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Seoni S, Jahmunah V, Salvi M, Barua PD, Molinari F, Acharya UR. Application of uncertainty quantification to artificial intelligence in healthcare: A review of last decade (2013-2023). Comput Biol Med 2023; 165:107441. [PMID: 37683529 DOI: 10.1016/j.compbiomed.2023.107441] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
Uncertainty estimation in healthcare involves quantifying and understanding the inherent uncertainty or variability associated with medical predictions, diagnoses, and treatment outcomes. In this era of Artificial Intelligence (AI) models, uncertainty estimation becomes vital to ensure safe decision-making in the medical field. Therefore, this review focuses on the application of uncertainty techniques to machine and deep learning models in healthcare. A systematic literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our analysis revealed that Bayesian methods were the predominant technique for uncertainty quantification in machine learning models, with Fuzzy systems being the second most used approach. Regarding deep learning models, Bayesian methods emerged as the most prevalent approach, finding application in nearly all aspects of medical imaging. Most of the studies reported in this paper focused on medical images, highlighting the prevalent application of uncertainty quantification techniques using deep learning models compared to machine learning models. Interestingly, we observed a scarcity of studies applying uncertainty quantification to physiological signals. Thus, future research on uncertainty quantification should prioritize investigating the application of these techniques to physiological signals. Overall, our review highlights the significance of integrating uncertainty techniques in healthcare applications of machine learning and deep learning models. This can provide valuable insights and practical solutions to manage uncertainty in real-world medical data, ultimately improving the accuracy and reliability of medical diagnoses and treatment recommendations.
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Affiliation(s)
- Silvia Seoni
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | | | - Massimo Salvi
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Prabal Datta Barua
- School of Business (Information System), University of Southern Queensland, Toowoomba, QLD, 4350, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia
| | - Filippo Molinari
- Biolab, PolitoBIOMedLab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia
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Chermat R, Ziaee M, Mak DY, Refet-Mollof E, Rodier F, Wong P, Carrier JF, Kamio Y, Gervais T. Radiotherapy on-chip: microfluidics for translational radiation oncology. LAB ON A CHIP 2022; 22:2065-2079. [PMID: 35477748 DOI: 10.1039/d2lc00177b] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The clinical importance of radiotherapy in the treatment of cancer patients justifies the development and use of research tools at the fundamental, pre-clinical, and ultimately clinical levels, to investigate their toxicities and synergies with systemic agents on relevant biological samples. Although microfluidics has prompted a paradigm shift in drug discovery in the past two decades, it appears to have yet to translate to radiotherapy research. However, the materials, dimensions, design versatility and multiplexing capabilities of microfluidic devices make them well-suited to a variety of studies involving radiation physics, radiobiology and radiotherapy. This review will present the state-of-the-art applications of microfluidics in these fields and specifically highlight the perspectives offered by radiotherapy on-a-chip in the field of translational radiobiology and precision medicine. This body of knowledge can serve both the microfluidics and radiotherapy communities by identifying potential collaboration avenues to improve patient care.
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Affiliation(s)
- Rodin Chermat
- μFO Lab, Polytechnique Montréal, Montréal, QC, Canada.
- Institut du Cancer de Montréal, (ICM), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Maryam Ziaee
- μFO Lab, Polytechnique Montréal, Montréal, QC, Canada.
- Institut du Cancer de Montréal, (ICM), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - David Y Mak
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Elena Refet-Mollof
- μFO Lab, Polytechnique Montréal, Montréal, QC, Canada.
- Institut du Cancer de Montréal, (ICM), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
| | - Francis Rodier
- Institut du Cancer de Montréal, (ICM), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Département de radiologie, radio-oncologie et médecine nucléaire, Université de Montréal, Montreal, QC, Canada
| | - Philip Wong
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Institut du Cancer de Montréal, (ICM), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Jean-François Carrier
- Département de radiologie, radio-oncologie et médecine nucléaire, Université de Montréal, Montreal, QC, Canada
- Département de Physique, Université de Montréal, Montréal, QC, Canada
- Département de Radio-oncologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
| | - Yuji Kamio
- Département de Radio-oncologie, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, QC, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montreal, QC, Canada
| | - Thomas Gervais
- μFO Lab, Polytechnique Montréal, Montréal, QC, Canada.
- Institut du Cancer de Montréal, (ICM), Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montreal, QC, Canada
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Alizadehsani R, Roshanzamir M, Hussain S, Khosravi A, Koohestani A, Zangooei MH, Abdar M, Beykikhoshk A, Shoeibi A, Zare A, Panahiazar M, Nahavandi S, Srinivasan D, Atiya AF, Acharya UR. Handling of uncertainty in medical data using machine learning and probability theory techniques: a review of 30 years (1991-2020). ANNALS OF OPERATIONS RESEARCH 2021:1-42. [PMID: 33776178 PMCID: PMC7982279 DOI: 10.1007/s10479-021-04006-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/23/2021] [Indexed: 05/17/2023]
Abstract
Understanding the data and reaching accurate conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have been widely used for this purpose in various fields. One critically important yet less explored aspect is capturing and analyzing uncertainties in the data and model. Proper quantification of uncertainty helps to provide valuable information to obtain accurate diagnosis. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, various novel deep learning techniques have been proposed to deal with such uncertainties and improve the performance in decision making.
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Affiliation(s)
- Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Mohamad Roshanzamir
- Department of Computer Engineering, Faculty of Engineering, Fasa University, 74617-81189 Fasa, Iran
| | - Sadiq Hussain
- System Administrator, Dibrugarh University, Dibrugarh, Assam 786004 India
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Afsaneh Koohestani
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | | | - Moloud Abdar
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Adham Beykikhoshk
- Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia
| | - Afshin Shoeibi
- Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
- Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Maryam Panahiazar
- Institute for Computational Health Sciences, University of California, San Francisco, USA
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia
| | - Dipti Srinivasan
- Dept. of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576 Singapore
| | - Amir F. Atiya
- Department of Computer Engineering, Faculty of Engineering, Cairo University, Cairo, 12613 Egypt
| | - U. Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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