Performance of a Machine Learning Algorithm on Lesions with a High Preoperative Suspicion of Invasive Melanoma

Authors

  • Filippos Giannopoulos Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
  • Martin Gillstedt Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
  • Sofia Lindskogen Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
  • John Paoli Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden
  • Sam Polesie Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Sahlgrenska University Hospital, Department of Dermatology and Venereology, Gothenburg, Sweden

DOI:

https://doi.org/10.2340/actadv.v104.40023

Keywords:

Neural Networks, Computer, Melanoma, Dermoscopy, Supervised Machine Learning, Artificial Intelligence

Abstract

Abstract is missing (Short communication)

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References

Polesie S, Gillstedt M, Kittler H, Rinner C, Tschandl P, Paoli J. Assessment of melanoma thickness based on dermoscopy images: an open, web-based, international, diagnostic study. J Eur Acad Dermatol Venereol 2022; 36: 2002-2007.

https://doi.org/10.1111/jdv.18436 DOI: https://doi.org/10.1111/jdv.18436

Polesie S, Jergeus E, Gillstedt M, Ceder H, Dahlen Gyllencreutz J, Fougelberg J, et al. Can dermoscopy be used to predict if a melanoma is in situ or invasive? Dermatol Pract Concept 2021; 11: e2021079.

https://doi.org/10.5826/dpc.1103a79 DOI: https://doi.org/10.5826/dpc.1103a79

Polesie S, Sundback L, Gillstedt M, Ceder H, Dahlen Gyllencreutz J, Fougelberg J, et al. Interobserver agreement on dermoscopic features and their associations with in situ and invasive cutaneous melanomas. Acta Derm Venereol 2021; 101: adv00570.

https://doi.org/10.2340/actadv.v101.281 DOI: https://doi.org/10.2340/actadv.v101.281

Swedish guidelines for malignant melanoma [in Swedish], Version 6.0, last updated: December 14, 2021: Available from: https://kunskapsbanken.cancercentrum.se/globalassets/cancerdiagnoser/hud/vardprogram/nationellt-vardprogram-malignt-melanom.pdf

Gillstedt M, Hedlund E, Paoli J, Polesie S. Discrimination between invasive and in situ melanomas using a convolutional neural network. J Am Acad Dermatol 2022; 86: 647-649.

https://doi.org/10.1016/j.jaad.2021.02.012 DOI: https://doi.org/10.1016/j.jaad.2021.02.012

Gillstedt M, Mannius L, Paoli J, Dahlen Gyllencreutz J, Fougelberg J, Johansson Backman E, et al. Evaluation of melanoma thickness with clinical close-up and dermoscopic images using a convolutional neural network. Acta Derm Venereol 2022; 102: adv00790.

https://doi.org/10.2340/actadv.v102.2681 DOI: https://doi.org/10.2340/actadv.v102.2681

Polesie S, Gillstedt M, Ahlgren G, Ceder H, Dahlen Gyllencreutz J, Fougelberg J, et al. Discrimination between invasive and in situ melanomas using clinical close-up images and a de novo convolutional neural network. Front Med (Lausanne) 2021; 8: 723914.

https://doi.org/10.3389/fmed.2021.723914 DOI: https://doi.org/10.3389/fmed.2021.723914

Gillstedt M, Segerholm K, Mannius L, Paoli J, Polesie S. How does a convolutional neural network trained to differentiate between invasive melanoma and melanoma in situ generalize when assessing dysplastic naevi? Acta Derm Venereol 2023; 103: adv00891.

https://doi.org/10.2340/actadv.v103.4822 DOI: https://doi.org/10.2340/actadv.v103.4822

Hernandez-Rodriguez JC, Duran-Lopez L, Dominguez-Morales JP, Ortiz-Alvarez J, Conejo-Mir J, Pereyra-Rodriguez JJ. Prediction of melanoma Breslow thickness using Deep Transfer Learning Algorithms. Clin Exp Dermatol 2023; 10.1093/ced/llad107.

https://doi.org/10.1093/ced/llad107 DOI: https://doi.org/10.1093/ced/llad107

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Published

2024-07-18

How to Cite

Giannopoulos, F., Gillstedt, M., Lindskogen, S., Paoli, J., & Polesie, S. (2024). Performance of a Machine Learning Algorithm on Lesions with a High Preoperative Suspicion of Invasive Melanoma. Acta Dermato-Venereologica, 104, adv40023. https://doi.org/10.2340/actadv.v104.40023

Issue

Vol. 104 (2024)

Section

Short Communication

License

Copyright (c) 2024 Filippos Giannopoulos, Martin Gillstedt, Sofia Lindskogen, John Paoli, Sam Polesie

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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