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Zhao Y, Qiu J, Li Y, Khan MA, Wan L, Chen L. Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint. SLAS Technol 2024:100149. [PMID: 38796035 DOI: 10.1016/j.slast.2024.100149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 05/01/2024] [Accepted: 05/22/2024] [Indexed: 05/28/2024]
Abstract
OBJECTIVE This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analysis of shoulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mineral density (BMD) measurements and clinical information based on machine learning (ML) models. METHODS A retrospective cohort of 89 patients was analyzed, including 63 with both OP and RCT (OPRCT) and 26 with OP only. The study analyzed a series of shoulder radiographs from April 2021 to April 2023. Grayscale values were measured after plotting ROIs based on AP X-rays of shoulder joint. Five kinds of ML models were developed and compared based on their performance in predicting the occurrence and severity of RCT from ROIs' greyscale values and clinical information (age, gender, advantage side, lumbar BMD, and acromion morphology (AM)). Further analysis using SHAP values illustrated the significant impact of selected features on model predictions. RESULTS R1-6 had a positive correlation with BMD respectively. The nine variables, including greyscale R1-6, age, BMD, and AM, were used in the prediction models. The RF model was determined to be superior in effectively diagnosing RCT in OP patients, with high AUC scores of 0.998, 0.889, and 0.95 in the training, validation, and testing sets, respectively. SHAP values revealed that the most influential factors on the diagnostic outcomes were the grayscale values of all cancellous bones in ROIs. A column-line graph prediction model based on nine variables was constructed, and DCA curves indicated that RCT prediction in OP patients was favored based on this model. Furthermore, the RF model was also the most superior in predicting the types of RCT within the OPRCT group, with an accuracy of 86.364% and 73.684% in the training and test sets, respectively. SHAP values indicated that the most significant factor affecting the predictive outcomes was the AM, followed by the grayscale values of the greater tubercle, among others. CONCLUSIONS ML models, particularly the RF algorithm, show significant promise in diagnosing RCT occurrence and severity in OP patients using conventional shoulder X-rays based on the nine variables. This method presents a cost-effective, accessible, and non-invasive diagnostic strategy that has the potential to substantially enhance the early detection and management of RCT in OP patient population.
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Affiliation(s)
- Yu Zhao
- Postgraduate College, Guangzhou University of Chinese Medicine, Guangzhou 510080, China
| | - Jingjing Qiu
- Shenzhen Bao'an Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518100, China; Postgraduate College, Guangzhou University of Chinese Medicine, Guangzhou 510080, China
| | - Yang Li
- College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Lei Wan
- The Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou 510006, China.
| | - Lihua Chen
- Shenzhen Bao'an Traditional Chinese Medicine Hospital, Guangzhou University of Chinese Medicine, Shenzhen 518100, China; Postgraduate College, Guangzhou University of Chinese Medicine, Guangzhou 510080, China.
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Savchenko E, Bunimovich-Mendrazitsky S. Investigation toward the economic feasibility of personalized medicine for healthcare service providers: the case of bladder cancer. Front Med (Lausanne) 2024; 11:1388685. [PMID: 38808135 PMCID: PMC11130437 DOI: 10.3389/fmed.2024.1388685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 04/26/2024] [Indexed: 05/30/2024] Open
Abstract
In today's complex healthcare landscape, the pursuit of delivering optimal patient care while navigating intricate economic dynamics poses a significant challenge for healthcare service providers (HSPs). In this already complex dynamic, the emergence of clinically promising personalized medicine-based treatment aims to revolutionize medicine. While personalized medicine holds tremendous potential for enhancing therapeutic outcomes, its integration within resource-constrained HSPs presents formidable challenges. In this study, we investigate the economic feasibility of implementing personalized medicine. The central objective is to strike a balance between catering to individual patient needs and making economically viable decisions. Unlike conventional binary approaches to personalized treatment, we propose a more nuanced perspective by treating personalization as a spectrum. This approach allows for greater flexibility in decision-making and resource allocation. To this end, we propose a mathematical framework to investigate our proposal, focusing on Bladder Cancer (BC) as a case study. Our results show that while it is feasible to introduce personalized medicine, a highly efficient but highly expensive one would be short-lived relative to its less effective but cheaper alternative as the latter can be provided to a larger cohort of patients, optimizing the HSP's objective better.
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Jovanovic L, Damaševičius R, Matic R, Kabiljo M, Simic V, Kunjadic G, Antonijevic M, Zivkovic M, Bacanin N. Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics. PeerJ Comput Sci 2024; 10:e2031. [PMID: 38855236 PMCID: PMC11157549 DOI: 10.7717/peerj-cs.2031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/09/2024] [Indexed: 06/11/2024]
Abstract
Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.
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Affiliation(s)
- Luka Jovanovic
- Faculty of Technical Sciences, Singidunum University, Belgrade, Serbia
| | | | - Rade Matic
- Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia
| | - Milos Kabiljo
- Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia
| | - Vladimir Simic
- Faculty of Transport and Traffic Engineering, University of Belgrade, Belgrade, Serbia
- College of Engineering, Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan City, Taiwan
| | - Goran Kunjadic
- Higher Colleges of Technology, Abu Dhabi, United Arab Emirates
| | - Milos Antonijevic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
- MEU Research Unit, Middle East University, Amman, Jordan
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Chong LM, Wang P, Lee VV, Vijayakumar S, Tan HQ, Wang FQ, Yeoh TDYY, Truong ATL, Tan LWJ, Tan SB, Senthil Kumar K, Hau E, Vellayappan BA, Blasiak A, Ho D. Radiation therapy with phenotypic medicine: towards N-of-1 personalization. Br J Cancer 2024:10.1038/s41416-024-02653-3. [PMID: 38514762 DOI: 10.1038/s41416-024-02653-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/29/2024] [Accepted: 03/04/2024] [Indexed: 03/23/2024] Open
Abstract
In current clinical practice, radiotherapy (RT) is prescribed as a pre-determined total dose divided over daily doses (fractions) given over several weeks. The treatment response is typically assessed months after the end of RT. However, the conventional one-dose-fits-all strategy may not achieve the desired outcome, owing to patient and tumor heterogeneity. Therefore, a treatment strategy that allows for RT dose personalization based on each individual response is preferred. Multiple strategies have been adopted to address this challenge. As an alternative to current known strategies, artificial intelligence (AI)-derived mechanism-independent small data phenotypic medicine (PM) platforms may be utilized for N-of-1 RT personalization. Unlike existing big data approaches, PM does not engage in model refining, training, and validation, and guides treatment by utilizing prospectively collected patient's own small datasets. With PM, clinicians may guide patients' RT dose recommendations using their responses in real-time and potentially avoid over-treatment in good responders and under-treatment in poor responders. In this paper, we discuss the potential of engaging PM to guide clinicians on upfront dose selections and ongoing adaptations during RT, as well as considerations and limitations for implementation. For practicing oncologists, clinical trialists, and researchers, PM can either be implemented as a standalone strategy or in complement with other existing RT personalizations. In addition, PM can either be used for monotherapeutic RT personalization, or in combination with other therapeutics (e.g. chemotherapy, targeted therapy). The potential of N-of-1 RT personalization with drugs will also be presented.
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Affiliation(s)
- Li Ming Chong
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - Peter Wang
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - V Vien Lee
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
| | - Smrithi Vijayakumar
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
| | - Hong Qi Tan
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, 168583, Singapore
| | - Fu Qiang Wang
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, 168583, Singapore
| | | | - Anh T L Truong
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - Lester Wen Jeit Tan
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - Shi Bei Tan
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore
| | - Kirthika Senthil Kumar
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore
| | - Eric Hau
- Department of Radiation Oncology, Westmead Hospital, Sydney, NSW, Australia
- Department of Radiation Oncology, Blacktown Haematology and Cancer Care Centre, Sydney, NSW, Australia
- Westmead Medical School, The University of Sydney, Sydney, NSW, Australia
- Centre for Cancer Research, Westmead Institute of Medical Research, Sydney, NSW, Australia
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute, Singapore, 119074, Singapore.
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore.
| | - Agata Blasiak
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
| | - Dean Ho
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore, 117583, Singapore.
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, 117456, Singapore.
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117456, Singapore.
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117600, Singapore.
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Truong ATL, Tan SB, Wang GZ, Yip AWJ, Egermark M, Yeung W, Lee VV, Chan MY, Kumar KS, Tan LWJ, Vijayakumar S, Blasiak A, Wang LYT, Ho D. CURATE.AI-assisted dose titration for anti-hypertensive personalized therapy: study protocol for a multi-arm, randomized, pilot feasibility trial using CURATE.AI (CURATE.AI ADAPT trial). EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:41-49. [PMID: 38264697 PMCID: PMC10802822 DOI: 10.1093/ehjdh/ztad063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/11/2023] [Accepted: 09/06/2023] [Indexed: 01/25/2024]
Abstract
Aims Artificial intelligence-driven small data platforms such as CURATE.AI hold potential for personalized hypertension care by assisting physicians in identifying personalized anti-hypertensive doses for titration. This trial aims to assess the feasibility of a larger randomized controlled trial (RCT), evaluating the efficacy of CURATE.AI-assisted dose titration intervention. We will also collect preliminary efficacy and safety data and explore stakeholder feedback in the early design process. Methods and results In this open-label, randomized, pilot feasibility trial, we aim to recruit 45 participants with primary hypertension. Participants will be randomized in 1:1:1 ratio into control (no intervention), home blood pressure monitoring (active control; HBPM), or CURATE.AI arms (intervention; HBPM and CURATE.AI-assisted dose titration). The home treatments include 1 month of two-drug anti-hypertensive regimens. Primary endpoints assess the logistical (e.g. dose adherence) and scientific (e.g. percentage of participants for which CURATE.AI profiles can be generated) feasibility, and define the progression criteria for the RCT in a 'traffic light system'. Secondary endpoints assess preliminary efficacy [e.g. mean change in office blood pressures (BPs)] and safety (e.g. hospitalization events) associated with each treatment protocol. Participants with both baseline and post-treatment BP measurements will form the intent-to-treat analysis. Following their involvement with the CURATE.AI intervention, feedback from CURATE.AI participants and healthcare providers will be collected via exit survey and interviews. Conclusion Findings from this study will inform about potential refinements of the current treatment protocols before proceeding with a larger RCT, or potential expansion to collect additional information. Positive results may suggest the potential efficacy of CURATE.AI to improve BP control. Trial registration number NCT05376683.
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Affiliation(s)
- Anh T L Truong
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Shi-Bei Tan
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Golda Z Wang
- Department of Pharmacy, Alexandra Hospital, Singapore 15996, Singapore
| | - Alexander W J Yip
- Department of Medicine, Alexandra Hospital, Singapore 159964, Singapore
- Department of Healthcare Redesign, Alexandra Hospital, Singapore 159964, Singapore
| | - Mathias Egermark
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
| | - Wesley Yeung
- National University Heart Centre, National University Hospital Singapore, Singapore 119074, Singapore
| | - V Vien Lee
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Mark Y Chan
- National University Heart Centre, National University Hospital Singapore, Singapore 119074, Singapore
| | - Kirthika S Kumar
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Lester W J Tan
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Smrithi Vijayakumar
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
| | - Laureen Y T Wang
- Department of Medicine, Alexandra Hospital, Singapore 159964, Singapore
- Department of Healthcare Redesign, Alexandra Hospital, Singapore 159964, Singapore
- National University Heart Centre, National University Hospital Singapore, Singapore 119074, Singapore
| | - Dean Ho
- The N.1 Institute for Health (N.1), National University of Singapore, 28 Medical Drive, Singapore 117456, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- The Bia-Echo Asia Centre for Reproductive Longevity and Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
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Hussain W, Mabrok M, Gao H, Rabhi FA, Rashed EA. Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems. Digit Health 2024; 10:20552076241258757. [PMID: 38817839 PMCID: PMC11138196 DOI: 10.1177/20552076241258757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.
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Affiliation(s)
- Walayat Hussain
- Peter Faber Business School, Australian Catholic University, North Sydney, Australia
| | - Mohamed Mabrok
- Department of Mathematics and Statistics, Qatar University, Doha, Qatar
| | - Honghao Gao
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Fethi A. Rabhi
- School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, Australia
| | - Essam A. Rashed
- Graduate School of Information Science, University of Hyogo, Kobe, Japan
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Alanzi T, Alhajri A, Almulhim S, Alharbi S, Alfaifi S, Almarhoun E, Mulla R, Alasafra ZO, Alalwan Z, Alnasser F, Almukhtar F, Al Ghadeer F, Amro S, Alodhayb I, Alanzi N. Artificial Intelligence and Patient Autonomy in Obesity Treatment Decisions: An Empirical Study of the Challenges. Cureus 2023; 15:e49725. [PMID: 38161816 PMCID: PMC10757560 DOI: 10.7759/cureus.49725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
Background This study aims to explore the factors associated with artificial intelligence (AI) and patient autonomy in obesity treatment decision-making. Methodology A cross-sectional, online, descriptive survey design was adopted in this study. The survey instrument incorporated the Ideal Patient Autonomy Scale (IPAS) and other factors affecting patient autonomy in the AI-patient relationship. The study participants included 74 physicians, 55 dieticians, and 273 obese patients. Results Different views were expressed in the scales AI knows the best (μ = 2.95-3.15) and the patient should decide (μ = 2.95-3.16). Ethical concerns (μ = 3.24) and perceived privacy risks (μ = 3.58) were identified as having a more negative influence on patient autonomy compared to personal innovativeness (μ = 2.41) and trust (μ = 2.85). Physicians and dieticians expressed significantly higher trust in AI compared to patients (p < 0.05). Conclusions Patient autonomy in the AI-patient relationship is significantly affected by privacy, trust, and ethical issues. As trust is a multifaceted factor and AI is a novel technology in healthcare, it is essential to fully explore the various factors influencing trust and patient autonomy.
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Affiliation(s)
- Turki Alanzi
- Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Ahlam Alhajri
- College of Agricultural and Food Sciences, King Faisal University, Al Hofuf, SAU
| | - Sara Almulhim
- College of Medicine, King Faisal University, Al Hofuf, SAU
| | - Sara Alharbi
- College of Applied Medical Sciences, Umm Al-Qura University, Makkah, SAU
| | - Samya Alfaifi
- College of Pharmacy, Umm Al-Qura University, Makkah, SAU
| | - Eslam Almarhoun
- Family Medicine, South Khobar Primary Healthcare Center, Khobar, SAU
| | - Raghad Mulla
- College of Medicine, King Abdulaziz University, Jeddah, SAU
| | | | - Zainab Alalwan
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Fatima Alnasser
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Fatima Almukhtar
- Medicine and Surgery, King Fahad University Hospital, Khobar, SAU
| | | | - Sara Amro
- Family Medicine, King Fahad Armed Forces Hospital, Jeddah, SAU
| | - Ibrahim Alodhayb
- College of Agriculture and Veterinary Medicine, Qassim University, Buraydah, SAU
| | - Nouf Alanzi
- Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Jouf university, Sakakah, SAU
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Vijayakumar S, Lee VV, Leong QY, Hong SJ, Blasiak A, Ho D. Physicians' Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform. JMIR Hum Factors 2023; 10:e48476. [PMID: 37902825 PMCID: PMC10644191 DOI: 10.2196/48476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/24/2023] [Accepted: 09/10/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are being explored. Therefore, understanding physicians' perceptions can be critical toward clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSSs), there remains a need to gain an in-depth understanding of physicians' perceptions and expectations toward their downstream implementation. This paper explores physicians' perceptions of integrating CURATE.AI, a novel artificial intelligence (AI)-based and clinical stage personalized dosing CDSSs, into clinical practice. OBJECTIVE This study aims to understand physicians' perspectives of integrating CURATE.AI for clinical work and to gather insights on considerations of the implementation of AI-based CDSS tools. METHODS A total of 12 participants completed semistructured interviews examining their knowledge, experience, attitudes, risks, and future course of the personalized combination therapy dosing platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, and coded manually. The data were thematically analyzed. RESULTS Overall, 3 broad themes and 9 subthemes were identified through thematic analysis. The themes covered considerations that physicians perceived as significant across various stages of new technology development, including trial, clinical implementation, and mass adoption. CONCLUSIONS The study laid out the various ways physicians interpreted an AI-based personalized dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that physicians' expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation that are relevant for technology developers and researchers.
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Affiliation(s)
- Smrithi Vijayakumar
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - V Vien Lee
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Qiao Ying Leong
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Soo Jung Hong
- Department of Communications and New Media, National University of Singapore, Singapore, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dean Ho
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Remus A, Tadeo X, Kai GNS, Blasiak A, Kee T, Vijayakumar S, Nguyen L, Raczkowska MN, Chai QY, Aliyah F, Rusalovski Y, Teo K, Yeo TT, Wong ALA, Chia D, Asplund CL, Ho D, Vellayappan BA. CURATE.AI COR-Tx platform as a digital therapy and digital diagnostic for cognitive function in patients with brain tumour postradiotherapy treatment: protocol for a prospective mixed-methods feasibility clinical trial. BMJ Open 2023; 13:e077219. [PMID: 37879700 PMCID: PMC10603439 DOI: 10.1136/bmjopen-2023-077219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 09/29/2023] [Indexed: 10/27/2023] Open
Abstract
INTRODUCTION Conventional interventional modalities for preserving or improving cognitive function in patients with brain tumour undergoing radiotherapy usually involve pharmacological and/or cognitive rehabilitation therapy administered at fixed doses or intensities, often resulting in suboptimal or no response, due to the dynamically evolving patient state over the course of disease. The personalisation of interventions may result in more effective results for this population. We have developed the CURATE.AI COR-Tx platform, which combines a previously validated, artificial intelligence-derived personalised dosing technology with digital cognitive training. METHODS AND ANALYSIS This is a prospective, single-centre, single-arm, mixed-methods feasibility clinical trial with the primary objective of testing the feasibility of the CURATE.AI COR-Tx platform intervention as both a digital intervention and digital diagnostic for cognitive function. Fifteen patient participants diagnosed with a brain tumour requiring radiotherapy will be recruited. Participants will undergo a remote, home-based 10-week personalised digital intervention using the CURATE.AI COR-Tx platform three times a week. Cognitive function will be assessed via a combined non-digital cognitive evaluation and a digital diagnostic session at five time points: preradiotherapy, preintervention and postintervention and 16-weeks and 32-weeks postintervention. Feasibility outcomes relating to acceptability, demand, implementation, practicality and limited efficacy testing as well as usability and user experience will be assessed at the end of the intervention through semistructured patient interviews and a study team focus group discussion at study completion. All outcomes will be analysed quantitatively and qualitatively. ETHICS AND DISSEMINATION This study has been approved by the National Healthcare Group (NHG) DSRB (DSRB2020/00249). We will report our findings at scientific conferences and/or in peer-reviewed journals. TRIAL REGISTRATION NUMBER NCT04848935.
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Affiliation(s)
- Alexandria Remus
- Heat Resilence and Performance Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Xavier Tadeo
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
| | - Grady Ng Shi Kai
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Social Sciences, Yale-NUS College, Singapore
| | - Agata Blasiak
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Theodore Kee
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Smrithi Vijayakumar
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
| | - Le Nguyen
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
| | - Marlena N Raczkowska
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
| | - Qian Yee Chai
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
| | - Fatin Aliyah
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
| | - Yaromir Rusalovski
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
| | - Kejia Teo
- Department of Surgery, Division of Neurosurgery, National University Hospital, Singapore
| | - Tseng Tsai Yeo
- Department of Surgery, Division of Neurosurgery, National University Hospital, Singapore
| | - Andrea Li Ann Wong
- Department of Hematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
| | - David Chia
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Christopher L Asplund
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Social Sciences, Yale-NUS College, Singapore
| | - Dean Ho
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The N.1 Institute for Health (N1), National University of Singapore, Singapore
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- The Bia-Echo Asia Centre for Reproductive Longevity and Equality, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Balamurugan A Vellayappan
- Department of Radiation Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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10
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Sufyan M, Shokat Z, Ashfaq UA. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective. Comput Biol Med 2023; 165:107356. [PMID: 37688994 DOI: 10.1016/j.compbiomed.2023.107356] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 07/21/2023] [Accepted: 08/12/2023] [Indexed: 09/11/2023]
Abstract
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
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Affiliation(s)
- Muhammad Sufyan
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Zeeshan Shokat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Pakistan.
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11
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Iqbal J, Cortés Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, Butt AN, Sikto JT, Kaur P, Lak MA, Augustine M, Shahzad R, Arain M. Reimagining Healthcare: Unleashing the Power of Artificial Intelligence in Medicine. Cureus 2023; 15:e44658. [PMID: 37799217 PMCID: PMC10549955 DOI: 10.7759/cureus.44658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2023] [Indexed: 10/07/2023] Open
Abstract
Artificial intelligence (AI) has opened new medical avenues and revolutionized diagnostic and therapeutic practices, allowing healthcare providers to overcome significant challenges associated with cost, disease management, accessibility, and treatment optimization. Prominent AI technologies such as machine learning (ML) and deep learning (DL) have immensely influenced diagnostics, patient monitoring, novel pharmaceutical discoveries, drug development, and telemedicine. Significant innovations and improvements in disease identification and early intervention have been made using AI-generated algorithms for clinical decision support systems and disease prediction models. AI has remarkably impacted clinical drug trials by amplifying research into drug efficacy, adverse events, and candidate molecular design. AI's precision and analysis regarding patients' genetic, environmental, and lifestyle factors have led to individualized treatment strategies. During the COVID-19 pandemic, AI-assisted telemedicine set a precedent for remote healthcare delivery and patient follow-up. Moreover, AI-generated applications and wearable devices have allowed ambulatory monitoring of vital signs. However, apart from being immensely transformative, AI's contribution to healthcare is subject to ethical and regulatory concerns. AI-backed data protection and algorithm transparency should be strictly adherent to ethical principles. Vigorous governance frameworks should be in place before incorporating AI in mental health interventions through AI-operated chatbots, medical education enhancements, and virtual reality-based training. The role of AI in medical decision-making has certain limitations, necessitating the importance of hands-on experience. Therefore, reaching an optimal balance between AI's capabilities and ethical considerations to ensure impartial and neutral performance in healthcare applications is crucial. This narrative review focuses on AI's impact on healthcare and the importance of ethical and balanced incorporation to make use of its full potential.
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Affiliation(s)
| | - Diana Carolina Cortés Jaimes
- Epidemiology, Universidad Autónoma de Bucaramanga, Bucaramanga, COL
- Medicine, Pontificia Universidad Javeriana, Bogotá, COL
| | - Pallavi Makineni
- Medicine, All India Institute of Medical Sciences, Bhubaneswar, Bhubaneswar, IND
| | - Sachin Subramani
- Medicine and Surgery, Employees' State Insurance Corporation (ESIC) Medical College, Gulbarga, IND
| | - Sarah Hemaida
- Internal Medicine, Istanbul Okan University, Istanbul, TUR
| | - Thanmai Reddy Thugu
- Internal Medicine, Sri Padmavathi Medical College for Women, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, IND
| | - Amna Naveed Butt
- Medicine/Internal Medicine, Allama Iqbal Medical College, Lahore, PAK
| | | | - Pareena Kaur
- Medicine, Punjab Institute of Medical Sciences, Jalandhar, IND
| | | | | | - Roheen Shahzad
- Medicine, Combined Military Hospital (CMH) Lahore Medical College and Institute of Dentistry, Lahore, PAK
| | - Mustafa Arain
- Internal Medicine, Civil Hospital Karachi, Karachi, PAK
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12
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Gupta NS, Kumar P. Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine. Comput Biol Med 2023; 162:107051. [PMID: 37271113 DOI: 10.1016/j.compbiomed.2023.107051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 06/06/2023]
Abstract
Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.
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Affiliation(s)
- Nancy Sanjay Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, India.
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13
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Poalelungi DG, Musat CL, Fulga A, Neagu M, Neagu AI, Piraianu AI, Fulga I. Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare. J Pers Med 2023; 13:1214. [PMID: 37623465 PMCID: PMC10455458 DOI: 10.3390/jpm13081214] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 07/27/2023] [Accepted: 07/28/2023] [Indexed: 08/26/2023] Open
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology with immense potential in the field of medicine. By leveraging machine learning and deep learning, AI can assist in diagnosis, treatment selection, and patient monitoring, enabling more accurate and efficient healthcare delivery. The widespread implementation of AI in healthcare has the role to revolutionize patients' outcomes and transform the way healthcare is practiced, leading to improved accessibility, affordability, and quality of care. This article explores the diverse applications and reviews the current state of AI adoption in healthcare. It concludes by emphasizing the need for collaboration between physicians and technology experts to harness the full potential of AI.
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Affiliation(s)
- Diana Gina Poalelungi
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
| | - Carmina Liana Musat
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Ana Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Marius Neagu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Anca Iulia Neagu
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
- ‘Saint John’ Clinical Emergency Hospital for Children, 800487 Galati, Romania
| | - Alin Ionut Piraianu
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
| | - Iuliu Fulga
- Saint Apostle Andrew Emergency County Clinical Hospital, 177 Brailei st., 800578 Galati, Romania; (D.G.P.); (M.N.); (A.I.P.); (I.F.)
- Faculty of Medicine and Pharmacy, Dunarea de Jos University of Galati, 35 AI Cuza st., 800010 Galati, Romania;
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14
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Kao TW, Liao PJ. Phenotype-directed clinically driven low-dose direct oral anticoagulant for atrial fibrillation. Future Cardiol 2023; 19:405-417. [PMID: 37650492 DOI: 10.2217/fca-2022-0109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023] Open
Abstract
Clinically-driven dose reduction of direct oral anticoagulants in individuals with atrial fibrillation is prevalent worldwide. However, a paucity of evidence to tailor dose selection remained as clinical unmet need. Current doses of anticoagulant were determined largely by landmark clinical trials, in which the enrolled subjects were carefully selected and without major comorbidities. Our study reviewed the relevant real-world studies in specific patient phenotypes, including renal and hepatic diseases, elderly, low body weight, Asians and presence of concomitant drug-drug interactions. Thorough investigations toward the efficacy and safety of direct oral anticoagulants in reduced doses will facilitate substituting current universal approach with individualized prescriptions.
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Affiliation(s)
- Ting-Wei Kao
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, 100, Taiwan
| | - Pin-Jyun Liao
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, 100, Taiwan
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15
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Pang J, Xiu W, Ma X. Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors. J Clin Med 2023; 12:jcm12082818. [PMID: 37109155 PMCID: PMC10144939 DOI: 10.3390/jcm12082818] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/01/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023] Open
Abstract
Artificial intelligence (AI), also known as machine intelligence, is widely utilized in the medical field, promoting medical advances. Malignant tumors are the critical focus of medical research and improvement of clinical diagnosis and treatment. Mediastinal malignancy is an important tumor that attracts increasing attention today due to the difficulties in treatment. Combined with artificial intelligence, challenges from drug discovery to survival improvement are constantly being overcome. This article reviews the progress of the use of AI in the diagnosis, treatment, and prognostic prospects of mediastinal malignant tumors based on current literature findings.
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Affiliation(s)
- Jiyun Pang
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- West China School of Medicine, Sichuan University, Chengdu 610041, China
| | - Weigang Xiu
- Division of Thoracic Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
- West China School of Medicine, Sichuan University, Chengdu 610041, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610041, China
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16
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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17
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Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023; 189:106706. [PMID: 36813095 DOI: 10.1016/j.phrs.2023.106706] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.
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Affiliation(s)
- Anita Bakrania
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
| | | | - Xun Zhao
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Gang Zheng
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Division of Gastroenterology, Department of Medicine, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Medical Sciences, Toronto, ON, Canada.
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18
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Tan P, Chen X, Zhang H, Wei Q, Luo K. Artificial intelligence aids in development of nanomedicines for cancer management. Semin Cancer Biol 2023; 89:61-75. [PMID: 36682438 DOI: 10.1016/j.semcancer.2023.01.005] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/28/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023]
Abstract
Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.
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Affiliation(s)
- Ping Tan
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaoting Chen
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hu Zhang
- Amgen Bioprocessing Centre, Keck Graduate Institute, Claremont, CA 91711, USA
| | - Qiang Wei
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Kui Luo
- Department of Urology, and Department of Radiology, Institute of Urology, and Huaxi MR Research Center (HMRRC), Animal Experimental Center, National Clinical Research Center for Geriatrics, Frontiers Science Center for Disease-Related Molecular Network, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, China.
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19
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Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care. Cancer Treat Rev 2023; 112:102498. [PMID: 36527795 DOI: 10.1016/j.ctrv.2022.102498] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/03/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.
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20
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Rai A, Shah K, Dewangan HK. Review on the Artificial Intelligence-based Nanorobotics Targeted Drug Delivery System for Brain-specific Targeting. Curr Pharm Des 2023; 29:3519-3531. [PMID: 38111114 DOI: 10.2174/0113816128279248231210172053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 11/07/2023] [Indexed: 12/20/2023]
Abstract
Contemporary medical research increasingly focuses on the blood-brain barrier (BBB) to maintain homeostasis in healthy individuals and provide solutions for neurological disorders, including brain cancer. Specialized in vitro modules replicate the BBB's complex structure and signalling using micro-engineered perfusion devices and advanced 3D cell cultures, thus advancing the understanding of neuropharmacology. This research explores nanoparticle-based biomolecular engineering for precise control, targeting, and transport of theranostic payloads across the BBB using nanorobots. The review summarizes case studies on delivering therapeutics for brain tumors and neurological disorders, such as Alzheimer's, Parkinson's, and multiple sclerosis. It also examines the advantages and disadvantages of nano-robotics. In conclusion, integrating machine learning and AI with robotics aims to develop safe nanorobots capable of interacting with the BBB without adverse effects. This comprehensive review is valuable for extensive analysis and is of great significance to healthcare professionals, engineers specializing in robotics, chemists, and bioengineers involved in pharmaceutical development and neurological research, emphasizing transdisciplinary approaches.
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Affiliation(s)
- Akriti Rai
- School of Pharmacy, Lingayas Vidyapeeth, Nachauli, Jasana Road, Faridabad, Haryana 121002, India
| | - Kamal Shah
- Institute of Pharmaceutical Research (IPR), GLA University Mathura, NH-2 Delhi Mathura Road, Po Chaumuhan, Mathura, Uttar Pradesh 281406, India
| | - Hitesh Kumar Dewangan
- University Institute of Pharma Sciences (UIPS), Chandigarh University, NH-95, Chandigarh Ludhiana Highway, Mohali, Punjab, India
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21
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Qureshi SA, Hsiao WWW, Hussain L, Aman H, Le TN, Rafique M. Recent Development of Fluorescent Nanodiamonds for Optical Biosensing and Disease Diagnosis. BIOSENSORS 2022; 12:bios12121181. [PMID: 36551148 PMCID: PMC9775945 DOI: 10.3390/bios12121181] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/07/2022] [Accepted: 12/16/2022] [Indexed: 05/24/2023]
Abstract
The ability to precisely monitor the intracellular temperature directly contributes to the essential understanding of biological metabolism, intracellular signaling, thermogenesis, and respiration. The intracellular heat generation and its measurement can also assist in the prediction of the pathogenesis of chronic diseases. However, intracellular thermometry without altering the biochemical reactions and cellular membrane damage is challenging, requiring appropriately biocompatible, nontoxic, and efficient biosensors. Bright, photostable, and functionalized fluorescent nanodiamonds (FNDs) have emerged as excellent probes for intracellular thermometry and magnetometry with the spatial resolution on a nanometer scale. The temperature and magnetic field-dependent luminescence of naturally occurring defects in diamonds are key to high-sensitivity biosensing applications. Alterations in the surface chemistry of FNDs and conjugation with polymer, metallic, and magnetic nanoparticles have opened vast possibilities for drug delivery, diagnosis, nanomedicine, and magnetic hyperthermia. This study covers some recently reported research focusing on intracellular thermometry, magnetic sensing, and emerging applications of artificial intelligence (AI) in biomedical imaging. We extend the application of FNDs as biosensors toward disease diagnosis by using intracellular, stationary, and time-dependent information. Furthermore, the potential of machine learning (ML) and AI algorithms for developing biosensors can revolutionize any future outbreak.
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Affiliation(s)
- Shahzad Ahmad Qureshi
- Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad 45650, Pakistan
| | - Wesley Wei-Wen Hsiao
- Department of Chemical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
| | - Lal Hussain
- Department of Computer Science and Information Technology, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
- Department of Computer Science and Information Technology, Neelum Campus, University of Azad Jammu and Kashmir, Athmuqam 13230, Pakistan
| | - Haroon Aman
- School of Mathematics and Physics, The University of Queensland, St Lucia, QLD 4072, Australia
- National Institute of Lasers and Optronics College, PIEAS, Islamabad 45650, Pakistan
| | - Trong-Nghia Le
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei 106, Taiwan
| | - Muhammad Rafique
- Department of Physics, King Abdullah Campus Chatter Kalas, University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan
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22
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Hanis TM, Ruhaiyem NIR, Arifin WN, Haron J, Wan Abdul Rahman WF, Abdullah R, Musa KI. Over-the-Counter Breast Cancer Classification Using Machine Learning and Patient Registration Records. Diagnostics (Basel) 2022; 12:diagnostics12112826. [PMID: 36428886 PMCID: PMC9689364 DOI: 10.3390/diagnostics12112826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 10/13/2022] [Accepted: 10/15/2022] [Indexed: 11/18/2022] Open
Abstract
This study aims to determine the feasibility of machine learning (ML) and patient registration record to be utilised to develop an over-the-counter (OTC) screening model for breast cancer risk estimation. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia for breast-related problems. Eight ML models were used: k-nearest neighbour (kNN), elastic-net logistic regression, multivariate adaptive regression splines, artificial neural network, partial least square, random forest, support vector machine (SVM), and extreme gradient boosting. Features utilised for the development of the screening models were limited to information in the patient registration form. The final model was evaluated in terms of performance across a mammographic density. Additionally, the feature importance of the final model was assessed using the model agnostic approach. kNN had the highest Youden J index, precision, and PR-AUC, while SVM had the highest F2 score. The kNN model was selected as the final model. The model had a balanced performance in terms of sensitivity, specificity, and PR-AUC across the mammographic density groups. The most important feature was the age at examination. In conclusion, this study showed that ML and patient registration information are feasible to be used as the OTC screening model for breast cancer.
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Affiliation(s)
- Tengku Muhammad Hanis
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
- Correspondence: (T.M.H.); (K.I.M.)
| | | | - Wan Nor Arifin
- Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Juhara Haron
- Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
- Breast Cancer Awareness and Research Unit, Hospital Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Wan Faiziah Wan Abdul Rahman
- Breast Cancer Awareness and Research Unit, Hospital Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
- Department of Pathology, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
| | - Rosni Abdullah
- School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Penang, Malaysia
| | - Kamarul Imran Musa
- Department of Community Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian 16150, Kelantan, Malaysia
- Correspondence: (T.M.H.); (K.I.M.)
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23
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Blasiak A, Sapanel Y, Leitman D, Ng WY, De Nicola R, Lee VV, Todorov A, Ho D. Omnichannel Communication to Boost Patient Engagement and Behavioural Change with Digital Health Interventions: Viewpoint (Preprint). J Med Internet Res 2022; 24:e41463. [DOI: 10.2196/41463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/27/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022] Open
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The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens. NPJ Digit Med 2022; 5:83. [PMID: 35773329 PMCID: PMC9244889 DOI: 10.1038/s41746-022-00627-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/01/2022] [Indexed: 12/15/2022] Open
Abstract
IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.
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25
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Promoting Sustainability through Next-Generation Biologics Drug Development. SUSTAINABILITY 2022. [DOI: 10.3390/su14084401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The fourth industrial revolution in 2011 aimed to transform the traditional manufacturing processes. As part of this revolution, disruptive innovations in drug development and data science approaches have the potential to optimize CMC (chemistry, manufacture, and control). The real-time simulation of processes using “digital twins” can maximize efficiency while improving sustainability. As part of this review, we investigate how the World Health Organization’s 17 sustainability goals can apply toward next-generation drug development. We analyze the state-of-the-art laboratory leadership, inclusive personnel recruiting, the latest therapy approaches, and intelligent process automation. We also outline how modern data science techniques and machine tools for CMC help to shorten drug development time, reduce failure rates, and minimize resource usage. Finally, we systematically analyze and compare existing approaches to our experiences with the high-throughput laboratory KIWI-biolab at the TU Berlin. We describe a sustainable business model that accelerates scientific innovations and supports global action toward a sustainable future.
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26
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Determination of Significant Parameters on the Basis of Methods of Mathematical Statistics, and Boolean and Fuzzy Logic. MATHEMATICS 2022. [DOI: 10.3390/math10071133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Among the set of parameters for which data are collected for decision-making based on artificial intelligence methods, often only some of the parameters are significant. This article compares methods for determining the significant parameters based on the theory of mathematical statistics, and fuzzy and boolean logic. The testing was conducted on several test data sets with a different number of parameters and different variability of parameter values. It was shown that for data sets with a small number of parameters (<5), the most accurate result was given for a method based on the theory of mathematical statistics and boolean logic. For a data set with a large number of parameters—the most suitable is the method of fuzzy logic.
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27
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Xiang W, Lam YH, Periyasamy G, Chuah C. Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress. Int J Mol Sci 2022; 23:ijms23052863. [PMID: 35270002 PMCID: PMC8910862 DOI: 10.3390/ijms23052863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/26/2022] [Accepted: 02/28/2022] [Indexed: 11/27/2022] Open
Abstract
Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the accessibility to hematologic malignancy patient samples and recent advances in high-throughput technologies, large amounts of biological data that are clinically relevant for diagnosis, risk stratification and targeted drug development have been generated. Recent studies highlight the potential of implementing genomic-based and phenotypic-based screens in clinics to improve survival in patients with refractory AML. In this review, we will discuss successful applications as well as challenges of most up-to-date high-throughput technologies, including artificial intelligence (AI) approaches, in the development of personalized medicine for AML, and recent clinical studies for evaluating the utility of integrating genomics-guided and drug sensitivity testing-guided treatment approaches for AML patients.
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Affiliation(s)
- Wei Xiang
- Department of Haematology, Singapore General Hospital, Singapore 169608, Singapore; (W.X.); (Y.H.L.)
| | - Yi Hui Lam
- Department of Haematology, Singapore General Hospital, Singapore 169608, Singapore; (W.X.); (Y.H.L.)
| | - Giridharan Periyasamy
- High Throughput Phenomics Platform, Experimental Drug Development Centre, Agency for Science, Technology and Research (A*STAR), Singapore 139632, Singapore;
| | - Charles Chuah
- Department of Haematology, Singapore General Hospital, Singapore 169608, Singapore; (W.X.); (Y.H.L.)
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore 169857, Singapore
- Correspondence:
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28
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You K, Wang P, Ho D. N-of-1 Healthcare: Challenges and Prospects for the Future of Personalized Medicine. Front Digit Health 2022; 4:830656. [PMID: 35224536 PMCID: PMC8873079 DOI: 10.3389/fdgth.2022.830656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/20/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Kui You
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore
| | - Peter Wang
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore
| | - Dean Ho
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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29
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Martínez-García M, Hernández-Lemus E. Data Integration Challenges for Machine Learning in Precision Medicine. Front Med (Lausanne) 2022; 8:784455. [PMID: 35145977 PMCID: PMC8821900 DOI: 10.3389/fmed.2021.784455] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 12/28/2021] [Indexed: 12/19/2022] Open
Abstract
A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on different databases about the molecular and environmental origins of disease, into analytic frameworks, allowing the development of individualized, context-dependent diagnostics, and therapeutic approaches. In this regard, artificial intelligence and machine learning approaches can be used to build analytical models of complex disease aimed at prediction of personalized health conditions and outcomes. Such models must handle the wide heterogeneity of individuals in both their genetic predisposition and their social and environmental determinants. Computational approaches to medicine need to be able to efficiently manage, visualize and integrate, large datasets combining structure, and unstructured formats. This needs to be done while constrained by different levels of confidentiality, ideally doing so within a unified analytical architecture. Efficient data integration and management is key to the successful application of computational intelligence approaches to medicine. A number of challenges arise in the design of successful designs to medical data analytics under currently demanding conditions of performance in personalized medicine, while also subject to time, computational power, and bioethical constraints. Here, we will review some of these constraints and discuss possible avenues to overcome current challenges.
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Affiliation(s)
- Mireya Martínez-García
- Clinical Research Division, National Institute of Cardiology ‘Ignacio Chávez’, Mexico City, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autnoma de Mexico, Mexico City, Mexico
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30
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Chen Z, Lin L, Wu C, Li C, Xu R, Sun Y. Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine. Cancer Commun (Lond) 2021; 41:1100-1115. [PMID: 34613667 PMCID: PMC8626610 DOI: 10.1002/cac2.12215] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/10/2021] [Accepted: 09/01/2021] [Indexed: 12/12/2022] Open
Abstract
Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti-cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI-powered cancer care.
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Affiliation(s)
- Zi‐Hang Chen
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
- Zhongshan School of MedicineSun Yat‐sen UniversityGuangzhouGuangdong510080P. R. China
| | - Li Lin
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Chen‐Fei Wu
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Chao‐Feng Li
- Artificial Intelligence LaboratoryState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Rui‐Hua Xu
- Department of Medical OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
| | - Ying Sun
- Department of Radiation OncologyState Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat‐sen University Cancer CenterGuangzhouGuangdong510060P. R. China
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Tan BKJ, Teo CB, Tadeo X, Peng S, Soh HPL, Du SDX, Luo VWY, Bandla A, Sundar R, Ho D, Kee TW, Blasiak A. Personalised, Rational, Efficacy-Driven Cancer Drug Dosing via an Artificial Intelligence SystEm (PRECISE): A Protocol for the PRECISE CURATE.AI Pilot Clinical Trial. Front Digit Health 2021; 3:635524. [PMID: 34713106 PMCID: PMC8521832 DOI: 10.3389/fdgth.2021.635524] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/04/2021] [Indexed: 01/02/2023] Open
Abstract
Introduction: Oncologists have traditionally administered the maximum tolerated doses of drugs in chemotherapy. However, these toxicity-guided doses may lead to suboptimal efficacy. CURATE.AI is an indication-agnostic, mechanism-independent and efficacy-driven personalised dosing platform that may offer a more optimal solution. While CURATE.AI has already been applied in a variety of clinical settings, there are no prior randomised controlled trials (RCTs) on CURATE.AI-guided chemotherapy dosing for solid tumours. Therefore, we aim to assess the technical and logistical feasibility of a future RCT for CURATE.AI-guided solid tumour chemotherapy dosing. We will also collect exploratory data on efficacy and toxicity, which will inform RCT power calculations. Methods and analysis: This is an open-label, single-arm, two-centre, prospective pilot clinical trial, recruiting adults with metastatic solid tumours and raised baseline tumour marker levels who are planned for palliative-intent, capecitabine-based chemotherapy. As CURATE.AI is a small data platform, it will guide drug dosing for each participant based only on their own tumour marker levels and drug doses as input data. The primary outcome is the proportion of participants in whom CURATE.AI is successfully applied to provide efficacy-driven personalised dosing, as judged based on predefined considerations. Secondary outcomes include the timeliness of dose recommendations, participant and physician adherence to CURATE.AI-recommended doses, and the proportion of clinically significant dose changes. We aim to initially enrol 10 participants from two hospitals in Singapore, perform an interim analysis, and consider either cohort expansion or an RCT. Recruitment began in August 2020. This pilot clinical trial will provide key data for a future RCT of CURATE.AI-guided personalised dosing for precision oncology. Ethics and dissemination: The National Healthcare Group (NHG) Domain Specific Review Board has granted ethical approval for this study (DSRB 2020/00334). We will distribute our findings at scientific conferences and publish them in peer-reviewed journals. Trial registration number: NCT04522284
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Affiliation(s)
- Benjamin Kye Jyn Tan
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chong Boon Teo
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xavier Tadeo
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore, Singapore
| | - Siyu Peng
- Department of Medicine, National University Health System, Singapore, Singapore
| | - Hazel Pei Lin Soh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Sherry De Xuan Du
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Vilianty Wen Ya Luo
- Haematology-Oncology Research Group, National University Cancer Institute, Singapore (NCIS), Singapore, Singapore
| | - Aishwarya Bandla
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore
| | - Raghav Sundar
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Haematology-Oncology Research Group, National University Cancer Institute, Singapore (NCIS), Singapore, Singapore.,Department of Haematology-Oncology, National University Health System, Singapore, Singapore
| | - Dean Ho
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore, Singapore.,Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Smart Systems Institute, National University of Singapore, Singapore, Singapore
| | - Theodore Wonpeum Kee
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore, Singapore.,The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Department of Biomedical Engineering, NUS Engineering, National University of Singapore, Singapore, Singapore
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32
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Wolff J, Pauling J, Keck A, Baumbach J. Success Factors of Artificial Intelligence Implementation in Healthcare. Front Digit Health 2021; 3:594971. [PMID: 34713083 PMCID: PMC8521923 DOI: 10.3389/fdgth.2021.594971] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 04/22/2021] [Indexed: 01/31/2023] Open
Abstract
Background: Artificial Intelligence (AI) in healthcare has demonstrated high efficiency in academic research, while only few, and predominantly small, real-world AI applications exist in the preventive, diagnostic and therapeutic contexts. Our identification and analysis of success factors for the implementation of AI aims to close the gap between recent years' significant academic AI advancements and the comparably low level of practical application in healthcare. Methods: A literature and real life cases analysis was conducted in Scopus and OpacPlus as well as the Google advanced search database. The according search queries have been defined based on success factor categories for AI implementation derived from a prior World Health Organization survey about barriers of adoption of Big Data within 125 countries. The eligible publications and real life cases were identified through a catalog of in- and exclusion criteria focused on concrete AI application cases. These were then analyzed to deduct and discuss success factors that facilitate or inhibit a broad-scale implementation of AI in healthcare. Results: The analysis revealed three categories of success factors, namely (1) policy setting, (2) technological implementation, and (3) medical and economic impact measurement. For each of them a set of recommendations has been deducted: First, a risk adjusted policy frame is required that distinguishes between precautionary and permissionless principles, and differentiates among accountability, liability, and culpability. Second, a "privacy by design" centered technology infrastructure shall be applied that enables practical and legally compliant data access. Third, the medical and economic impact need to be quantified, e.g., through the measurement of quality-adjusted life years while applying the CHEERS and PRISMA reporting criteria. Conclusions: Private and public institutions can already today leverage AI implementation based on the identified results and thus drive the translation from scientific development to real world application. Additional success factors could include trust-building measures, data categorization guidelines, and risk level assessments and as the success factors are interlinked, future research should elaborate on their optimal interaction to utilize the full potential of AI in real world application.
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Affiliation(s)
- Justus Wolff
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.,Syte - Strategy Institute for Digital Health, Hamburg, Germany
| | - Josch Pauling
- LipiTUM, Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany
| | - Andreas Keck
- Syte - Strategy Institute for Digital Health, Hamburg, Germany
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Munich, Germany.,Chair of Computational Systems Biology, University of Hamburg, Hamburg, Germany
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Yang F, Darsey JD, Ghosh A, Li HY, Yang MQ, Wang S. Artificial Intelligence and Cancer Drug Development. Recent Pat Anticancer Drug Discov 2021; 17:2-8. [PMID: 34323201 DOI: 10.2174/1574892816666210728123758] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/06/2021] [Accepted: 05/07/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND The development of cancer drugs is among the most focused "bench to bedside activities" to improve human health. Because of the amount of data publicly available to cancer research, drug development for cancers has significantly benefited from big data and AI. In the meantime, challenges, like curating the data of low quality, remain to be resolved. OBJECTIVE This review focused on the recent advancements in and challenges of AI in developing cancer drugs. METHOD We discussed target validation, drug repositioning, de novo design, and compounds' synthetic strategies. RESULTS AND CONCLUSION AI can be applied to all stages during drug development, and some excellent reviews detailing the applications of AI in specific stages are available.
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Affiliation(s)
- Fan Yang
- Healthville Primary Care, Little Rock, AR 72211, United States
| | - Jerry D Darsey
- Department of Chemistry, University of Arkansas at Little Rock, AR 72204, United States
| | - Anindya Ghosh
- Department of Chemistry, University of Arkansas at Little Rock, AR 72204, United States
| | - Hong-Yu Li
- University of Arkansas for Medical Sciences, Little Rock, AR 72204, United States
| | - Mary Q Yang
- Department of Information Science, University of Arkansas at Little Rock, AR 72204, United States
| | - Shanzhi Wang
- Department of Chemistry, University of Arkansas at Little Rock, AR 72204, United States
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Janković SM. Personalization of drug dose thematic issue: introduction article. Expert Opin Drug Metab Toxicol 2021; 17:1363-1367. [PMID: 34197236 DOI: 10.1080/17425255.2021.1951220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION The need to individualize a drug dosage and adjust it to a patient's physiological and/or pathophysiological status is rarely satisfied in routine clinical practice, primarily because of complexity of the adjustment task. AREAS COVERED The aim of this article is to shed light to basic principles of drug dosage individualization in the most frequent clinical states that affect pharmacokinetics of drugs. The principles are derived from published clinical studies conducted on diverse patient populations, using non-compartmental pharmacokinetic model. EXPERT OPINION Simple, but sufficiently exact, way to calculate appropriate drug dose for a patient is the one based on target average steady-state concentration and non-compartmental pharmacokinetic model. If target steady-state drug concentration and dosage interval are considered fixed, maintenance dose could be adjusted on the basis of expected changes of total drug clearance and bioavailability, while loading dose should be related to changes of volume of distribution and bioavailability. Relative increase or decrease of these pharmacokinetic parameters in regard to normal values in healthy persons is translated to relative (percentual) increase or decrease of maintenance and loading doses recommended in the drug monograph.
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Affiliation(s)
- Slobodan M Janković
- Faculty of Medical Sciences, and University Clinical Center, University of Kragujevac, Kragujevac, Serbia
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35
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Abdulkareem M, Petersen SE. The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype. Front Artif Intell 2021; 4:652669. [PMID: 34056579 PMCID: PMC8160471 DOI: 10.3389/frai.2021.652669] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/13/2021] [Indexed: 12/24/2022] Open
Abstract
COVID-19 has created enormous suffering, affecting lives, and causing deaths. The ease with which this type of coronavirus can spread has exposed weaknesses of many healthcare systems around the world. Since its emergence, many governments, research communities, commercial enterprises, and other institutions and stakeholders around the world have been fighting in various ways to curb the spread of the disease. Science and technology have helped in the implementation of policies of many governments that are directed toward mitigating the impacts of the pandemic and in diagnosing and providing care for the disease. Recent technological tools, artificial intelligence (AI) tools in particular, have also been explored to track the spread of the coronavirus, identify patients with high mortality risk and diagnose patients for the disease. In this paper, areas where AI techniques are being used in the detection, diagnosis and epidemiological predictions, forecasting and social control for combating COVID-19 are discussed, highlighting areas of successful applications and underscoring issues that need to be addressed to achieve significant progress in battling COVID-19 and future pandemics. Several AI systems have been developed for diagnosing COVID-19 using medical imaging modalities such as chest CT and X-ray images. These AI systems mainly differ in their choices of the algorithms for image segmentation, classification and disease diagnosis. Other AI-based systems have focused on predicting mortality rate, long-term patient hospitalization and patient outcomes for COVID-19. AI has huge potential in the battle against the COVID-19 pandemic but successful practical deployments of these AI-based tools have so far been limited due to challenges such as limited data accessibility, the need for external evaluation of AI models, the lack of awareness of AI experts of the regulatory landscape governing the deployment of AI tools in healthcare, the need for clinicians and other experts to work with AI experts in a multidisciplinary context and the need to address public concerns over data collection, privacy, and protection. Having a dedicated team with expertise in medical data collection, privacy, access and sharing, using federated learning whereby AI scientists hand over training algorithms to the healthcare institutions to train models locally, and taking full advantage of biomedical data stored in biobanks can alleviate some of problems posed by these challenges. Addressing these challenges will ultimately accelerate the translation of AI research into practical and useful solutions for combating pandemics.
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Affiliation(s)
- Musa Abdulkareem
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
| | - Steffen E. Petersen
- Barts Heart Centre, Barts Health National Health Service (NHS) Trust, London, United Kingdom
- National Institute for Health Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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Bhinder B, Gilvary C, Madhukar NS, Elemento O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov 2021; 11:900-915. [PMID: 33811123 DOI: 10.1158/2159-8290.cd-21-0090] [Citation(s) in RCA: 162] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 11/16/2022]
Abstract
Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.
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Affiliation(s)
- Bhavneet Bhinder
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York.,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York
| | | | | | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York. .,Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York.,OneThree Biotech, New York, New York
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Smidova V, Michalek P, Goliasova Z, Eckschlager T, Hodek P, Adam V, Heger Z. Nanomedicine of tyrosine kinase inhibitors. Theranostics 2021; 11:1546-1567. [PMID: 33408767 PMCID: PMC7778595 DOI: 10.7150/thno.48662] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 09/21/2020] [Indexed: 12/24/2022] Open
Abstract
Recent progress in nanomedicine and targeted therapy brings new breeze into the field of therapeutic applications of tyrosine kinase inhibitors (TKIs). These drugs are known for many side effects due to non-targeted mechanism of action that negatively impact quality of patients' lives or that are responsible for failure of the drugs in clinical trials. Some nanocarrier properties provide improvement of drug efficacy, reduce the incidence of adverse events, enhance drug bioavailability, helps to overcome the blood-brain barrier, increase drug stability or allow for specific delivery of TKIs to the diseased cells. Moreover, nanotechnology can bring new perspectives into combination therapy, which can be highly efficient in connection with TKIs. Lastly, nanotechnology in combination with TKIs can be utilized in the field of theranostics, i.e. for simultaneous therapeutic and diagnostic purposes. The review provides a comprehensive overview of advantages and future prospects of conjunction of nanotransporters with TKIs as a highly promising approach to anticancer therapy.
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Affiliation(s)
- Veronika Smidova
- Department of Chemistry and Biochemistry Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic
| | - Petr Michalek
- Department of Chemistry and Biochemistry Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Zita Goliasova
- Department of Chemistry and Biochemistry Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic
| | - Tomas Eckschlager
- Department of Paediatric Haematology and Oncology, 2nd Faculty of Medicine, Charles University, and University Hospital Motol, V Uvalu 84, Prague 5 CZ-15006, Czech Republic
| | - Petr Hodek
- Department of Biochemistry, Faculty of Science, Charles University, Albertov 2030, 128 40 Prague 2, Czech Republic
| | - Vojtech Adam
- Department of Chemistry and Biochemistry Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
| | - Zbynek Heger
- Department of Chemistry and Biochemistry Mendel University in Brno, Zemedelska 1, 613 00 Brno, Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic
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Kletecka-Pulker M, Mondal H, Wang D, Parra RG, Maigoro AY, Lee S, Garg T, Mulholland EJ, Devkota HP, Konwar B, Patnaik SS, Lordan R, Nawaz FA, Tsagkaris C, Rayan RA, Louka AM, De R, Badhe P, Schaden E, Willschke H, Maleczek M, Boyina HK, Khalid GM, Uddin MS, Sanusi, Khan J, Odimegwu JI, Yeung AWK, Akram F, Sai CS, Bucher S, Paswan SK, Singla RK, Shen B, Di Lonardo S, Tosevska A, Simal-Gandara J, Zec M, González-Burgos E, Habijan M, Battino M, Giampieri F, Tikhonov A, Cianciosi D, Forbes-Hernandez TY, Quiles JL, Mezzetti B, Babiaka SB, Ahmed ME, Piccard P, Urquiza MS, Depew JR, Schultz F, Sur D, Pai SR, Găman MA, Cenanovic M, Tzvetkov NT, Tripathi SK, Kharat KR, Garcia-Sosa AT, Sieber S, Atanasov AG. Impacts of biomedical hashtag-based Twitter campaign: #DHPSP utilization for promotion of open innovation in digital health, patient safety, and personalized medicine. CURRENT RESEARCH IN BIOTECHNOLOGY 2021. [DOI: 10.1016/j.crbiot.2021.04.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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Two new species of the genus Mecyclothorax Sharp from New Guinea (Coleoptera: Carabidae: Psydrinae). TIJDSCHRIFT VOOR ENTOMOLOGIE 2008; 19:ijerph19158979. [PMID: 35897349 PMCID: PMC9332044 DOI: 10.3390/ijerph19158979] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/15/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022]
Abstract
Chronic diseases typically require long-term management through healthy lifestyle practices and pharmacological intervention. Although efficacious treatments exist, disease control is often sub-optimal leading to chronic disease-related sequela. Poor disease control can partially be explained by the ‘one size fits all’ pharmacological approach. Precision medicine aims to tailor treatments to the individual. CURATE.AI is a dosing optimisation platform that considers individual factors to improve the precision of drug therapies. CURATE.AI has been validated in other therapeutic areas, such as cancer, but has yet to be applied in chronic disease care. We will evaluate the CURATE.AI system through a single-arm feasibility study (n = 20 hypertensives and n = 20 type II diabetics). Dosing decisions will be based on CURATE.AI recommendations. We will prospectively collect clinical and qualitative data and report on the clinical effect, implementation challenges, and acceptability of using CURATE.AI. In addition, we will explore how to enhance the algorithm further using retrospective patient data. For example, the inclusion of other variables, the simultaneous optimisation of multiple drugs, and the incorporation of other artificial intelligence algorithms. Overall, this project aims to understand the feasibility of using CURATE.AI in clinical practice. Barriers and enablers to CURATE.AI will be identified to inform the system’s future development.
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