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Life
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Noninvasive Skin Imaging: The Present and the Future in General...
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Noninvasive Skin Imaging: The Present and the Future in General Dermatology

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: 28 February 2025 | Viewed by 2296

Special Issue Editors

Dr. Bijan Safai

E-Mail Website
Guest Editor
Dermatology Department, NYC Health + Hospitals/Metropolitan, New York, NY, USA
Interests: molecular dermatology; targeted therapy; cutaneous oncology; immunodermatology; cutaneous biology
Dr. Banu Farabi

E-Mail Website
Guest Editor
Dermatology Department, Metropolitan Hospital Center, New York, NY, USA
Interests: dermatopathology; clinical dermatology; histopathology; melanoma; skin biology; dermatosurgery; basal cell carcinoma; dermatology imaging; squamous cell carcinoma; skin cancer

Special Issue Information

Dear Colleagues,

We invite scholars and researchers to contribute to our upcoming Special Issue on “Noninvasive Skin Imaging: The Present and the Future in General Dermatology”. This issue aims to explore recent advancements, innovative methodologies, and significant findings in the field of skin imaging. Topics of interest include, but are not limited to, the following:

  • Diagnostic applications;
  • Imaging techniques;
  • Machine learning applications;
  • Clinical correlations;
  • Therapeutic implications;
  • Emerging trends and technologies.

We encourage submissions of original research articles, reviews, and perspectives that will contribute to the advancement of knowledge in noninvasive skin imaging. Join us in shaping the discourse and fostering collaboration in this rapidly evolving domain.

Dr. Bijan Safai
Dr. Banu Farabi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • skin imaging
  • dermatoscopy
  • dermatology imaging
  • dermatosurgery
  • clinical dermatology

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

20 pages, 3262 KiB  
Article
Predicting Prognosis of Early-Stage Mycosis Fungoides with Utilization of Machine Learning
by Banu İsmail Mendi, Hatice Şanlı, Mert Akın Insel, Beliz Bayındır Aydemir and Mehmet Fatih Atak
Life 2024, 14(11), 1371; https://doi.org/10.3390/life14111371 - 25 Oct 2024
Viewed by 620
Abstract
Mycosis fungoides (MF) is the most prevalent type of cutaneous T cell lymphomas. Studies on the prognosis of MF are limited, and no research exists on the potential of artificial intelligence to predict MF prognosis. This study aimed to compare the predictive capabilities [...] Read more.
Mycosis fungoides (MF) is the most prevalent type of cutaneous T cell lymphomas. Studies on the prognosis of MF are limited, and no research exists on the potential of artificial intelligence to predict MF prognosis. This study aimed to compare the predictive capabilities of various machine learning (ML) algorithms in predicting progression, treatment response, and relapse and to assess their predictive power against that of the Cox proportional hazards (CPH) model in patients with early-stage MF. The data of patients aged 18 years and over who were diagnosed with early-stage MF at Ankara University Faculty of Medicine Hospital from 2006 to 2024 were retrospectively reviewed. ML algorithms were utilized to predict complete response, relapse, and disease progression using patient data. Of the 185 patients, 94 (50.8%) were female, and 91 (49.2%) were male. Complete response was observed in 114 patients (61.6%), while relapse and progression occurred in 69 (37.3%) and 54 (29.2%) patients, respectively. For predicting progression, the Support Vector Machine (SVM) algorithm demonstrated the highest success rate, with an accuracy of 75%, outperforming the CPH model (C-index: 0.652 for SVM vs. 0.501 for CPH). The most successful model for predicting complete response was the Ensemble model, with an accuracy of 68.89%, surpassing the CPH model (C-index: 0.662 for the Ensemble model vs. 0.543 for CPH). For predicting relapse, the decision tree classifier showed the highest performance, with an accuracy of 78.17%, outperforming the CPH model (C-index: 0.782 for the decision tree classifier vs. 0.505 for CPH). The results suggest that ML algorithms may be useful in predicting prognosis in early-stage MF patients. Full article
(This article belongs to the Special Issue Noninvasive Skin Imaging: The Present and the Future in General Dermatology)
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14 pages, 4599 KiB  
Article
Deep Learning Method Applied to Autonomous Image Diagnosis for Prick Test
by Ramon Hernany Martins Gomes, Edson Luiz Pontes Perger, Lucas Hecker Vasques, Elaine Gagete and Rafael Plana Simões
Life 2024, 14(10), 1256; https://doi.org/10.3390/life14101256 - 2 Oct 2024
Viewed by 705
Abstract
Background: The skin prick test (SPT) is used to diagnose sensitization to antigens. This study proposes a deep learning approach to infer wheal dimensions, aiming to reduce dependence on human interpretation. Methods: A dataset of SPT images (n = 5844) was used to [...] Read more.
Background: The skin prick test (SPT) is used to diagnose sensitization to antigens. This study proposes a deep learning approach to infer wheal dimensions, aiming to reduce dependence on human interpretation. Methods: A dataset of SPT images (n = 5844) was used to infer a convolutional neural network for wheal segmentation (ML model). Three methods for inferring wheal dimensions were evaluated: the ML model; the standard protocol (MA1); and approximation of the area as an ellipse using diameters measured by an allergist (MA2). The results were compared with assisted image segmentation (AIS), the most accurate method. Bland–Altman analysis, distribution analyses, and correlation tests were applied to compare the methods. This study also compared the percentage deviation among these methods in determining the area of wheals with regular geometric shapes (n = 150) and with irregular shapes (n = 150). Results: The Bland–Altman analysis showed that the difference between methods was not correlated with the absolute area. The ML model achieved a segmentation accuracy of 85.88% and a strong correlation with the AIS method (ρ = 0.88), outperforming all other methods. Additionally, MA1 showed significant error (13.44 ± 13.95%) for pseudopods. Conclusions: The ML protocol can potentially automate the reading of SPT, offering greater accuracy than the standard protocol. Full article
(This article belongs to the Special Issue Noninvasive Skin Imaging: The Present and the Future in General Dermatology)
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12 pages, 793 KiB  
Article
Adversarial Training Based Domain Adaptation of Skin Cancer Images
by Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques and Hee-Cheol Kim
Life 2024, 14(8), 1009; https://doi.org/10.3390/life14081009 - 14 Aug 2024
Viewed by 678
Abstract
Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur [...] Read more.
Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were performed using two different domain adaptation architectures. The domain adversarial neural network with two gradient reversal layers and VGG13 as a feature extractor achieved the highest accuracy and F1 score of 0.7567 and 0.75, respectively, representing an 18.47% improvement in accuracy over the baseline model. Full article
(This article belongs to the Special Issue Noninvasive Skin Imaging: The Present and the Future in General Dermatology)
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