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Improving Teledermatology Referral with Edge-AI: Mobile App to Foster Skin Lesion Imaging Standardization

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Biomedical Engineering Systems and Technologies (BIOSTEC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1710))

Abstract

Every year, the number of skin cancer cases has been increasing which, consequently, increases the strain on the health care systems around the globe. With the growth of processing power and camera quality on smartphones, the investment in telemedicine and the development of mobile teledermatology applications can, not only contribute to the standardization of image acquisitions but also, facilitate early diagnosis. This paper presents a new process for real-time automated image acquisition of macroscopic skin images with the merging of an automated focus assessment feature-based machine learning algorithm with conventional computer vision techniques to segment dermatological images. Three datasets were used to develop and evaluate the proposed methodology. One comprised of 3428 images acquired with a mobile phone for this purpose and 1380 from the other two datasets which are publicly available. The best focus assessment model achieved an accuracy of 88.3% and an F1-Score of 86.8%. The segmentation algorithm obtained a Jaccard index of 85.81% for the SMARTSKINS dataset and 68.59% for the Dermofit dataset. The algorithms were deployed to a mobile application, available in Android and iOS, without causing any performance hindrances. The application was tested in a real environment, being used in a 10-month pilot study with six General and Family Medicine doctors and one Dermatologist. The easiness to acquire dermatological images, image quality, and standardization were referred to as the main advantages of the application.

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Acknowledgements

This work was done under the scope of project “DERM.AI: Usage of Artificial Intelligence to Power Teledermatological Screening”, and supported by national funds through ‘FCT—Foundation for Science and Technology, I.P.’, with reference DSAIPA/AI/0031/2018. The authors thank doctors from Unidade Local de Saúde da Guarda that participated in the trial.

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Authors and Affiliations

  1. Fraunhofer Portugal AICOS, 4200-135, Porto, Portugal

    Maria João M. Vasconcelos, Dinis Moreira, Pedro Alves, Ricardo Graça & Luís Rosado

  2. Shared Services of the Ministry of Health, Lisbon, Portugal

    Rafael Franco

Authors
  1. Maria João M. Vasconcelos
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  2. Dinis Moreira
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  3. Pedro Alves
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  4. Ricardo Graça
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  5. Rafael Franco
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  6. Luís Rosado
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Correspondence to Maria João M. Vasconcelos .

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Editors and Affiliations

  1. INSA-Lyon, Villeurbanne, France

    Claudine Gehin

  2. FEMTO-ST, Besancon, France

    Bruno Wacogne

  3. Toronto Metropolitan University, Toronto, ON, Canada

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  4. University of Vienna, Vienna, Austria

    Ronny Lorenz

  5. Charles River Analytics, Inc., Cambridge, MA, USA

    Bethany Bracken

  6. University of Lisbon, Lisbon, Portugal

    Cátia Pesquita

  7. Instituto de Telecomunicações, Aveiro, Portugal

    Ana Fred

  8. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Vasconcelos, M.J.M., Moreira, D., Alves, P., Graça, R., Franco, R., Rosado, L. (2022). Improving Teledermatology Referral with Edge-AI: Mobile App to Foster Skin Lesion Imaging Standardization. In: Gehin, C., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2021. Communications in Computer and Information Science, vol 1710. Springer, Cham. https://doi.org/10.1007/978-3-031-20664-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-20664-1_9

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