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10 results on '"Mzurikwao, Deogratias"'

1. FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Author

Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, and Starmans, Martijn P A

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.0, I.4.0, I.5.0
Abstract

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

Published
2023

2. Application of deep neural network in healthcare data

Author

Mzurikwao, Deogratias, Ang, Jim, Marcelli, Gianluca, and Efstratiou, Christos

Subjects
362.10285
Abstract

Biomedical data analysis has been playing an important role in healthcare provision services. For decades, medical practitioners and researchers have been extracting and analyse biomedical data to derive different health-related information. Recently, there has been a significant rise in the amount of biomedical data collection. This is due to the availability of biomedical devices for the extraction of biomedical data which are more portable, easy to use and affordable, as an effect technology advancement. As the amount of biomedical data produced every day increases, the risk of human making analytical and diagnostic mistakes also increases. For example, there are approximately 40 million diagnostic errors involving medical imaging annually worldwide, hence rise a need for the development of fast, accurate, reliable and automatic means for analysis of biomedical data. Conventional machine learning has been used to assist in the analysis and interpretation of biomedical data automatically, but always limited with the need for feature extraction process to train the built models. To achieve this, three studies have been conducted. Two studies were conducted by using EEG signals and one study by using microscopic images of cancer cells. In the first study with EEG signals, our method managed to interpret motor imaginary activities from a 64 channels EEG device with 99% classification accuracy when all the 64 channels were used and 91.5% classification when the number of channels was selected to eight (8) channels. In a second study which involved steady-state visual evoked potential form of EEG signals, our method achieved an average of 94% classification accuracy by using two channels, skin like EEG sensor. In the third study for authentication of cancer cell lines by using microscopic images, our method managed to attain an average of 0.91 F1-score in the authentication of eight classes of cancer cell lines. Studies reported in this thesis, significantly shows that CNN can play a major role in the development of a computerised way in the analysis of biomedical data. Towards provision of better healthcare by using CNN in analysis of different formats of biomedical data, this thesis has three major contributions, i) introduction of a new method for EEG channels selection towards development of portable EEG sensors for real-life application, and ii) introduction of a method for cancer cell lines authentication in the laboratory environment towards development of anti-cancer drugs, and iii) Introduction of a method for authentication of isogenic cancer cell lines.

Published
2020
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4. The use of artificial intelligence-based innovations in the health sector in Tanzania : A scoping review

Author

Sukums, Felix, Mzurikwao, Deogratias, Sabas, Deodatus, Chaula, Rebecca, Mbuke, Juliana, Kabika, Twaha, Kaswija, John, Ngowi, Bernard, Noll, Josef, Winkler, Andrea S., Wamala, Sarah, Sukums, Felix, Mzurikwao, Deogratias, Sabas, Deodatus, Chaula, Rebecca, Mbuke, Juliana, Kabika, Twaha, Kaswija, John, Ngowi, Bernard, Noll, Josef, Winkler, Andrea S., and Wamala, Sarah

Abstract

Background: Artificial Intelligence (AI) has great potential to transform health systems to improve the quality of healthcare services. However, AI is still new in Tanzania, and there is limited knowledge about the application of AI technology in the Tanzanian health sector.Objectives: This study aims to explore the current status, challenges, and opportunities for AI application in the health system in Tanzania. Methods: A scoping review was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis Extensions for Scoping Review (PRISMA-ScR). We searched different electronic databases such as PubMed, Embase, African Journal Online, and Google Scholar.Results: Eighteen (18) studies met the inclusion criteria out of 2,017 studies from different electronic databases and known AI-related project websites. Amongst AI-driven solutions, the studies mostly used machine learning (ML) and deep learning for various purposes, including prediction and diagnosis of diseases and vaccine stock optimisation. The most commonly used algorithms were conventional machine learning, including Random Forest and Neural network, Naive Bayes K-Nearest Neighbour and Logistic regression. Conclusions: This review shows that AI-based innovations may have a role in improving health service delivery, including early outbreak prediction and detection, disease diagnosis and treatment, and efficient management of healthcare resources in Tanzania. Our results indicate the need for developing national AI policies and regulatory frameworks for adopting responsible and ethical AI solutions in the health sector in accordance with the World Health Organisation (WHO) guidance on ethics and governance of AI for health.

Published
2023
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6. Application of Deep Neural Network in Healthcare data

Author

Mzurikwao, Deogratias, Ang, Jim, Marcelli, Gianluca, and Efstratiou, Christos

Abstract

Biomedical data analysis has been playing an important role in healthcare provision services. For decades, medical practitioners and researchers have been extracting and analyse biomedical data to derive different health-related information. Recently, there has been a significant rise in the amount of biomedical data collection. This is due to the availability of biomedical devices for the extraction of biomedical data which are more portable, easy to use and affordable, as an effect technology advancement. As the amount of biomedical data produced every day increases, the risk of human making analytical and diagnostic mistakes also increases. For example, there are approximately 40 million diagnostic errors involving medical imaging annually worldwide, hence rise a need for the development of fast, accurate, reliable and automatic means for analysis of biomedical data. Conventional machine learning has been used to assist in the analysis and interpretation of biomedical data automatically, but always limited with the need for feature extraction process to train the built models. To achieve this, three studies have been conducted. Two studies were conducted by using EEG signals and one study by using microscopic images of cancer cells. In the first study with EEG signals, our method managed to interpret motor imaginary activities from a 64 channels EEG device with 99% classification accuracy when all the 64 channels were used and 91.5% classification when the number of channels was selected to eight (8) channels. In a second study which involved steady-state visual evoked potential form of EEG signals, our method achieved an average of 94% classification accuracy by using two channels, skin like EEG sensor. In the third study for authentication of cancer cell lines by using microscopic images, our method managed to attain an average of 0.91 F1-score in the authentication of eight classes of cancer cell lines. Studies reported in this thesis, significantly shows that CNN can play a major role in the development of a computerised way in the analysis of biomedical data. Towards provision of better healthcare by using CNN in analysis of different formats of biomedical data, this thesis has three major contributions, i) introduction of a new method for EEG channels selection towards development of portable EEG sensors for real-life application, and ii) introduction of a method for cancer cell lines authentication in the laboratory environment towards development of anti-cancer drugs, and iii) Introduction of a method for authentication of isogenic cancer cell lines.

Published
2021

7. A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer networks

Author

Samuel, Oluwarotimi Williams, primary, Yang, Bin, additional, Geng, Yanjuan, additional, Asogbon, Mojisola Grace, additional, Pirbhulal, Sandeep, additional, Mzurikwao, Deogratias, additional, Idowu, Oluwagbenga Paul, additional, Ogundele, Tunde Joseph, additional, Li, Xiangxin, additional, Chen, Shixiong, additional, Naik, Ganesh R., additional, Fang, Peng, additional, Han, Fanghai, additional, and Li, Guanglin, additional

Published
2020
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8. A Channel Selection Approach Based on Convolutional Neural Network for Multi-channel EEG Motor Imagery Decoding

Author

Mzurikwao, Deogratias, Williams Samuel, Oluwarotimi, Grace Asogbon, Mojisola, Li, Xiangxin, Li, Guanglin, Yeo, Woon-Hong, Efstratiou, Christos, Siang Ang, Chee, Mzurikwao, Deogratias, Williams Samuel, Oluwarotimi, Grace Asogbon, Mojisola, Li, Xiangxin, Li, Guanglin, Yeo, Woon-Hong, Efstratiou, Christos, and Siang Ang, Chee

Abstract

For many disabled people, brain computer interface (BCI) may be the only way to communicate with others and to control things around them. Using motor imagery paradigm, one can decode an individual's intention by using their brainwaves to help them interact with their environment without having to make any physical movement. For decades, machine learning models, trained on features extracted from acquired electroencephalogram (EEG) signals have been used to decode motor imagery activities. This method has several limitations and constraints especially during feature extraction. Large number of channels on the current EEG devices make them hard to use in real-life as they are bulky, uncomfortable to wear, and takes lot of time in preparation. In this paper, we introduce a technique to perform channel selection using convolutional neural network (CNN) and to decode multiple classes of motor imagery intentions from four participants who are amputees. A CNN model trained on EEG data of 64 channels achieved a mean classification accuracy of 99.7% with five classes. Channel selection based on weights extracted from the trained model has been performed with subsequent models trained on eight selected channels achieved a reasonable accuracy of 91.5%. Training the model in time domain and frequency domain was also compared, different window sizes were experimented to test the possibilities of realtime application. Our method of channel selection was then evaluated on a publicly available motor imagery EEG dataset.

Published
2019

9. Fully portable and wireless universal brain-machine interfaces enabled by flexible scalp electronics and deep-learning algorithm

Author

Mahmood, Musa, Mzurikwao, Deogratias, Kim, Yun-Soung, Lee, Yongkuk, Mishra, Saswat, Herbert, Robert, Duarte, Audrey, Ang, Chee Siang, Yeo, Woon-Hong, Mahmood, Musa, Mzurikwao, Deogratias, Kim, Yun-Soung, Lee, Yongkuk, Mishra, Saswat, Herbert, Robert, Duarte, Audrey, Ang, Chee Siang, and Yeo, Woon-Hong

Abstract

Variation in human brains creates difficulty in implementing electroencephalography (EEG) into universal brain-machine interfaces (BMI). Conventional EEG systems typically suffer from motion artifacts, extensive preparation time, and bulky equipment, while existing EEG classification methods require training on a per-subject or per-session basis. Here, we introduce a fully portable, wireless, flexible scalp electronic system, incorporating a set of dry electrodes and flexible membrane circuit. Time domain analysis using convolutional neural networks allows for an accurate, real-time classification of steady-state visually evoked potentials on the occipital lobe. Simultaneous comparison of EEG signals with two commercial systems captures the improved performance of the flexible electronics with significant reduction of noise and electromagnetic interference. The two-channel scalp electronic system achieves a high information transfer rate (122.1 ± 3.53 bits per minute) with six human subjects, allowing for a wireless, real-time, universal EEG classification for an electronic wheelchair, motorized vehicle, and keyboard-less presentation.

Published
2019

10. Rethinking global digital health and AI-for-health innovation challenges.

Author

Farlow A, Hoffmann A, Tadesse GA, Mzurikwao D, Beyer R, Akogo D, Weicken E, Matika T, Nweje MI, Wamae W, Arts S, Wiegand T, Bennett C, Farhat MR, and Gröschel MI

Abstract

Digital health technologies can help tackle challenges in global public health. Digital and AI-for-Health Challenges, controlled events whose goal is to generate solutions to a given problem in a defined period of time, are one way of catalysing innovation. This article proposes an expanded investment framework for Global Health AI and digitalhealth Innovation that goes beyond traditional factors such as return on investment. Instead, we propose non monetary and non GDP metrics, such as Disability Adjusted Life Years or achievement of universal health coverage. Furthermore, we suggest a venture building approach around global health, which includes filtering of participants to reduce opportunity cost, close integration of implementation scientists and an incubator for the long-term development of ideas resulting from the challenge. Finally, we emphasize the need to strengthen human capital across a range of areas in local innovation, implementation-science, and in health services., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Farlow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2023
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