SADE: Software de Análise Dermatológica - Um sistema de coleta, gerenciamento e triagem de lesões de pele
Resumo
Skin cancer is a global public health challenge, accounting for approximately one-third of cancer diagnoses worldwide. The state of Espírito Santo has tens of thousands of inhabitants of European descent. Most of them have fair skin and are engaged in family farming, often exposed to the sun. The combination of this vulnerable phenotype with such sun exposure results in a high incidence of skin cancer in the state. Since 1987, the Federal University of Espírito Santo has maintained a dermatological and surgical assistance program, providing free care to the most vulnerable population. Starting from a partnership that began in 2018, the Dermatological Analysis Software (SADE) was developed, a system used to collect, manage, and screen skin lesions during the program’s care. Since its implementation, the software has had a significant impact on assisting the population, reducing both waiting and service times. Additionally, SADE has enabled a range of technical and scientific achievements, such as publications, awards, and participation in events.
Palavras-chave:
Dermatology, Skin Cancer, Software, Application, Deep Learning
Referências
Giuseppe Argenziano and H Peter Soyer. 2001. Dermoscopy of pigmented skin lesions – a valuable tool for early. The Lancet Oncology 2, 7 (2001), 443–449.
Pedro BC Castro, Breno Krohling, Andre GC Pacheco, and Renato A Krohling. 2020. An app to detect melanoma using deep learning: an approach to handle imbalanced data based on evolutionary algorithms. In IEEE International Joint Conference on Neural Networks. IEEE, 1–6.
Gabriel G de Angelo, Andre G C Pacheco, and Renato A Krohling. 2019. Skin lesion segmentation using deep learning for images acquired from smartphones. In IEEE International Joint Conference on Neural Networks. 1–8.
Hao Feng, Juliana Berk-Krauss, Paula W. Feng, and Jennifer A. Stein. 2018. Comparison of Dermatologist Density Between Urban and Rural Counties in the United States. JAMA Dermatology 154, 11 (2018), 1265–1271.
Klaus Granzov. 2009. Pomeranos sob o Cruzeiro do Sul: colonos Alemães no Brasil. Arquivo Público do Estado do Espírito Santo, Vitória.
INCA. 2023. Incidência do câncer no Brasil. Instituto Nacional do Câncer (INCA). Disponível em: [link]. Último acesso em: 10 de Agosto 2023.
OMS. 2017. Radiation: Ultraviolet (UV) radiation and skin cancer. World Health Organization (WHO). Disponível em: [link]. Último acesso em: 10 de Agosto 2023..
Andre GC Pacheco et al. 2020. PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones. Data in Brief 32 (2020), 1–10.
Andre GC Pacheco, Abder-Rahman Ali, and Thomas Trappenberg. 2019. Skin cancer detection based on deep learning and entropy to detect outlier samples. In Medical Image Computing and Computer Assisted Intervention (MICCAI) at Skin Lesion Analysis Towards Melanoma Detection (ISIC) challenge. 1–6.
Andre GC Pacheco and Renato A Krohling. 2019. Recent advances in deep learning applied to skin cancer detection. In Neural Information Processing Systems at Retrospectives workshop. 1–8.
Andre GC Pacheco and Renato A Krohling. 2020. The impact of patient clinical information on automated skin cancer detection. Computers in Biology and Medicine 116 (2020), 103545.
Andre GC Pacheco and Renato A Krohling. 2020. Learning dynamic weights for an ensemble of deep models applied to medical imaging classification. In IEEE International Joint Conference on Neural Networks. 1–8.
Andre GC Pacheco and Renato A Krohling. 2021. An attention-based mechanism to combine images and metadata in deep learning models applied to skin cancer classification. IEEE journal of biomedical and health informatics 25, 9 (2021), 3554–3563.
Richard M. Scheffler et al. 2008. Forecasting the global shortage of physicians: an economic-and needs-based approach. Bulletin of the World Health Organization 86 (2008), 516–523B.
Pedro BC Castro, Breno Krohling, Andre GC Pacheco, and Renato A Krohling. 2020. An app to detect melanoma using deep learning: an approach to handle imbalanced data based on evolutionary algorithms. In IEEE International Joint Conference on Neural Networks. IEEE, 1–6.
Gabriel G de Angelo, Andre G C Pacheco, and Renato A Krohling. 2019. Skin lesion segmentation using deep learning for images acquired from smartphones. In IEEE International Joint Conference on Neural Networks. 1–8.
Hao Feng, Juliana Berk-Krauss, Paula W. Feng, and Jennifer A. Stein. 2018. Comparison of Dermatologist Density Between Urban and Rural Counties in the United States. JAMA Dermatology 154, 11 (2018), 1265–1271.
Klaus Granzov. 2009. Pomeranos sob o Cruzeiro do Sul: colonos Alemães no Brasil. Arquivo Público do Estado do Espírito Santo, Vitória.
INCA. 2023. Incidência do câncer no Brasil. Instituto Nacional do Câncer (INCA). Disponível em: [link]. Último acesso em: 10 de Agosto 2023.
OMS. 2017. Radiation: Ultraviolet (UV) radiation and skin cancer. World Health Organization (WHO). Disponível em: [link]. Último acesso em: 10 de Agosto 2023..
Andre GC Pacheco et al. 2020. PAD-UFES-20: a skin lesion dataset composed of patient data and clinical images collected from smartphones. Data in Brief 32 (2020), 1–10.
Andre GC Pacheco, Abder-Rahman Ali, and Thomas Trappenberg. 2019. Skin cancer detection based on deep learning and entropy to detect outlier samples. In Medical Image Computing and Computer Assisted Intervention (MICCAI) at Skin Lesion Analysis Towards Melanoma Detection (ISIC) challenge. 1–6.
Andre GC Pacheco and Renato A Krohling. 2019. Recent advances in deep learning applied to skin cancer detection. In Neural Information Processing Systems at Retrospectives workshop. 1–8.
Andre GC Pacheco and Renato A Krohling. 2020. The impact of patient clinical information on automated skin cancer detection. Computers in Biology and Medicine 116 (2020), 103545.
Andre GC Pacheco and Renato A Krohling. 2020. Learning dynamic weights for an ensemble of deep models applied to medical imaging classification. In IEEE International Joint Conference on Neural Networks. 1–8.
Andre GC Pacheco and Renato A Krohling. 2021. An attention-based mechanism to combine images and metadata in deep learning models applied to skin cancer classification. IEEE journal of biomedical and health informatics 25, 9 (2021), 3554–3563.
Richard M. Scheffler et al. 2008. Forecasting the global shortage of physicians: an economic-and needs-based approach. Bulletin of the World Health Organization 86 (2008), 516–523B.
Publicado
23/10/2023
Como Citar
PACHECO, Andre G. C.; VICENTE, Clayton Oliveira; MAGESK, Eduarda Pylro; LUCAS, Gabriel Schettino; CALDANA, Guilherme Teixeira; FRASSON, Patricia H. L..
SADE: Software de Análise Dermatológica - Um sistema de coleta, gerenciamento e triagem de lesões de pele. In: WORKSHOP DE FERRAMENTAS E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 111-114.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2023.235905.
