Minicursos do XXI Simpósio Brasileiro de Computação Aplicada à Saúde


Andrey Ricardo Pimentel (ed.)
Lucas Ferrari de Oliveira (ed.)


O Livro de Minicursos do XXI Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2021) aborda temas de interesse para a comunidade de Informática na Saúde. Estes temas vão de biomarcadores para imagens biomédicas, passando pela aplicação de tecnologias como IoT e Blockchain na área da Saúde, terminando com a apresentação de uma norma para requisitos para sistemas de saúde.

O primeiro capítulo, chamado “Precision Radiomic Biomarkers: a brief introduction, some technical development, and several clinical applications” apresenta alguns biomarcadores radiômicos robustos identificados nos últimos anos para diferentes padrões de imagens patológicas. O capítulo apresenta a teoria básica sobre biomarcadores radiômicos e aborda biomarcadores de última geração para três diferentes doenças.

O capítulo “Internet das coisas, blockchain e contratos inteligentes aplicados à saúde” apresenta pesquisas recentes que utilizam IoT, blockchain e contratos inteligentes na área da saúde. É detalhado como empregar estas tecnologias na área da saúde e é apresentado a um exemplo prático construindo uma aplicação descentralizada usando os conceitos apresentados.

O terceiro capítulo “Fundamentals of IEC 62304 with an Agile Software Development Model” apresenta os fundamentos e definições da norma IEC 62304 que visa fornecer requisitos para os fabricantes de sistemas de saúde com Software para demonstrar sua capacidade de fornecer Software que atenda consistentemente aos requisitos do cliente e requisitos regulatórios. Este capítulo também apresenta um Modelo Ágil de Desenvolvimento de Software compatível com a IEC 62304 descrevendo suas principais fases.

Os temas apresentados neste livro tem como objetivo atender interesses tanto de estudantes, professores quanto de profissionais da área de Informática na Saúde.


1. Biomarcadores Radiômicos de Precisão: uma breve introdução, alguns desenvolvimentos técnicos e várias aplicações clínicas
José Raniery Ferreira Junior
2. Internet das coisas, blockchain e contratos inteligentes aplicados à saúde
Jauberth Abijaude, Henrique Serra, Rita Barretto, Aprígio Bezerra, Péricles Sobreira, Fabíola Greve
3. Fundamentos do IEC 62304 com um Modelo Ágil de Desenvolvimento de Software
Johnny Marques, Lilian Barros, Sarasuaty Yelisetty, Talita Slavov


Não há dados estatísticos.


Abijaude, J., Serra, Henrique Bezerra, A., Barretto, R., Sobreira, P., e Greve, F. (2021). Internet das coisas, blockchain e contratos inteligentes aplicados à saúde. Acessado em 09/06/2021.

Adibi, S. (2015). A mobile health network disaster management system. In 2015 seventh international conference on ubiquitous and future networks, pages 424±428. IEEE.

Aerts, H., Velazquez, E., Leijenaar, R., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M., Leemans, C., Dekker, A., Quackenbush, J., Gillies, R., and Lambin, P. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 5(4006).

Ahmad, R. W., Salah, K., Jayaraman, R., Yaqoob, I., Ellahham, S., e Omar, M. (2021). The role of blockchain technology in telehealth and telemedicine. International Journal of Medical Informatics, page 104399.

Al Omar, A., Rahman, M. S., Basu, A., e Kiyomoto, S. (2017). Medibchain: A blockchain based privacy preserving platform for healthcare data. In International conference on security, privacy and anonymity in computation, communication and storage, pages 534±543. Springer.

Albeyatti, A. (2018). Meddicalchain white paper.

Alphand, O., Amoretti, M., Claeys, T., Dall’Asta, S., Duda, A., Ferrari, G., Rousseau, F., Tourancheau, B., Veltri, L., e Zanichelli, F. (2018). Iotchain: A blockchain security architecture for the internet of things. In 2018 IEEE wireless communications and networking conference (WCNC), pages 1±6. IEEE.

Amadasun, M. and King, R. (1989). Textural features corresponding to textural properties. IEEE Transactions on Systems, Man, and Cybernetics, 19(5):1264±1274.

Ambler, S. (2002). Agile Modeling. Wiley, Nova Iorque, Estados Unidos.

Anderson, J. (2018). Securing, standardizing, and simplifying electronic health record audit logs through permissioned blockchain technology.

Antonopoulos, A. (2017). Mastering Bitcoin: Programming the Open Blockchain. O’reilly, 2nd edition.

Araujo, P., Viana, W., Veras, N., Farias, E. J., e de Castro Filho, J. A. (2019). Exploring students perceptions and performance in flipped classroom designed with adaptive learning techniques: A study in distributed systems courses. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE), volume 30, page 219.

Astels, D. (2003). Test-driven Development: A Practical Guide. Pearson Education.

Au, M. H., Yuen, T. H., Liu, J. K., Susilo,W., Huang, X., Xiang, Y., e Jiang, Z. L. (2017). A general framework for secure sharing of personal health records in cloud system. Journal of Computer and System Sciences, 90:46±62.

Azaria, A., Ekblaw, A., Vieira, T., e Lippman, A. (2016). Medrec: Using blockchain for medical data access and permission management. In 2016 2nd International Conference on Open and Big Data (OBD), pages 25±30. IEEE.

Azevedo-Marques, P. M. and Ferreira Junior, J. R. (2021). Medical image analyst: A radiology career focused on comprehensive quantitative imaging analytics to improve healthcare. Academic Radiology. DOI:10.1016/j.acra.2020.11.028.

Bach, L. M., Mihaljevic, B., e Zagar, M. (2018). Comparative analysis of blockchain consensus algorithms. In 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1545±1550. IEEE.

Baeßler, B., Mannil, M., Maintz, D., Alkadhi, H., and Manka, R. (2018). Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-preliminary results. European Journal of Radiology, 102:61±67.

Beck, K. (2000). Extreme Programming Explained: Embrace Change. Addison-Wesley.

Beck, K., Fulano, Beltrano, and Ciclano (2001). Manifesto for agile software development.

Bender, D. e Sartipi, K. (2013). Hl7 fhir: An agile and restful approach to healthcare information exchange. In Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pages 326±331.

Bessani, A., Sousa, J., e Alchieri, E. E. (2014). State machine replication for the masses with bft-smart. In 2014 44th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, pages 355±362. IEEE.

Bianco, C. (2011). Integrating a risk-based approach and iso 62304 into a quality system for medical devices. In Nineteenth Safety-Critical Systems Symposium.

Bianconi, F. and Fernandez, A. (2007). Evaluation of the effects of Gabor filter parameters on texture classification. Pattern Recognition, 40(12):3325±3335.

Buterin, V. e outros. (2014). A next-generation smart contract and decentralized application platform. white paper.

Cachin, C., Caro, A. D., Christidis, K., e Yellick, J. (2016). Architecture of the hyperledger blockchain fabric. Technical report, IBM Research - Zurich.

Caffery, F. M., Burton, J., and Richardson, I. (2010). Risk management capability model for the development of medical device software. Software Quality Journal, 18(1):81±107.

Calheiros, J. L. L., Amorim, L. B. V., Lima, L. L., Lima Filho, A. F., Ferreira Junior, J. R., and Oliveira, M. C. (2021). The effects of perinodular features on solid lung nodule classification. Journal of Digital Imaging. DOI:10.1007/s10278-021-00453-2.

Carvalho, S., Leijenaar, R. T., Troost, E. G., van Timmeren, J. E., Oberije, C., van Elmpt, W., de Geus-Oei, L.-F., Bussink, J., and Lambin, P. (2018). 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)- Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC)±A prospective externally validated study. PloS One, 13(3):e0192859.

Castro, M., Liskov, B., e outros. (1999). Practical byzantine fault tolerance. In OSDI, volume 99, pages 173±186.

Cheema, P. and Burkes, R. (2013). Overall survival should be the primary endpoint in clinical trials for advanced non-small-cell lung cancer. Current Oncology, 20(2):e150.

Chiu, W. H. K., Vardhanabhuti, V., Poplavskiy, D., Yu, P. L. H., Du, R., Yap, A. Y. H., Zhang, S., Fong, A. H.-T., Chin, T.W.-Y., Lee, J. C. Y., et al. (2020). Detection of COVID-19 using deep learning algorithms on chest radiographs. Journal of Thoracic Imaging, 35(6):369±376.

Chrissis, M. B., Konrad, M., and Shrum, S. (2011). CMMI for Development: Guidelines for Process Integration and Product Improvement. Software Engineering Institute.

Cockburn, A. (2004). Crystal Clear: a Human-powered Methodology for Small Teams. Addison-Wesley.

Coinbase (2018). Coinbase wallet. Acessado em 03/03/2021.

Cruciani, F. and Vicario, E. (2011). Reducing complexity of data flow testing in the verification of a iec-62304 flexible workflow system. In 30th International Conference (SAFECOMP).

da Conceição, A. F., da Silva, F. S. C., Rocha, V., Locoro, A., e Barguil, J. M. (2018). Eletronic health records using blockchain technology. arXiv preprint arXiv:1804.10078.

da Saúde, M. (2020). Rede nacional de dados em saúde. Acessado em 05/06/2021.

Davis, B. (2013). Agile Practices for Waterfall Projects. J.ROSS.

Davnall, F., Yip, C. S., Ljungqvist, G., Selmi, M., Ng, F., Sanghera, B., Ganeshan, B., Miles, K. A., Cook, G. J., and Goh, V. (2012). Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Into Imaging, 3(6):573±589.

Dawes, T. J., de Marvao, A., Shi, W., Fletcher, T., Watson, G. M., Wharton, J., Rhodes, C. J., Howard, L. S., Gibbs, J. S. R., Rueckert, D., et al. (2017). Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology, 283(2):381±390.

De Aguiar, E. J., Faiçal, B. S., Krishnamachari, B., e Ueyama, J. (2020). A survey of blockchain-based strategies for healthcare. ACM Computing Surveys (CSUR), 53(2):1±27.

De Angelis, S., Aniello, L., Baldoni, R., Lombardi, F., Margheri, A., e Sassone, V. (2018). Pbft vs proof-of-authority: Applying the cap theorem to permissioned blockchain.

Debe, M., Salah, K., Rehman, M. H. U., e Svetinovic, D. (2019). Iot public fog nodes reputation system: A decentralized solution using ethereum blockchain. IEEE Access, 7:178082±178093.

Dettling, W. (2018). How to teach blockchain in a business school. In Business Information Systems and Technology 4.0, pages 213±225. Springer.

Digumarthy, S. R., Padole, A. M., Gullo, R. L., Sequist, L. V., and Kalra, M. K. (2019). Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status? Medicine, 98(1):e13963.

Dorri, A., Kanhere, S. S., Jurdak, R., e Gauravaram, P. (2017). Blockchain for iot security and privacy: The case study of a smart home. In 2017 IEEE international conference on pervasive computing and communications workshops (PerCom workshops), pages 618±623. IEEE.

Dou, T. H., Coroller, T. P., van Griethuysen, J. J., Mak, R. H., and Aerts, H. J. (2018). Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC. PloS One, 13(11):e0206108.

Dubovitskaya, A., Xu, Z., Ryu, S., Schumacher, M., e Wang, F. (2017). Secure and trustable electronic medical records sharing using blockchain. In AMIA annual symposium proceedings, volume 2017, page 650. American Medical Informatics Association.

Dziembowski, S., Faust, S., Kolmogorov, V., e Pietrzak, K. (2015). Proofs of space. In Annual Cryptology Conference, pages 585±605. Springer.

Echegaray, S., Gevaert, O., Shah, R., Kamaya, A., Louie, J., Kothary, N., and Napel, S. (2015). Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma. Journal of Medical Imaging, 2(4):041011.

Ethereum, W. (2014). A secure decentralised generalised transaction ledger [j]. Ethereum project yellow paper, 151:1±32.

Ethfiddle (2017). Ethfiddle editor rinkeby. Acessado em 03/03/2021.

European Centre for Disease Prevention and Control (2021). COVID-19 situation update worldwide. Online (last update on Jun 3, 2021); last access on Jun 4, 2021. Available at [link].

Faleiros, M. C., Nogueira-Barbosa, M. H., Dalto, V. F., Ferreira Junior, J. R., Tenorio, A. P. M., Luppino-Assad, R., Louzada-Junior, P., Rangayyan, R. M., and Azevedo-Marques, P. M. (2020). Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging. Advances in Rheumatology, 60:1±10. DOI:10.1186/s42358-020-00126-8.

Ferreira Junior, J. R. (2019). Framework for Classification, Content-Based Retrieval, and Radiomics of Medical Images: an investigation of quantitative biomarkers for lung cancer. PhD thesis, Sao Carlos School of Engineering, University of Sao Paulo. DOI:10.11606/T.82.2020.tde-27022020-113956.

Ferreira Junior, J. R. and Cardona Cardenas, D. A. (2021). The potential role of radiogenomics in precision medicine for covid-19. Journal of Thoracic Imaging, 36(3):W34.

Ferreira Junior, J. R. and Oliveira, M. C. (2015). Evaluating margin sharpness analysis on similar pulmonary nodule retrieval. In Computer-Based Medical Systems, IEEE 28th International Symposium on, pages 60±65. DOI:10.1109/CBMS.2015.16.

Ferreira Junior, J. R., Cardenas, D. A. C., Moreno, R. A., Rebelo, M. d. F. d. S., Krieger, J. E., and Gutierrez, M. A. (2021a). A general fully automated deep-learning method to detect cardiomegaly in chest x-rays. In SPIE Medical Imaging 2021: Computer-Aided Diagnosis, volume 11597, page 115972B. DOI:10.1117/12.2581980.

Ferreira Junior, J. R., Cardenas, D. A. C., Moreno, R. A., Rebelo, M. F. S., Krieger, J. E., and Gutierrez, M. A. (2020a). Multiview ensemble convolutional neural network to improve classification of pneumonia in low contrast chest x-ray images. In 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, pages 1238±1241. DOI:10.1109/EMBC44109.2020.9176517.

Ferreira Junior, J. R., Cardenas, D. A. C., Moreno, R. A., Rebelo, M. F. S., Krieger, J. E., and Gutierrez, M. A. (2021b). Novel chest radiographic biomarkers for COVID-19 using radiomic features associated with diagnostics and outcomes. Journal of Digital Imaging. DOI:10.1007/s10278-021-00421-w.

Ferreira Junior, J. R., Koenigkam-Santos, M., de Vita Graves, C., Correia, N. S. C., Cipriano, F. E. G., Fabro, A. T., and Azevedo-Marques, P. M. (2021c). Quantifying intratumor heterogeneity of lung neoplasms with radiomics. Clinical Imaging, 74:27±30.

Ferreira Junior, J. R., Koenigkam-Santos, M., Machado, C. V. B., Faleiros, M. C., Correia, N. S. C., Cipriano, F. E. G., Fabro, A. T., and Azevedo-Marques, P. M. (2021d). Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity. Radiologia Brasileira, 54(2):87±93.

Ferreira Junior, J. R., Oliveira, M. C., and Azevedo-Marques, P. M. (2018). Characterization of pulmonary nodules based on features of margin sharpness and texture. Journal of Digital Imaging, 31(4):451±463.

Ferreira Junior, J. R., Oliveira, M. C., and de Azevedo-Marques, P. M. (2017). Integrating 3D image descriptors of margin sharpness and texture on a GPU-optimized similar pulmonary nodule retrieval engine. The Journal of Supercomputing, 73(8):3451±3467.

Ferreira Junior, J. R., Santos, M. K., Tenorio, A. P. M., Faleiros, M. C., Cipriano, F. E. G., Fabro, A. T., Nappi, J., Yoshida, H., and Azevedo Marques, P. M. (2020b). CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. International Journal of Computer Assisted Radiology and Surgery, 15:163±172.

Ferreira, J. R., Azevedo-Marques, P. M., and Oliveira, M. C. (2017). Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval. International Journal of Computer Assisted Radiology and Surgery, 12(3):509±517.

Frankenfield, J. (2018). Proof of burn. Acessado em 04/05/2021.

Galloway, M. (1975). Texture analysis using gray level run lengths. Computer Graphics and Image Processing, 4(2):172±179.

George, B., Seals, S., and Aban, I. (2014). Survival analysis and regression models. Journal of Nuclear Cardiology, 21(4):686±694.

Gilhuijs, K. G., Giger, M. L., and Bick, U. (1998). Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Medical Physics, 25(9):1647±1654.

Gonzalez, R. C. and Woods, R. E. (2007). Digital Image Processing. Prentice Hall, New Jersey, USA.

Greenhalgh, T. et al. (2020). Management of post-acute covid-19 in primary care. BMJ, 370:m3026.

Greve, F. G. P. (2005). Protocolos fundamentais para o desenvolvimento de aplicaçoes robustas. In Minicursos SBRC 2005: Brazilian Symposium on Computer Networks, pages 330±398.

Greve, F. G., Sampaio, L. S., Abijaude, J. A., Coutinho, A. C., Valcy, Í. V., e Queiroz, S. Q. (2018). Blockchain e a revolução do consenso sob demanda. Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos (SBRC)-Minicursos.

Guo, R., Shi, H., Zhao, Q., e Zheng, D. (2018). Secure attributebased signature scheme with multiple authorities for blockchain in electronic health records systems. IEEE access, 6:11676±11686.

Guo, R., Shi, H., Zheng, D., Jing, C., Zhuang, C., e Wang, Z. (2019). Flexible and efficient blockchain-based abe scheme with multi-authority for medical on demand in telemedicine system. IEEE Access, 7:88012±88025.

Halamka, J. D., Alterovitz, G., Buchanan, W. J., Cenaj, T., Clauson, K. A., Dhillon, V., Hudson, F. D., Mokhtari, M. M., Porto, D. A., Rutschman, A., e outros. (2019). Top 10 blockchain predictions for the (near) future of healthcare. Blockchain in Healthcare Today.

Haralick, R., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6):610±621.

Herlihy, M. (2018). Atomic cross-chain swaps. In Proceedings of the 2018 ACM symposium on principles of distributed computing, pages 245±254.

Highsmith, J. A. (2000). Adaptive Software Development: a Collaborative Approach to Managing Complex Systems. Dorset House Publishing.

Höss, A., Lampe, C., Panse, R., Ackermann, B., Naumann, J., and Jäkel, O. (2014). First experiences with the implementation of the european standard en 62304 on medical device software for the quality assurance of a radiotherapy unit. Radiat Oncol, 9(79):1±10.

Hoy, M. B. (2017). An introduction to the blockchain and its implications for libraries and medicine. Medical reference services quarterly, 36(3):273±279.

Huhn, M. and Zechner, A. (2010). Arguing for software quality in an iec 62304 compliant development process. In 4th International Symposium on Leveraging Applications.

Hussien, H. M., Yasin, S. M., Udzir, N. I., Ninggal, M. I. H., e Salman, S. (2021). Blockchain technology in the healthcare industry: Trends and opportunities. Journal of Industrial Information Integration, 22:100217.

IEC (2006). Iec 62304:2006 medical device software - software life-cycle processes ± amendment 1. Technical report, International Electrotechnical Commission.

IEC (2010a). Iec 61010-1:2010 safety requirements for electrical equipment for measurement, control, and laboratory use - part 1: General requirementssafety requirements for electrical equipment for measurement, control, and laboratory use - part 1: General requirements. Technical report, International Electrotechnical Commission.

IEC (2010b). Iec61508-3:2010 functional safety of electrical/electronic/programmable electronic safety related sysyste - software requirements.

IEC (2015). Iec 62304:2006/amd 1:2015 medical device software - software life-cycle processes ± amendment 1. Technical report, International Electrotechnical Commission.

IEC (2016). Iec 82304-1:2016 health software - part 1: General requirements for product safety. Technical report, International Electrotechnical Commission.

IEC (2020). Iec 60601-1-12:2014/amd 1:2020 medical electrical equipment part 1-12: General requirements for basic safety and essential performance - collateral standard: Requirements for medical electrical equipment and medical electrical systems intended for use in the emergency medical services environment. Technical report, International Electrotechnical Commission.

ISO (2016a). Iso 13485:2016 medical devices - quality management systems-requirements for regulatory purposes. Technical report, International Standardization.

ISO (2016b). Iso tr 29110:2016 systems and software engineering - lifecycle profiles for very small entities (vses). Technical report, International Standardization Organization.

ISO (2017). Iso/iec/ieee 12207:2017 systems and software engineering - software life cycle processes. Technical report, Internation Standardization Organization.

ISO (2018). Iso/iec/ieee 90003:2018 software engineering - guidelines for the application of iso 9001:2008 to computer software. Technical report, International Standardization.

ISO (2019). Iso 14971:2019 medical devices - application of risk management to medical devices. Technical report, International Standardization.

Jaxx (2018). Jaxx safely manager ethereum. Acessado em 03/03/2021.

Jiang, J.,Wen, S., Yu, S., Xiang, Y., e Zhou,W. (2016). Identifying propagation sources in networks: State-of-the-art and comparative studies. IEEE Communications Surveys & Tutorials, 19(1):465±481.

Jordan, P. (2006). Standard iec 62304 - medical device software - software lifecycle processes. In IET Seminar on Software for Medical devices.

Kaplan, E. and Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282):457±481.

Kasisopha, N. and Meananeatra, P. (2019). Applying iso/iec 29110 to iso/iec 62304 for medical device software sme. In 2nd International Conference on Computing and Big Data.

Kazmi, H. S. Z., Nazeer, F., Mubarak, S., Hameed, S., Basharat, A., e Javaid, N. (2019). Trusted remote patient monitoring using blockchain-based smart contracts. In International Conference on Broadband and Wireless Computing, Communication and Applications, pages 765±776. Springer.

Keek, S. A., Leijenaar, R. T., Jochems, A., and Woodruff, H. C. (2018). A review on radiomics and the future of theragnostics for patient selection in precision medicine. The British Journal of Radiology, 91(1091):20170926.

Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5):1122±1131.

Kiayias, A., Russell, A., David, B., e Oliynykov, R. (2017). Ouroboros: A provably secure proof-of-stake blockchain protocol. In Annual International Cryptology Conference, pages 357±388. Springer.

Kickingereder, P., Bonekamp, D., Nowosielski, M., Kratz, A., Sill, M., Burth, S., Wick, A., Eidel, O., Schlemmer, H.-P., Radbruch, A., et al. (2016). Radiogenomics of glioblastoma: machine learning±based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology, 281(3):907±918.

Kolan, A., Tjoa, S., e Kieseberg, P. Medical blockchains and privacy in austria-technical and legal aspects.

Kolossvary, M., Kellermayer, M., Merkely, B., and Maurovich-Horvat, P. (2018). Cardiac computed tomography radiomics: A comprehensive review on radiomic techniques. Journal of Thoracic Imaging, 33(1):26±34.

Kotz, D., Gunter, C. A., Kumar, S., e Weiner, J. P. (2016). Privacy and security in mobile health: A research agenda. Computer, 49(6):22±30.

Kwon, J. (2014). Tendermint: Consensus without mining. Draft v. 0.6, fall, 1(11).

Lambin, P. (2021). Radiomics: transforming standard imaging into mineable data for diagnostic and theragnostic applications. In SPIE Medical Imaging 2021: Physics of Medical Imaging, volume 11595, page 1159502. DOI:10.1117/12.2585711.

Lamport, L., Shostak, R., e Pease, M. (1982). The byzantine generals problem. ACM Trans. Program. Lang. Syst., 4(3):382±401.

Lao, L., Li, Z., Hou, S., Xiao, B., Guo, S., e Yang, Y. (2020). A survey of iot applications in blockchain systems: Architecture, consensus, and traffic modeling. ACM Computing Surveys (CSUR), 53(1):1±32.

Larimer, D. (2014). Delegated proof-of-stake (dpos). Bitshare whitepaper, 81:85.

Larson, B., Hatcliff, J., Procter, S., and Chalin, P. (2012). Requirements specification for apps in medical application platforms. In 4th International Workshop on Software Engineering in Health Care (SEHC).

Larue, R. T., Defraene, G., De Ruysscher, D., Lambin, P., and Van Elmpt, W. (2017). Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. The British Journal of Radiology, 90(1070):20160665.

Laukkarinen, T., Kuusinen, K., and Mikkonen, T. (2017). Devops in regulated software development: Case medical devices. In 2017 IEEE/ACM 39th International Conference on Software Engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER).

LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436±444.

Lee, C. K. (2019). Blockchain application with health token in medical & health industrials. In 2nd International Conference on Social Science, Public Health and Education (SSPHE 2018), pages 233±236. Atlantis Press.

Lee, G. R., Gommers, R., Waselewski, F., Wohlfahrt, K., and O’Leary, A. (2019). Pywavelets: A python package for wavelet analysis. Journal of Open Source Software, 4(36):1237.

Lee, I. e Lee, K. (2015). The internet of things (iot): Applications, investments, and challenges for enterprises. Business Horizons, 58(4):431±440.

Leger, S., Zwanenburg, A., Pilz, K., Lohaus, F., Linge, A., Zophel, K., Kotzerke, J., Schreiber, A., Tinhofer, I., Budach, V., et al. (2017). A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling. Scientific Reports, 7(1):13206.

Lerner, S. D. (2015). Dagcoin: a cryptocurrency without blocks. White paper.

Levman, J. E. and Martel, A. L. (2011). A margin sharpness measurement for the diagnosis of breast cancer from magnetic resonance imaging examinations. Academic Radiology, 18(12):1577±1581.

Li, M., Lei, P., Zeng, B., Li, Z., Yu, P., Fan, B., Wang, C., Li, Z., Zhou, J., Hu, S., et al. (2020). Coronavirus disease (COVID-19): spectrum of CT findings and temporal progression of the disease. Academic Radiology, 27(5):603±608.

Liang, G. and Zheng, L. (2019). A transfer learning method with deep residual network for pediatric pneumonia diagnosis. Computer Methods and Programs in Biomedicine. Ahead of print. DOI:10.1016/j.cmpb.2019.06.023.

Liu, C., Liu, H., Cao, Z., Chen, Z., Chen, B., e Roscoe, B. (2018). Reguard: finding reentrancy bugs in smart contracts. In 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion), pages 65±68. IEEE.

Lu, M. T., Ivanov, A., Mayrhofer, T., Hosny, A., Aerts, H. J., and Hoffmann, U. (2019). Deep learning to assess long-term mortality from chest radiographs. JAMA Network Open, 2(7):e197416±e197416.

Lubner, M. G., Smith, A. D., Sandrasegaran, K., Sahani, D. V., and Pickhardt, P. J. (2017). CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 37(5):1483±1503.

Magnuson, A. (2012). Iec/iso 62304 regulations for the development of medical software devices. Master’s thesis, Chalmers University of Technology.

Mannaro, K., Baralla, G., Pinna, A., e Ibba, S. (2018). A blockchain approach applied to a teledermatology platform in the sardinian region (italy). Information, 9(2):44.

Marques, J. (2019). Uma análise das características de especificação de requisitos de software em normas de ambientes regulados. In 22ë Workshop de Engenharia de Requisitos (WER 2019).

Marques, J. and Cunha, A. (2019). Ares: An agile requirements specification process for regulated environments. International Journal of Software Engineering and Knowledge Engineering (IJSEKE), 29(10):1403±1438.

Marques, J., Yelisetty, S., and Barros, L. (2021). Um mapeamento sistemático da literatura no uso da iec 62304. Journal of Health Informatics.

Mauer, T. and Marin, H. (2017). Instrumento de avaliação de implantação de sistemas de informação em saúde. Journal of Health Informatics, 9(4):111±118.

Mazieres, D. (2015). The stellar consensus protocol: A federated model for internet-level consensus. Stellar Development Foundation, 32.

McGhin, T., Choo, K.-K. R., Liu, C. Z., e He, D. (2019). Blockchain in healthcare applications: Research challenges and opportunities. Journal of Network and Computer Applications, 135:62±75.

Mchugh, M., Caffery, F. M., and Casey, V. (2012). Software process improvement to assist medical device software development organizations to comply with the amendments to the medical device directive. IET Software, 6(5):431±437.

Metamask (2018). Metamask crypto wallet and gateway. Acessado em 03/03/2021.

Milutinovic, M., He, W., Wu, H., e Kanwal, M. (2016). Proof of luck: An efficient blockchain consensus protocol. In proceedings of the 1st Workshop on System Software for Trusted Execution, pages 1±6.

Munch, J., Armbrunt, O., Kowalczyk, M., and Soto, M. (2012). Software Process Definition and Management. Springer-Verlag, Berlim, Germany.

MyCrypto (2019). Mycrypto. Acessado em 03/03/2021.

Myetherwallet (2019). Myetherwallet original wallet. Acessado em 03/03/2021.

Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Technical report, Bitcoin Org.

Narayanan, A., Bonneau, J., Felten, E., Miller, A., e Goldfeder, S. (2016). Bitcoin and cryptocurrency technologies. Princeton University Press.

of Health, U. D., Services, H., e outros. (2008). Personal health records and the hipaa privacy rule. Washington, DC. URL: [link] [accessed 2016-06-20][WebCite Cache].

Osman, A. H., Aljahdali, H. M., Altarrazi, S. M., and Ahmed, A. (2021). SOM-LWL method for identification of COVID-19 on chest x-rays. PloS One. DOI:10.1371/journal.pone.0247176.

Pairo-Castineira, E., Clohisey, S., Klaric, L., Bretherick, A. D., Rawlik, K., Pasko, D., Walker, S., Parkinson, N., Fourman, M. H., Russell, C. D., et al. (2021). Genetic mechanisms of critical illness in covid-19. Nature, 591(7848):92±98.

Palmer, S. R. and Felsing, J. M. (2002). A Practical Guide to Feature Driven Development. Pearson Educational.

Parker, J. R. (2011). Algorithms for Image Processing and Computer Vision. Wiley Publishing, Indianapolis, USA.

Patel, V. (2019). A framework for secure and decentralized sharing of medical imaging data via blockchain consensus. Health informatics journal, 25(4):1398±1411.

Peterson, K., Vakkalanka, S., and Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1±18.

Phillips, I., Ajaz, M., Ezhil, V., Prakash, V., Alobaidli, S., McQuaid, S. J., South, C., Scuffham, J., Nisbet, A., and Evans, P. (2017). Clinical applications of textural analysis in non-small cell lung cancer. The British Journal of Radiology, 91(1081):20170267.

PoET (2018). Poet 1.0 specification. Acessado em 04/05/2021.

Popov, S. (2018). The tangle, iota whitepaper. Acessado em 03/03/2021.

Popov, S., Moog, H., e et al. (2020). The coordicide. white paper Iota Foundation.

Pressman, R. and Maxim, B. (2015). Software Engineering: A Practitioner’s Approach. McGraw-Hill Education.

Raikwar, M., Mazumdar, S., Ruj, S., Gupta, S. S., Chattopadhyay, A., e Lam, K.-Y. (2018). A blockchain framework for insurance processes. In 2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pages 1±4. IEEE.

Rajaraman, S., Sornapudi, S., Alderson, P. O., Folio, L. R., and Antani, S. K. (2020). Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs. PloS One, 15(11):e0242301.

Ramachandran, S., Kiruthika, O. O., Ramasamy, A., Vanaja, R., e Mukherjee, S. (2020). A review on blockchain-based strategies for management of electronic health records (ehrs). In 2020 International Conference on Smart Electronics and Communication (ICOSEC), pages 341±346. IEEE.

Rao, A. R. e Dave, R. (2019). Developing hands-on laboratory exercises for teaching stem students the internet-of-things, cloud computing and blockchain applications. In 2019 IEEE Integrated STEM Education Conference (ISEC), pages 191±198. IEEE.

Regan, G., Caffery, F. M., Daid, K. M., and D. Flood, D. (2013). Medical device standards’ requirements for traceability during the software development lifecycle and implementation of a traceability assessment model. Computer Standards Interfaces, 36(1):3±9.

Remix (2015). Remix ide. Acessado em 03/03/2021.

Roehrs, A., Da Costa, C. A., e da Rosa Righi, R. (2017). Omniphr: A distributed architecture model to integrate personal health records. Journal of biomedical informatics, 71:70±81.

Rudwaleit, M., Jurik, A.-G., Hermann, K. A., LandewÂe, R., van der Heijde, D., Baraliakos, X., Marzo-Ortega, H., éstergaard, M., Braun, J., and Sieper, J. (2009). Defining active sacroiliitis on magnetic resonance imaging (MRI) for classification of axial spondyloarthritis: a consensual approach by the ASAS/OMERACT MRI group. Annals of the Rheumatic Diseases, 68(10):1520±1527.

Rust, P., Flood, D., and McCaffery, F. (2016). Creation of an iec 62304 compliant software development plan. Journal of Software Evolution and Process, 28(11):1.1±1.10.

Sacconi, B., Anzidei, M., Leonardi, A., Boni, F., Saba, L., Scipione, R., Anile, M., Rengo, M., Longo, F., Bezzi, M., et al. (2017). Analysis of ct features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with egfr mutations and survival rates. Clinical Radiology, 72(6):443±450.

Salah, K., Alfalasi, A., Alfalasi, M., Alharmoudi, M., Alzaabi, M., Alzyeodi, A., e Ahmad, R. W. (2020). Iot-enabled shipping container with environmental monitoring and location tracking. In 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), pages 1±6. IEEE.

Samaniego, M., Jamsrandorj, U., e Deters, R. (2016). Blockchain as a service for iot. In 2016 IEEE international conference on internet of things (iThings) and IEEE green computing and communications (GreenCom) and IEEE cyber, physical and social computing (CPSCom) and IEEE smart data (SmartData), pages 433±436. IEEE.

Sandgaard, J. e Wishstar, S. (2018). Medchain white paper. Acessado em 04/06/2021.

Santos, B. P., Silva, L. A., Celes, C., Borges, J. B., Neto, B. S. P., Vieira, M. A. M., Vieira, L. F. M., Goussevskaia, O. N., e Loureiro, A. (2016). Internet das coisas: da teoria à prática. Minicursos SBRC-Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, 31.

Santos, M. K., Ferreira Junior, J. R., Wada, D. T., Tenorio, A. P. M., Barbosa, M. H. N., and Azevedo Marques, P. M. (2019). Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia Brasileira, 52(6):387±396.

Saweros, E. e Song, Y.-T. (2019). Connecting heterogeneous electronic health record systems using tangle. In International Conference on Ubiquitous Information Management and Communication, pages 858±869. Springer.

Schneider, C., Rasband, W., and Eliceiri, K. (2012). NIH Image to ImageJ: 25 years of image analysis. Nature Methods, 9(7):671±675.

Schwaber, K. and Beedle, M. (2001). Agile Software Development with SCRUM. Prentice-Hall.

Shahnaz, A., Qamar, U., e Khalid, A. (2019). Using blockchain for electronic health records. IEEE Access, 7:147782±147795.

Sieper, J., Rudwaleit, M., Baraliakos, X., Brandt, J., Braun, J., Burgos-Vargas, R., Dougados, M., Hermann, K., Landewe, R., Maksymowych, W., et al. (2009). The Assessment of SpondyloArthritis international Society (ASAS) handbook: a guide to assess spondyloarthritis. Annals of the Rheumatic Diseases, 68(Suppl 2):ii1±ii44.

Silvano, W. F. e Marcelino, R. (2020). Iota tangle: A cryptocurrency to communicate internet-of-things data. Future Generation Computer Systems, 112:307±319.

Siyal, A. A., Junejo, A. Z., Zawish, M., Ahmed, K., Khalil, A., e Soursou, G. (2019). Applications of blockchain technology in medicine and healthcare: Challenges and future perspectives. Cryptography, 3(1):3.

Sobreira, P. (2005). Desenvolvimento de uma arquitetura microcontrolada para tratamento de sinais biológicos. Master’s thesis, Universidade Federal de Pernambuco.

Sommerville, I. (2015). Software Engineering. Pearson.

Sousa, J., Bessani, A., e Vukolic, M. (2018). A byzantine faulttolerant ordering service for the hyperledger fabric blockchain platform. In 2018 48th annual IEEE/IFIP international conference on dependable systems and networks (DSN), pages 51±58. IEEE.

Stapleton, J. (1997). DSDM - Dynamic Systems Development Method. Addison-Wesley.

Status (2019). Status private, secure communication. Acessado em 03/03/2021.

Stober, T. and Hansmann, U. (2010). Agile Software Development - Best Practices for Large Software Projects. Springer.

StudioEthereum (2019). Studio ethereum. Acessado em 03/03/2021.

Sun, C. andWee,W. G. (1983). Neighboring gray level dependence matrix for texture classification. Computer Vision, Graphics, and Image Processing, 23(3):341±352.

Sun, H., Chen, Y., Huang, Q., Lui, S., Huang, X., Shi, Y., Xu, X., Sweeney, J. A., and Gong, Q. (2017). Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis. Radiology, 287(2):620±630.

Sutherland, J. (2010). SCRUM Handbook,. Scrum Training Institute Press.

Szabo, N. (1997). Formalizing and securing relationships on public networks. First Monday, 2(9).

Sztajnberg, A., da Silva Macedo, R., e Stutzel, M. (2018). Protocolos de aplicação para a internet das coisas: conceitos e aspectos práticos. Sociedade Brasileira de Computação.

Tamura, H., Mori, S., and Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics, 8(6):460±473.

Tang, X. (1998). Texture information in run-length matrices. IEEE Transactions on Image Processing, 7(11):1602±1609.

Tenorio, A. P. M., Faleiros, M. C., Junior, J. R. F., Dalto, V. F., Assad, R. L., Louzada-Junior, P., Yoshida, H., Nogueira-Barbosa, M. H., and Azevedo- Marques, P. M. (2020). A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis. International Journal of Computer Assisted Radiology and Surgery, 15(10):1737±1748.

Thatcher, C. e Acharya, S. (2018). Pharmaceutical uses of blockchain technology. In 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), pages 1±6. IEEE.

Thibault, G., Angulo, J., and Meyer, F. (2013). Advanced statistical matrices for texture characterization: application to cell classification. IEEE Transactions on Biomedical Engineering, 61(3):630±637.

Todd, P. (2015). Ripple protocol consensus algorithm review. Ripple Labs Inc White Paper (May, 2015) [link].

Tomaszewski, M. R. and Gillies, R. J. (2021). The biological meaning of radiomic features. Radiology, 298(3):505±516.

Trust (2019). Trust wallet - secure crypto wallet. Acessado em 03/03/2021.

Tsui, F., Karam, O., and Bernal, B. (2015). Essentials of Software Engineering. Jones Bartlett Learning.

Van Griethuysen, J. J., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G., Fillion-Robin, J.-C., Pieper, S., and Aerts, H. J. (2017). Computational radiomics system to decode the radiographic phenotype. Cancer Research, 77(21):e104±e107.

van Timmeren, J. E., van Elmpt, W., Leijenaar, R. T., Reymen, B., Monshouwer, R., Bussink, J., Paelinck, L., Bogaert, E., De Wagter, C., Elhaseen, E., et al. (2019). Longitudinal radiomics of cone-beam ct images from non-small cell lung cancer patients: evaluation of the added prognostic value for overall survival and locoregional recurrence. Radiotherapy and Oncology, 136:78±85.

Varri, A. and de la Cruz, P. K.-Z. R. (2019). Software life cycle standard for health software. Stud Health Technol Inform, 264:868±872.

Vuori, M. (2011). Agile development of safety-critical software. Technical report, Tampere University of Technology.

Wasson, C. (2015). System Engineering Analysis, Design, and Development: Concepts, Principles, and Practices. Wiley Series in Systems Engineering and Management.

Web3js (2016). Ethereum javascript api. Acessado em 03/03/2021.

Wehbe, R. M., Sheng, J., Dutta, S., Chai, S., Dravid, A., Barutcu, S., Wu, Y., Cantrell, D. R., Xiao, N., Allen, B. D., et al. (2020). DeepCOVID-XR: An artificial intelligence algorithm to detect COVID-19 on chest radiographs trained and tested on a large US clinical dataset. Radiology. DOI:10.1148/radiol.2020203511.

Weissman, S. M., Zellmer, K., Gill, N., e Wham, D. (2018). Implementing a virtual health telemedicine program in a community setting. Journal of genetic counseling, 27(2):323±325.

Wong, H. Y. F., Lam, H. Y. S., Fong, A. H.-T., Leung, S. T., Chin, T. W.-Y., Lo, C. S. Y., Lui, M. M.-S., Lee, J. C. Y., Chiu, K.W.-H., Chung, T.W.-H., et al. (2020). Frequency and distribution of chest radiographic findings in patients positive for COVID-19. Radiology, 296(2):E72±E78.

Wong, K. and Callaghan, C. (2012). Managing requirements baselines for medical device software development. In 2012 IEEE International Systems Conference (SysCon).

Wu, M., Wang, K., Cai, X., Guo, S., Guo, M., e Rong, C. (2019). A comprehensive survey of blockchain: From theory to iot applications and beyond. IEEE Internet of Things Journal, 6(5):8114±8154.

Xu, J., Napel, S., Greenspan, H., Beaulieu, C. F., Agrawal, N., and Rubin, D. (2012). Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval. Medical Physics, 39:5405±5418.

Yang, J., Zhang, L., Fave, X., Fried, D., Stingo, F., Ng, C., and Court, L. (2016). Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors. Computerized Medical Imaging and Graphics, 48:1±8.

Yip, S. S., Liu, Y., Parmar, C., Li, Q., Liu, S., Qu, F., Ye, Z., Gillies, R. J., and Aerts, H. J. (2017). Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Scientific Reports, 7(1):3519.

Yli-Huumo, J., Ko, D., Choi, S., Park, S., e Smolander, K. (2016). Where is current research on blockchain technology?-a systematic review. PloS one, 11(10):e0163477.

Yüksel, B., Küpçü, A., e Öznur Özkasap (2017). Research issues for privacy and security of electronic health services. Future Generation Computer Systems, 68:1±13.

Zhang, L., Fried, D., Fave, X., Hunter, L., Yang, J., and Court, L. (2015). IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Medical Physics, 42(3):1341±1353.

Zhang, R., Tie, X., Qi, Z., Bevins, N. B., Zhang, C., Griner, D., Song, T. K., Nadig, J. D., Schiebler, M. L., Garrett, J. W., et al. (2021). Diagnosis of coronavirus disease 2019 pneumonia by using chest radiography: Value of artificial intelligence. Radiology, 298(2):E88±E97.

Zhang, Y., Liu, T., Li, K., e Zhang, J. (2017). Improved visual correlation analysis for multidimensional data. Journal of Visual Languages and Computing, 41:121±132.

Zheng, Z., Xie, S., Dai, H.-N., Chen, X., e Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4):352±375.

Zhu, X., Dong, D., Chen, Z., Fang, M., Zhang, L., Song, J., Yu, D., Zang, Y., Liu, Z., Shi, J., et al. (2018). Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. European radiology, 28(7):2772±2778.

Zwanenburg, A., Vallieres, M., Abdalah, M. A., Aerts, H. J., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020). The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology, 295(2):328±338.

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