ArticlePloS one2018
Acral melanoma detection using a convolutional neural network for dermoscopy images.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 42 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled it, 131 citations in OpenAlex.
- Diagnostic accuracy of artificial intelligence compared to family physicians and dermatologists for skin conditions: a systematic review and meta-analysis.BMC primary care · 2025Pooled it
- Diagnostic Accuracy of Novel Optical Imaging Techniques for Melanoma Detection: A Systematic Review and Meta-Analysis.International journal of dermatology · 2025Pooled it
- Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review.Dermatology (Basel, Switzerland) · 2023Pooled it
- The Use of Artificial Intelligence for Skin Cancer Detection in Asia-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Using the power of artificial intelligence to improve the diagnosis and management of nonmelanoma skin cancer.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Review
- Artificial Intelligence in the Non-Invasive Detection of Melanoma.Life (Basel, Switzerland) · 2024Review
- Review
- Deep Learning Techniques for the Dermoscopic Differential Diagnosis of Benign/Malignant Melanocytic Skin Lesions: From the Past to the Present.Bioengineering (Basel, Switzerland) · 2024Review
- Image Quality Assessment Using Convolutional Neural Network in Clinical Skin Images.JID innovations : skin science from molecules to population health · 2024Article
- Evidence of Cannabidiol Effectiveness Associated or Not with Tetrahydrocannabinol in Topical Administration: A Scope Review.Pharmaceuticals (Basel, Switzerland) · 2024Review
- A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.NPJ digital medicine · 2024Review
- An Automated Approach for Diagnosing Allergic Contact Dermatitis Using Deep Learning to Support Democratization of Patch Testing.Mayo Clinic proceedings. Digital health · 2024Article
- A European Multicentric Investigation of Atypical Melanocytic Skin Lesions of Palms and Soles: TheDiagnostics (Basel, Switzerland) · 2024Article
- Skin Type Diversity in Skin Lesion Datasets: A Review.Current dermatology reports · 2024Review
- A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study.EClinicalMedicine · 2024Article
- Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations.JID innovations : skin science from molecules to population health · 2023Review
- Artificial Intelligence in Dermatology: Challenges and Perspectives.Dermatology and therapy · 2022Article
- A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub-epidermal moisture measurements.International wound journal · 2022Article
- Development and validation of the interpretability analysis system based on deep learning model for smart image follow-up of nail pigmentation.Annals of translational medicine · 2022Article
- Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology.Journal of clinical medicine · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background/purposeAcral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions.
methodsA total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study. To perform the 2-fold cross validation, we split them into two mutually exclusive subsets: half of the total image dataset was selected for training and the rest for testing, and we calculated the accuracy of diagnosis comparing it with the dermatologist's and non-expert's evaluation.
resultsThe accuracy (percentage of true positive and true negative from all images) of the convolutional neural network was 83.51% and 80.23%, which was higher than the non-expert's evaluation (67.84%, 62.71%) and close to that of the expert (81.08%, 81.64%). Moreover, the convolutional neural network showed area-under-the-curve values like 0.8, 0.84 and Youden's index like 0.6795, 0.6073, which were similar score with the expert.
conclusionAlthough further data analysis is necessary to improve their accuracy, convolutional neural networks would be helpful to detect acral melanoma from dermoscopy images of the hands and feet.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.