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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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References

  1. Alves, J., Moreira, D., Alves, P., Rosado, L., Vasconcelos, M.: Automatic focus assessment on dermoscopic images acquired with smartphones. Sensors (Switzerland) 19(22), 4957 (2019). https://doi.org/10.3390/s19224957

    Article  Google Scholar 

  2. Andrade, C., Teixeira, L.F., Vasconcelos, M.J.M., Rosado, L.: Deep learning models for segmentation of mobile-acquired dermatological images. In: Campilho, A., Karray, F., Wang, Z. (eds.) ICIAR 2020. LNCS, vol. 12132, pp. 228–237. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50516-5_20

    Chapter  Google Scholar 

  3. Apalla, Z., Nashan, D., Weller, R.B., Castellsagué, X.: Skin cancer: epidemiology, disease burden, pathophysiology, diagnosis, and therapeutic approaches. Dermatol. Ther. 7(1), 5–19 (2017)

    Article  Google Scholar 

  4. Bangor, A., Kortum, P., Miller, J.: Determining what individual SUS scores mean: adding an adjective rating scale. J. Usability Stud. 4(3), 114–123 (2009)

    Google Scholar 

  5. Brooke, J.: SUS: a retrospective. J. Usability Stud. 8(2), 29–40 (2013)

    Google Scholar 

  6. Börve, A., et al.: Smartphone teledermoscopy referrals: a novel process for improved triage of skin cancer patients. Acta Dermato-Venereologica 95 (2014). https://doi.org/10.2340/00015555-1906

  7. Carvalho, R., Morgado, A.C., Andrade, C., Nedelcu, T., Carreiro, A., Vasconcelos, M.J.M.: Integrating domain knowledge into deep learning for skin lesion risk prioritization to assist teledermatology referral. Diagnostics 12(1) (2022). https://doi.org/10.3390/diagnostics12010036

  8. de Carvalho, T.M., Noels, E., Wakkee, M., Udrea, A., Nijsten, T.: Development of smartphone apps for skin cancer risk assessment: progress and promise. JMIR Dermatol. 2(1), e13376 (2019)

    Article  Google Scholar 

  9. Commissioning PC: Quality standards for teledermatology using ‘store and forward’ images (2011). https://sad.org.ar/wp-content/uploads/2020/12/Teledermatology-Quality-Standards.pdf. Accessed 15 Nov 2022

  10. Dahlén Gyllencreutz, J., Johansson Backman, E., Terstappen, K., Paoli, J.: Teledermoscopy images acquired in primary health care and hospital settings - a comparative study of image quality. J. Eur. Acad. Dermatol. Venereol. 32(6), 1038–1043 (2018). https://doi.org/10.1111/jdv.14565

    Article  Google Scholar 

  11. Dugonik, B., Dugonik, A., Marovt, M., Golob, M.: Image quality assessment of digital image capturing devices for melanoma detection. Appl. Sci. (Switzerland) 10(8), 2876 (2020). https://doi.org/10.3390/APP10082876

    Article  Google Scholar 

  12. Errichetti, E., Stinco, G.: Dermoscopy in general dermatology: a practical overview. Dermatol. Ther. 6(4), 471–507 (2016)

    Article  Google Scholar 

  13. Faria, J., Almeida, J., Vasconcelos, M.J.M., Rosado, L.: Automated mobile image acquisition of skin wounds using real-time deep neural networks. In: Zheng, Y., Williams, B.M., Chen, K. (eds.) MIUA 2019. CCIS, vol. 1065, pp. 61–73. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39343-4_6

    Chapter  Google Scholar 

  14. Ferlay, J., et al.: Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int. J. Cancer 144(8), 1941–1953 (2019)

    Article  Google Scholar 

  15. Fernandes, K., Cruz, R., Cardoso, J.S.: Deep image segmentation by quality inference. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)

    Google Scholar 

  16. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Advances in Neural Information Processing Systems, pp. 2962–2970 (2015)

    Google Scholar 

  17. Finnane, A., et al.: Proposed technical guidelines for the acquisition of clinical images of skin-related conditions. JAMA Dermatol. 153(5), 453–457 (2017). https://doi.org/10.1001/jamadermatol.2016.6214

  18. Finnane, A., Dallest, K., Janda, M., Soyer, H.P.: Teledermatology for the diagnosis and management of skin cancer: a systematic review. JAMA Dermatol. 153(3), 319–327 (2017)

    Article  Google Scholar 

  19. Flores, E., Scharcanski, J.: Segmentation of melanocytic skin lesions using feature learning and dictionaries. Expert Syst. Appl. 56, 300–309 (2016)

    Article  Google Scholar 

  20. Gonçalves, J., Conceiçao, T., Soares, F.: Inter-observer reliability in computer-aided diagnosis of diabetic retinopathy. In: HEALTHINF, pp. 481–491 (2019)

    Google Scholar 

  21. EI Ltd: Dermofit image library - Edinburgh innovations (2019). https://licensing.eri.ed.ac.uk/i/software/dermofit-image-library.html. Accessed 11 June 2019

  22. Lubax, I.: Dermpic (2019). https://apps.apple.com/app/dermpic-dermoscopy/id1413455878?src=AppAgg.com (mobile software)

  23. Moreira, D., Alves, P., Veiga, F., Rosado, L., Vasconcelos, M.: Automated mobile image acquisition of macroscopic dermatological lesions. In: Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF, pp. 122–132. SCITEPRESS-Science and Technology Publications, Lda (2021)

    Google Scholar 

  24. Munteanu, C.: Spotmole (2016). https://play.google.com/store/apps/details?id=com.spotmole &hl=en (mobile software)

  25. Oliveira, R.B., Marranghello, N., Pereira, A.S., Tavares, J.M.R.: A computational approach for detecting pigmented skin lesions in macroscopic images. Expert Syst. Appl. 61, 53–63 (2016)

    Article  Google Scholar 

  26. Pertuz, S., Puig, D., Garcia, M.A.: Analysis of focus measure operators for shape-from-focus. Pattern Recogn. 46(5), 1415–1432 (2013). https://doi.org/10.1016/j.patcog.2012.11.011

    Article  MATH  Google Scholar 

  27. Rat, C., et al.: Use of smartphones for early detection of melanoma: systematic review. J. Med. Internet Res. 20(4), e135 (2018)

    Article  Google Scholar 

  28. Rosado, L., Da Costa, J.M.C., Elias, D., Cardoso, J.S.: Mobile-based analysis of malaria-infected thin blood smears: automated species and life cycle stage determination. Sensors 17(10), 2167 (2017)

    Article  Google Scholar 

  29. Rosado, L., Vasconcelos, M.: Automatic segmentation methodology for dermatological images acquired via mobile devices. In: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 5, pp. 246–251. SCITEPRESS-Science and Technology Publications, Lda (2015)

    Google Scholar 

  30. Santos, A., Ortiz de Solórzano, C., Vaquero, J.J., Pena, J.M., Malpica, N., del Pozo, F.: Evaluation of autofocus functions in molecular cytogenetic analysis. J. Microscopy 188(3), 264–272 (1997)

    Google Scholar 

  31. Udrea, A., Lupu, C.: Real-time acquisition of quality verified nonstandardized color images for skin lesions risk assessment - a preliminary study. In: 2014 18th International Conference on System Theory, Control and Computing, ICSTCC 2014, pp. 199–204. Institute of Electrical and Electronics Engineers Inc. (2014). https://doi.org/10.1109/ICSTCC.2014.6982415

  32. Vasconcelos, M.J.M., Rosado, L.: No-reference blur assessment of dermatological images acquired via mobile devices. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2014. LNCS, vol. 8509, pp. 350–357. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07998-1_40

    Chapter  Google Scholar 

  33. Vasconcelos, M.J.M., Rosado, L., Ferreira, M.: Principal axes-based asymmetry assessment methodology for skin lesion image analysis. In: Bebis, G., et al. (eds.) ISVC 2014. LNCS, vol. 8888, pp. 21–31. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-14364-4_3

    Chapter  Google Scholar 

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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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Corresponding author

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

    Alexandre Douplik

  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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