Untitled


Viewpoint

LesionMap: A Method and Tool for the Semantic Annotation of
Dermatological Lesions for Documentation and Machine Learning

Bell Raj Eapen1, MD, MSc; Norm Archer1, PhD; Kamran Sartipi2, PhD
1Information Systems, McMaster University, Hamilton, ON, Canada
2Department of Computer Science, East Carolina University, Greenville, NC, United States

Corresponding Author:
Bell Raj Eapen, MD, MSc
Information Systems
McMaster University
DSB - 211
1280 Main Street West
Hamilton, ON, L8S 4L8
Canada
Phone: 1 905 525 9140 ext 26392
Email: eapenbp@mcmaster.ca

Abstract

Diagnosis and follow-up of patients in dermatology rely on visual cues. Documentation of skin lesions in dermatology is
time-consuming and inaccurate. Digital photography is resource-intensive, difficult to standardize, and has privacy concerns. We
propose a simple method—LesionMap—and an electronic health software tool—LesionMapper—for semantically annotating
dermatological lesions on a body wireframe. We discuss how the type, distribution, and progression of lesions can be represented
in a standardized way. The tool is an open-source JavaScript package that can be integrated into web-based electronic medical
records. We believe that LesionMapper will facilitate documentation in dermatology that can be used for machine learning in a
privacy-preserving manner.

(JMIR Dermatol 2020;3(1):e18149) doi: 10.2196/18149

KEYWORDS

LesionMap; LesionMapper; digital imaging; machine learning; dermatology

Introduction

Documenting the origin, distribution, and nature of
dermatological lesions in a textual form is inefficient and
imprecise. Dermatologists often document the images of the
patient or draw the lesions on a body wireframe for later
reference. Digital photography for clinical documentation is
time-consuming and resource intensive to capture, organize,
and maintain [1]. Additionally, there is a growing
privacy-related concern over the use of these images [2].

Capturing a detailed account of dermatological lesions in a
privacy-preserving way is becoming increasingly important in
the era of machine learning and artificial intelligence (AI).
Documentation in electronic medical records (EMRs) requires
a simple and efficient tool that fits into the clinical workflow.
There is a growing need for a standardized methodology and
an annotation schema to facilitate the capture of rich data related
to dermatological conditions for machine learning. For this, we
propose a simple method—LesionMap (LM)—and an electronic

health (eHealth) software tool—LesionMapper (LMR)—that
fits into the clinical workflow.

The sharing of clinical images between dermatologists for
learning purposes is common, and most images are published
with the consent of the patient [2]. However, increasingly, social
media platforms are used for the easy sharing of such resources,
with the associated implications on privacy [3]. Machine
learning and AI applications need access to a large volume of
data to build machine learning models for clinical decision
support. Emerging techniques in machine learning and AI such
as convolutional neural networks (CNN) and transfer learning
[4] have several applications in dermatology [5]. Interestingly,
some computer-vision methods can be applied to
machine-generated images in addition to digital images [6].

In this paper, we describe common skin lesions, the semantic
annotation methodology (LM), and a software tool (LMR) that
can be used for semantic annotations. The tool is designed as
an extensible software library (JavaScript) that can be

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incorporated into web-based EMRs. We briefly describe two
such integrations with open-source EMRs—OpenMRS and
OSCAR EMR.

Classification of Skin Lesions

Dermatologists use numerous descriptive terms to identify and
describe skin lesions [7]. Flat skin lesions that are small are
called macules, and when they exceed 1 cm in size, they are
called patches. An elevated dome-shaped lesion is called a
nodule, whereas a flat elevated lesion is called a plaque. Small
fluid-filled lesions are called vesicles, and if they exceed 1 cm,
they are called bullae. If vesicles are filled with pus instead of
clear fluid, they are called pustules.

Scales refer to a thickened outer layer of skin while the crust is
a liquid debris. An ulcer is an irregularly shaped, deep loss of
skin, and if it is superficial it is called an erosion. Atrophy is a
thinning of skin, and a fissure is a linear cleft. Necrosis is dead
skin tissue, and the scar is the replacement of lost skin by
connective tissue. Localized hemorrhage into the skin is called
purpura, and petechiae, when the hemorrhagic lesions are small.

The color of the lesion can provide diagnostic cues, along with
the shape, arrangement, and distribution. Discoid and annular

are terms used to describe the shape. The distribution can be
grouped, discrete, linear, serpiginous, reticular, generalized,
symmetrical, or photodistributed. The size, location, and severity
are also important. Although this is not an exhaustive list of
dermatological descriptions, the most common descriptions are
included here. Discrepancies in the terminology of
dermatological lesions exist in the literature [8]. LM does not
attempt to formalize the ontology, but proposes a pragmatic
standard using the iconographic method.

Iconographic Representations of Skin
Lesions

Most descriptive terms used in dermatology can be represented
by iconographic images representative of the lesion or feature.
The use of iconography in clinical documentation has been
demonstrated in the context of pain [9]. The type of lesion can
be easily represented by icons due to their visual similarity. The
list of icons can be supplemented with custom icons for
representing descriptive characteristics, such as the site of onset.
LMR provides a set of icons for representing visual and
nonvisual characteristics of common skin lesions and additional
icons for descriptive characteristics (see Figure 1A and B).

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Figure 1. The LesionMapper interface: (A,B) icons for descriptive characteristics; (C) large nodule; (D) ulcer on lateral side; (E) multiple discrete
plaques; (F) Christmas tree pattern in pityriasis rosea; (G) vitiligo patch showing variation in opacity.

In addition to the type of lesions, there are five other
characteristics of each icon that can be changed: size, position,
number, orientation, and opacity. Additional information
pertaining to the lesion can be encoded using the following
characteristics:

• The size of the icon can be used to indicate the average size
of the lesion in conditions where lesion size points toward
a diagnosis or a particular subtype of the primary diagnosis.

For example, the size of the plaques can be a differentiating
feature for small-plaque and large-plaque parapsoriasis.
The original size of the icon, when placed on the LM, can
be used for comparison (see the large nodule in Figure 1C).

• The position of icon placement indicates the distribution
of the lesions. The front and back of the body are depicted
in the LM. The lateral view is not included to simplify the
interface. To represent lateral distribution, the icons can be

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placed in the corresponding edge of the wireframe with an
overlap of 50% (see the ulcer on the legs in Figure 1D).

• Multiple icons of the same type can be used to represent
discrete lesions, and a single large icon can be used to
represent confluent distribution (see discrete plaques in
Figure 1E).

• The orientation can be used to indicate a pathognomonic
distribution, such as the Christmas tree pattern in pityriasis
rosea (see Figure 1F).

• The opacity of the lesion can be used to indicate the severity
of the presentation. For example, it can be used to represent
the degree of depigmentation in a vitiligo patch or the
severity of contact dermatitis (see Figure 1G).

Mapping lesions consistently and accurately requires a tool that
supports the various functions described above. In addition,
from a design perspective, the tool should have the capability
to integrate with other health information systems and EMRs.

LesionMapper

LMR is a prototype implementation of the LM method described
above. We adopted the design science principles of Hevner et
al [10] for information systems to design LMR. We searched
the literature for similar approaches and available tools to
address the problem of lesional documentation. Based on the
success of similar approaches (Pain-QuILT for annotating pain
[9]), we chose iconography as the method and standardized it
based on our domain expertise in dermatology. Thereafter, we
distinguished some of the easily identifiable characteristics of
icons that can be programmatically controlled, such as size,
orientation, and transparency. Subsequently, we converged on
a popular framework (VueJS JavaScript framework [11]) for
implementation. We designed the artifact adopting a modular
pattern—as a JavaScript package shared as open source (see
the GitHub repository [12])—that can be incorporated into
web-based EMRs. LMR provides buttons to add various icons
to the canvas. These icons can be independently moved and
resized. The opacity and orientation can also be independently
modified. The LM can be exported as an image or as a
JavaScript Object Notation (JSON) string. LMR supports
freehand drawing in the canvas to represent features that are
not represented by icons though machine interpretation of the
freehand drawing is challenging.

Next, we describe the integration of LMR into two open-source
EMRs—OpenMRS and OSCAR.

Integration With Electronic Medical
Records

The modular design helps in the integration of LMR into
web-based EMRs. The prototype is created using the VueJS
JavaScript framework following the Universal Module
Definition (UMD) pattern [13] that can be imported by different
module loaders into other browser-based applications. The icons
are converted into Base64 strings and included in the JavaScript
files.

Open Medical Records System (OpenMRS) is an open-source,
Java-based EMR for developing countries with a modular and
extensible architecture [14]. OpenMRS supports the Open Web
Apps (OWA) specifications that make it possible to design
external applications that extend the core functions. The OWA
communicates with OpenMRS using REST APIs
(representational state transfer application programming
interfaces), a software architectural style used for creating Web
services, and is embedded in the same server instance.
OpenMRS has a custom concept dictionary that helps map data
points to a uniform terminology. Nontextual data such as images
are stored as ”complex concepts” outside the relational database.
LMR can easily fit into an OWA design pattern, and the
exported LM images can be stored as complex observations in
the patient record. We have a prototype integration that can be
used as an example [15].

OSCAR EMR is a web-based EMR system initially developed
for primary care practitioners in Canada. OSCAR EMR has a
complex data model, and additional data points are supported
by an electronic form (eForm) module that stores data as
key-value pairs [16]. eForms do not support images or other
nontextual data. The ability of LMR to save LMs as a JSON
string makes the integration of the LMR module into eForms
possible.

Machine Learning Applications

Dermatological diseases have diverse presentations, with skin
type and skin color adding to this variation. Some of these
diseases involve hair, nails, and mucous membranes in addition
to the skin. Traditional computer-vision algorithms such as
convolutional neural networks (CNNs) and other variants of
neural networks have limited application when there are many
decision alternatives [17]. Hence, AI algorithms have had
limited application in dermatology except in problems associated
with classification (eg, the presence or absence of cancer) [18].
Such algorithms can classify only a given lesion rather than the
patient as a whole (ie, a lesion is cancerous vs patient has
cancer). Although few CNN-based image search algorithms
have proven to be useful, AI algorithms for diagnostic decision
making in clinical dermatology lag far behind areas such as
radiology [17].

Text analytics and natural language processing (NLP) can be
more useful than image analytics when the decision alternatives
are numerous, as in dermatology. Multimodal approaches where
an image is combined with metadata have shown promise [19].
Machine learning models built using LMs—especially the
models created using the JSON representation—resemble text
more than an image. Some relevant metadata such as the position
and distribution of the lesions, which are difficult to be captured
in text and hard to precisely decipher with NLP, are implicitly
captured in LMs. The icons represent ontological concepts from
dermatology and can map to any standard terminology system
[7]. We posit that LMs are semantically rich enough to be used
for machine learning applications. Machine learning models
from LMs are likely to be more “explainable” than traditional
black box algorithms [20]. The implicit metadata captured by

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LMs can supplement regular digital images, leading to better
machine learning models.

Advantages and Limitations

LMs may save time for busy practitioners while capturing the
type, distribution, and characteristics of the lesion; these data
can be used to assess clinical progress. The LM exported as
JSON resembles a markup language amenable to data mining
and machine learning methods [21]. LMs are portable and can
be easily and safely exchanged without privacy concerns.

LMR can export LMs as images. These images can be used as
a proxy for patient images in some computer vision–based
applications. Computer vision has been successfully applied to
identify metabolic defects from gene expression maps [22].
MNIST (Modified National Institute of Standards and
Technology database), a dataset widely used in machine
learning, consists of images of handwritten digits [23].

It is widely accepted that machine learning can reinforce some
health care disparities in dermatology [24]. Skin color is a
significant background noise that needs to be accounted for in
any machine learning model. It is possible that some of the
existing models are biased toward particular skin types that
predominate in the training data set. Such models tend to be
less sensitive in making predictions on different skin types [25].
The LMs are not affected by such bias.

LMs, however, do not capture all the features, both explainable
and unexplainable, captured by a digital image. Hence, LMs
are not useful in scenarios where accurate and sensitive
extraction of features from an image is important for prediction.
For example, LMs are not appropriate for skin cancer
classification [5] and mole mapping [26]. LMs do not support
annotating dermatopathology images [27]; they are also not
applicable for dermatoscopic images that rely on pixel-level
analysis [28]. Examination findings such as fluctuation,
consistency, and tenderness are not represented by icons at
present to keep the interface simple. More icons can be added
if the user community requires them.

LMR and the LM method have not been clinically tested. The
integration of LMR into existing EMRs may be difficult. Despite

its anticipated ease of use, the actual impact of LMR on
physician workflow, if any, needs to be investigated further.

Discussion

The skin is the largest organ in the human body, and as such,
skin conditions are commonly encountered in any health care
practice. Although dermatology is a specialization within clinical
medicine, 50% of skin conditions are assessed and documented
by nondermatologists [29].

There is no universal standard for pictographic documentation
of the type, distribution, progression, and severity of lesions in
dermatology, as in dentistry [30] and ophthalmology [31]. The
LM standardizes visual representation using icons that can be
extended to accommodate different use cases in clinical and
cosmetic dermatology. The simplicity of the mapping rules
facilitates use by nondermatologists in the skincare industry;
LMs are also semantically rich enough to capture most relevant
information about a skin condition with minimal effort.

Image analytics in dermatology is not as popular, as it is in
visually oriented medical specialties such as radiology and
pathology; the exception is the field of skin cancer diagnostics.
This is because of the privacy concerns associated with
dermatological images and the difficulties in standardizing
image capture. The LM is not a replacement for a digital image
of the lesion. However, some of the diagnostic aspects that are
difficult to be captured in images, such as distribution and
progression, can be useful for machine learning applications,
especially when combined with the textual representation of a
patient’s history. Such multimodal approaches mimic the clinical
workflow more so than CNN-based algorithms [32]. New
computer-vision algorithms are proving to be capable of learning
from computer-generated images [22]. We believe that LMs
can be similarly used with computer-vision methods. Finally,
we urge the open-source community to help us improve LMR
and potential users to report issues on the repository [12] so that
we can fix them. We will work on a 3D wireframe for better
accuracy, and we welcome other feature requests from the user
community.

Conflicts of Interest
None declared.

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Abbreviations
AI: artificial intelligence
API: application programming interface
CNN: convolutional neural network
eForm: electronic form
eHealth: electronic health
EMR: electronic medical record
JSON: JavaScript Object Notation
LM: LesionMap
LMR: LesionMapper
MNIST: Modified National Institute of Standards and Technology database
NLP: natural language processing
OWA: Open Web Apps
REST: representational state transfer
UMD: Universal Module Definition

Edited by G Eysenbach; submitted 06.02.20; peer-reviewed by F Kaliyadan, A Kt; comments to author 15.02.20; revised version
received 26.02.20; accepted 27.02.20; published 20.04.20

Please cite as:
Eapen BR, Archer N, Sartipi K
LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning
JMIR Dermatol 2020;3(1):e18149
URL: http://derma.jmir.org/2020/1/e18149/
doi: 10.2196/18149
PMID:

©Bell Raj R Eapen, Norm Archer, Kamran Sartipi. Originally published in JMIR Dermatology (http://derma.jmir.org), 20.04.2020.
This is an open-access article distributed under the terms of the Creative Commons Attribution License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work, first published in JMIR Dermatology Research, is properly cited. The complete bibliographic
information, a link to the original publication on http://derma.jmir.org, as well as this copyright and license information must be
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JMIR Dermatol 2020 | vol. 3 | iss. 1 | e18149 | p. 7http://derma.jmir.org/2020/1/e18149/
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