set of anatomic terms to describe human surface anatomy.<h4>Objective</h4>To establish expert consensus on a standardized set of terms that describe clinically relevant human surface anatomy.<h4>Methods</h4>We 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, th ...[more]

PMID: 32770737

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Project description:BackgroundPrevious studies of artificial intelligence (AI) applied to dermatology have shown AI to have higher diagnostic classification accuracy than expert dermatologists; however, these studies did not adequately assess clinically realistic scenarios, such as how AI systems behave when presented with images of disease categories that are not included in the training dataset or images drawn from statistical distributions with significant shifts from training distributions. We aimed to simulate these real-world scenarios and evaluate the effects of image source institution, diagnoses outside of the training set, and other image artifacts on classification accuracy, with the goal of informing clinicians and regulatory agencies about safety and real-world accuracy.MethodsWe designed a large dermoscopic image classification challenge to quantify the performance of machine learning algorithms for the task of skin cancer classification from dermoscopic images, and how this performance is affected by shifts in statistical distributions of data, disease categories not represented in training datasets, and imaging or lesion artifacts. Factors that might be beneficial to performance, such as clinical metadata and external training data collected by challenge participants, were also evaluated. 25 331 training images collected from two datasets (in Vienna [HAM10000] and Barcelona [BCN20000]) between Jan 1, 2000, and Dec 31, 2018, across eight skin diseases, were provided to challenge participants to design appropriate algorithms. The trained algorithms were then tested for balanced accuracy against the HAM10000 and BCN20000 test datasets and data from countries not included in the training dataset (Turkey, New Zealand, Sweden, and Argentina). Test datasets contained images of all diagnostic categories available in training plus other diagnoses not included in training data (not trained category). We compared the performance of the algorithms against that of 18 dermatologists in a simulated setting that reflected intended clinical use.Findings64 teams submitted 129 state-of-the-art algorithm predictions on a test set of 8238 images. The best performing algorithm achieved 58·8% balanced accuracy on the BCN20000 data, which was designed to better reflect realistic clinical scenarios, compared with 82·0% balanced accuracy on HAM10000, which was used in a previously published benchmark. Shifted statistical distributions and disease categories not included in training data contributed to decreases in accuracy. Image artifacts, including hair, pen markings, ulceration, and imaging source institution, decreased accuracy in a complex manner that varied based on the underlying diagnosis. When comparing algorithms to expert dermatologists (2460 ratings on 1269 images), algorithms performed better than experts in most categories, except for actinic keratoses (similar accuracy on average) and images from categories not included in training data (26% correct for experts vs 6% correct for algorithms, p<0·0001). For the top 25 submitted algorithms, 47·1% of the images from categories not included in training data were misclassified as malignant diagnoses, which would lead to a substantial number of unnecessary biopsies if current state-of-the-art AI technologies were clinically deployed.InterpretationWe have identified specific deficiencies and safety issues in AI diagnostic systems for skin cancer that should be addressed in future diagnostic evaluation protocols to improve safety and reliability in clinical practice.FundingMelanoma Research Alliance and La Marató de TV3.

Dataset Information

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

Background

Objective

Methods

Results

Conclusions

Publications

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

Similar Datasets

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