Dermatology: Practical and Conceptual


Research Letter | Dermatol Pract Concept. 2022;12(4):e2022181 1

Application of Machine Learning Technologies to 
Improve the Diagnostic Value of Dermatoscopy, 

Combined with Digital Photo-fixation  
of Skin Neoplasms

Marian Voloshynovych1, Galyna Girnyk1, Valerii Chmut1, Nataliia Matkovska2,  
Iryna Blaga1, Nataliia Kozak1

1 Department of Dermatology and Venereology, Ivano-Frankivsk National Medical University, Ivano-Frankivsk, Ukraine

2 Department of Therapy and Family Medicine of Postgraduate Education, Ivano-Frankivsk National Medical University,  

Ivano-Frankivsk, Ukraine

Key words: dermatoscopy, machine learning, skin neoplasms

Citation: Voloshynovych M, Girnyk G, Chmut V, Matkovska N, Blaga I, Kozak N. Application of machine learning technologies to 
improve the diagnostic value of dermatoscopy, combined with digital photo-fixation of skin neoplasms. Dermatol Pract Concept. 
2022;12(4):e2022181. DOI: https://doi.org/10.5826/dpc.1204a181

Accepted: March 17, 2022; Published: October 2022

Copyright: ©2022 Voloshynovych et al. This is an open-access article distributed under the terms of the Creative Commons 
Attribution-Non Commercial License (BY-NC-4.0), https://creativecommons.org/licenses/by-nc/4.0/, which permits unrestricted 
noncommercial use, distribution, and reproduction in any medium, provided the original authors and source are credited.

Funding: None.

Competing interests: None.

Authorship: All authors have contributed significantly to this publication.

Corresponding author: Marian Voloshynovych PhD, Associate professor, Department of Dermatology and Venereology,  
Ivano-Frankivsk National Medical University, Ivano-Frankivsk, street. Kropyvnytskogo 16, 76018, Ukraine. tel. 0954011955  
E-mail: mvoloshynovych@gmail.com

Introduction

Many of us who use the phone camera in professional prac-

tice have repeatedly encountered a situation where the visible 

image differs from the image obtained through the eyepiece 

of the dermatoscope.

I would like to bring to your attention in order to com-

pare several clinical cases that have been refined in the Pixel-

mator Pro program with the ML Enhance tool. It is based on 

the use of machine learning technology to achieve the highest 

quality presentation of photographic material.

Case Presentation

Patient A (56 years old) has an area of redness with scal-

ing on the surface in the area of projection of the left chin 

bone (Figure 1A). The picture is typical for actinic keratosis 

Grade 2 (AK) [1]. The symptom of "strawberry" is present, 

there are isolated areas of doubtful delicate pigmentation, 

the vessels are not clearly visualized (Figure 1B). In the same 

photo after improving the image with ML Enhance there are 

clearly visible pigment deposits that limit the follicular open-

ings, a pronounced symptom of "strawberry", linear slightly 

branched vessels are traced over a longer period including 

outside the visual boundaries of the formation (Figure 1C). 

Photo processing optimizes the picture of pigmented AK. 

The extent of the lesion is slightly larger (1-2 mm), and the 

accumulation of melanin imposes certain restrictions on the 

use of photodynamic therapy as a treatment option.

Patient B (62 years old) has a papular element in the 

forehead on the right, dark in color, with keratin masses 

and peeling in the center (Figure 2A). During dermatoscopy, 



2 Research Letter | Dermatol Pract Concept. 2022;12(4):e2022181

the obtained image shows different sizes of blue and pur-

ple globules, single linear blood vessels in the thickness of 

the formation, and weak erythema of the surrounding tis-

sues. The center is occupied by dark horny masses, there is a 

radiance that becomes lighter to the periphery (Figure 2B). 

This tends to consider this formation as a pigmented form of 

nodular basal cell carcinoma [2]. After photo processing, the 

deep occurrence of melanocyte structures of gray-blue color, 

multiple sparsely branched blood vessels, larger in the center 

and smaller - to the periphery of the formation, interspersed 

with white and radial lines, is clearly visualized. The visual 

size of the formation expands after photo processing mainly 

due to perifocal erythema. Such changes are more typical of 

nodular melanoma. Surgical excision was performed and the 

diagnosis of melanoma was confirmed.

Conclusions

Judging by the presented photos, the use of digital filters 

based on machine learning technology, in particular ML En-

hance from the Pixelmator Pro package, in certain situations 

allows the dermatologist to improve the visualization of 

Figure 1. Patient A. Pigmented actinic keratosis, photofixation with the iPhone 11 Pro camera. (A) Macro-

photo. (B) Microphoto without the use of ML Enhance. (C) Microphoto after application of ML Enhance.

Figure 2. Patient B. Nodular melanoma, photofixation with iPhone 11 Pro camera. (A) Macrophoto.  

(B) Microphoto without the use of ML Enhance. (C) Microphoto after application of ML Enhance.



Research Letter | Dermatol Pract Concept. 2022;12(4):e2022181 3

changes in vascular pattern elements and pigment structures, 

which, in turn, facilitates the work of the doctor.

I would like to note that the use of the above tool in no 

way leads to the emergence of new elements of the dermo-

scopic picture. All of them are available for control during 

direct inspection through the eyepiece of the device, but are 

lost during photofixation, and become inaccessible during 

dynamic observation.

References

1. Zalaudek I, Argenziano G. Dermoscopy of actinic keratosis, 

intraepidermal carcinoma and squamous cell carcinoma.  Curr 

Probl Dermatol. 2015;46:70-76. DOI:10.1159/000366539. 

PMID: 25561209.

2. Wozniak-Rito A, Zalaudek I, Rudnicka L. Dermoscopy of 

basal cell carcinoma. Clin Exp Dermatol. 2018;43(3):241-247. 

DOI:10.1111/ced.13387. PMID: 29341291.