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Sheng B, Pushpanathan K, Guan Z, Lim QH, Lim ZW, Yew SME, Goh JHL, Bee YM, Sabanayagam C, Sevdalis N, Lim CC, Lim CT, Shaw J, Jia W, Ekinci EI, Simó R, Lim LL, Li H, Tham YC. Artificial intelligence for diabetes care: current and future prospects. Lancet Diabetes Endocrinol 2024; 12:569-595. [PMID: 39054035 DOI: 10.1016/s2213-8587(24)00154-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/28/2024] [Accepted: 05/16/2024] [Indexed: 07/27/2024]
Abstract
Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.
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Affiliation(s)
- Bin Sheng
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China; Key Laboratory of Artificial Intelligence, Ministry of Education, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Krithi Pushpanathan
- Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Zhouyu Guan
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Quan Hziung Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Zhi Wei Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Samantha Min Er Yew
- Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | | | - Yong Mong Bee
- Department of Endocrinology, Singapore General Hospital, Singapore; SingHealth Duke-National University of Singapore Diabetes Centre, Singapore Health Services, Singapore
| | - Charumathi Sabanayagam
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Nick Sevdalis
- Centre for Behavioural and Implementation Science Interventions, National University of Singapore, Singapore
| | | | - Chwee Teck Lim
- Department of Biomedical Engineering, National University of Singapore, Singapore; Institute for Health Innovation and Technology, National University of Singapore, Singapore; Mechanobiology Institute, National University of Singapore, Singapore
| | - Jonathan Shaw
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Weiping Jia
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Elif Ilhan Ekinci
- Australian Centre for Accelerating Diabetes Innovations, Melbourne Medical School and Department of Medicine, University of Melbourne, Melbourne, VIC, Australia; Department of Endocrinology, Austin Health, Melbourne, VIC, Australia
| | - Rafael Simó
- Diabetes and Metabolism Research Unit, Vall d'Hebron University Hospital and Vall d'Hebron Research Institute, Barcelona, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Madrid, Spain
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Asia Diabetes Foundation, Hong Kong Special Administrative Region, China
| | - Huating Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
| | - Yih-Chung Tham
- Centre of Innovation and Precision Eye Health, Department of Ophthalmology, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-National University of Singapore Medical School, Singapore; Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.
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Wichmann J, Gesk TS, Leyer M. Acceptance of AI in Health Care for Short- and Long-Term Treatments: Pilot Development Study of an Integrated Theoretical Model. JMIR Form Res 2024; 8:e48600. [PMID: 39024565 PMCID: PMC11294784 DOI: 10.2196/48600] [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: 05/21/2023] [Revised: 02/14/2024] [Accepted: 04/29/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND As digital technologies and especially artificial intelligence (AI) become increasingly important in health care, it is essential to determine whether and why potential users intend to use related health information systems (HIS). Several theories exist, but they focus mainly on aspects of health care or information systems, in addition to general psychological theories, and hence provide a small number of variables to explain future behavior. Thus, research that provides a larger number of variables by combining several theories from health care, information systems, and psychology is necessary. OBJECTIVE This study aims to investigate the intention to use new HIS for decisions concerning short- and long-term medical treatments using an integrated approach with several variables to explain future behavior. METHODS We developed an integrated theoretical model based on theories from health care, information systems, and psychology that allowed us to analyze the duality approach of adaptive and nonadaptive appraisals and their influence on the intention to use HIS. We applied the integrated theoretical model to the short-term treatment using AI-based HIS for surgery and the long-term treatment of diabetes tracking using survey data with structured equation modeling. To differentiate between certain levels of AI involvement, we used several scenarios that include treatments by physicians only, physicians with AI support, and AI only to understand how individuals perceive the influence of AI. RESULTS Our results showed that for short- and long-term treatments, the variables perceived threats, fear (disease), perceived efficacy, attitude (HIS), and perceived norms are important to consider when determining the intention to use AI-based HIS. Furthermore, the results revealed that perceived efficacy and attitude (HIS) are the most important variables to determine intention to use for all treatments and scenarios. In contrast, abilities (HIS) were important for short-term treatments only. For our 9 scenarios, adaptive and nonadaptive appraisals were both important to determine intention to use, depending on whether the treatment is known. Furthermore, we determined R² values that varied between 57.9% and 81.7% for our scenarios, which showed that the explanation power of our model is medium to good. CONCLUSIONS We contribute to HIS literature by highlighting the importance of integrating disease- and technology-related factors and by providing an integrated theoretical model. As such, we show how adaptive and nonadaptive appraisals should be arranged to report on medical decisions in the future, especially in the short and long terms. Physicians and HIS developers can use our insights to identify promising rationale for HIS adoption concerning short- and long-term treatments and adapt and develop HIS accordingly. Specifically, HIS developers should ensure that future HIS act in terms of HIS functions, as our study shows that efficient HIS lead to a positive attitude toward the HIS and ultimately to a higher intention to use.
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Affiliation(s)
- Johannes Wichmann
- Working group Digitalization and Process Management, Department of Business, Philipps-University Marburg, Marburg, Germany
| | - Tanja Sophie Gesk
- Working group Digitalization and Process Management, Department of Business, Philipps-University Marburg, Marburg, Germany
| | - Michael Leyer
- Working group Digitalization and Process Management, Department of Business, Philipps-University Marburg, Marburg, Germany
- Management Department, Queensland University of Technology, Brisbane, Australia
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Mai HN, Win TT, Kim HS, Pae A, Att W, Nguyen DD, Lee DH. Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance. J Prosthodont 2024. [PMID: 39010644 DOI: 10.1111/jopr.13900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 06/06/2024] [Indexed: 07/17/2024] Open
Abstract
PURPOSE This study aimed to examine the satisfaction of dental professionals, including dental students, dentists, and dental technicians, with computer-aided design (CAD) software performance using deep learning (DL) and explainable artificial intelligence (XAI)-based behavioral analysis concepts. MATERIALS AND METHODS This study involved 436 dental professionals with diverse CAD experiences to assess their satisfaction with various dental CAD software programs. Through exploratory factor analysis, latent factors affecting user satisfaction were extracted from the observed variables. A multilayer perceptron artificial neural network (MLP-ANN) model was developed along with permutation feature importance analysis (PFIA) and the Shapley additive explanation (Shapley) method to gain XAI-based insights into individual factors' significance and contributions. RESULTS The MLP-ANN model outperformed a standard logistic linear regression model, demonstrating high accuracy (95%), precision (84%), and recall rates (84%) in capturing complex psychological problems related to human attitudes. PFIA revealed that design adjustability was the most important factor impacting dental CAD software users' satisfaction. XAI analysis highlighted the positive impacts of features supporting the finish line and crown design, while the number of design steps and installation time had negative impacts. Notably, finish-line design-related features and the number of design steps emerged as the most significant factors. CONCLUSIONS This study sheds light on the factors influencing dental professionals' decisions in using and selecting CAD software. This approach can serve as a proof-of-concept for applying DL-XAI-based behavioral analysis in dentistry and medicine, facilitating informed software selection and development.
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Affiliation(s)
- Hang-Nga Mai
- Institute for Translational Research in Dentistry, Kyungpook National University, Daegu, Republic of Korea
- Hanoi University of Business and Technology, Hanoi, Vietnam
| | - Thaw Thaw Win
- Department of Prosthodontics, School of Dentistry, Kyungpook National University, Daegu, Republic of Korea
| | - Hyeong-Seob Kim
- Department of Prosthodontics, Kyung Hee University College of Dentistry, Kyung Hee University Medical Center, Seoul, Republic of Korea
| | - Ahran Pae
- Department of Prosthodontics, Kyung Hee University College of Dentistry, Kyung Hee University Medical Center, Seoul, Republic of Korea
| | - Wael Att
- Center for Dental Medicine, Department of Prosthetic Dentistry, Faculty of Medicine, University of Freiburg, Freiburg, Germany
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Dang Dinh Nguyen
- Department of Prosthodontics, University of Iowa College of Dentistry and Dental Clinics, Iowa City, Iowa, USA
| | - Du-Hyeong Lee
- Institute for Translational Research in Dentistry, Kyungpook National University, Daegu, Republic of Korea
- Department of Prosthodontics, School of Dentistry, Kyungpook National University, Daegu, Republic of Korea
- The Face Dental Group, Boston, Massachusetts, USA
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Weatherall T, Avsar P, Nugent L, Moore Z, McDermott JH, Sreenan S, Wilson H, McEvoy NL, Derwin R, Chadwick P, Patton D. The impact of machine learning on the prediction of diabetic foot ulcers - A systematic review. J Tissue Viability 2024:S0965-206X(24)00109-8. [PMID: 39019690 DOI: 10.1016/j.jtv.2024.07.004] [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: 01/11/2024] [Revised: 06/24/2024] [Accepted: 07/10/2024] [Indexed: 07/19/2024]
Abstract
INTRODUCTION Globally, diabetes mellitus poses a significant health challenge as well as the associated complications of diabetes, such as diabetic foot ulcers (DFUs). The early detection of DFUs is important in the healing process and machine learning may be able to help inform clinical staff during the treatment process. METHODS A PRISMA-informed search of the literature was completed via the Cochrane Library and MEDLINE (OVID), EMBASE, CINAHL Plus and Scopus databases for reports published in English and in the last ten years. The primary outcome of interest was the impact of machine learning on the prediction of DFUs. The secondary outcome was the statistical performance measures reported. Data were extracted using a predesigned data extraction tool. Quality appraisal was undertaken using the evidence-based librarianship critical appraisal tool. RESULTS A total of 18 reports met the inclusion criteria. Nine reports proposed models to identify two classes, either healthy skin or a DFU. Nine reports proposed models to predict the progress of DFUs, for example, classing infection versus non-infection, or using wound characteristics to predict healing. A variety of machine learning techniques were proposed. Where reported, sensitivity = 74.53-98 %, accuracy = 64.6-99.32 %, precision = 62.9-99 %, and the F-measure = 52.05-99.0 %. CONCLUSIONS A variety of machine learning models were suggested to successfully classify DFUs from healthy skin, or to inform the prediction of DFUs. The proposed machine learning models may have the potential to inform the clinical practice of managing DFUs and may help to improve outcomes for individuals with DFUs. Future research may benefit from the development of a standard device and algorithm that detects, diagnoses and predicts the progress of DFUs.
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Affiliation(s)
- Teagan Weatherall
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Pinar Avsar
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Linda Nugent
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia.
| | - Zena Moore
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia; School of Nursing and Midwifery, Griffith University, Southport, Queensland, Australia; Lida Institute, Shanghai, China; Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia; Department of Public Health, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; University of Wales, Cardiff, UK; National Health and Medical Research Council Centre of Research Excellence in Wiser Wound Care, Menzies Health Institute Queensland, Southport, Queensland, Australia.
| | - John H McDermott
- Department of Endocrinology, Royal College of Surgeons in Ireland, Connolly Hospital Blanchardstown, Dublin, Ireland.
| | - Seamus Sreenan
- Department of Endocrinology, Royal College of Surgeons in Ireland, Connolly Hospital Blanchardstown, Dublin, Ireland.
| | - Hannah Wilson
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Natalie L McEvoy
- School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Rosemarie Derwin
- School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland.
| | - Paul Chadwick
- Birmingham City University, Birmingham, UK; Spectral MD, London, UK.
| | - Declan Patton
- Skin Wounds and Trauma (SWaT) Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland; School of Nursing and Midwifery, RCSI University of Medicine and Health Sciences, Dublin, Ireland; Fakeeh College of Medical Sciences, Jeddah, Saudi Arabia; School of Nursing and Midwifery, Griffith University, Southport, Queensland, Australia; Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, New South Wales, Australia.
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Ameksa M, Elamrani Abou Elassad Z, Lamjadli S, Mousannif H. Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics. Comput Methods Biomech Biomed Engin 2024:1-18. [PMID: 38902976 DOI: 10.1080/10255842.2024.2363946] [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: 05/09/2023] [Accepted: 05/28/2024] [Indexed: 06/22/2024]
Abstract
Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance. The research utilizes Grid Search optimized models, such as Random Forest, Support Vector Machine, K Nearest Neighbors, AdaBoost, Gradient Boosting, Light Gradient Boosting, Categorical Boosting, and eXtreme Gradient Boosting for in-depth stroke analysis. Since stroke events are rare and unpredictable, classification outcomes can be deceptive due to imbalanced data. This article examines stroke probability by comparing three data balancing methods: over-sampling, under-sampling, and tomek-link synthetic minority over-sampling (SMOTE-TL) to enhance prediction accuracy. The findings reveal that AdaBoost as a meta-classifier attains the highest performance in the fusion framework, with a peak of 88.09% Recall and 83.66% F1 score. This innovative approach provides crucial insights into stroke prediction and can be a valuable resource for strengthening intervention efforts in advanced healthcare systems.
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Affiliation(s)
- Mohammed Ameksa
- LISI Laboratory, Computer Science Department, FSSM, Cadi Ayyad University, Marrakesh, Morocco
| | | | - Saad Lamjadli
- Immunology Laboratory, Arrazi Hospital, CHU Mohamed VI, Marrakech, Morocco
| | - Hajar Mousannif
- LISI Laboratory, Computer Science Department, FSSM, Cadi Ayyad University, Marrakesh, Morocco
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Nag S, Mitra O, Maturi B, Kaur SP, Saini A, Nama M, Roy S, Samanta S, Chacko L, Dutta R, Sayana SB, Subramaniyan V, Bhatti JS, Kandimalla R. Autophagy and mitophagy as potential therapeutic targets in diabetic heart condition: Harnessing the power of nanotheranostics. Asian J Pharm Sci 2024; 19:100927. [PMID: 38948399 PMCID: PMC11214300 DOI: 10.1016/j.ajps.2024.100927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 03/29/2024] [Accepted: 04/13/2024] [Indexed: 07/02/2024] Open
Abstract
Autophagy and mitophagy pose unresolved challenges in understanding the pathology of diabetic heart condition (DHC), which encompasses a complex range of cardiovascular issues linked to diabetes and associated cardiomyopathies. Despite significant progress in reducing mortality rates from cardiovascular diseases (CVDs), heart failure remains a major cause of increased morbidity among diabetic patients. These cellular processes are essential for maintaining cellular balance and removing damaged or dysfunctional components, and their involvement in the development of diabetic heart disease makes them attractive targets for diagnosis and treatment. While a variety of conventional diagnostic and therapeutic strategies are available, DHC continues to present a significant challenge. Point-of-care diagnostics, supported by nanobiosensing techniques, offer a promising alternative for these complex scenarios. Although conventional medications have been widely used in DHC patients, they raise several concerns regarding various physiological aspects. Modern medicine places great emphasis on the application of nanotechnology to target autophagy and mitophagy in DHC, offering a promising approach to deliver drugs beyond the limitations of traditional therapies. This article aims to explore the potential connections between autophagy, mitophagy and DHC, while also discussing the promise of nanotechnology-based theranostic interventions that specifically target these molecular pathways.
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Affiliation(s)
- Sagnik Nag
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia
| | - Oishi Mitra
- Department of Bio-Sciences, School of Bio-Sciences & Technology (SBST), Vellore Institute of Technology (VIT), Tiruvalam Road, Vellore 632014, Tamil Nadu, India
| | - Bhanu Maturi
- Department of Internal Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Simran Preet Kaur
- Department of Microbiology, University of Delhi (South Campus), Benito Juarez Road, New Delhi 110021, India
| | - Ankita Saini
- Department of Microbiology, University of Delhi (South Campus), Benito Juarez Road, New Delhi 110021, India
| | - Muskan Nama
- Department of Bio-Sciences, School of Bio-Sciences & Technology (SBST), Vellore Institute of Technology (VIT), Tiruvalam Road, Vellore 632014, Tamil Nadu, India
| | - Soumik Roy
- Department of Biotechnology, Indian Institute of Technology, Hyderabad (IIT-H), Sangareddy, Telangana 502284, India
| | - Souvik Samanta
- Department of Bio-Sciences, School of Bio-Sciences & Technology (SBST), Vellore Institute of Technology (VIT), Tiruvalam Road, Vellore 632014, Tamil Nadu, India
| | - Leena Chacko
- BioAnalytical Lab, Meso Scale Discovery, 1601 Research Blvd, Rockville, MD, USA
| | - Rohan Dutta
- Department of Bio-Sciences, School of Bio-Sciences & Technology (SBST), Vellore Institute of Technology (VIT), Tiruvalam Road, Vellore 632014, Tamil Nadu, India
| | - Suresh Babu Sayana
- Department of Pharmacology, Government Medical College, Suryapet, Telangana, India
| | - Vetriselvan Subramaniyan
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, 47500 Bandar Sunway, Selangor, Malaysia
| | - Jasvinder Singh Bhatti
- Laboratory of Translational Medicine and Nanotherapeutics, Department of Human Genetics and Molecular Medicine, School of Health Sciences, Central University of Punjab, Bathinda, India
| | - Ramesh Kandimalla
- Department of Biochemistry, Kakatiya Medical College, Warangal 506007, India
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Nedbal C, Adithya S, Naik N, Gite S, Juliebø-Jones P, Somani BK. Can Machine Learning Correctly Predict Outcomes of Flexible Ureteroscopy with Laser Lithotripsy for Kidney Stone Disease? Results from a Large Endourology University Centre. EUR UROL SUPPL 2024; 64:30-37. [PMID: 38832122 PMCID: PMC11145425 DOI: 10.1016/j.euros.2024.05.004] [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: 05/12/2024] [Indexed: 06/05/2024] Open
Abstract
Background and objective The integration of machine learning (ML) in health care has garnered significant attention because of its unprecedented opportunities to enhance patient care and outcomes. In this study, we trained ML algorithms for automated prediction of outcomes of ureteroscopic laser lithotripsy (URSL) on the basis of preoperative characteristics. Methods Data were retrieved for patients treated with ureteroscopy for urolithiasis by a single experienced surgeon over a 7-yr period. Sixteen ML classification algorithms were trained to investigate correlation between preoperative characteristics and postoperative outcomes. The outcomes assessed were primary stone-free status (SFS, defined as the presence of only stone fragments <2 mm on endoscopic visualisation and at 3-mo imaging) and postoperative complications. An ensemble model was constructed from the best-performing algorithms for prediction of complications and for prediction of SFS. Simultaneous prediction of postoperative characteristics was then investigated using a multitask neural network, and explainable artificial intelligence (AI) was used to demonstrate the predictive power of the best models. Key findings and limitations An ensemble ML model achieved accuracy of 93% and precision of 87% for prediction of SFS. Complications were mainly associated with a preoperative positive urine culture (1.44). Logistic regression revealed that SFS was impacted by the total stone burden (0.34), the presence of a preoperative stent (0.106), a positive preoperative urine culture (0.14), and stone location (0.09). Explainable AI results emphasised the key features and their contributions to the output. Conclusions and clinical implications Technological advances are helping urologists to overcome the classic limits of ureteroscopy, namely stone size and the risk of complications. ML represents an excellent aid for correct prediction of outcomes after training on pre-existing data sets. Our ML model achieved accuracy of >90% for prediction of SFS and complications, and represents a basis for the development of an accessible predictive model for endourologists and patients in the URSL setting. Patient summary We tested the ability of artificial intelligence to predict treatment outcomes for patients with kidney stones. We trained 16 different machine learning tools with data before surgery, such as patient age and the stone characteristics. Our final model was >90% accurate in predicting stone-free status after surgery and the occurrence of complications.
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Affiliation(s)
- Carlotta Nedbal
- University Hospital Southampton NHS Trust, Southampton, UK
- Urology Unit, Azienda Ospedaliero-Universitaria Delle Marche, Università Politecnica Delle Marche, Ancona, Italy
| | | | - Nithesh Naik
- Manipal Academy of Higher Education, Manipal, India
| | - Shilpa Gite
- Symbiosis Institute of Technology, Pune, India
| | - Patrick Juliebø-Jones
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Urology, Haukeland University Hospital, Bergen, Norway
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Wang P, Tadeo X, Chew HSJ, Sapanel Y, Ong YH, Leung NYT, Chow EKH, Ho D. N-of-1 health optimization: Digital monitoring of biomarker dynamics to gamify adherence to metabolic switching. PNAS NEXUS 2024; 3:pgae214. [PMID: 38881838 PMCID: PMC11179112 DOI: 10.1093/pnasnexus/pgae214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024]
Abstract
The digital health field is experiencing substantial growth due to its potential for sustained and longitudinal deployment. In turn, this may drive improved monitoring and intervention as catalysts for behavioral change compared to traditional point-of-care practices. In particular, the increase in incidence of population health challenges such as diabetes, heart disease, fatty liver disease, and other disorders coupled with rising healthcare costs have emphasized the importance of exploring technical, economics, and implementation considerations, among others in the decentralization of health and healthcare innovations. Both healthy individuals and patients stand to benefit from continued technical advances and studies in these domains. To address these points, this study reports a N-of-1 study comprised of sustained regimens of intermittent fasting, fitness (strength and cardiovascular training), and high protein, low carbohydrate diet and parallel monitoring. These regimens were paired with serial blood ketone, blood glucose (wearable and finger stick) and blood pressure readings, as well as body weight measurements using a collection of devices. Collectively this suite of platforms and approaches were used to monitor metabolic switching from glucose to ketones as energy sources-a process associated with potential cardio- and neuroprotective functions. In addition to longitudinal biomarker dynamics, this work discusses user perspectives on the potential role of harnessing digital devices to these dynamics as potential gamification factors, as well as considerations for the role of biomarker monitoring in health regimen development, user stratification, and potentially informing downstream population-scale studies to address metabolic disease, healthy aging and longevity, among other indications.
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Affiliation(s)
- Peter Wang
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore 117456, Singapore
| | - Xavier Tadeo
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore 117456, Singapore
| | - Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Yoann Sapanel
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Singapore's Health District @ Queenstown, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Yoong Hun Ong
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore 117456, Singapore
| | - Nicole Yong Ting Leung
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore 117456, Singapore
| | - Edward Kai-Hua Chow
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore 117456, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, Singapore 117599, Singapore
- NUS Centre for Cancer Research (N2CR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117599, Singapore
| | - Dean Ho
- Department of Biomedical Engineering, College of Design and Engineering, National University of Singapore, Singapore 117583, Singapore
- Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- The N.1 Institute for Health (N.1), National University of Singapore, Singapore 117456, Singapore
- Singapore's Health District @ Queenstown, 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
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9
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Adelson RP, Garikipati A, Zhou Y, Ciobanu M, Tawara K, Barnes G, Singh NP, Mao Q, Das R. Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes. Diagnostics (Basel) 2024; 14:1152. [PMID: 38893680 PMCID: PMC11172278 DOI: 10.3390/diagnostics14111152] [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: 05/03/2024] [Revised: 05/24/2024] [Accepted: 05/29/2024] [Indexed: 06/21/2024] Open
Abstract
Type 2 diabetes (T2D) is a global health concern with increasing prevalence. Comorbid hypothyroidism (HT) exacerbates kidney, cardiac, neurological and other complications of T2D; these risks can be mitigated pharmacologically upon detecting HT. The current HT standard of care (SOC) screening in T2D is infrequent, delaying HT diagnosis and treatment. We present a first-to-date machine learning algorithm (MLA) clinical decision tool to classify patients as low vs. high risk for developing HT comorbid with T2D; the MLA was developed using readily available patient data from harmonized multinational datasets. The MLA was trained on data from NIH All of US (AoU) and UK Biobank (UKBB) (Combined dataset) and achieved a high negative predictive value (NPV) of 0.989 and an AUROC of 0.762 in the Combined dataset, exceeding AUROCs for the models trained on AoU or UKBB alone (0.666 and 0.622, respectively), indicating that increasing dataset diversity for MLA training improves performance. This high-NPV automated tool can supplement SOC screening and rule out T2D patients with low HT risk, allowing for the prioritization of lab-based testing for at-risk patients. Conversely, an MLA output that designates a patient to be at risk of developing HT allows for tailored clinical management and thereby promotes improved patient outcomes.
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Affiliation(s)
| | | | | | | | | | | | | | - Qingqing Mao
- Montera, Inc. dba Forta, 548 Market St, PMB 89605, San Francisco, CA 94104-5401, USA; (R.P.A.); (A.G.); (Y.Z.); (M.C.); (K.T.); (G.B.); (N.P.S.); (R.D.)
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10
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Eghbali-Zarch M, Masoud S. Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review. Artif Intell Med 2024; 151:102868. [PMID: 38632030 DOI: 10.1016/j.artmed.2024.102868] [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: 08/18/2023] [Revised: 03/03/2024] [Accepted: 04/03/2024] [Indexed: 04/19/2024]
Abstract
Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper conducts a comprehensive review of current literature on the application of machine learning (ML) in insulin management for diabetes patients, particularly focusing on enhancing affordability and accessibility within the United States. The review encompasses various facets of insulin management, including dosage calculation and response, prediction of blood glucose and insulin sensitivity, initial insulin estimation, resistance prediction, treatment adherence, complications, hypoglycemia prediction, and lifestyle modifications. Additionally, the study identifies key limitations in the utilization of ML within the insulin management literature and suggests future research directions aimed at furthering accessible and affordable insulin treatments. These proposed directions include exploring insurance coverage, optimizing insulin type selection, assessing the impact of biosimilar insulin and market competition, considering mental health factors, evaluating insulin delivery options, addressing cost-related issues affecting insulin usage and adherence, and selecting appropriate patient cost-sharing programs. By examining the potential of ML in addressing insulin management affordability and accessibility, this work aims to envision improved and cost-effective insulin management practices. It not only highlights existing research gaps but also offers insights into future directions, guiding the development of innovative solutions that have the potential to revolutionize insulin management and benefit patients reliant on this life-saving treatment.
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Affiliation(s)
- Maryam Eghbali-Zarch
- Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA
| | - Sara Masoud
- Department of Industrial and Systems Engineering, Wayne State University, Detroit, MI 48202, USA.
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11
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Gupta S, Sharma N, Arora S, Verma S. Diabetes: a review of its pathophysiology, and advanced methods of mitigation. Curr Med Res Opin 2024; 40:773-780. [PMID: 38512073 DOI: 10.1080/03007995.2024.2333440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/18/2024] [Indexed: 03/22/2024]
Abstract
Diabetes mellitus (DM) is a long-lasting metabolic non-communicable disease often characterized by an increase in the level of glucose in the blood or hyperglycemia. Approximately, 415 million people between the ages of 20 and 79 years had DM in 2015 and this figure will rise by 200 million by 2040. In a study conducted by CARRS, it's been found that in Delhi the prevalence of diabetes is around 27% and for prediabetic cases, it is more than 46%. The disease DM can be both short-term and long-term and is often associated with one or more diseases like cardiovascular disease, liver disorder, or kidney malfunction. Early identification of diabetes may help avoid catastrophic repercussions because untreated DM can result in serious complications. Diabetes' primary symptoms are persistently high blood glucose levels, frequent urination, increased thirst, and increased hunger. Therefore, DM is classified into four major categories, namely, Type 1, Type 2, Gestational diabetes, and secondary diabetes. There are various oral and injectable formulations available in the market like insulin, biguanides, sulphonylureas, etc. for the treatment of DM. Recent attention can be given to the various nano approaches undertaken for the treatment, diagnosis, and management of diabetes mellitus. Various nanoparticles like Gold Nanoparticles, carbon nanomaterials, and metallic nanoparticles are some of the approaches mentioned in this review. Besides nanotechnology, artificial intelligence (AI) has also found its application in diabetes care. AI can be used for screening the disease, helping in decision-making, predictive population-level risk stratification, and patient self-management tools. Early detection and diagnosis of diabetes also help the patient avoid expensive treatments later in their life with the help of IoT (internet of medical things) and machine learning models. These tools will help healthcare physicians to predict the disease early. Therefore, the Nano drug delivery system along with AI tools holds a very bright future in diabetes care.
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Affiliation(s)
- Sarika Gupta
- Centre for Pharmaceutics, Industrial Pharmacy and Drugs Regulatory Affairs, Amity Institute of Pharmacy, Amity University, Noida, India
| | - Nitin Sharma
- Centre for Pharmaceutics, Industrial Pharmacy and Drugs Regulatory Affairs, Amity Institute of Pharmacy, Amity University, Noida, India
| | - Sandeep Arora
- Centre for Pharmaceutics, Industrial Pharmacy and Drugs Regulatory Affairs, Amity Institute of Pharmacy, Amity University, Noida, India
| | - Saurabh Verma
- Centre for Pharmaceutics, Industrial Pharmacy and Drugs Regulatory Affairs, Amity Institute of Pharmacy, Amity University, Noida, India
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12
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Han Y, Kim DY, Woo J, Kim J. Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients. Heliyon 2024; 10:e29030. [PMID: 38638954 PMCID: PMC11024573 DOI: 10.1016/j.heliyon.2024.e29030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/20/2024] Open
Abstract
Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels, posing significant health risks such as cardiovascular disease, and nerve, kidney, and eye damage. Effective management of blood glucose is essential for individuals with diabetes to mitigate these risks. This study introduces the Glu-Ensemble, a deep learning framework designed for precise blood glucose forecasting in patients with type 2 diabetes. Unlike other predictive models, Glu-Ensemble addresses challenges related to small sample sizes, data quality issues, reliance on strict statistical assumptions, and the complexity of models. It enhances prediction accuracy and model generalizability by utilizing larger datasets and reduces bias inherent in many predictive models. The framework's unified approach, as opposed to patient-specific models, eliminates the need for initial calibration time, facilitating immediate blood glucose predictions for new patients. The obtained results indicate that Glu-Ensemble surpasses traditional methods in accuracy, as measured by root mean square error, mean absolute error, and error grid analysis. The Glu-Ensemble framework emerges as a promising tool for blood glucose level prediction in type 2 diabetes patients, warranting further investigation in clinical settings for its practical application.
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Affiliation(s)
- Yechan Han
- Department of Medical Science, Soonchunhyang University, Asan, 31538, Republic of Korea
| | - Dae-Yeon Kim
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, 31151, Republic of Korea
| | - Jiyoung Woo
- Department of AI and Big Data, Soonchunhyang University, Asan, 31538, Republic of Korea
| | - Jaeyun Kim
- Department of AI and Big Data, Soonchunhyang University, Asan, 31538, Republic of Korea
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13
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Alhalafi A, Alqahtani SM, Alqarni NA, Aljuaid AT, Aljaber GT, Alshahrani LM, Mushait H, Nandi PA. Utilizing Artificial Intelligence Among Patients With Diabetes: A Systematic Review and Meta-Analysis. Cureus 2024; 16:e58713. [PMID: 38779284 PMCID: PMC11110080 DOI: 10.7759/cureus.58713] [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: 03/27/2024] [Accepted: 04/21/2024] [Indexed: 05/25/2024] Open
Abstract
Diabetes mellitus, a condition characterized by dysregulation of blood glucose levels, poses significant health challenges globally. This meta-analysis and systematic review aimed to evaluate the effectiveness of artificial intelligence (AI) in managing diabetes, underpinned by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review scrutinized articles published between January 2019 and February 2024, sourced from six electronic databases: Web of Science, Google Scholar, PubMed, Cochrane Library, EMBASE, and MEDLINE, using keywords such as "Artificial intelligence use in medicine, Diabetes management, Health technology, Machine learning, Diabetic patients, AI applications, and Health informatics." The analysis revealed a notable variance in the prevalence of diabetes symptoms between patients managed with AI models and those receiving standard treatments or other machine learning models, with a risk ratio (RR) of 0.98 (95% CI: 0.88-1.08, I2 = 0%). Sub-group analyses, focusing on symptom detection and management, consistently showed outcomes favoring AI interventions, with RRs of 0.97 (95% CI: 0.87-1.08, I2 = 0%) for symptom detection and 0.97 (95% CI: 0.56-1.57, I2 = 0%) for management, respectively. The findings underscore the potential of AI in enhancing diabetes care, particularly in early disease detection and personalized lifestyle recommendations, addressing the significant health risks associated with diabetes, including increased morbidity and mortality. This study highlights the promising role of AI in revolutionizing diabetes management, advocating for its expanded use in healthcare settings to improve patient outcomes and optimize treatment efficacy.
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Affiliation(s)
- Abdullah Alhalafi
- Department of Family and Community Medicine, University of Bisha, Bisha, SAU
| | | | | | | | - Ghade T Aljaber
- Department of Medicine, Batterjee Medical College, Aseer, SAU
| | | | | | - Partha A Nandi
- Department of Family and Community Medicine, University of Bisha, Bisha, SAU
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14
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Jairoun AA, Al-Hemyari SS, Shahwan M, Al-Qirim T, Shahwan M. Benefit-Risk Assessment of ChatGPT Applications in the Field of Diabetes and Metabolic Illnesses: A Qualitative Study. Clin Med Insights Endocrinol Diabetes 2024; 17:11795514241235514. [PMID: 38495947 PMCID: PMC10943713 DOI: 10.1177/11795514241235514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/06/2024] [Indexed: 03/19/2024] Open
Abstract
Background The use of ChatGPT and artificial intelligence (AI) in the management of metabolic and endocrine disorders presents both significant opportunities and notable risks. Objectives To investigate the benefits and risks associated with the application of ChatGPT in managing diabetes and metabolic illnesses by exploring the perspectives of endocrinologists and diabetologists. Methods and materials The study employed a qualitative research approach. A semi-structured in-depth interview guide was developed. A convenience sample of 25 endocrinologists and diabetologists was enrolled and interviewed. All interviews were audiotaped and verbatim transcribed; then, thematic analysis was used to determine the themes in the data. Results The findings of the thematic analysis resulted in 19 codes and 9 major themes regarding the benefits of implementing AI and ChatGPT in managing diabetes and metabolic illnesses. Moreover, the extracted risks of implementing AI and ChatGPT in managing diabetes and metabolic illnesses were categorized into 7 themes and 14 codes. The benefits of heightened diagnostic precision, tailored treatment, and efficient resource utilization have potential to improve patient results. Concurrently, the identification of potential challenges, such as data security concerns and the need for AI that can be explained, enables stakeholders to proactively tackle these issues. Conclusions Regulatory frameworks must evolve to keep pace with the rapid adoption of AI in healthcare. Sustained attention to ethical considerations, including obtaining patient consent, safeguarding data privacy, ensuring accountability, and promoting fairness, remains critical. Despite its potential impact on the human aspect of healthcare, AI will remain an integral component of patient-centered care. Striking a balance between AI-assisted decision-making and human expertise is essential to uphold trust and provide comprehensive patient care.
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Affiliation(s)
- Ammar Abdulrahman Jairoun
- Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Malaysia
- Health and Safety Department, Dubai Municipality, Dubai, United Arab Emirates
| | - Sabaa Saleh Al-Hemyari
- Discipline of Clinical Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia (USM), Pulau Pinang, Malaysia
- Pharmacy Department, Emirates Health Services, Dubai, United Arab Emirates
| | - Moyad Shahwan
- College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
- Centre of Medical and Bio-allied Health Sciences Research, Ajman University, United Arab Emirates
| | - Tariq Al-Qirim
- Faculty of Pharmacy, Al-Zaytoonah University of Jordan, Amman, Jordan
| | - Monzer Shahwan
- Diabetes Clinic, AL-Swity Center for Dermatology and Chronic Diseases, Ramallah, Palestine
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15
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Chafai N, Bonizzi L, Botti S, Badaoui B. Emerging applications of machine learning in genomic medicine and healthcare. Crit Rev Clin Lab Sci 2024; 61:140-163. [PMID: 37815417 DOI: 10.1080/10408363.2023.2259466] [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: 06/19/2023] [Accepted: 09/12/2023] [Indexed: 10/11/2023]
Abstract
The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.
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Affiliation(s)
- Narjice Chafai
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
| | - Luigi Bonizzi
- Department of Biomedical, Surgical and Dental Science, University of Milan, Milan, Italy
| | - Sara Botti
- PTP Science Park, Via Einstein - Loc. Cascina Codazza, Lodi, Italy
| | - Bouabid Badaoui
- Laboratory of Biodiversity, Ecology, and Genome, Faculty of Sciences, Department of Biology, Mohammed V University in Rabat, Rabat, Morocco
- African Sustainable Agriculture Research Institute (ASARI), Mohammed VI Polytechnic University (UM6P), Laâyoune, Morocco
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16
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Abbas A, Rehman MS, Rehman SS. Comparing the Performance of Popular Large Language Models on the National Board of Medical Examiners Sample Questions. Cureus 2024; 16:e55991. [PMID: 38606229 PMCID: PMC11007479 DOI: 10.7759/cureus.55991] [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: 03/11/2024] [Indexed: 04/13/2024] Open
Abstract
INTRODUCTION Large language models (LLMs) have transformed various domains in medicine, aiding in complex tasks and clinical decision-making, with OpenAI's GPT-4, GPT-3.5, Google's Bard, and Anthropic's Claude among the most widely used. While GPT-4 has demonstrated superior performance in some studies, comprehensive comparisons among these models remain limited. Recognizing the significance of the National Board of Medical Examiners (NBME) exams in assessing the clinical knowledge of medical students, this study aims to compare the accuracy of popular LLMs on NBME clinical subject exam sample questions. METHODS The questions used in this study were multiple-choice questions obtained from the official NBME website and are publicly available. Questions from the NBME subject exams in medicine, pediatrics, obstetrics and gynecology, clinical neurology, ambulatory care, family medicine, psychiatry, and surgery were used to query each LLM. The responses from GPT-4, GPT-3.5, Claude, and Bard were collected in October 2023. The response by each LLM was compared to the answer provided by the NBME and checked for accuracy. Statistical analysis was performed using one-way analysis of variance (ANOVA). RESULTS A total of 163 questions were queried by each LLM. GPT-4 scored 163/163 (100%), GPT-3.5 scored 134/163 (82.2%), Bard scored 123/163 (75.5%), and Claude scored 138/163 (84.7%). The total performance of GPT-4 was statistically superior to that of GPT-3.5, Claude, and Bard by 17.8%, 15.3%, and 24.5%, respectively. The total performance of GPT-3.5, Claude, and Bard was not significantly different. GPT-4 significantly outperformed Bard in specific subjects, including medicine, pediatrics, family medicine, and ambulatory care, and GPT-3.5 in ambulatory care and family medicine. Across all LLMs, the surgery exam had the highest average score (18.25/20), while the family medicine exam had the lowest average score (3.75/5). Conclusion: GPT-4's superior performance on NBME clinical subject exam sample questions underscores its potential in medical education and practice. While LLMs exhibit promise, discernment in their application is crucial, considering occasional inaccuracies. As technological advancements continue, regular reassessments and refinements are imperative to maintain their reliability and relevance in medicine.
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Affiliation(s)
- Ali Abbas
- Medical School, University of Texas Southwestern Medical School, Dallas, USA
| | - Mahad S Rehman
- Medical School, University of Texas Southwestern Medical School, Dallas, USA
| | - Syed S Rehman
- Nephrology, Baptist Hospitals of Southeast Texas, Beaumont, USA
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Xu Y, Jiang Z, Ting DSW, Kow AWC, Bello F, Car J, Tham YC, Wong TY. Medical education and physician training in the era of artificial intelligence. Singapore Med J 2024; 65:159-166. [PMID: 38527300 PMCID: PMC11060639 DOI: 10.4103/singaporemedj.smj-2023-203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 02/08/2024] [Indexed: 03/27/2024]
Abstract
ABSTRACT With the rise of generative artificial intelligence (AI) and AI-powered chatbots, the landscape of medicine and healthcare is on the brink of significant transformation. This perspective delves into the prospective influence of AI on medical education, residency training and the continuing education of attending physicians or consultants. We begin by highlighting the constraints of the current education model, challenges in limited faculty, uniformity amidst burgeoning medical knowledge and the limitations in 'traditional' linear knowledge acquisition. We introduce 'AI-assisted' and 'AI-integrated' paradigms for medical education and physician training, targeting a more universal, accessible, high-quality and interconnected educational journey. We differentiate between essential knowledge for all physicians, specialised insights for clinician-scientists and mastery-level proficiency for clinician-computer scientists. With the transformative potential of AI in healthcare and service delivery, it is poised to reshape the pedagogy of medical education and residency training.
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Affiliation(s)
- Yueyuan Xu
- Tsinghua Medicine, School of Medicine, Tsinghua University, Beijing, China
| | - Zehua Jiang
- Tsinghua Medicine, School of Medicine, Tsinghua University, Beijing, China
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Eye Academic Clinical Program, Duke-NUS Medical School, Singapore
- Byers Eye Institute, Stanford University, Palo Alto, CA, USA
| | - Alfred Wei Chieh Kow
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Fernando Bello
- Technology Enhanced Learning and Innovation Department, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Josip Car
- Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Eye Academic Clinical Program, Duke-NUS Medical School, Singapore
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tien Yin Wong
- Tsinghua Medicine, School of Medicine, Tsinghua University, Beijing, China
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
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Guldemond N. What is meant by 'integrated personalized diabetes management': A view into the future and what success should look like. Diabetes Obes Metab 2024; 26 Suppl 1:14-29. [PMID: 38328815 DOI: 10.1111/dom.15476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 02/09/2024]
Abstract
Integrated personalized diabetes management (IPDM) has emerged as a promising approach to improving outcomes in patients with diabetes mellitus (DM). This care approach emphasizes the integration and coordination of different providers, including physicians, nurses, dietitians, social workers and pharmacists. The goal of IPDM is to provide patients with personalized care that is tailored to their needs. This review addresses the concept of integrated care and the use of technology (including data, software applications and artificial intelligence) as well as managerial, regulatory and financial aspects. The implementation and upscaling of digitally enabled IPDM are discussed, with elaboration of successful practices and related evidence. Finally, recommendations are made. It is concluded that the adoption of digitally enabled IPDM on a global level is inevitable, considering the challenges created by an increasing prevalence of patients with DM and the need for better outcomes and improvement of health system sustainability.
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Affiliation(s)
- Nick Guldemond
- Department of Public Health and Primary Care, Leiden Universitair Medisch Centrum, Leiden, Netherlands
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19
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Zhang D, Wu C, Yang Z, Yin H, Liu Y, Li W, Huang H, Jin Z. The application of artificial intelligence in EUS. Endosc Ultrasound 2024; 13:65-75. [PMID: 38947752 PMCID: PMC11213611 DOI: 10.1097/eus.0000000000000053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.
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Affiliation(s)
- Deyu Zhang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Chang Wu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhenghui Yang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan 750004, Ningxia Hui Autonomous Region, China
| | - Yue Liu
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Wanshun Li
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Haojie Huang
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
| | - Zhendong Jin
- Department of Gastroenterology, Changhai hospital, Naval Medical University, Shanghai 200433, China
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Mackenzie SC, Sainsbury CAR, Wake DJ. Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges. Diabetologia 2024; 67:223-235. [PMID: 37979006 PMCID: PMC10789841 DOI: 10.1007/s00125-023-06038-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/22/2023] [Indexed: 11/19/2023]
Abstract
The discourse amongst diabetes specialists and academics regarding technology and artificial intelligence (AI) typically centres around the 10% of people with diabetes who have type 1 diabetes, focusing on glucose sensors, insulin pumps and, increasingly, closed-loop systems. This focus is reflected in conference topics, strategy documents, technology appraisals and funding streams. What is often overlooked is the wider application of data and AI, as demonstrated through published literature and emerging marketplace products, that offers promising avenues for enhanced clinical care, health-service efficiency and cost-effectiveness. This review provides an overview of AI techniques and explores the use and potential of AI and data-driven systems in a broad context, covering all diabetes types, encompassing: (1) patient education and self-management; (2) clinical decision support systems and predictive analytics, including diagnostic support, treatment and screening advice, complications prediction; and (3) the use of multimodal data, such as imaging or genetic data. The review provides a perspective on how data- and AI-driven systems could transform diabetes care in the coming years and how they could be integrated into daily clinical practice. We discuss evidence for benefits and potential harms, and consider existing barriers to scalable adoption, including challenges related to data availability and exchange, health inequality, clinician hesitancy and regulation. Stakeholders, including clinicians, academics, commissioners, policymakers and those with lived experience, must proactively collaborate to realise the potential benefits that AI-supported diabetes care could bring, whilst mitigating risk and navigating the challenges along the way.
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Affiliation(s)
- Scott C Mackenzie
- Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Chris A R Sainsbury
- Institute for Applied Health Research, University of Birmingham, Birmingham, UK
| | - Deborah J Wake
- Usher Institute, The University of Edinburgh, Edinburgh, UK.
- Edinburgh Centre for Endocrinology and Diabetes, NHS Lothian, Edinburgh, UK.
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Abstract
BACKGROUND The coronavirus pandemic of 2019 (COVID-19) forced worldwide recognition and implementation of telehealth as a means of providing continuity of care by varied health care institutions. Diabetes is a global health threat with rates that continue to accelerate, thereby causing an increased need for clinicians to provide diabetes care and education to keep up with demand. Utilizing technology to provide education via phone/smartphone, video/audio, web, text message, mobile apps, or a combination of these methods can help improve patient access and clinical outcomes, cut costs, and close gaps in care. METHODS While numerous publications have summarized the various tools and technologies available for capturing remote clinical data and their relevance to diabetes care and self-management, this review focuses on self-educational telehealth tools available for diabetes self-management, their advantages and disadvantages, and factors that need to be considered prior to implementation. Recent relevant studies indexed by PubMed were included. RESULTS The widespread use and popularity of phones/smartphones, tablets, computers, and the Internet by patients of all age groups, cultures, socioeconomic and geographic areas allow for increased outreach, flexibility, and engagement with diabetes education, either in combination or as an adjunct to traditional in-person visits. Demonstrated benefits of using health technologies for diabetes self-management education include improved lifestyle habits, reduced hemoglobin A1C levels, decreased health care costs, and better medication adherence. Potential drawbacks include lack of regulation, need for staff training on methodologies used, the requirement for patients to be tech savvy, privacy concerns, lag time with technology updates/glitches, and the need for more long-term research data on efficacy. CONCLUSIONS Telehealth technologies for diabetes self-education improve overall clinical outcomes and have come a long way. With increasing numbers of patients with diabetes, it is expected that more optimal and user-friendly methodologies will be developed to fully engage and help patients communicate with their physicians.
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Affiliation(s)
- Vidya Sharma
- Department of Nutrition & Dietetics, College for Health, Community and Policy, The University of Texas at San Antonio, San Antonio, TX, USA
| | | | - Ramaswamy Sharma
- Department of Cell Systems and Anatomy, Joe R. & Teresa Lozano Long School of Medicine, UT Health San Antonio, San Antonio, TX, USA
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Li C, Ye G, Jiang Y, Wang Z, Yu H, Yang M. Artificial Intelligence in battling infectious diseases: A transformative role. J Med Virol 2024; 96:e29355. [PMID: 38179882 DOI: 10.1002/jmv.29355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/01/2023] [Accepted: 12/17/2023] [Indexed: 01/06/2024]
Abstract
It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era in the realm of infectious disease prevention and control. This evolution encompasses early warning of outbreaks, contact tracing, infection diagnosis, drug discovery, and the facilitation of drug design, alongside other facets of epidemic management. This article presents an overview of the utilization of AI systems in the field of infectious diseases, with a specific focus on their role during the COVID-19 pandemic. The article also highlights the contemporary challenges that AI confronts within this domain and posits strategies for their mitigation. There exists an imperative to further harness the potential applications of AI across multiple domains to augment its capacity in effectively addressing future disease outbreaks.
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Affiliation(s)
- Chunhui Li
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Guoguo Ye
- Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, The Third People's Hospital of Shenzhen, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China
| | - Yinghan Jiang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Zhiming Wang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
| | - Haiyang Yu
- Hangzhou Yalla Information Technology Service Co., Ltd., Hangzhou, People's Republic of China
| | - Minghui Yang
- School of Life Science, Advanced Research Institute of Multidisciplinary Science, Key Laboratory of Molecular Medicine and Biotherapy, Beijing Institute of Technology, Beijing, People's Republic of China
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23
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Tsai MC, Yen HH, Tsai HY, Huang YK, Luo YS, Kornelius E, Sung WW, Lin CC, Tseng MH, Wang CC. Artificial intelligence system for the detection of Barrett's esophagus. World J Gastroenterol 2023; 29:6198-6207. [PMID: 38186865 PMCID: PMC10768395 DOI: 10.3748/wjg.v29.i48.6198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 11/13/2023] [Accepted: 12/12/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Barrett's esophagus (BE), which has increased in prevalence worldwide, is a precursor for esophageal adenocarcinoma. Although there is a gap in the detection rates between endoscopic BE and histological BE in current research, we trained our artificial intelligence (AI) system with images of endoscopic BE and tested the system with images of histological BE. AIM To assess whether an AI system can aid in the detection of BE in our setting. METHODS Endoscopic narrow-band imaging (NBI) was collected from Chung Shan Medical University Hospital and Changhua Christian Hospital, resulting in 724 cases, with 86 patients having pathological results. Three senior endoscopists, who were instructing physicians of the Digestive Endoscopy Society of Taiwan, independently annotated the images in the development set to determine whether each image was classified as an endoscopic BE. The test set consisted of 160 endoscopic images of 86 cases with histological results. RESULTS Six pre-trained models were compared, and EfficientNetV2B2 (accuracy [ACC]: 0.8) was selected as the backbone architecture for further evaluation due to better ACC results. In the final test, the AI system correctly identified 66 of 70 cases of BE and 85 of 90 cases without BE, resulting in an ACC of 94.37%. CONCLUSION Our AI system, which was trained by NBI of endoscopic BE, can adequately predict endoscopic images of histological BE. The ACC, sensitivity, and specificity are 94.37%, 94.29%, and 94.44%, respectively.
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Affiliation(s)
- Ming-Chang Tsai
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
| | - Hsu-Heng Yen
- Division of Gastroenterology, Changhua Christian Hospital, Changhua 500, Taiwan
- Artificial Intelligence Development Center, Changhua Christian Hospital, Changhua 500, Taiwan
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 400, Taiwan
| | - Hui-Yu Tsai
- Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
| | - Yu-Kai Huang
- Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Yu-Sin Luo
- Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Edy Kornelius
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
- Department of Endocrinology and Metabolism, Chung-Shan Medical University Hospital, Taichung 402, Taiwan
| | - Wen-Wei Sung
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
- Department of Urology, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Chun-Che Lin
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
| | - Ming-Hseng Tseng
- Department of Medical Informatics, Chung Shan Medical University, Taichung 402, Taiwan
- Information Technology Office, Chung Shan Medical University Hospital, Taichung 402, Taiwan
| | - Chi-Chih Wang
- Division of Gastroenterology and Hepatology, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 402, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung 402, Taiwan
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Tahir F, Farhan M. Exploring the progress of artificial intelligence in managing type 2 diabetes mellitus: a comprehensive review of present innovations and anticipated challenges ahead. FRONTIERS IN CLINICAL DIABETES AND HEALTHCARE 2023; 4:1316111. [PMID: 38161783 PMCID: PMC10757318 DOI: 10.3389/fcdhc.2023.1316111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024]
Abstract
A significant worldwide health issue, Type 2 Diabetes Mellitus (T2DM) calls for creative solutions. This in-depth review examines the growing severity of T2DM and the requirement for individualized management approaches. It explores the use of artificial intelligence (AI) in the treatment of diabetes, highlighting its potential for diagnosis, customized treatment plans, and patient self-management. The paper highlights the roles played by AI applications such as expert systems, machine learning algorithms, and deep learning approaches in the identification of retinopathy, the interpretation of clinical guidelines, and prediction models. Examined are difficulties with individualized diabetes treatment, including complex technological issues and patient involvement. The review highlights the revolutionary potential of AI in the management of diabetes and calls for a balanced strategy in which AI supports clinical knowledge. It is crucial to pay attention to ethical issues, data privacy, and joint research initiatives.
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Affiliation(s)
- Farwa Tahir
- Department of Pharmacy, Rashid Latif Medical Complex, Lahore, Pakistan
| | - Muhammad Farhan
- Department of Pharmacy, University of Lahore, Islamabad, Pakistan
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25
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Karimi F, Karimi-Maleh H, Rouhi J, Zare N, Karaman C, Baghayeri M, Fu L, Rostamnia S, Dragoi EN, Ayati A, Krivoshapkin P. Revolutionizing cancer monitoring with carbon-based electrochemical biosensors. ENVIRONMENTAL RESEARCH 2023; 239:117368. [PMID: 37827366 DOI: 10.1016/j.envres.2023.117368] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 10/05/2023] [Accepted: 10/09/2023] [Indexed: 10/14/2023]
Abstract
Cancer monitoring plays a critical role in improving patient outcomes by providing early detection, personalized treatment options, and treatment response tracking. Carbon-based electrochemical biosensors have emerged in recent years as a revolutionary technology with the potential to revolutionize cancer monitoring. These sensors are useful for clinical applications because of their high sensitivity, selectivity, rapid response, and compatibility with miniaturized equipment. This review paper gives an in-depth look at the latest developments and the possibilities of carbon-based electrochemical sensors in cancer surveillance. The essential principles of carbon-based electrochemical sensors are discussed, including their structure, operating mechanisms, and critical qualities that make them suited for cancer surveillance. Furthermore, we investigate their applicability in detecting specific cancer biomarkers, evaluating therapy responses, and detecting cancer recurrence early. Additionally, a comparison of carbon-based electrochemical sensor performance measures, including sensitivity, selectivity, accuracy, and limit of detection, is presented in contrast to existing monitoring methods and upcoming technologies. Finally, we discuss prospective tactics, future initiatives, and commercialization opportunities for improving the capabilities of these sensors and integrating them into normal clinical practice. The review highlights the potential impact of carbon-based electrochemical sensors on cancer diagnosis, treatment, and patient outcomes, as well as the importance of ongoing research, collaboration, and validation studies to fully realize their potential in revolutionizing cancer monitoring.
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Affiliation(s)
- Fatemeh Karimi
- School of Resources and Environment, University of Electronic Science and Technology of China, P.O. Box 611731, Xiyuan Ave, Chengdu, PR China
| | - Hassan Karimi-Maleh
- School of Resources and Environment, University of Electronic Science and Technology of China, P.O. Box 611731, Xiyuan Ave, Chengdu, PR China; School of Engineering, Lebanese American University, Byblos, Lebanon
| | - Jalal Rouhi
- Faculty of Physics, University of Tabriz, Tabriz, 51566, Iran.
| | - Najmeh Zare
- School of Resources and Environment, University of Electronic Science and Technology of China, P.O. Box 611731, Xiyuan Ave, Chengdu, PR China
| | - Ceren Karaman
- School of Engineering, Lebanese American University, Byblos, Lebanon; Department of Electricity and Energy, Akdeniz University, Antalya, 07070, Turkey
| | - Mehdi Baghayeri
- School of Engineering, Lebanese American University, Byblos, Lebanon; Department of Chemistry, Faculty of Science, Hakim Sabzevari University, PO. B 397, Sabzevar, Iran
| | - Li Fu
- College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, 310018, PR China
| | - Sadegh Rostamnia
- Organic and Nano Group (ONG), Department of Chemistry, Iran University of Science and Technology (IUST), PO Box 16846-13114, Tehran, Iran
| | - Elena Niculina Dragoi
- "Cristofor Simionescu" Faculty of Chemical Engineering and Environmental Protection, "Gheorghe Asachi" Technical University, Bld Mangeron No 73, Iasi, 700050, Romania
| | - Ali Ayati
- EnergyLab, ITMO University, Lomonosova Street 9, Saint Petersburg, 191002, Russia
| | - Pavel Krivoshapkin
- EnergyLab, ITMO University, Lomonosova Street 9, Saint Petersburg, 191002, Russia
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26
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Vas P, Chockalingam N. Improving Physical, Physiological, and Psychological Health Outcomes in Patients with Diabetic Foot Ulcers - State of the Art. Clin Cosmet Investig Dermatol 2023; 16:3547-3560. [PMID: 38107668 PMCID: PMC10725647 DOI: 10.2147/ccid.s333660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 11/30/2023] [Indexed: 12/19/2023]
Abstract
Diabetic foot disease is a complex and challenging complication of diabetes mellitus, which imposes a significant burden of disease on patients, their carers, and the wider health systems. Recurrence rates are high, and current evidence indicates a high mortality associated with it. While management algorithms have primarily focused on the physical aspects of healing, there is increasing recognition of the critical role played by psychological and biomechanical factors in the development and resolution of diabetic foot disease. Therefore, in this paper, we aim to explore how diabetic foot outcomes can be improved by addressing not only the physical but also the psychological and biomechanical aspects that are integral to the development of this condition and its optimal resolution. We explore new technologies that allow for non-invasive objective assessment of the diabetic foot at risk, and we also explore the role of understanding biomechanics, which is essential to determining risk of foot disease, but also the potential for recurrence. In addition, we discuss the evidence linking depression and cognitive impairment to diabetic foot disease and offer our insight on the research direction required before implementing novel information into front-line clinics.
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Affiliation(s)
- Prashanth Vas
- Department of Diabetes and Diabetic Foot, King’s College Hospital NHS Foundation Trust, London, UK
- Centre for Biomechanics and Rehabilitation Technologies, Staffordshire University, Stoke on Trent, UK
- Department of Diabetes and Endocrinology, Guy’s and St Thomas’ Hospitals NHS Foundation Trust, London, UK
| | - Nachiappan Chockalingam
- Centre for Biomechanics and Rehabilitation Technologies, Staffordshire University, Stoke on Trent, UK
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27
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Tanhapour M, Peimani M, Rostam Niakan Kalhori S, Nasli Esfahani E, Shakibian H, Mohammadzadeh N, Qorbani M. The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials. Acta Diabetol 2023; 60:1599-1631. [PMID: 37542200 DOI: 10.1007/s00592-023-02133-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/09/2023] [Indexed: 08/06/2023]
Abstract
AIMS Type 2 diabetes (T2D) is rising worldwide. Self-care prevents diabetic complications. Lack of knowledge is one reason patients fail at self-care. Intelligent digital health (IDH) solutions have a promising role in training self-care behaviors based on patients' needs. This study reviews the effects of RCTs offering individualized self-care training systems for T2D patients. METHODS PubMed, Web of Science, Scopus, Cochrane Library, and Science Direct databases were searched. The included RCTs provided data-driven, individualized self-care training advice for T2D patients. Due to the repeated studies measurements, an all-time-points meta-analysis was conducted to analyze the trends over time. The revised Cochrane risk-of-bias tool (RoB 2.0) was used for quality assessment. RESULTS In total, 22 trials met the inclusion criteria, and 19 studies with 3071 participants were included in the meta-analysis. IDH interventions led to a significant reduction of HbA1c level in the intervention group at short-term (in the third month: SMD = - 0.224 with 95% CI - 0.319 to - 0.129, p value < 0.0; in the sixth month: SMD = - 0.548 with 95% CI - 0.860 to - 0.237, p value < 0.05). The difference in HbA1c reduction between groups varied based on patients' age and technological forms of IDH services delivery. The descriptive results confirmed the impact of M-Health technologies in improving HbA1c levels. CONCLUSIONS IDH systems had significant and small effects on HbA1c reduction in T2D patients. IDH interventions' impact needs long-term RCTs. This review will help diabetic clinicians, self-care training system developers, and researchers interested in using IDH solutions to empower T2D patients.
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Affiliation(s)
- Mozhgan Tanhapour
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Maryam Peimani
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Science, Tehran, Iran
| | - Sharareh Rostam Niakan Kalhori
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 38106, Braunschweig, Germany
| | - Ensieh Nasli Esfahani
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Science, Tehran, Iran
| | - Hadi Shakibian
- Department of Computer Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran
| | - Niloofar Mohammadzadeh
- Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran.
| | - Mostafa Qorbani
- Non-communicable Disease Research Center, Alborz University of Medical Sciences, Karaj, Iran
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Howard T, Ahluwalia R, Papanas N. The Advent of Artificial Intelligence in Diabetic Foot Medicine: A New Horizon, a New Order, or a False Dawn? INT J LOW EXTR WOUND 2023; 22:635-640. [PMID: 34488463 DOI: 10.1177/15347346211041866] [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: 11/15/2022]
Abstract
In a world where automation is becoming increasingly common, easier collection of mass of data and powerful computer processing has meant a transformation in the field of artificial intelligence (AI). The diabetic foot is a multifactorial problem; its issues render it suitable for analysis, interrogation, and development of AI. The latter has the potential to deliver many solutions to issues of delayed diagnosis, compliance, and defining preventative treatments. We describe the use of AI and the development of artificial neural networks that may supplement the failed networks in the diabetic foot. The potential of this technology, current developing applications, and their limitations for diabetic foot care are suggested.
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Affiliation(s)
| | - Raju Ahluwalia
- King's College Hospital, London, UK
- King's Diabetic Foot Clinic, King's College Hospital, London, UK
| | - Nikolas Papanas
- Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupoli, Greece
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29
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Ashraf FUN, Ghouri K, Someshwar F, Kumar S, Kumar N, Kumari K, Bano S, Ahmad S, Khawar MH, Ramchandani L, Salame T, Varrassi G, Khatri M, Kumar S, Mohamad T. Insulin Resistance and Coronary Artery Disease: Untangling the Web of Endocrine-Cardiac Connections. Cureus 2023; 15:e51066. [PMID: 38269234 PMCID: PMC10806385 DOI: 10.7759/cureus.51066] [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: 12/19/2023] [Accepted: 12/25/2023] [Indexed: 01/26/2024] Open
Abstract
The relationship between insulin resistance and coronary artery disease (CAD) is a crucial study area in understanding the complex connection between metabolic dysregulation and cardiovascular morbidity. This scholarly investigation examines the intricate relationship between insulin resistance, a key characteristic of metabolic syndrome, and CAD development. The goal is to understand the detailed molecular and physiological connections that underlie the dangerous connection between the endocrine and cardiac systems. The recognition of insulin resistance as a key player in cardiovascular disease highlights the need to study the complex relationships between insulin signaling pathways and the development of atherosclerosis. This research analyzes the molecular processes by which insulin resistance leads to disruptions in lipid metabolism, inflammatory reactions, and malfunction of the blood vessel's inner lining. These processes create an environment that promotes the development and advancement of CAD. As we begin this scientific exploration, it becomes clear that insulin resistance acts as a metabolic indicator and a potent mediator of endothelial dysfunction, oxidative stress, and systemic inflammation. The complex interaction between insulin-sensitive tissues and the vascular endothelium plays a crucial role in defining the pathophysiological landscape of CAD. Furthermore, this discussion highlights the mutual interaction between the endocrine and cardiac systems, where CAD produced by myocardial ischemia worsens insulin resistance through complex molecular pathways. Discovering new therapeutic targets that disrupt the harmful cycle between insulin resistance and the development of CAD shows potential for creating specific therapies to reduce cardiovascular risk in people with insulin resistance. This study aims to clarify the complexities of the connection between the endocrine system and the heart, establishing the basis for a thorough comprehension of how insulin resistance contributes to the development and advancement of CAD.
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Affiliation(s)
- Fakhar Un Nisa Ashraf
- Medicine, The Royal Wolverhampton National Health Service (NHS) Trust, Wolverhampton, GBR
| | | | - Fnu Someshwar
- Medicine, Liaquat National Hospital and Medical College, Karachi, PAK
| | - Sunny Kumar
- Medicine, Liaquat National Hospital and Medical College, Karachi, PAK
| | | | - Komal Kumari
- Medicine, New Medical Centre (NMC) Royal Family Medical Centre, Abu Dhabi, ARE
| | - Saira Bano
- Medicine, Faisalabad Medical College and University, Faisalabad, PAK
| | - Saad Ahmad
- Medicine, Shalamar Medical and Dental College, Lahore, PAK
| | | | - Lata Ramchandani
- Medicine, People's University of Medical and Health Sciences for Women, Nawabshah, PAK
| | | | | | - Mahima Khatri
- Internal Medicine/Cardiology, Dow University of Health Sciences, Civil Hospital Karachi, Karachi, PAK
| | - Satish Kumar
- Medicine, Shaheed Mohtarma Benazir Bhutto Medical College, Karachi, PAK
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Ansari MA, Chauhan W, Shoaib S, Alyahya SA, Ali M, Ashraf H, Alomary MN, Al-Suhaimi EA. Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions. Int J Obes (Lond) 2023; 47:1179-1199. [PMID: 37696926 DOI: 10.1038/s41366-023-01369-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 07/04/2023] [Accepted: 08/17/2023] [Indexed: 09/13/2023]
Abstract
Diabetes is a serious health issue that causes a progressive dysregulation of carbohydrate metabolism due to insufficient insulin hormone, leading to consistently high blood glucose levels. According to the epidemiological data, the prevalence of diabetes has been increasing globally, affecting millions of individuals. It is a long-term condition that increases the risk of various diseases caused by damage to small and large blood vessels. There are two main subtypes of diabetes: type 1 and type 2, with type 2 being the most prevalent. Genetic and molecular studies have identified several genetic variants and metabolic pathways that contribute to the development and progression of diabetes. Current treatments include gene therapy, stem cell therapy, statin therapy, and other drugs. Moreover, recent advancements in therapeutics have also focused on developing novel drugs targeting these pathways, including incretin mimetics, SGLT2 inhibitors, and GLP-1 receptor agonists, which have shown promising results in improving glycemic control and reducing the risk of complications. However, these treatments are often expensive, inaccessible to patients in underdeveloped countries, and can have severe side effects. Peptides, such as glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), are being explored as a potential therapy for diabetes. These peptides are postprandial glucose-dependent pancreatic beta-cell insulin secretagogues and have received much attention as a possible treatment option. Despite these advances, diabetes remains a major health challenge, and further research is needed to develop effective treatments and prevent its complications. This review covers various aspects of diabetes, including epidemiology, genetic and molecular basis, and recent advancements in therapeutics including herbal and synthetic peptides.
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Affiliation(s)
- Mohammad Azam Ansari
- Department of Epidemic Disease Research, Institute for Research and Medical Consultations (IRMC), Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia.
| | - Waseem Chauhan
- Department of Hematology, Duke University, Durham, NC, 27710, USA
| | - Shoaib Shoaib
- Department of Biochemistry, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Sami A Alyahya
- Wellness and Preventive Medicine Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia
| | - Mubashshir Ali
- USF Health Byrd Alzheimer's Center and Neuroscience Institute, Department of Molecular Medicine, Tampa, FL, USA
| | - Hamid Ashraf
- Rajiv Gandhi Center for Diabetes and Endocrinology, Faculty of Medicine, Aligarh Muslim University, Aligarh, Uttar Pradesh, India
| | - Mohammad N Alomary
- Advanced Diagnostic and Therapeutic Institute, King Abdulaziz City for Science and Technology (KACST), Riyadh, 11442, Saudi Arabia.
| | - Ebtesam A Al-Suhaimi
- King Abdulaziz & his Companions Foundation for Giftedness & Creativity, Riyadh, Saudi Arabia.
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Lin Z, Cheng YT, Cheung BMY. Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018. Ann Med 2023; 55:2209336. [PMID: 37162442 DOI: 10.1080/07853890.2023.2209336] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND Hypokalaemia is a side-effect of diuretics. We aimed to use machine learning to identify features predicting hypokalaemia risk in hypertensive patients. METHODS Participants with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018 were included for analysis. To select the most suitable algorithm, we tested and evaluated five machine learning algorithms commonly employed in epidemiological studies: Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting. These algorithms were accessed using a set of 38 screened features. We then selected the key hypokalaemia-associated features in the hypertension group and their cardiovascular diseases (CVD) subgroup using the SHapley Additive exPlanations (SHAP) values. Using SHAP values, the key features and their impact pattern on hypokalaemia risk were determined. RESULTS A total of 25,326 hypertensive participants were included for analysis, of whom 4,511 had known CVD. The Random Forest algorithm had the highest AUROC (hypertension dataset: 0.73 [95%CI, 0.71-0.76]; CVD subgroup: 0.72 [95%CI, 0.66-0.78]). Moreover, the nomogram based on the top twelve key features screened by random forest retained good performance: age, sex, race, poverty income ratio, body mass index, systolic and diastolic blood pressure, non-potassium-sparing diuretics use and duration, renin-angiotensin blockers use and duration, and CVD history in hypertension dataset; while in CVD subgroup, the additional key features were comorbid diabetes, education level, smoking status, and use of bronchodilators. CONCLUSION Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms. Hypokalaemia-associated key features have been identified in hypertensive patients and the subgroup with CVD. These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients.
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Affiliation(s)
- Ziying Lin
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, China
| | - Yuen Ting Cheng
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, China
| | - Bernard Man Yung Cheung
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, China
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Pokfulam, China
- Institute of Cardiovascular Science and Medicine, The University of Hong Kong, Pokfulam, China
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Zaidan AM. The leading global health challenges in the artificial intelligence era. Front Public Health 2023; 11:1328918. [PMID: 38089037 PMCID: PMC10711066 DOI: 10.3389/fpubh.2023.1328918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
Millions of people's health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the "digital revolution," characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
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Affiliation(s)
- Amal Mousa Zaidan
- King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia
- Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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Pereira MG, Vilaça M, Braga D, Madureira A, Da Silva J, Santos D, Carvalho E. Healing profiles in patients with a chronic diabetic foot ulcer: An exploratory study with machine learning. Wound Repair Regen 2023; 31:793-803. [PMID: 38073283 DOI: 10.1111/wrr.13141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/21/2023] [Accepted: 10/16/2023] [Indexed: 12/26/2023]
Abstract
Diabetic foot ulcers (DFU) are one of the most frequent and debilitating complications of diabetes. DFU wound healing is a highly complex process, resulting in significant medical, economic and social challenges. Therefore, early identification of patients with a high-risk profile would be important to adequate treatment and more successful health outcomes. This study explores risk assessment profiles for DFU healing and healing prognosis, using machine learning predictive approaches and decision tree algorithms. Patients were evaluated at baseline (T0; N = 158) and 2 months later (T1; N = 108) on sociodemographic, clinical, biochemical and psychological variables. The performance evaluation of the models comprised F1-score, accuracy, precision and recall. Only profiles with F1-score >0.7 were selected for analysis. According to the two profiles generated for DFU healing, the most important predictive factors were illness representations on T1 IPQ-B (IPQ-B ≤ 9.5 and < 10.5) and the DFU duration (≤ 13 weeks). The two predictive models for DFU healing prognosis suggest that biochemical factors are the best predictors of a favorable healing prognosis, namely IL-6, microRNA-146a-5p and PECAM-1 at T0 and angiopoietin-2 at T1. Illness perception at T0 (IPQ-B ≤ 39.5) also emerged as a relevant predictor for healing prognosis. The results emphasize the importance of DFU duration, illness perception and biochemical markers as predictors of healing in chronic DFUs. Future research is needed to confirm and test the obtained predictive models.
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Affiliation(s)
- M Graça Pereira
- Psychology Research Center (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Margarida Vilaça
- Psychology Research Center (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Diogo Braga
- Interdisciplinary Studies Research Center (ISRC), ISEP, Porto, Portugal
| | - Ana Madureira
- Interdisciplinary Studies Research Center (ISRC), ISEP, Porto, Portugal
- ISEP, Polytechnic of Porto, Porto, Portugal
- Institute for Systems and Computer Engineering, Technology and Science (INOV), Lisboa, Portugal
| | - Jéssica Da Silva
- PhD Program in Experimental Biology and Biomedicine (PDBEB), Institute for Interdisciplinary Research, Coimbra, Portugal
- Center for Neuroscience and Cell Biology (CNC), Center for Innovative Biotechnology and Biomedicine (CIBB), University of Coimbra, Coimbra, Portugal
- Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Diana Santos
- PhD Program in Experimental Biology and Biomedicine (PDBEB), Institute for Interdisciplinary Research, Coimbra, Portugal
- Center for Neuroscience and Cell Biology (CNC), Center for Innovative Biotechnology and Biomedicine (CIBB), University of Coimbra, Coimbra, Portugal
- Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Eugénia Carvalho
- Center for Neuroscience and Cell Biology (CNC), Center for Innovative Biotechnology and Biomedicine (CIBB), University of Coimbra, Coimbra, Portugal
- Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
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Gong D, Chen X, Yang L, Zhang Y, Zhong Q, Liu J, Yan C, Cai Y, Yang W, Wang J. From normal population to prediabetes and diabetes: study of influencing factors and prediction models. Front Endocrinol (Lausanne) 2023; 14:1225696. [PMID: 37964953 PMCID: PMC10640999 DOI: 10.3389/fendo.2023.1225696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/29/2023] [Indexed: 11/09/2023] Open
Abstract
Objective The purpose of this study is to investigate the independent influencing factors of the transition from normal population to prediabetes, and from prediabetes to diabetes, and to further construct clinical prediction models to provide a basis for the prevention and management of prediabetes and diabetes. Materials and methods The data for this study were based on clinical information of participants from the Health Management Center of Peking University Shenzhen Hospital. Participants were classified into normal group, prediabetes group, and diabetes group according to their functional status of glucose metabolism. Spearman's correlation coefficients were calculated for the variables, and a matrix diagram was plotted. Further, univariate and multivariate logistic regression analysis were conducted to explore the independent influencing factors. The independent influencing factors were used as predictors to construct the full-variable prediction model (Full.model) and simplified prediction model (Simplified.model). Results This study included a total of 5310 subjects and 22 variables, among which there were 1593(30%) in the normal group, 3150(59.3%) in the prediabetes group, and 567(10.7%) in the diabetes group. The results of the multivariable logistic regression analysis showed that there were significant differences in 9 variables between the normal group and the prediabetes group, including age(Age), body mass index(BMI), systolic blood pressure(SBP), urinary glucose(U.GLU), urinary protein(PRO), total protein(TP), globulin(GLB), alanine aminotransferase(ALT), and high-density lipoprotein cholesterol(HDL-C). There were significant differences in 7 variables between the prediabetes group and the diabetes group, including Age, BMI, SBP, U.GLU, PRO, triglycerides(TG), and HDL.C. The Full.model and Simplified.model constructed based on the above influencing factors had moderate discriminative power in both the training set and the test set. Conclusion Age, BMI, SBP, U.GLU, PRO, TP, and ALT are independent risk factors, while GLB and HDL.C are independent protective factors for the development of prediabetes in the normal population. Age, BMI, SBP, U.GLU, PRO, and TG are independent risk factors, while HDL.C is an independent protective factor for the progression from prediabetes to diabetes. The Full.model and Simplified.model developed based on these influencing factors have moderate discriminative power.
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Affiliation(s)
- Di Gong
- Shenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong, China
- The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, Guangdong, China
| | - Xiaohong Chen
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Lin Yang
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Yongjian Zhang
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Qianqian Zhong
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Jing Liu
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Chen Yan
- Shenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong, China
| | - Yongjiang Cai
- Center of Health Management, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Weihua Yang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, Guangdong, China
| | - Jiantao Wang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, Guangdong, China
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Liu T, Ren Z, Xiong C, Peng W, Wu J, Huang S, Liang G, Sun B. Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks. Heliyon 2023; 9:e20796. [PMID: 37842612 PMCID: PMC10569993 DOI: 10.1016/j.heliyon.2023.e20796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 09/11/2023] [Accepted: 10/06/2023] [Indexed: 10/17/2023] Open
Abstract
A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak-peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.
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Affiliation(s)
- Tao Liu
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Zhong Ren
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
- Key Laboratory of Optic-electronic Detection and Information Processing of Nanchang City, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Chengxin Xiong
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Wenping Peng
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Junli Wu
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Shuanggen Huang
- Agricultural Equipment Key Laboratory of Jiangxi Provincial, Jiangxi Agriculture University, 330045 Nanchang, Jiangxi, China
| | - Gaoqiang Liang
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
| | - Bingheng Sun
- Key Laboratory of Optic-electronic and Communication, Jiangxi Science and Technology Normal University, 330038 Nanchang, Jiangxi, China
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Mora T, Roche D, Rodríguez-Sánchez B. Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms. Diabetes Res Clin Pract 2023; 204:110910. [PMID: 37722566 DOI: 10.1016/j.diabres.2023.110910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 08/01/2023] [Accepted: 09/15/2023] [Indexed: 09/20/2023]
Abstract
AIMS Using machine learning algorithms and administrative data, we aimed to predict the risk of being diagnosed with several diabetes-related complications after one-, two- and three-year post-diabetes diagnosis. METHODS We used longitudinal data from administrative registers of 610,019 individuals in Catalonia with a diagnosis of diabetes and checked the presence of several complications after diabetes onset from 2013 to 2017: hypertension, renal failure, myocardial infarction, cardiovascular disease, retinopathy, congestive heart failure, cerebrovascular disease, peripheral vascular disease and stroke. Four different machine learning (ML) algorithms (logistic regression (LR), Decision tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB)) will be used to assess their prediction performance and to evaluate the prediction accuracy of complications changes over the period considered. RESULTS 610,019 people with diabetes were included. After three years since diabetes diagnosis, the area under the curve values ranged from 60% (retinopathy) to 69% (congestive heart failure), whereas accuracy rates varied between 60% (retinopathy) to 75% (hypertension). RF was the most relevant technique for hypertension, myocardial and retinopathy, and LR for the rest of the comorbidities. The Shapley additive explanations values showed that age was associated with an elevated risk for all diabetes-related complications except retinopathy. Gender, other comorbidities, co-payment levels and age were the most relevant factors for comorbidity diagnosis prediction. CONCLUSIONS Our ML models allow for the identification of individuals newly diagnosed with diabetes who are at increased risk of developing diabetes-related complications. The prediction performance varied across complications but within acceptable ranges as prediction tools.
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Affiliation(s)
- Toni Mora
- Research Institute for Evaluation and Public Policies (IRAPP), Universitat Internacional de Catalunya (UIC), Carrer de la Immaculada, 22, 08017 Barcelona, Spain
| | - David Roche
- Research Institute for Evaluation and Public Policies (IRAPP), Universitat Internacional de Catalunya (UIC), Carrer de la Immaculada, 22, 08017 Barcelona, Spain
| | - Beatriz Rodríguez-Sánchez
- Applied Economics, Public Economics and Political Economy, Faculty of Law, Universidad Complutense de Madrid, Plaza Menéndez Pelayo, 4, 28040 Madrid, Spain.
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Alqahtani A, Alsubai S, Rahamathulla MP, Gumaei A, Sha M, Zhang YD, Khan MA. Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification. Diagnostics (Basel) 2023; 13:2831. [PMID: 37685369 PMCID: PMC10486793 DOI: 10.3390/diagnostics13172831] [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: 06/24/2023] [Revised: 08/09/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance.
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Affiliation(s)
- Abdullah Alqahtani
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; (S.A.); (A.G.)
| | - Mohamudha Parveen Rahamathulla
- School of Podiatric Medicine, The University of Texas Rio Grande Valley, Harlingen, TX 78550, USA;
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
| | - Abdu Gumaei
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia; (S.A.); (A.G.)
| | - Mohemmed Sha
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia;
| | - Yu-Dong Zhang
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Muhammad Attique Khan
- Department of CS, HITEC University, Taxila 47080, Pakistan;
- Department of Computer Science and Mathematics, Lebanese American University, Beirut 1102-2801, Lebanon
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Ribba B, Peck R, Hutchinson L, Bousnina I, Motti D. Digital Therapeutics as a New Therapeutic Modality: A Review from the Perspective of Clinical Pharmacology. Clin Pharmacol Ther 2023; 114:578-590. [PMID: 37392464 DOI: 10.1002/cpt.2989] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/24/2023] [Indexed: 07/03/2023]
Abstract
The promise of transforming digital technologies into treatments is what drives the development of digital therapeutics (DTx), generally known as software applications embedded within accessible technologies-such as smartphones-to treat, manage, or prevent a pathological condition. Whereas DTx solutions that successfully demonstrate effectiveness and safety could drastically improve the life of patients in multiple therapeutic areas, there is a general consensus that generating therapeutic evidence for DTx presents challenges and open questions. We believe there are three main areas where the application of clinical pharmacology principles from the drug development field could benefit DTx development: the characterization of the mechanism of action, the optimization of the intervention, and, finally, its dosing. We reviewed DTx studies to explore how the field is approaching these topics and to better characterize the challenges associated with them. This leads us to emphasize the role that the application of clinical pharmacology principles could play in the development of DTx and to advocate for a development approach that merges such principles from development of traditional therapeutics with important considerations from the highly attractive and fast-paced world of digital solutions.
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Affiliation(s)
- Benjamin Ribba
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Richard Peck
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Lucy Hutchinson
- Roche Information Solutions, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Imein Bousnina
- Genentech, A Member of the Roche Group, Washington, DC, USA
| | - Dario Motti
- Roche Information Solutions, F. Hoffmann-La Roche Ltd., Basel, Switzerland
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Prioleau T, Bartolome A, Comi R, Stanger C. DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions. Sci Data 2023; 10:556. [PMID: 37612336 PMCID: PMC10447420 DOI: 10.1038/s41597-023-02469-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023] Open
Abstract
Objective digital data is scarce yet needed in many domains to enable research that can transform the standard of healthcare. While data from consumer-grade wearables and smartphones is more accessible, there is critical need for similar data from clinical-grade devices used by patients with a diagnosed condition. The prevalence of wearable medical devices in the diabetes domain sets the stage for unique research and development within this field and beyond. However, the scarcity of open-source datasets presents a major barrier to progress. To facilitate broader research on diabetes-relevant problems and accelerate development of robust computational solutions, we provide the DiaTrend dataset. The DiaTrend dataset is composed of intensive longitudinal data from wearable medical devices, including a total of 27,561 days of continuous glucose monitor data and 8,220 days of insulin pump data from 54 patients with diabetes. This dataset is useful for developing novel analytic solutions that can reduce the disease burden for people living with diabetes and increase knowledge on chronic condition management in outpatient settings.
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Affiliation(s)
- Temiloluwa Prioleau
- Dartmouth College, Department of Computer Science, Hanover, 03755, USA.
- Dartmouth College, Center for Technology and Behavioral Health, Lebanon, 03766, USA.
| | - Abigail Bartolome
- Dartmouth College, Department of Computer Science, Hanover, 03755, USA
| | - Richard Comi
- Dartmouth Health, Geisel School of Medicine, Lebanon, 03766, USA
| | - Catherine Stanger
- Dartmouth College, Center for Technology and Behavioral Health, Lebanon, 03766, USA
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Sugandh F, Chandio M, Raveena F, Kumar L, Karishma F, Khuwaja S, Memon UA, Bai K, Kashif M, Varrassi G, Khatri M, Kumar S. Advances in the Management of Diabetes Mellitus: A Focus on Personalized Medicine. Cureus 2023; 15:e43697. [PMID: 37724233 PMCID: PMC10505357 DOI: 10.7759/cureus.43697] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 08/17/2023] [Indexed: 09/20/2023] Open
Abstract
Diabetes mellitus poses a substantial global health challenge, necessitating innovative approaches to improve patient outcomes. Conventional one-size-fits-all treatment strategies have shown limitations in addressing the diverse nature of the disease. In recent years, personalized medicine has emerged as a transformative solution, tailoring treatment plans based on individual genetic makeup, lifestyle factors, and health characteristics. This review highlights the role of genetic screening in predicting diabetes susceptibility and response to treatment, as well as the potential of pharmacogenomics in optimizing medication choices. Moreover, it discusses the incorporation of lifestyle modifications and behavioral interventions to empower patients in their health journey. Telemedicine and remote patient monitoring are also examined for their role in enhancing accessibility and adherence. Ethical considerations and challenges in implementing personalized medicine are addressed. The review envisions a future where personalized medicine becomes a cornerstone in diabetes management, ensuring improved patient outcomes and fostering more effective and patient-centric care on a global scale.
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Affiliation(s)
- Fnu Sugandh
- Medicine, Ghulam Muhammad Mahar Medical College, Sukkur, PAK
- Medicine, Civil Hospital Karachi, Karachi, PAK
| | - Maria Chandio
- Medicine, Liaquat University of Medical and Health Sciences, Jamshoro, PAK
| | - Fnu Raveena
- Medicine, Ghulam Muhammad Mahar Medical College, Sukkur, PAK
| | - Lakshya Kumar
- General Medicine, Pandit Deendayal Upadhyay Medical College, Rajkot, IND
| | - Fnu Karishma
- Medical School, Jinnah Sindh Medical University, Karachi, PAK
| | - Sundal Khuwaja
- Medicine, Liaquat University of Medical and Health Sciences, Jamshoro, PAK
| | - Unaib Ahmed Memon
- Neurology, Internal Medicine, Liaquat University of Medical and Health Sciences, Jamshoro, PAK
| | - Karoona Bai
- Internal Medicine, Dow University of Health Sciences, Civil Hospital Karachi, Karachi, PAK
| | - Maham Kashif
- Medicine, Khawaja Muhammad Safdar Medical College, Sialkot, PAK
| | | | - Mahima Khatri
- Medicine and Surgery, Dow University of Health Sciences, Karachi, PAK
| | - Satesh Kumar
- Medicine and Surgery, Shaheed Mohtarma Benazir Bhutto Medical College, Karachi, PAK
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Jyotsna F, Ahmed A, Kumar K, Kaur P, Chaudhary MH, Kumar S, Khan E, Khanam B, Shah SU, Varrassi G, Khatri M, Kumar S, Kakadiya KA. Exploring the Complex Connection Between Diabetes and Cardiovascular Disease: Analyzing Approaches to Mitigate Cardiovascular Risk in Patients With Diabetes. Cureus 2023; 15:e43882. [PMID: 37746454 PMCID: PMC10511351 DOI: 10.7759/cureus.43882] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 08/21/2023] [Indexed: 09/26/2023] Open
Abstract
Cardiovascular disease (CVD) is the primary cause of morbidity and mortality in individuals diagnosed with diabetes mellitus. This narrative review offers a comprehensive examination of the complex correlation between diabetes and cardiovascular complications. The objective of this review is to analyze the most recent evidence on preventive measures and treatment options for mitigating cardiovascular risk in patients with diabetes, by synthesizing existing literature. Insulin resistance plays a crucial role in connecting diabetes and CVD, leading to the development of dyslipidemia and atherogenesis. As a result, the risk of cardiovascular events in individuals with diabetes is significantly elevated. Moreover, the presence of hyperglycemia-induced oxidative stress and inflammation serves to intensify endothelial dysfunction and vascular damage, thereby exacerbating the risk of cardiovascular complications. The interaction between diabetes and CVD frequently speeds up the development of atherosclerotic plaque, making the plaque more prone to rupture. This can lead to severe cardiovascular events such as myocardial infarction and stroke. It is crucial to comprehend the intricate relationship between diabetes and CVD in order to formulate effective strategies aimed at enhancing patient outcomes and mitigating the burden associated with these interconnected chronic conditions. Healthcare practitioners can enhance the quality of life and reduce mortality rates associated with CVD in diabetic patients by thoroughly examining evidence-based preventive measures and treatment options. This approach allows them to make informed decisions when managing cardiovascular risk. In summary, this narrative review provides a valuable resource for healthcare professionals and researchers, presenting a comprehensive analysis of the complex relationship between diabetes and CVD. By providing a comprehensive analysis of the latest evidence and elucidating the underlying mechanisms, this review seeks to establish a foundation for the development of innovative strategies in diabetes management. These strategies have the potential to significantly improve cardiovascular outcomes and enhance overall patient care.
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Affiliation(s)
- Fnu Jyotsna
- Medicine, Dr. B. R. Ambedkar Medical College & Hospital, Mohali, IND
| | - Areeba Ahmed
- Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Kamal Kumar
- Medicine, Chandka Medical College, Larkana, PAK
| | - Paramjeet Kaur
- Internal Medicine, Guru Gobind Singh Medical College, Faridkot, IND
| | | | - Sagar Kumar
- Medicine, Chandka Medical College, Larkana, PAK
| | - Ejaz Khan
- Dermatology, All India Institute of Medical Sciences, New Delhi, New Delhi, IND
| | - Bushra Khanam
- Internal Medicine, National Tuberculosis Control Center, Kathmandu, NPL
| | | | | | - Mahima Khatri
- Medicine and Surgery, Dow University of Health Sciences, Karachi, PAK
| | - Satesh Kumar
- Medicine and Surgery, Shaheed Mohtarma Benazir Bhutto Medical College, Karachi, PAK
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Phan P, Mitragotri S, Zhao Z. Digital therapeutics in the clinic. Bioeng Transl Med 2023; 8:e10536. [PMID: 37476062 PMCID: PMC10354777 DOI: 10.1002/btm2.10536] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 04/05/2023] [Accepted: 04/12/2023] [Indexed: 07/22/2023] Open
Abstract
Digital therapeutics are emerging as a new form of therapeutic interventions. Unlike conventional therapeutics, digital therapeutics deliver interventions directly to patients using an evidence-based, clinically evaluated software to treat, manage, or prevent diseases. Digital therapeutics manifest in diverse forms such as web-based applications, mobile applications on smart devices, virtual reality, and video games. As its own product category for FDA approval, digital therapeutics can function as stand-alone treatments or in combination with conventional therapeutics to improve adherence and/or efficacy. Here, we review the clinical landscape of digital therapeutics. We summarize FDA-approved products and their clinical use, overview >300 ongoing clinical trials, and discuss challenges for their clinical translation and strategies to overcome the same.
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Affiliation(s)
- Philana Phan
- Department of Pharmaceutical Sciences, College of PharmacyUniversity of Illinois ChicagoChicagoIllinoisUSA
| | - Samir Mitragotri
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMassachusettsUSA
- Wyss Institute for Biologically Inspired Engineering at Harvard UniversityBostonMassachusettsUSA
| | - Zongmin Zhao
- Department of Pharmaceutical Sciences, College of PharmacyUniversity of Illinois ChicagoChicagoIllinoisUSA
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43
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Yang S, Kim SH, Kang M, Joo JY. Harnessing deep learning into hidden mutations of neurological disorders for therapeutic challenges. Arch Pharm Res 2023:10.1007/s12272-023-01450-5. [PMID: 37261600 DOI: 10.1007/s12272-023-01450-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 05/26/2023] [Indexed: 06/02/2023]
Abstract
The relevant study of transcriptome-wide variations and neurological disorders in the evolved field of genomic data science is on the rise. Deep learning has been highlighted utilizing algorithms on massive amounts of data in a human-like manner, and is expected to predict the dependency or druggability of hidden mutations within the genome. Enormous mutational variants in coding and noncoding transcripts have been discovered along the genome by far, despite of the fine-tuned genetic proofreading machinery. These variants could be capable of inducing various pathological conditions, including neurological disorders, which require lifelong care. Several limitations and questions emerge, including the use of conventional processes via limited patient-driven sequence acquisitions and decoding-based inferences as well as how rare variants can be deduced as a population-specific etiology. These puzzles require harnessing of advanced systems for precise disease prediction, drug development and drug applications. In this review, we summarize the pathophysiological discoveries of pathogenic variants in both coding and noncoding transcripts in neurological disorders, and the current advantage of deep learning applications. In addition, we discuss the challenges encountered and how to outperform them with advancing interpretation.
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Affiliation(s)
- Sumin Yang
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea
| | - Sung-Hyun Kim
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea
| | - Mingon Kang
- Department of Computer Science, University of Nevada, Las Vegas, NV, 89154, USA
| | - Jae-Yeol Joo
- Department of Pharmacy, College of Pharmacy, Hanyang University, Rm 407, Bldg.42, 55 Hanyangdaehak-Ro, Sangnok-Gu Ansan, Ansan, Gyeonggi-Do, 15588, Republic of Korea.
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44
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Ratajczak R, Cockerill RG. Artificial Intelligence in Violence Risk Assessment: Addressing Racial Bias and Inequity. J Psychiatr Pract 2023; 29:239-245. [PMID: 37200144 DOI: 10.1097/pra.0000000000000713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Although there has been no shortage of technological innovation in recent decades, a solution to sociodemographic disparities in the forensic setting has remained elusive. Artificial intelligence (AI) is a uniquely powerful emerging technology that is likely to either exacerbate or mitigate existing disparities and biases. This column argues that the implementation of AI in forensic settings is inevitable, and that practitioners and researchers should focus on developing AI systems that reduce bias and advance sociodemographic equity rather than attempt to impede the use of this novel technology.
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Affiliation(s)
- Robert Ratajczak
- RATAJCZAK and COCKERILL: Department of Psychiatry and Behavioral Neuroscience, University of Chicago Pritzker School of Medicine, Chicago, IL
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45
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Dorri M. AI and clinical decision making. Br Dent J 2023; 234:711. [PMID: 37237181 DOI: 10.1038/s41415-023-5928-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 04/05/2023] [Indexed: 05/28/2023]
Affiliation(s)
- M Dorri
- Norwich and Norfolk University Hospitals Foundation Trust, Norwich, United Kingdom.
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46
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Agliata A, Giordano D, Bardozzo F, Bottiglieri S, Facchiano A, Tagliaferri R. Machine Learning as a Support for the Diagnosis of Type 2 Diabetes. Int J Mol Sci 2023; 24:ijms24076775. [PMID: 37047748 PMCID: PMC10095542 DOI: 10.3390/ijms24076775] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/07/2023] Open
Abstract
Diabetes is a chronic, metabolic disease characterized by high blood sugar levels. Among the main types of diabetes, type 2 is the most common. Early diagnosis and treatment can prevent or delay the onset of complications. Previous studies examined the application of machine learning techniques for prediction of the pathology, and here an artificial neural network shows very promising results as a possible valuable aid in the management and prevention of diabetes. Additionally, its superior ability for long-term predictions makes it an ideal choice for this field of study. We utilized machine learning methods to uncover previously undiscovered associations between an individual’s health status and the development of type 2 diabetes, with the goal of accurately predicting its onset or determining the individual’s risk level. Our study employed a binary classifier, trained on scratch, to identify potential nonlinear relationships between the onset of type 2 diabetes and a set of parameters obtained from patient measurements. Three datasets were utilized, i.e., the National Center for Health Statistics’ (NHANES) biennial survey, MIMIC-III and MIMIC-IV. These datasets were then combined to create a single dataset with the same number of individuals with and without type 2 diabetes. Since the dataset was balanced, the primary evaluation metric for the model was accuracy. The outcomes of this study were encouraging, with the model achieving accuracy levels of up to 86% and a ROC AUC value of 0.934. Further investigation is needed to improve the reliability of the model by considering multiple measurements from the same patient over time.
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Affiliation(s)
- Antonio Agliata
- Dipartimento di Scienze Aziendali, Management and Innovation Systems, Università degli Studi di Salerno, 84084 Fisciano, Italy
- BC Soft, Centro Direzionale, Via Taddeo da Sessa Isola F10, 80143 Napoli, Italy
| | - Deborah Giordano
- National Research Council, Institute of Food Science, Via Roma 64, 83100 Avellino, Italy
| | - Francesco Bardozzo
- Dipartimento di Scienze Aziendali, Management and Innovation Systems, Università degli Studi di Salerno, 84084 Fisciano, Italy
| | | | - Angelo Facchiano
- National Research Council, Institute of Food Science, Via Roma 64, 83100 Avellino, Italy
| | - Roberto Tagliaferri
- Dipartimento di Scienze Aziendali, Management and Innovation Systems, Università degli Studi di Salerno, 84084 Fisciano, Italy
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47
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Ansari RM, Harris MF, Hosseinzadeh H, Zwar N. Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes. Healthcare (Basel) 2023; 11:healthcare11060903. [PMID: 36981560 PMCID: PMC10048183 DOI: 10.3390/healthcare11060903] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
The use of Artificial intelligence in healthcare has evolved substantially in recent years. In medical diagnosis, Artificial intelligence algorithms are used to forecast or diagnose a variety of life-threatening illnesses, including breast cancer, diabetes, heart disease, etc. The main objective of this study is to assess self-management practices among patients with type 2 diabetes in rural areas of Pakistan using Artificial intelligence and machine learning algorithms. Of particular note is the assessment of the factors associated with poor self-management activities, such as non-adhering to medications, poor eating habits, lack of physical activities, and poor glycemic control (HbA1c %). The sample of 200 participants was purposefully recruited from the medical clinics in rural areas of Pakistan. The artificial neural network algorithm and logistic regression classification algorithms were used to assess diabetes self-management activities. The diabetes dataset was split 80:20 between training and testing; 80% (160) instances were used for training purposes and 20% (40) instances were used for testing purposes, while the algorithms' overall performance was measured using a confusion matrix. The current study found that self-management efforts and glycemic control were poor among diabetes patients in rural areas of Pakistan. The logistic regression model performance was evaluated based on the confusion matrix. The accuracy of the training set was 98%, while the test set's accuracy was 97.5%; each set had a recall rate of 79% and 75%, respectively. The output of the confusion matrix showed that only 11 out of 200 patients were correctly assessed/classified as meeting diabetes self-management targets based on the values of HbA1c < 7%. We added a wide range of neurons (32 to 128) in the hidden layers to train the artificial neural network models. The results showed that the model with three hidden layers and Adam's optimisation function achieved 98% accuracy on the validation set. This study has assessed the factors associated with poor self-management activities among patients with type 2 diabetes in rural areas of Pakistan. The use of a wide range of neurons in the hidden layers to train the artificial neural network models improved outcomes, confirming the model's effectiveness and efficiency in assessing diabetes self-management activities from the required data attributes.
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Affiliation(s)
- Rashid M Ansari
- School of Public Health and Community Medicine, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia
| | - Mark F Harris
- Centre for Primary Health Care and Equity, University of New South Wales, Sydney, NSW 2052, Australia
| | - Hassan Hosseinzadeh
- School of Health and Society, Faculty of Science, Medicine and Health, University of Wollongong, Sydney, NSW 2522, Australia
| | - Nicholas Zwar
- Faculty of Health Sciences and Medicine, Queensland University, Brisbane, QLD 4072, Australia
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48
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Guo S, Wang S, Meng J, Gu D, Yang Y. Immobilized enzyme for screening and identification of anti-diabetic components from natural products by ligand fishing. Crit Rev Biotechnol 2023; 43:242-257. [PMID: 35156475 DOI: 10.1080/07388551.2021.2025034] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Diabetes is a chronic metabolic disease caused by insufficient insulin secretion and insulin resistance. Natural product is one of the most important resources for anti-diabetic drug. However, due to the extremely complex composition, this research is facing great challenges. After the advent of ligand fishing technology based on enzyme immobilization, the efficiency of screening anti-diabetic components has been greatly improved. In order to provide critical knowledge for future research in this field, the application progress of immobilized enzyme in screening anti-diabetic components from complex natural extracts in recent years was reviewed comprehensively, including novel preparation technologies and strategies of immobilized enzyme and its outstanding application prospect in many aspects. The basic principles and preparation steps of immobilized enzyme were briefly described, including entrapment, physical adsorption, covalent binding, affinity immobilization, multienzyme system and carrier-free immobilization. New formatted immobilized enzymes with different carriers, hollow fibers, magnetic materials, microreactors, metal organic frameworks, etc., were widely used to screen anti-diabetic compositions from various natural products, such as Ginkgo biloba, Morus alba, lotus leaves, Pueraria lobata, Prunella vulgaris, and Magnolia cortex. Furthermore, the challenges and future prospects in this field were put forward in this review.
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Affiliation(s)
- Shuang Guo
- School of Light Industry and Chemical Engineering, Dalian Polytechnic University, Dalian, China
| | - Shuai Wang
- School of Light Industry and Chemical Engineering, Dalian Polytechnic University, Dalian, China
| | - Jing Meng
- School of Light Industry and Chemical Engineering, Dalian Polytechnic University, Dalian, China
| | - Dongyu Gu
- School of Light Industry and Chemical Engineering, Dalian Polytechnic University, Dalian, China.,College of Marine Science and Environment, Dalian Ocean University, Dalian, China
| | - Yi Yang
- School of Light Industry and Chemical Engineering, Dalian Polytechnic University, Dalian, China
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49
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Mudaliar S. The Evolution of Diabetes Treatment Through the Ages: From Starvation Diets to Insulin, Incretins, SGLT2-Inhibitors and Beyond. J Indian Inst Sci 2023; 103:1-11. [PMID: 36845885 PMCID: PMC9942084 DOI: 10.1007/s41745-023-00357-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 01/05/2023] [Indexed: 02/23/2023]
Abstract
Diabetes is an ancient disease and for centuries extreme diets and herbal remedies were used to treat diabetes symptoms. The discovery of insulin in 1921 transformed the landscape of diabetes treatment and was followed by the discovery of several new therapies which improved glycemia and increased patient life span. However, as patients with diabetes lived longer, they developed classic microvascular and macrovascular diabetes complications. In the 1990s, the DCCT and the UKPDS trials demonstrated that tight glucose control reduced the microvascular complications of diabetes, but had marginal effects on cardiovascular disease, the leading cause of death in patients with diabetes. In 2008, the FDA directed that all new diabetes medications demonstrate cardiovascular safety. From this recommendation emerged novel therapeutic classes, the GLP-1 receptor agonists and SGLT2-Inhibitors, which not only improve glycemia, but also provide robust cardio-renal protection. In parallel, developments in diabetes technology like continuous glucose monitoring systems, insulin pumps, telemedicine and precision medicine have advanced diabetes management. Remarkably, a century later, insulin remains a cornerstone of diabetes treatment. Also, diet and physical activity remain important components of any diabetes treatment. Today type 2 diabetes is preventable and long-term remission of diabetes is possible. Finally, progress continues in the field of islet transplantation, perhaps the ultimate frontier in diabetes management.
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Affiliation(s)
- Sunder Mudaliar
- Veterans Affairs Medical Center, San Diego, CA USA
- Diabetes/Metabolism Section, VA San Diego HealthCare System, Department of Medicine, San Diego School of Medicine, University of California at San Diego, 3350 La Jolla Village Drive (Mail Code: 111G), San Diego, CA 92161 USA
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50
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Gardner C, Wake D, Brodie D, Silverstein A, Young S, Cunningham S, Sainsbury C, Ilia M, Lucas A, Willis T, Halligan J. Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method. Digit Health 2023; 9:20552076221128677. [PMID: 36644660 PMCID: PMC9834412 DOI: 10.1177/20552076221128677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 09/08/2022] [Indexed: 01/10/2023] Open
Abstract
The prevalence of type 2 diabetes in North West London (NWL) is relatively high compared to other parts of the United Kingdom with outcomes suboptimal. This presents a need for more effective strategies to identify people living with type 2 diabetes who need additional support. An emerging subset of web-based interventions for diabetes self-management and population management has used artificial intelligence and machine learning models to stratify the risk of complications from diabetes and identify patients in need of immediate support. In this study, two prototype risk prediction tools on the MyWay Diabetes and MyWay Clinical platforms were evaluated with six clinicians and six people living with type 2 diabetes in NWL using the think aloud method. The results of the sessions with people living with type 2 diabetes showed that the concept of the tool was intuitive, however, more instruction on how to correctly use the risk prediction tool would be valuable. The feedback from the sessions with clinicians was that the data presented in the tool aligned with the key diabetes targets in NWL, and that this would be useful for identifying and inviting patients to the practice who are overdue for tests and at risk of complications. The findings of the evaluation have been used to support the development of the prototype risk predictions tools. This study demonstrates the value of conducting usability testing on web-based interventions designed to support the targeted management of type 2 diabetes in local communities.
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Affiliation(s)
- Clarissa Gardner
- Department of Surgery and Cancer, Institute of Global Health Innovation, Imperial College London, London, UK,Clarissa Gardner, Institute of Global Health Innovation, Imperial College London, Level 10, QEQM Building, St Mary’s Hospital, Paddington Basin, London W2 1NY, UK.
| | - Deborah Wake
- MyWay Digital Health, Dundee, UK,Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK
| | | | - Alex Silverstein
- Local Care Directorate, NHS North West London Integrated Care Board, London, UK
| | - Sophie Young
- Information Directorate, Imperial College Health Partners, London, UK
| | | | | | - Maria Ilia
- Information Directorate, Imperial College Health Partners, London, UK
| | - Amanda Lucas
- Information Directorate, Imperial College Health Partners, London, UK
| | - Tony Willis
- Diabetes Transformation Team, NHS North West London Collaboration of CCGs, London, UK
| | - Jack Halligan
- Department of Surgery and Cancer, Institute of Global Health Innovation, Imperial College London, London, UK
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