ArticleSkin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)2022
Discriminative deep learning based benignity/malignancy diagnosis of dermatologic ultrasound skin lesions with pretrained artificial intelligence architecture.
Article in Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Deep Learning-Based Skin Lesion Classification: A CNN Approach on High-Frequency Ultrasound Imaging.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- The Transformative Potential of AI in Ultrasound for Facial Aesthetics.Journal of cosmetic dermatology · 2025Review
- Clinical Application of Artificial Intelligence in Ultrasound Imaging for Oncology.JMA journal · 2025Review
- Applied deep learning in neurosurgery: identifying cerebrospinal fluid (CSF) shunt systems in hydrocephalus patients.Acta neurochirurgica · 2024Article
- Review
- Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods.Current oncology reports · 2023Review
- Discriminative deep learning based benignity/malignancy diagnosis of dermatologic ultrasound skin lesions with pretrained artificial intelligence architecture.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2022Article
- The State of Artificial Intelligence in Skin Cancer Publications.Journal of cutaneous medicine and surgeryArticle
- High-Frequency Ultrasound Evaluation of Cutaneous Surgical Wound Healing: An Outpatient Experience.Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair SocietyArticle
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8 authors.
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Abstract
backgroundDeep-learning algorithms (DLAs) have been used in artificial intelligence aided ultrasonography diagnosis of thyroid and breast lesions. However, its use has not been described in the case of dermatologic ultrasound lesions. Our purpose was to train a DLA to discriminate benign form malignant lesions in dermatologic ultrasound images. MATERIALS AND
methodsWe trained a prebuilt neural network architecture (EfficientNet B4) in a commercial artificial intelligence platform (Peltarion, Stockholm, Sweden) with 235 color Doppler images of both benign and malignant ultrasound images of 235 excised and histologically confirmed skin lesions (84.3% training, 15.7% validation). An additional 35 test images were used for testing the algorithm discrimination for correct benign/malignant diagnosis. One dermatologist with more than 5 years of experience in dermatologic ultrasound blindly evaluated the same 35 test images for malignancy or benignity.
resultsEfficientNet B4 trained dermatologic ultrasound algorithm sensitivity; specificity; predictive positive values, and predicted negative values for validation algorithm were 0.8, 0.86, 0.86, and 0.8, respectively for malignancy diagnosis. When tested with 35 previously unevaluated images sets, the algorithm´s accuracy for correct benign/malignant diagnosis was 77.1%, not statistically significantly different from the dermatologist's evaluation (74.1%).
conclusionAn adequately trained algorithm, even with a limited number of images, is at least as accurate as a dermatologic-ultrasound experienced dermatologist in the evaluation of benignity/malignancy of ultrasound skin tumor images devoid of clinical data.
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