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Srivastava V, Kumar R, Wani MY, Robinson K, Ahmad A. Role of artificial intelligence in early diagnosis and treatment of infectious diseases. Infect Dis (Lond) 2025; 57:1-26. [PMID: 39540872 DOI: 10.1080/23744235.2024.2425712] [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: 06/01/2024] [Revised: 09/19/2024] [Accepted: 10/30/2024] [Indexed: 11/16/2024] Open
Abstract
Infectious diseases remain a global health challenge, necessitating innovative approaches for their early diagnosis and effective treatment. Artificial Intelligence (AI) has emerged as a transformative force in healthcare, offering promising solutions to address this challenge. This review article provides a comprehensive overview of the pivotal role AI can play in the early diagnosis and treatment of infectious diseases. It explores how AI-driven diagnostic tools, including machine learning algorithms, deep learning, and image recognition systems, enhance the accuracy and efficiency of disease detection and surveillance. Furthermore, it delves into the potential of AI to predict disease outbreaks, optimise treatment strategies, and personalise interventions based on individual patient data and how AI can be used to gear up the drug discovery and development (D3) process.The ethical considerations, challenges, and limitations associated with the integration of AI in infectious disease management are also examined. By harnessing the capabilities of AI, healthcare systems can significantly improve their preparedness, responsiveness, and outcomes in the battle against infectious diseases.
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Affiliation(s)
- Vartika Srivastava
- Department of Clinical Microbiology and Infectious Diseases, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Department of Inflammation and Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | - Ravinder Kumar
- Department of Pathology, College of Medicine, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Mohmmad Younus Wani
- Department of Chemistry, College of Science, University of Jeddah, Jeddah, Saudi Arabia
| | - Keven Robinson
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Aijaz Ahmad
- Department of Clinical Microbiology and Infectious Diseases, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
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Chan PZ, Jin E, Jansson M, Chew HSJ. AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review. J Med Internet Res 2024; 26:e58892. [PMID: 39561353 PMCID: PMC11615544 DOI: 10.2196/58892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/24/2024] [Accepted: 10/08/2024] [Indexed: 11/21/2024] Open
Abstract
BACKGROUND Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience. OBJECTIVE This review aimed to map the use cases of artificial intelligence (AI) in NIBGM. METHODS A systematic scoping review was conducted according to the Arksey O'Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI. RESULTS A total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data. CONCLUSIONS Efforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management.
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Affiliation(s)
- Pin Zhong Chan
- Department of Nursing, Ng Teng Fong General Hospital, Singapore, Singapore
| | - Eric Jin
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Miia Jansson
- Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland
| | - Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Price JE, Fujihara K, Kodama S, Yamazaki K, Maegawa H, Yamazaki T, Sone H. Machine learning algorithms mimicking specialists decision making on initial treatment for people with type 2 diabetes mellitus in Japan diabetes data management study (JDDM76). Diabetes Metab Syndr 2024; 18:103168. [PMID: 39644730 DOI: 10.1016/j.dsx.2024.103168] [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: 01/09/2024] [Revised: 11/25/2024] [Accepted: 11/28/2024] [Indexed: 12/09/2024]
Abstract
OBJECTIVE To evaluate whether typical machine learning models that mimic specialists' care can successfully reproduce information, not only on whether to prescribe medications but also which hypoglycemic agents to prescribe as initial treatment for type 2 diabetes. RESEARCH DESIGN AND METHODS A medical records database containing prescriptions for medications for 16,005 patients who visited a diabetologist's office for the first time was utilized to train five typical machine learning models as well-as a model used for logistic analysis. Prescribed were no medications (diet and exercise therapy), insulin, biguanides (BG), sulfonylureas (SU), dipeptidyl peptidase-4 inhibitors (DPP-4I), alpha-glucosidase inhibitors (α-GI) or glinides. Models were compared based on the F1 score and ROC/AUC scores. RESULTS XGBoost, which splits decision-making into three sections, was the top performing model (42 % accuracy) among five models and conventional logistic regression (35 % accuracy). The second highest scoring model was Support Vector Machines, which had an accuracy of 40 %. When using XGBoost to compare decisions on no medication needed vs. needing medication the AUC was 0.96. Insulin vs. oral medications had an AUC of 0.78. With all remaining oral medications removed, the AUC was 0.76. CONCLUSIONS Among the five models investigated, XGBoost outperformed the other machine learning models examined as well as the traditional logistic model, suggesting that its accuracy had the potential to assist non-specialists in decision-making regarding treatment of patients with type 2 diabetes in the future.
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Affiliation(s)
| | - Kazuya Fujihara
- Department of Hematology, Endocrinology and Metabolism, University Faculty of Medicine, Niigata, Japan
| | - Satoru Kodama
- Department of Hematology, Endocrinology and Metabolism, University Faculty of Medicine, Niigata, Japan
| | | | - Hiroshi Maegawa
- Department of Medicine, Shiga University of Medical Science, Shiga, Japan
| | | | - Hirohito Sone
- Department of Hematology, Endocrinology and Metabolism, University Faculty of Medicine, Niigata, Japan.
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Kataria P, Madhu S, Upadhyay MK. Role of Artificial Intelligence in Diabetes Mellitus Care: A SWOT Analysis. Indian J Endocrinol Metab 2024; 28:562-568. [PMID: 39881760 PMCID: PMC11774413 DOI: 10.4103/ijem.ijem_183_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/05/2024] [Accepted: 08/19/2024] [Indexed: 01/31/2025] Open
Abstract
Diabetes mellitus has become one of the major public health problems in India. Chronic nature and the rising epidemic of diabetes have adverse consequences on India's economy and health status. Recently, machine learning (ML) methods are becoming popular in the healthcare sector. Human medicine is a complex field, and it cannot be solely handled by algorithms, especially diabetes, which is a lifelong multisystem disorder. But ML methods have certain attributes which can make a physician's job easier and can also be helpful in health system management. This article covers multiple dimensions of using artificial intelligence (AI) for diabetes care under the headings Strengths, Weaknesses, Opportunities, and Threats (SWOT), specifically for the Indian healthcare system with a few examples of the latest studies in India. We briefly discuss the scope of using AI for diabetes care in rural India, followed by recommendations. Identifying the potential and challenges with respect to AI use in diabetes care is a fundamental step to improve the management of disease with best possible use of technology.
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Affiliation(s)
- Priya Kataria
- Department of Community Medicine, University College of Medical Sciences and GTB Hospital, New Delhi, India
| | - Srivenkata Madhu
- Department of Endocrinology, Centre for Diabetes, Endocrinology and Metabolism, University College of Medical Sciences and GTB Hospital, New Delhi, India
| | - Madhu K. Upadhyay
- Department of Community Medicine, University College of Medical Sciences and GTB Hospital, New Delhi, India
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Wang J, Leung L, Jackson N, McClean M, Rose D, Lee ML, Stockdale SE. The association between population health management tools and clinician burnout in the United States VA primary care patient-centered medical home. BMC PRIMARY CARE 2024; 25:164. [PMID: 38750457 PMCID: PMC11094957 DOI: 10.1186/s12875-024-02410-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/26/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Technological burden and medical complexity are significant drivers of clinician burnout. Electronic health record(EHR)-based population health management tools can be used to identify high-risk patient populations and implement prophylactic health practices. Their impact on clinician burnout, however, is not well understood. Our objective was to assess the relationship between ratings of EHR-based population health management tools and clinician burnout. METHODS We conducted cross-sectional analyses of 2018 national Veterans Health Administration(VA) primary care personnel survey, administered as an online survey to all VA primary care personnel (n = 4257, response rate = 17.7%), using bivariate and multivariate logistic regressions. Our analytical sample included providers (medical doctors, nurse practitioners, physicians' assistants) and nurses (registered nurses, licensed practical nurses). The outcomes included two items measuring high burnout. Primary predictors included importance ratings of 10 population health management tools (eg. VA risk prediction algorithm, recent hospitalizations and emergency department visits, etc.). RESULTS High ratings of 9 tools were associated with lower odds of high burnout, independent of covariates including VA tenure, team role, gender, ethnicity, staffing, and training. For example, clinicians who rated the risk prediction algorithm as important were less likely to report high burnout levels than those who did not use or did not know about the tool (OR 0.73; CI 0.61-0.87), and they were less likely to report frequent burnout (once per week or more) (OR 0.71; CI 0.60-0.84). CONCLUSIONS Burned-out clinicians may not consider the EHR-based tools important and may not be using them to perform care management. Tools that create additional technological burden may need adaptation to become more accessible, more intuitive, and less burdensome to use. Finding ways to improve the use of tools that streamline the work of population health management and/or result in less workload due to patients with poorly managed chronic conditions may alleviate burnout. More research is needed to understand the causal directional of the association between burnout and ratings of population health management tools.
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Affiliation(s)
- Jane Wang
- Department of Medicine, Stanford University, Stanford, USA
| | - Lucinda Leung
- Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, USA
- Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, 16111 Plummer Avenue, North Hills, CA, 91343, USA
| | - Nicholas Jackson
- Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, USA
- Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, 16111 Plummer Avenue, North Hills, CA, 91343, USA
| | - Michael McClean
- Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, 16111 Plummer Avenue, North Hills, CA, 91343, USA
| | - Danielle Rose
- Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, 16111 Plummer Avenue, North Hills, CA, 91343, USA
| | - Martin L Lee
- Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, 16111 Plummer Avenue, North Hills, CA, 91343, USA
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, USA
| | - Susan E Stockdale
- Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System, 16111 Plummer Avenue, North Hills, CA, 91343, USA.
- Department of Psychiatry and Biobehavioral Sciences, UCLA, Los Angeles, USA.
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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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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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Annuzzi G, Apicella A, Arpaia P, Bozzetto L, Criscuolo S, De Benedetto E, Pesola M, Prevete R. Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI. IEEE J Biomed Health Inform 2024; 28:3123-3133. [PMID: 38157465 DOI: 10.1109/jbhi.2023.3348334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Type 1 diabetes mellitus (T1DM) is characterized by insulin deficiency and blood sugar control issues. The state-of-the-art solution is the artificial pancreas (AP), which integrates basal insulin delivery and glucose monitoring. However, APs are unable to manage postprandial glucose response (PGR) due to limited knowledge of its determinants, requiring additional information for accurate bolus delivery, such as estimated carbohydrate intake. This study aims to quantify the influence of various meal-related factors on predicting postprandial blood glucose levels (BGLs) at different time intervals (15 min, 60 min, and 120 min) after meals by using deep neural network (DNN) models. The prediction models incorporate preprandial blood glucose values, insulin dosage, and various meal-related nutritional factors such as intake of energy, carbohydrates, proteins, lipids, fatty acids, fibers, glycemic index, and glycemic load as input variables. The impact of input features was assessed by exploiting eXplainable Artificial Intelligence (XAI) methodologies, specifically SHapley Additive exPlanations (SHAP), which provide insights into each feature's contribution to the model predictions. By leveraging XAI methodologies, this study aims to enhance the interpretability and transparency of BGL prediction models and validate clinical literature hypotheses. The findings can aid in the development of decision-support tools for individuals with T1DM, facilitating PGR management and reducing the risks of adverse events. The improved understanding of PGR determinants may lead to advancements in AP technology and improve the overall quality of life for T1DM patients.
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Hennrich J, Ritz E, Hofmann P, Urbach N. Capturing artificial intelligence applications' value proposition in healthcare - a qualitative research study. BMC Health Serv Res 2024; 24:420. [PMID: 38570809 PMCID: PMC10993548 DOI: 10.1186/s12913-024-10894-4] [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: 10/26/2023] [Accepted: 03/25/2024] [Indexed: 04/05/2024] Open
Abstract
Artificial intelligence (AI) applications pave the way for innovations in the healthcare (HC) industry. However, their adoption in HC organizations is still nascent as organizations often face a fragmented and incomplete picture of how they can capture the value of AI applications on a managerial level. To overcome adoption hurdles, HC organizations would benefit from understanding how they can capture AI applications' potential.We conduct a comprehensive systematic literature review and 11 semi-structured expert interviews to identify, systematize, and describe 15 business objectives that translate into six value propositions of AI applications in HC.Our results demonstrate that AI applications can have several business objectives converging into risk-reduced patient care, advanced patient care, self-management, process acceleration, resource optimization, and knowledge discovery.We contribute to the literature by extending research on value creation mechanisms of AI to the HC context and guiding HC organizations in evaluating their AI applications or those of the competition on a managerial level, to assess AI investment decisions, and to align their AI application portfolio towards an overarching strategy.
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Affiliation(s)
- Jasmin Hennrich
- FIM Research Institute for Information Management, University of Bayreuth, Branch Business and Information Systems Engineering of the Fraunhofer FIT, Wittelsbacherring 10, 95444, Bayreuth, Germany.
| | - Eva Ritz
- University St. Gallen, Dufourstrasse 50, 9000, St. Gallen, Switzerland
| | - Peter Hofmann
- FIM Research Institute for Information Management, University of Bayreuth, Branch Business and Information Systems Engineering of the Fraunhofer FIT, Wittelsbacherring 10, 95444, Bayreuth, Germany
- appliedAI Initiative GmbH, August-Everding-Straße 25, 81671, Munich, Germany
| | - Nils Urbach
- FIM Research Institute for Information Management, University of Bayreuth, Branch Business and Information Systems Engineering of the Fraunhofer FIT, Wittelsbacherring 10, 95444, Bayreuth, Germany
- Faculty Business and Law, Frankfurt University of Applied Sciences, Nibelungenplatz 1, 60318, Frankfurt Am Main, Germany
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Kulzer B, Aberle J, Haak T, Kaltheuner M, Kröger J, Landgraf R, Kellerer M. Fundamentals of Diabetes Management. Exp Clin Endocrinol Diabetes 2024; 132:171-180. [PMID: 38378015 DOI: 10.1055/a-2166-6566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Affiliation(s)
- Bernhard Kulzer
- Diabetes Centre Mergentheim, Research Institute of the Diabetes Academy Bad Mergentheim, University of Bamberg, Germany
| | - Jens Aberle
- Section Endocrinology and Diabetology, University Obesity Centre Hamburg, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Thomas Haak
- Diabetes Center Mergentheim, Bad Mergentheim, Germany
| | - Matthias Kaltheuner
- dialev, Diabetes Centre for Internal and General Medicine, Leverkusen, Germany
| | - Jens Kröger
- diabetesDE-German Diabetes Aid, Berlin, Germany
| | | | - Monika Kellerer
- Department of Internal Medicine, Marienhospital, Stuttgart, Germany
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11
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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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12
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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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Avoke D, Elshafeey A, Weinstein R, Kim CH, Martin SS. Digital Health in Diabetes and Cardiovascular Disease. Endocr Res 2024; 49:124-136. [PMID: 38605594 PMCID: PMC11484505 DOI: 10.1080/07435800.2024.2341146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 03/11/2024] [Accepted: 04/04/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD). PURPOSE OF THE REVIEW In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease. CONCLUSION We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.
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Affiliation(s)
- Dorothy Avoke
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
| | | | - Robert Weinstein
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Chang H Kim
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Seth S Martin
- Department of Medicine, Johns Hopkins Hospital, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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14
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Zhang H, Zeng T, Zhang J, Zheng J, Min J, Peng M, Liu G, Zhong X, Wang Y, Qiu K, Tian S, Liu X, Huang H, Surmach M, Wang P, Hu X, Chen L. Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China. Front Endocrinol (Lausanne) 2024; 15:1292346. [PMID: 38332892 PMCID: PMC10850228 DOI: 10.3389/fendo.2024.1292346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 01/11/2024] [Indexed: 02/10/2024] Open
Abstract
Objective Insulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the "common soil" of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings. Methods We analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models. Results The LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc. Conclusion The ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.
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Affiliation(s)
- Hao Zhang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Tianshu Zeng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Jiaoyue Zhang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Juan Zheng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Jie Min
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Miaomiao Peng
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Geng Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Xueyu Zhong
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Ying Wang
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Kangli Qiu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Shenghua Tian
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Xiaohuan Liu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Hantao Huang
- Department of Emergency Medicine, Yichang Yiling Hospital, Yichang, China
| | - Marina Surmach
- Department of Public Health and Health Services, Grodno State Medical University, Grodno, Belarus
| | - Ping Wang
- Precision Health Program, Department of Radiology, College of Human Medicine, Michigan State University, East Lansing, MI, United States
| | - Xiang Hu
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
| | - Lulu Chen
- Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China
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Soltanizadeh S, Naghibi SS. Hybrid CNN-LSTM for Predicting Diabetes: A Review. Curr Diabetes Rev 2024; 20:e201023222410. [PMID: 37867273 DOI: 10.2174/0115733998261151230925062430] [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: 05/07/2023] [Revised: 06/11/2023] [Accepted: 07/18/2023] [Indexed: 10/24/2023]
Abstract
BACKGROUND Diabetes is a common and deadly chronic disease caused by high blood glucose levels that can cause heart problems, neurological damage, and other illnesses. Through the early detection of diabetes, patients can live healthier lives. Many machine learning and deep learning techniques have been applied for noninvasive diabetes prediction. The results of some studies have shown that the CNN-LSTM method, a combination of CNN and LSTM, has good performance for predicting diabetes compared to other deep learning methods. METHOD This paper reviews CNN-LSTM-based studies for diabetes prediction. In the CNNLSTM model, the CNN includes convolution and max pooling layers and is applied for feature extraction. The output of the max-pooling layer was fed into the LSTM layer for classification. DISCUSSION The CNN-LSTM model performed well in extracting hidden features and correlations between physiological variables. Thus, it can be used to predict diabetes. The CNNLSTM model, like other deep neural network architectures, faces challenges such as training on large datasets and biological factors. Using large datasets can further improve the accuracy of detection. CONCLUSION The CNN-LSTM model is a promising method for diabetes prediction, and compared with other deep-learning models, it is a reliable method.
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Affiliation(s)
- Soroush Soltanizadeh
- Department of Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran
| | - Seyedeh Somayeh Naghibi
- Department of Biomedical Engineering, Mazandaran University of Science and Technology, Babol, Iran
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16
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Hasan SU, Siddiqui MAR. Diagnostic accuracy of smartphone-based artificial intelligence systems for detecting diabetic retinopathy: A systematic review and meta-analysis. Diabetes Res Clin Pract 2023; 205:110943. [PMID: 37805002 DOI: 10.1016/j.diabres.2023.110943] [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: 05/12/2023] [Revised: 07/28/2023] [Accepted: 10/05/2023] [Indexed: 10/09/2023]
Abstract
AIMS Diabetic retinopathy (DR) is a major cause of blindness globally, early detection is critical to prevent vision loss. Traditional screening that, rely on human experts are, however, costly, and time-consuming. The purpose of this systematic review is to assess the diagnostic accuracy of smartphone-based artificial intelligence(AI) systems for DR detection. METHODS Literature review was conducted on MEDLINE, Embase, Scopus, CINAHL Plus, and Cochrane from inception to December 2022. We included diagnostic test accuracy studies evaluating the use of smartphone-based AI algorithms for DR screening in patients with diabetes, with expert human grader as the reference standard. Random-effects model was used to pool sensitivity and specificity. Any DR(ADR) and referable DR(RDR) were analyzed separately. RESULTS Out of 968 identified articles, six diagnostic test accuracy studies met our inclusion criteria, comprising 3,931 patients. Four of these studies used the Medios AI algorithm. The pooled sensitivity and specificity for diagnosis of ADR were 88 % and 91.5 % respectively and for diagnosis of RDR were 98.2 % and 81.2 % respectively. The overall risk of bias across the studies was low. CONCLUSIONS Smartphone-based AI algorithms show high diagnostic accuracy for detecting DR. However, more high-quality comparative studies are needed to evaluate the effectiveness in real-world clinical settings.
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Affiliation(s)
- S Umar Hasan
- Department of Ophthalmology and Visual Sciences, Aga Khan University Hospital, National Stadium Road, Karachi, Pakistan
| | - M A Rehman Siddiqui
- Department of Ophthalmology and Visual Sciences, Aga Khan University Hospital, National Stadium Road, Karachi, Pakistan.
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Cambuli VM, Baroni MG. Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up? Int J Mol Sci 2023; 24:13139. [PMID: 37685946 PMCID: PMC10488097 DOI: 10.3390/ijms241713139] [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: 07/29/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Research in the treatment of type 1 diabetes has been addressed into two main areas: the development of "intelligent insulins" capable of auto-regulating their own levels according to glucose concentrations, or the exploitation of artificial intelligence (AI) and its learning capacity, to provide decision support systems to improve automated insulin therapy. This review aims to provide a synthetic overview of the current state of these two research areas, providing an outline of the latest development in the search for "intelligent insulins," and the results of new and promising advances in the use of artificial intelligence to regulate automated insulin infusion and glucose control. The future of insulin treatment in type 1 diabetes appears promising with AI, with research nearly reaching the possibility of finally having a "closed-loop" artificial pancreas.
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Affiliation(s)
- Valentina Maria Cambuli
- Diabetology and Metabolic Diseaseas, San Michele Hospital, ARNAS Giuseppe Brotzu, 09121 Cagliari, Italy;
| | - Marco Giorgio Baroni
- Department of Clinical Medicine, Public Health, Life and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
- Neuroendocrinology and Metabolic Diseases, IRCCS Neuromed, 86077 Pozzilli, Italy
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18
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Sun X, Yin Y, Yang Q, Huo T. Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur J Med Res 2023; 28:242. [PMID: 37475050 PMCID: PMC10360247 DOI: 10.1186/s40001-023-01065-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 07/22/2023] Open
Abstract
Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.
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Affiliation(s)
- Xiaoyu Sun
- National Institute of Hospital Administration, National Health Commission, Beijing, China
| | - Yuzhe Yin
- The Sixth Clinical Medical School, Capital Medical University, Beijing, China
| | - Qiwei Yang
- Department of Thorax, The First Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tianqi Huo
- National Institute of Hospital Administration, National Health Commission, Beijing, China.
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19
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Zhao A, Rasendran C, Aryal S, Yu J, Wadhwa RR, Lass JH. Trends in Ophthalmological Patents, 2005-2020. J Ocul Pharmacol Ther 2023; 39:365-370. [PMID: 37192496 PMCID: PMC11391888 DOI: 10.1089/jop.2022.0185] [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: 01/02/2023] [Accepted: 04/10/2023] [Indexed: 05/18/2023] Open
Abstract
Purpose: Technological development drives the optimization of therapeutics in ophthalmology, but quantifiable and systematic review of such innovation is lacking. To fill this gap, we characterize trends in ophthalmology-related patents in the United States from 2005 to 2020. Methods: Publicly available patent data from the US Patent and Trademark Office was analyzed with the R programming language. Ophthalmology-related patents were identified with a keyword search of their titles and claims text. Temporal trends were assessed with the Mann-Kendall trend test (α = 0.05, two-sided). Results: Of 4.5 million collected patents, some 21,000 (0.5%) were ophthalmology related. The number of annually granted ophthalmology patents increased over time (Mann-Kendall test: z = 4.91; P < 0.001), from 619 patents released in 2005 to 2,019 patents in 2020. Patent counts also increased over time for all ophthalmic subspecialties except oculoplastics, with steepest rises in retina (z = 4.91; P < 0.001) and cornea (z = 4.64; P < 0.001). The most cited patents were in biocompatible intraocular implants and implantable controlled-release drug delivery systems, which underscores particular advancement in therapeutic efficacy and safety in devices used in the treatment and management of common yet debilitating eye conditions. Conclusion: This exploratory analysis reveals hotspots for ophthalmology-related innovation in the United States that may predict current and future growth trends in device development and pharmacologic advancement in ophthalmology, paving the way for more diverse and effective treatment options for preserving vision.
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Affiliation(s)
- Alison Zhao
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Chandruganesh Rasendran
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Supriya Aryal
- School of Optometry and Vision Science, University of Waterloo, Waterloo, Ontario, Canada
| | - James Yu
- School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Raoul R. Wadhwa
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jonathan H. Lass
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
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20
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Puterman-Salzman L, Katz J, Bergman H, Grad R, Khanassov V, Gore G, Vedel I, Wilchesky M, Armanfard N, Ghourchian N, Abbasgholizadeh Rahimi S. Artificial Intelligence for Detection of Dementia Using Motion Data: A Scoping Review. Dement Geriatr Cogn Dis Extra 2023; 13:28-38. [PMID: 37927529 PMCID: PMC10624450 DOI: 10.1159/000533693] [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: 04/10/2023] [Accepted: 08/07/2023] [Indexed: 11/07/2023] Open
Abstract
Background Dementia is a neurodegenerative disease resulting in the loss of cognitive and psychological functions. Artificial intelligence (AI) may help in detection and screening of dementia; however, little is known in this area. Objectives The objective of this study was to identify and evaluate AI interventions for detection of dementia using motion data. Method The review followed the framework proposed by O'Malley's and Joanna Briggs Institute methodological guidance for scoping reviews. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist for reporting the results. An information specialist performed a comprehensive search from the date of inception until November 2020, in five bibliographic databases: MEDLINE, EMBASE, Web of Science Core Collection, CINAHL, and IEEE Xplore. We included studies aimed at the deployment and testing or implementation of AI interventions using motion data for the detection of dementia among a diverse population, encompassing varying age, sex, gender, economic backgrounds, and ethnicity, extending to their health care providers across multiple health care settings. Studies were excluded if they focused on Parkinson's or Huntington's disease. Two independent reviewers screened the abstracts, titles, and then read the full-texts. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. The reference lists of included studies were also screened. Results After removing duplicates, 2,632 articles were obtained. After title and abstract screening and full-text screening, 839 articles were considered for categorization. The authors categorized the papers into six categories, and data extraction and synthesis was performed on 20 included papers from the motion tracking data category. The included studies assessed cognitive performance (n = 5, 25%); screened dementia and cognitive decline (n = 8, 40%); investigated visual behaviours (n = 4, 20%); and analyzed motor behaviors (n = 3, 15%). Conclusions We presented evidence of AI systems being employed in the detection of dementia, showcasing the promising potential of motion tracking within this domain. Although some progress has been made in this field recently, there remain notable research gaps that require further exploration and investigation. Future endeavors need to compare AI interventions using motion data with traditional screening methods or other tech-enabled dementia detection mechanisms. Besides, future works should aim at understanding how gender and sex, and ethnic and cultural sensitivity can contribute to refining AI interventions, ensuring they are accessible, equitable, and beneficial across all society.
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Affiliation(s)
| | - Jory Katz
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Howard Bergman
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Roland Grad
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | | | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences and Engineering, McGill University, Montreal, QC, Canada
| | - Isabelle Vedel
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | - Machelle Wilchesky
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada
- Division of Geriatric Medicine, McGill University, Montreal, QC, Canada
- Donald Berman Maimonides Centre for Research in Aging, Montreal, QC, Canada
| | - Narges Armanfard
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada
| | | | - Samira Abbasgholizadeh Rahimi
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Jewish General Hospital, Lady Davis Institute for Medical Research, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Faculty of Dentistry Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
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21
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Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, Kleinberg S, Mathioudakis N, Swerdlow MA, Klonoff DC. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol 2023; 17:224-238. [PMID: 36121302 PMCID: PMC9846408 DOI: 10.1177/19322968221124583] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.
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Affiliation(s)
| | | | - David G. Armstrong
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - Ashley N. Battarbee
- Center for Women’s Reproductive Health,
The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jorge Cuadros
- Meredith Morgan Optometric Eye Center,
University of California, Berkeley, Berkeley, CA, USA
| | - Juan C. Espinoza
- Children’s Hospital Los Angeles,
University of Southern California, Los Angeles, CA, USA
| | | | | | - Mark A. Swerdlow
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - David C. Klonoff
- Diabetes Technology Society,
Burlingame, CA, USA
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
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22
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Sandeep Ganesh G, Kolusu AS, Prasad K, Samudrala PK, Nemmani KV. Advancing health care via artificial intelligence: From concept to clinic. Eur J Pharmacol 2022; 934:175320. [DOI: 10.1016/j.ejphar.2022.175320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 11/26/2022]
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23
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Rushlow DR, Croghan IT, Inselman JW, Thacher TD, Friedman PA, Yao X, Pellikka PA, Lopez-Jimenez F, Bernard ME, Barry BA, Attia IZ, Misra A, Foss RM, Molling PE, Rosas SL, Noseworthy PA. Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. Mayo Clin Proc 2022; 97:2076-2085. [PMID: 36333015 DOI: 10.1016/j.mayocp.2022.04.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/09/2022] [Accepted: 04/04/2022] [Indexed: 03/19/2023]
Abstract
OBJECTIVE To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting patients with low EF. METHODS Clinicians in 48 practice sites of a US Midwest health system were cluster-randomized by the care team to usual care or to receive a notification that suggested ordering an echocardiogram in patients flagged as potentially having low EF based on an AI-ECG algorithm. Enrollment was between June 26, 2019, and July 30, 2019; participation concluded on March 31, 2020. This report is focused on those clinicians randomized to receive the notification of the AI-ECG algorithm. At the patient level, data were analyzed for the proportion of patients with positive AI-ECG results. Adoption was defined as the clinician order of an echocardiogram after prompted by the alert. RESULTS A total of 165 clinicians and 11,573 patients were included in this analysis. Among patients with positive AI-ECG, high adopters (n=41) were twice as likely to diagnose patients with low EF (33.9%) vs low adopters, n=124, (16.9%); odds ratio, 1.62; 95% CI, 1.21 to 2.17). High adopters were more often advanced practice providers (eg, nurse practitioners and physician assistants) vs physicians, Family Medicine vs Internal Medicine specialty, and tended to have less complex patients. CONCLUSION Clinicians who most frequently followed the recommendations of an AI tool were twice as likely to diagnose low EF. Those clinicians with less complex patients were more likely to be high adopters. TRIAL REGISTRATION Clinicaltrials.gov Identifier: NCT04000087.
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Affiliation(s)
- David R Rushlow
- Department of Family Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Ivana T Croghan
- Department of Medicine, Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA; Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Jonathan W Inselman
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Tom D Thacher
- Department of Family Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Barbara A Barry
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Itzhak Z Attia
- Department of Cardiology, Mayo Clinic, Rochester, MN, USA
| | - Artika Misra
- Department of Family Medicine, Mayo Clinic Health System, Mankato, MN, USA
| | - Randy M Foss
- Department of Family Medicine, Mayo Clinic Health System, Lake City, MN, USA
| | - Paul E Molling
- Department of Family Medicine, Mayo Clinic Health System, Onalaska, WI, USA
| | - Steven L Rosas
- Department of Family Medicine, Mayo Clinic Health System, Menomonie, WI, USA
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Fasterholdt I, Naghavi-Behzad M, Rasmussen BSB, Kjølhede T, Skjøth MM, Hildebrandt MG, Kidholm K. Value assessment of artificial intelligence in medical imaging: a scoping review. BMC Med Imaging 2022; 22:187. [PMID: 36316665 PMCID: PMC9620604 DOI: 10.1186/s12880-022-00918-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 10/22/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging. METHODS We performed a systematic scoping review of published studies between January 2016 and September 2020 using 10 databases (Medline, Scopus, ProQuest, Google Scholar, and six related databases of grey literature). Information about the context (country, clinical area, and type of study) and mentioned domains with specific outcomes and items were extracted. An existing domain classification, from a European assessment framework, was used as a point of departure, and extracted data were grouped into domains and content analysis of data was performed covering predetermined themes. RESULTS Seventy-nine studies were included out of 5890 identified articles. An additional seven studies were identified by searching reference lists, and the analysis was performed on 86 included studies. Eleven domains were identified: (1) health problem and current use of technology, (2) technology aspects, (3) safety assessment, (4) clinical effectiveness, (5) economics, (6) ethical analysis, (7) organisational aspects, (8) patients and social aspects, (9) legal aspects, (10) development of AI algorithm, performance metrics and validation, and (11) other aspects. The frequency of mentioning a domain varied from 20 to 78% within the included papers. Only 15/86 studies were actual assessments of AI technologies. The majority of data were statements from reviews or papers voicing future needs or challenges of AI research, i.e. not actual outcomes of evaluations. CONCLUSIONS This review regarding value assessment of AI in medical imaging yielded 86 studies including 11 identified domains. The domain classification based on European assessment framework proved useful and current analysis added one new domain. Included studies had a broad range of essential domains about addressing AI technologies highlighting the importance of domains related to legal and ethical aspects.
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Affiliation(s)
- Iben Fasterholdt
- CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mohammad Naghavi-Behzad
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Benjamin S. B. Rasmussen
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Radiology, Odense University Hospital, Odense, Denmark
- CAI-X – Centre for Clinical Artificial Intelligence, Odense University Hospital, Odense, Denmark
| | - Tue Kjølhede
- CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
| | - Mette Maria Skjøth
- Department of Dermatology and Allergy Centre, Odense University Hospital, Odense, Denmark
| | - Malene Grubbe Hildebrandt
- CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
- Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark
| | - Kristian Kidholm
- CIMT – Centre for Innovative Medical Technology, Odense University Hospital, Sdr. Boulevard 29, Entrance 102, 4rd Floor, 5000 Odense C, Denmark
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Sikalidis AK, Kristo AS, Reaves SK, Kurfess FJ, DeLay AM, Vasilaky K, Donegan L. Capacity Strengthening Undertaking-Farm Organized Response of Workers against Risk for Diabetes: (C.S.U.-F.O.R.W.A.R.D. with Cal Poly)-A Concept Approach to Tackling Diabetes in Vulnerable and Underserved Farmworkers in California. SENSORS (BASEL, SWITZERLAND) 2022; 22:8299. [PMID: 36365994 PMCID: PMC9654638 DOI: 10.3390/s22218299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/25/2022] [Accepted: 10/28/2022] [Indexed: 06/16/2023]
Abstract
In our project herein, we use the case of farmworkers, an underserved and understudied population at high risk for Type-2 Diabetes Mellitus (T2DM), as a paradigm of an integrated action-oriented research, education and extension approach involving the development of long-term equitable strategies providing empowerment and tailored-made solutions that support practical decision-making aiming to reduce risk of T2DM and ensuing cardiovascular disease (CVD). A Technology-based Empowerment Didactic module (TEDm) and an Informed Decision-Making enhancer (IDMe) coupled in a smart application (app) for farmworkers aiming to teach, set goals, monitor, and support in terms of nutrition, hydration, physical activity, sleep, and circadian rhythm towards lowering T2DM risk, is to be developed and implemented considering the particular characteristics of the population and setting. In parallel, anthropometric, biochemical, and clinical assessments will be utilized to monitor risk parameters for T2DM and compliance to dietary and wellness plans. The app incorporating anthropometric/clinical/biochemical parameters, dietary/lifestyle behavior, and extent of goal achievement can be continuously refined and improved through machine learning and re-programming. The app can function as a programmable tool constantly learning, adapting, and tailoring its services to user needs helping optimization of practical informed decision-making towards mitigating disease symptoms and associated risk factors. This work can benefit apart from the direct beneficiaries being farmworkers, the stakeholders who will be gaining a healthier, more vibrant workforce, and in turn the local communities.
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Affiliation(s)
- Angelos K. Sikalidis
- Nutrition Program, Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, CA 93407, USA
- Cal Poly Personalized Nutrition Research Group, California Polytechnic State University, San Luis Obispo, CA 93407, USA
- Center for Health Research, California Polytechnic State University, San Luis Obispo, CA 94307, USA
| | - Aleksandra S. Kristo
- Nutrition Program, Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, CA 93407, USA
- Cal Poly Personalized Nutrition Research Group, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Scott K. Reaves
- Nutrition Program, Department of Food Science and Nutrition, California Polytechnic State University, San Luis Obispo, CA 93407, USA
- Cal Poly Personalized Nutrition Research Group, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Franz J. Kurfess
- Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Ann M. DeLay
- Department of Agriculture Education and Communication, California Polytechnic State University, San Luis Obispo, CA 93407, USA
| | - Kathryn Vasilaky
- Department of Economics, Orfalea College of Business, California Polytechnic State University, San Luis Obispo, CA 03497, USA
| | - Lorraine Donegan
- Department of Graphic Communication, California Polytechnic State University, San Luis Obispo, CA 93407, USA
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26
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Breast cancer image analysis using deep learning techniques – a survey. HEALTH AND TECHNOLOGY 2022. [DOI: 10.1007/s12553-022-00703-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Kulzer B, Aberle J, Haak T, Kaltheuner M, Kröger J, Landgraf R, Kellerer M. Grundlagen des Diabetesmanagements. DIABETOL STOFFWECHS 2022. [DOI: 10.1055/a-1916-2262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Bernhard Kulzer
- Diabetes Zentrum Mergentheim, Forschungsinstitut der Diabetes Akademie Bad Mergentheim, Universität Bamberg, Deutschland
| | - Jens Aberle
- Endokrinologie und Diabetologie, Universitäres Adipositas Centrum, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
| | - Thomas Haak
- Diabetes Zentrum Mergentheim, Bad Mergentheim, Deutschland
| | - Matthias Kaltheuner
- dialev, Diabetes Zentrum, Innere- und Allgemeinmedizin, Leverkusen, Deutschland
| | - Jens Kröger
- diabetesDE-Deutsche Diabetes-Hilfe, Berlin, Deutschland
| | | | - Monika Kellerer
- Zentrum für Innere Medizin, Marienhospital, Stuttgart, Deutschland
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28
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Potter E, Burstein F, Flynn D, Hwang ID, Dinh T, Goh TY, Mohammad Ebrahim M, Gilfillan C. Physician-Authored Feedback in a Type 2 Diabetes Self-management App: Acceptability Study. JMIR Form Res 2022; 6:e31736. [PMID: 35536614 PMCID: PMC9131138 DOI: 10.2196/31736] [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: 07/02/2021] [Revised: 01/04/2022] [Accepted: 01/25/2022] [Indexed: 11/16/2022] Open
Abstract
Background Type 2 diabetes (T2D) is increasingly prevalent in society, in part because of behavioral issues, with sedentary behavior, reduced exercise, and the consumption of foods with a high glycemic index being major contributors. There is evidence for the efficacy of mobile apps in promoting behavior change and lifestyle improvements in people with T2D. Many mobile phone apps help to monitor the condition of people with T2D and inform them about their health. Some of these digital interventions involve patients using apps on their own or in conjunction with health care professionals. Objective This study aimed to test the acceptability of receiving app-based, daily physician feedback for patients with T2D that is informed by the continuous monitoring of their activity, food choices, and glucose profiles, with the aim of encouraging healthier behavior. The GLOOK! app was designed and developed by an academic research team and pilot-tested at an Australian public hospital. Methods A total of 15 patients diagnosed with T2D wore a glucose monitor and an Apple Watch for 12 days. The uploaded data were integrated into the GLOOK! app on the patients’ smartphones, which also enabled the recording of activity and consumed food. A physician provided daily feedback to each individual through the app based on their data from each of the 12 days. At the beginning and end of the study, data were collected on vital signs, anthropometry, hemoglobin A1c level, fructosamine level, and fasting lipids level. Participants were also interviewed at the beginning and end of the study to assess the acceptability of the intervention and its potential impact on promoting positive behavior change. Results Over the 12 days of the study, there was a significant reduction of 0.22% (P=.004) in hemoglobin A1c level. There were favorable changes in fructosamine and lipid fractions; however, none reached significance. There was also a fall of 0.65 kg in body weight and falls in blood pressure and pulse rate that did not reach significance. Patient feedback on the GLOOK! system was positive. Of the 15 participants, 13 (87%) were enthusiastic about continuing to use the app system if some usability and reliability aspects were improved. All participants regarded the personalized physician feedback as supportive and helpful in understanding their own health behavior. Of the 15 participants, 4 (27%) felt that using the system encouraged long-term behavior changes. Conclusions A mobile app system that provides people with T2D daily, physician-generated, personalized feedback can produce favorable changes in glycemic and cardiovascular risk parameters—even in the short term—and encourage better self-management of their condition. Study participants found the experience of using the mobile app system acceptable and were motivated to establish longer-term lifestyle improvements through behavior changes.
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Affiliation(s)
- Eden Potter
- Design Health Collab, Monash Art, Design and Architecture, Monash University, Melbourne, Australia
| | - Frada Burstein
- Faculty of Information Technology, Monash University, Melbourne, Australia
| | - Daphne Flynn
- Design Health Collab, Monash Art, Design and Architecture, Monash University, Melbourne, Australia
| | - In Dae Hwang
- Design Health Collab, Monash Art, Design and Architecture, Monash University, Melbourne, Australia
| | - Tina Dinh
- Design Health Collab, Monash Art, Design and Architecture, Monash University, Melbourne, Australia
| | - Tian Yu Goh
- Faculty of Information Technology, Monash University, Melbourne, Australia
| | - Mina Mohammad Ebrahim
- Eastern Health Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
| | - Christopher Gilfillan
- Eastern Health Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
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Kulzer B, Aberle J, Haak T, Kaltheuner M, Kröger J, Landgraf R, Kellerer M. Fundamentals of Diabetes Management. Exp Clin Endocrinol Diabetes 2022; 130:S9-S18. [PMID: 35488178 DOI: 10.1055/a-1624-5080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Bernhard Kulzer
- Diabetes Centre Mergentheim, Research Institute of the Diabetes Academy Bad Mergentheim, University of Bamberg, Germany
| | - Jens Aberle
- Department of Endocrinology and Diabetology, University Obesity Centre, University Hospital Hamburg-Eppendorf, Hamburg, Germany
| | - Thomas Haak
- Diabetes Centre Mergentheim, Bad Mergentheim, Germany
| | - Matthias Kaltheuner
- dialev, Diabetes Centre for Internal and General Medicine, Leverkusen, Germany
| | - Jens Kröger
- diabetesDE- German Diabetes Aid, Berlin, Germany
| | | | - Monika Kellerer
- Department of Internal Medicine 1, Marienhospital, Stuttgart, Germany
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Juneja D, Gupta A, Singh O. Artificial intelligence in critically ill diabetic patients: current status and future prospects. Artif Intell Gastroenterol 2022; 3:66-79. [DOI: 10.35712/aig.v3.i2.66] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 04/21/2022] [Accepted: 04/28/2022] [Indexed: 02/06/2023] Open
Abstract
Recent years have witnessed increasing numbers of artificial intelligence (AI) based applications and devices being tested and approved for medical care. Diabetes is arguably the most common chronic disorder worldwide and AI is now being used for making an early diagnosis, to predict and diagnose early complications, increase adherence to therapy, and even motivate patients to manage diabetes and maintain glycemic control. However, these AI applications have largely been tested in non-critically ill patients and aid in managing chronic problems. Intensive care units (ICUs) have a dynamic environment generating huge data, which AI can extract and organize simultaneously, thus analysing many variables for diagnostic and/or therapeutic purposes in order to predict outcomes of interest. Even non-diabetic ICU patients are at risk of developing hypo or hyperglycemia, complicating their ICU course and affecting outcomes. In addition, to maintain glycemic control frequent blood sampling and insulin dose adjustments are required, increasing nursing workload and chances of error. AI has the potential to improve glycemic control while reducing the nursing workload and errors. Continuous glucose monitoring (CGM) devices, which are Food and Drug Administration (FDA) approved for use in non-critically ill patients, are now being recommended for use in specific ICU populations with increased accuracy. AI based devices including artificial pancreas and CGM regulated insulin infusion system have shown promise as comprehensive glycemic control solutions in critically ill patients. Even though many of these AI applications have shown potential, these devices need to be tested in larger number of ICU patients, have wider availability, show favorable cost-benefit ratio and be amenable for easy integration into the existing healthcare systems, before they become acceptable to ICU physicians for routine use.
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Affiliation(s)
- Deven Juneja
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
| | - Anish Gupta
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
| | - Omender Singh
- Institute of Critical Care Medicine, Max Super Speciality Hospital, Saket, New Delhi 110092, India
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31
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Wan TT, Matthews S, Luh H, Zeng Y, Wang Z, Yang L. A Proposed Multi-Criteria Optimization Approach to Enhance Clinical Outcomes Evaluation for Diabetes Care: A Commentary. Health Serv Res Manag Epidemiol 2022; 9:23333928221089125. [PMID: 35372638 PMCID: PMC8966128 DOI: 10.1177/23333928221089125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/04/2022] [Accepted: 03/06/2022] [Indexed: 11/30/2022] Open
Abstract
There are several challenges in diabetes care management including optimizing the currently used therapies, educating patients on selfmanagement, and improving patient lifestyle and systematic healthcare barriers. The purpose of performing a systems approach to implementation science aided by artificial intelligence techniques in diabetes care is two-fold: 1) to explicate the systems approach to formulate predictive analytics that will simultaneously consider multiple input and output variables to generate an ideal decision-making solution for an optimal outcome; and 2) to incorporate contextual and ecological variations in practicing diabetes care coupled with specific health educational interventions as exogenous variables in prediction. A similar taxonomy of modeling approaches proposed by Brennon et al (2006) is formulated to examining the determinants of diabetes care outcomes in program evaluation. The discipline-free methods used in implementation science research, applied to efficiency and quality-of-care analysis are presented. Finally, we illustrate a logically formulated predictive analytics with efficiency and quality criteria included for evaluation of behavioralchange intervention programs, with the time effect included, in diabetes care and research.
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Affiliation(s)
- Thomas T.H. Wan
- Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan and University of Central Florida, Orlando, FL, USA
| | - Sarah Matthews
- Health Communication Consultants, Inc., Orlando, FL, USA
| | - Hsing Luh
- College of Sciences, National Chengchi University, Taipei, Taiwan
| | - Yong Zeng
- Institute for Information Systems Engineering, Concordia University, Montreal, Canada
| | - Zhibo Wang
- College of Engineering and Computer Science, University of Central Florida, Orlando, Florida, USA
| | - Lin Yang
- Cancer Epidemiology and Prevention Research, University of Calgary, Alberta, Canada
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32
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Machine Learning and Smart Devices for Diabetes Management: Systematic Review. SENSORS 2022; 22:s22051843. [PMID: 35270989 PMCID: PMC8915068 DOI: 10.3390/s22051843] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/05/2022] [Accepted: 02/18/2022] [Indexed: 01/27/2023]
Abstract
(1) Background: The use of smart devices to better manage diabetes has increased significantly in recent years. These technologies have been introduced in order to make life easier for patients with diabetes by allowing better control of the stability of blood sugar levels and anticipating the occurrence of dangerous events (hypo/hyperglycemia), etc. That being said, the main objectives of the self-management of diabetes is to improve the lifestyle and life quality of patients with diabetes; (2) Methods: We performed a systematic review based on articles that focus on the use of smart devices for the monitoring and better management of diabetes. The search was focused on keywords related to the topic, such as “Diabetes”, “Technology”, “Self-management”, “Artificial Intelligence”, etc. This was performed using databases, such as Scopus, Google Scholar, and PubMed; (3) Results: A total of 89 studies, published between 2011 and 2021, were included. The majority of the selected research aims to solve a diabetes management problem (e.g., blood glucose prediction, early detection of risk events, and the automatic adjustment of insulin doses, etc.). In these studies, wearable devices were used in combination with artificial intelligence (AI) techniques; (4) Conclusions: Wearable devices have attracted a great deal of scientific interest in the field of healthcare for people with chronic conditions, such as diabetes. They are capable of assisting in the management of diabetes, as well as preventing complications associated with this condition. Furthermore, the usage of these devices has improved illness management and quality of life.
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Kulzer B, Aberle J, Haak T, Kaltheuner M, Kröger J, Landgraf R, Kellerer M. Grundlagen des Diabetesmanagements. DIABETOLOGE 2022. [DOI: 10.1007/s11428-022-00863-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Vargas E, Aiello EM, Pinsker JE, Teymourian H, Tehrani F, Church MM, Laffel LM, Doyle FJ, Patti ME, Dassau E, Wang J. Development of a Novel Insulin Sensor for Clinical Decision-Making. J Diabetes Sci Technol 2022:19322968211071132. [PMID: 35043720 PMCID: PMC10347992 DOI: 10.1177/19322968211071132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Clinical decision support systems that incorporate information from frequent insulin measurements to enhance individualized diabetes management remain an unmet goal. The development of a disposable insulin strip for fast decentralized point-of-care detection replacing the current centralized lab-based methods used in clinical practice would be highly desirable to improve the establishment of individual insulin absorption patterns and algorithm modeling processes. METHODS We carried out the development and optimization of a novel decentralized disposable insulin electrochemical sensor focusing on obtaining high analytical and operational performance toward achieving a true point-of-care insulin testing device for clinical on-site application. RESULTS Our novel insulin immunosensor demonstrated an attractive performance and efficient user-friendly operation by providing high sensitivity capability to detect endogenous and analog insulin with a limit of detection of 30.2 pM (4.3 µiU/mL), rapid time-to-result, stability toward remote site application, and scalable low-cost fabrication with an estimated cost-of-goods for disposable consumables of below $5, capable of near real-time insulin detection in a microliter (≤10 µL) sample droplet of undiluted serum within 30 minutes. CONCLUSIONS The results obtained in the optimization and characterization of our novel insulin sensor illustrate its suitability for its potential application in remote clinical environments for frequent insulin monitoring. Future work will test the insulin sensor in a clinical research setting to assess its efficacy in individuals with type 1 diabetes.
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Affiliation(s)
- Eva Vargas
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Eleonora M Aiello
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, USA
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | | | - Hazhir Teymourian
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Farshad Tehrani
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Lori M Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, USA
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | | | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Allston, MA, USA
- Sansum Diabetes Research Institute, Santa Barbara, CA, USA
| | - Joseph Wang
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
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Artificial intelligence perspective in the future of endocrine diseases. J Diabetes Metab Disord 2022; 21:971-978. [PMID: 35673469 PMCID: PMC9167325 DOI: 10.1007/s40200-021-00949-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 11/30/2021] [Indexed: 01/13/2023]
Abstract
In recent years, artificial intelligence (AI) shows promising results in the diagnosis, prediction, and management of diseases. The move from handwritten medical notes to electronic health records and a huge number of digital data commenced in the era of big data in medicine. AI can improve physician performance and help better clinical decision making which is called augmented intelligence. The methods applied in the research of AI and endocrinology include machine learning, artificial neural networks, and natural language processing. Current research in AI technology is making major efforts to improve decision support systems for patient use. One of the best-known applications of AI in endocrinology was seen in diabetes management, which includes prediction, diagnosis of diabetes complications (measuring microalbuminuria, retinopathy), and glycemic control. AI-related technologies are being found to assist in the diagnosis of other endocrine diseases such as thyroid cancer and osteoporosis. This review attempts to provide insight for the development of prospective for AI with a focus on endocrinology.
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Xue Y, Thalmayer AS, Zeising S, Fischer G, Lübke M. Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:425. [PMID: 35062385 PMCID: PMC8780031 DOI: 10.3390/s22020425] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 12/27/2021] [Accepted: 12/29/2021] [Indexed: 12/25/2022]
Abstract
Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.
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Affiliation(s)
| | | | | | - Georg Fischer
- Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 9, 91058 Erlangen, Germany; (Y.X.); (A.S.T.); (S.Z.)
| | - Maximilian Lübke
- Institute for Electronics Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstr. 9, 91058 Erlangen, Germany; (Y.X.); (A.S.T.); (S.Z.)
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Singla R, Aggarwal S, Bindra J, Garg A, Singla A. Developing Clinical Decision Support System using Machine Learning Methods for Type 2 Diabetes Drug Management. Indian J Endocrinol Metab 2022; 26:44-49. [PMID: 35662766 PMCID: PMC9162252 DOI: 10.4103/ijem.ijem_435_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/08/2021] [Accepted: 01/09/2022] [Indexed: 11/04/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Application of artificial intelligence/machine learning (AI/ML) for automation of diabetes management can enhance equitable access to care and ensure delivery of minimum standards of care. Objective of the current study was to create a clinical decision support system using machine learning approach for diabetes drug management in people living with Type 2 diabetes. METHODOLOGY Study was conducted at an Endocrinology clinic and data collected from the electronic clinic management system. 15485 diabetes prescriptions of 4974 patients were accessed. A data subset of 1671 diabetes prescriptions of 940 patients with information on diabetes drugs, demographics (age, gender, body mass index), biochemical parameters (HbA1c, fasting blood glucose, creatinine) and patient clinical parameters (diabetes duration, compliance to diet/exercise/medications, hypoglycemia, contraindication to any drug, summary of patient self monitoring of blood glucose data, diabetes complications) was used in analysis. An input of patient variables were used to predict all diabetes drug classes to be prescribed. Random forest algorithms were used to create decision trees for all diabetes drugs. RESULTS AND CONCLUSION Accuracy for predicting use of each individual drug class varied from 85% to 99.4%. Multi-drug accuracy, indicating that all drug predictions in a prescription are correct, stands at 72%. Multi drug class accuracy in clinical application may be higher than this result, as in a lot of clinical scenarios, two or more diabetes drugs may be used interchangeably. This report presents a first positive step in developing a robust clinical decision support system to transform access and quality of diabetes care.
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Affiliation(s)
- Rajiv Singla
- Department of Endocrinology and Health Informatics, Kalpavriksh Healthcare, Dwarka, Delhi, India
| | - Shivam Aggarwal
- Department of Health Informatics, Kalpavriksh Healthcare, Dwarka, Delhi, India
| | - Jatin Bindra
- Department of Health Informatics, Kalpavriksh Healthcare, Dwarka, Delhi, India
| | - Arpan Garg
- Department of Health Informatics, Kalpavriksh Healthcare, Dwarka, Delhi, India
| | - Ankush Singla
- Department of Health Informatics, Kalpavriksh Healthcare, Dwarka, Delhi, India
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Kulzer B, Aberle J, Haak T, Kaltheuner M, Kröger J, Landgraf R, Kellerer M. Grundlagen des Diabetesmanagements. DIABETOL STOFFWECHS 2021. [DOI: 10.1055/a-1590-7867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Bernhard Kulzer
- Diabetes Zentrum Mergentheim, Forschungsinstitut der Diabetes Akademie Bad Mergentheim, Universität Bamberg
| | - Jens Aberle
- Endokrinologie und Diabetologie, Universitäres Adipositas Centrum, Universitätsklinikum Hamburg-Eppendorf, Hamburg
| | - Thomas Haak
- Diabetes Zentrum Mergentheim, Bad Mergentheim
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39
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Sun R, Blayney DW, Hernandez-Boussard T. Health management via telemedicine: Learning from the COVID-19 experience. J Am Med Inform Assoc 2021; 28:2536-2540. [PMID: 34459475 PMCID: PMC8499808 DOI: 10.1093/jamia/ocab145] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 06/09/2021] [Accepted: 06/30/2021] [Indexed: 11/26/2022] Open
Abstract
At the onset of the COVID-19 (coronavirus disease 2019) pandemic, telemedicine was rapidly implemented to protect patients and healthcare providers from infection. It is unlikely that care delivery will fully return to the pre-COVID form. Telemedicine offers many opportunities to improve care efficiency, accessibility, and patient outcomes, but many challenges exist related to technology interoperability, the digital divide, and usability. We propose that telemedicine evolve to support continuity of care throughout the patient journey, including multidisciplinary care teams and the seamless integration of data into the clinical workflow to support a learning healthcare system. Importantly, evidence is needed to support this paradigm shift in care delivery to ensure the quality and efficacy of care delivered via telemedicine. Here, we highlight gaps and opportunities that need to be addressed by the biomedical informatics community to move forward with safe and effective healthcare delivery via telemedicine.
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Affiliation(s)
- Ran Sun
- Department of Medicine, Stanford University, Stanford, California, USA
| | - Douglas W Blayney
- Stanford Cancer Institute, Stanford University, Stanford, California, USA
- Division of Medical Oncology, Department of Medicine, Stanford University, Stanford, California, USA
| | - Tina Hernandez-Boussard
- Department of Medicine, Stanford University, Stanford, California, USA
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
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40
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Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz? DIABETOLOGE 2021. [DOI: 10.1007/s11428-021-00818-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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41
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Clark CR, Wilkins CH, Rodriguez JA, Preininger AM, Harris J, DesAutels S, Karunakaram H, Rhee K, Bates DW, Dankwa-Mullan I. Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care. J Gen Intern Med 2021; 36:3188-3193. [PMID: 34027610 PMCID: PMC8481410 DOI: 10.1007/s11606-021-06846-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 04/22/2021] [Indexed: 01/21/2023]
Abstract
The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity.
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Affiliation(s)
| | | | | | | | - Joyce Harris
- Vanderbilt University Medical Center, Nashville, USA
| | | | | | - Kyu Rhee
- IBM Watson Health, Cambridge, USA
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42
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Zhu T, Li K, Herrero P, Georgiou P. Deep Learning for Diabetes: A Systematic Review. IEEE J Biomed Health Inform 2021; 25:2744-2757. [PMID: 33232247 DOI: 10.1109/jbhi.2020.3040225] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Diabetes is a chronic metabolic disorder that affects an estimated 463 million people worldwide. Aiming to improve the treatment of people with diabetes, digital health has been widely adopted in recent years and generated a huge amount of data that could be used for further management of this chronic disease. Taking advantage of this, approaches that use artificial intelligence and specifically deep learning, an emerging type of machine learning, have been widely adopted with promising results. In this paper, we present a comprehensive review of the applications of deep learning within the field of diabetes. We conducted a systematic literature search and identified three main areas that use this approach: diagnosis of diabetes, glucose management, and diagnosis of diabetes-related complications. The search resulted in the selection of 40 original research articles, of which we have summarized the key information about the employed learning models, development process, main outcomes, and baseline methods for performance evaluation. Among the analyzed literature, it is to be noted that various deep learning techniques and frameworks have achieved state-of-the-art performance in many diabetes-related tasks by outperforming conventional machine learning approaches. Meanwhile, we identify some limitations in the current literature, such as a lack of data availability and model interpretability. The rapid developments in deep learning and the increase in available data offer the possibility to meet these challenges in the near future and allow the widespread deployment of this technology in clinical settings.
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43
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Sheikh A, Bhatti A, Adeyemi O, Raja M, Sheikh I. The Utility of Smartphone-Based Artificial Intelligence Approaches for Diabetic Retinopathy: A Literature Review and Meta-Analysis. J Curr Ophthalmol 2021; 33:219-226. [PMID: 34765807 PMCID: PMC8579798 DOI: 10.4103/2452-2325.329064] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 04/25/2021] [Accepted: 04/27/2021] [Indexed: 12/28/2022] Open
Abstract
PURPOSE To assess the diagnostic accuracy measures such as sensitivity and specificity of smartphone-based artificial intelligence (AI) approaches in the detection of diabetic retinopathy (DR). METHODS A literature search of the EMBASE and MEDLINE databases (up to March 2020) was conducted. Only studies using both smartphone-based cameras and AI software for image analysis were included. The main outcome measures were pooled sensitivity and specificity, diagnostic odds ratios and relative risk of smartphone-based AI approaches in detecting DR (of all types), and referable DR (RDR) (moderate nonproliferative retinopathy or worse and/or the presence of diabetic macular edema). RESULTS Smartphone-based AI has a pooled sensitivity of 89.5% (95% confidence interval [CI]: 82.3%-94.0%) and pooled specificity of 92.4% (95% CI: 86.4%-95.9%) in detecting DR. For referable disease, sensitivity is 97.9% (95% CI: 92.6%-99.4%), and the pooled specificity is 85.9% (95% CI: 76.5%-91.9%). The technology is better at correctly identifying referable retinopathy. CONCLUSIONS The smartphone-based AI programs demonstrate high diagnostic accuracy for the detection of DR and RDR and are potentially viable substitutes for conventional diabetic screening approaches. Further, high-quality randomized controlled trials are required to establish the effectiveness of this approach in different populations.
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Affiliation(s)
- Aadil Sheikh
- Department of Health Services Research and Policy, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Ahsan Bhatti
- Department of Ophthalmology, Singleton Hospital, Swansea, Wales, UK
| | - Oluwaseun Adeyemi
- Department of Public Health Sciences, University of North Carolina, Charlotte, USA
| | - Muhammad Raja
- Department of Ophthalmology, James Paget University Hospitals NHS Foundation Trust, Great Yarmouth, UK
| | - Ijaz Sheikh
- Department of Ophthalmology, East Surrey Hospital, Redhil, Surrey, UK
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Allan K, Oren N, Hutchison J, Martin D. In search of a Goldilocks zone for credible AI. Sci Rep 2021; 11:13687. [PMID: 34211064 PMCID: PMC8249604 DOI: 10.1038/s41598-021-93109-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 06/17/2021] [Indexed: 02/06/2023] Open
Abstract
If artificial intelligence (AI) is to help solve individual, societal and global problems, humans should neither underestimate nor overestimate its trustworthiness. Situated in-between these two extremes is an ideal 'Goldilocks' zone of credibility. But what will keep trust in this zone? We hypothesise that this role ultimately falls to the social cognition mechanisms which adaptively regulate conformity between humans. This novel hypothesis predicts that human-like functional biases in conformity should occur during interactions with AI. We examined multiple tests of this prediction using a collaborative remembering paradigm, where participants viewed household scenes for 30 s vs. 2 min, then saw 2-alternative forced-choice decisions about scene content originating either from AI- or human-sources. We manipulated the credibility of different sources (Experiment 1) and, from a single source, the estimated-likelihood (Experiment 2) and objective accuracy (Experiment 3) of specific decisions. As predicted, each manipulation produced functional biases for AI-sources mirroring those found for human-sources. Participants conformed more to higher credibility sources, and higher-likelihood or more objectively accurate decisions, becoming increasingly sensitive to source accuracy when their own capability was reduced. These findings support the hypothesised role of social cognition in regulating AI's influence, raising important implications and new directions for research on human-AI interaction.
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Affiliation(s)
- Kevin Allan
- School of Psychology, University of Aberdeen, Aberdeen, AB24 2UB, UK.
| | - Nir Oren
- School of Natural and Computing Sciences, University of Aberdeen, Aberdeen, AB24 2UB, UK
| | - Jacqui Hutchison
- School of Psychology, University of Aberdeen, Aberdeen, AB24 2UB, UK
| | - Douglas Martin
- School of Psychology, University of Aberdeen, Aberdeen, AB24 2UB, UK
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45
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D Ancona G, Murero M, Feickert S, Kaplan H, Öner A, Ortak J, Ince H. Implantation of an Innovative Intracardiac Microcomputer System for Web-Based Real-Time Monitoring of Heart Failure: Usability and Patients' Attitudes. JMIR Cardio 2021; 5:e21055. [PMID: 33881400 PMCID: PMC8411428 DOI: 10.2196/21055] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 11/05/2020] [Accepted: 03/05/2021] [Indexed: 12/20/2022] Open
Abstract
Background Heart failure (HF) management guided by the measurement of intracardiac and pulmonary pressure values obtained through innovative permanent intracardiac microsensors has been recently proposed as a valid strategy to individualize treatment and anticipate hemodynamic destabilization. These sensors have potential to reduce patient hospitalization rates and optimize quality of life. Objective The aim of this study was to evaluate the usability and patients’ attitudes toward a new permanent intracardiac device implanted to remotely monitor left intra-atrial pressures (V-LAP, Vectorious Medical Technologies, Tel Aviv, Israel) in patients with chronic HF. Methods The V-LAP system is a miniaturized sensor implanted percutaneously across the interatrial septum. The system communicates wirelessly with a “companion device” (a wearable belt) that is placed on the patient’s chest at the time of acquisition/transmission of left heart pressure measurements. At first follow-up after implantation, the patients and health care providers were asked to fill out a questionnaire on the usability of the system, ease in performing the various required tasks (data acquisition and transmission), and overall satisfaction. Replies to the questions were mainly given using a 5-point Likert scale (1: very poor, 2: poor, 3: average, 4: good, 5: excellent). Further patient follow-ups were performed at 3, 6, and 12 months. Results Use and acceptance of the first 14 patients receiving the V-LAP technology worldwide and related health care providers have been analyzed to date. No periprocedural morbidity/mortality was observed. Before discharge, a tailored educational session was performed after device implantation with the patients and their health care providers. At the first follow-up, the mean score for overall comfort in technology use was 3.7 (SD 1.2) with 93% (13/14) of patients succeeding in applying and operating the system independently. For health care providers, the mean score for overall ease and comfort in use of the technology was 4.2 (SD 0.8). No significant differences were found between the patients’ and health care providers’ replies to the questionnaires. There was a general trend for higher scores in patients’ usability reports at later follow-ups, in which the score related to overall comfort with using the technology increased from 3.0 (SD 1.4) to 4.0 (SD 0.7) (P=.40) and comfort with wearing and adjusting the measuring thoracic belt increased from 2.8 (SD 1.0) to 4.2 (SD 0.4) (P=.02). Conclusions Despite the gravity of their HF pathology and the complexity of their comorbid profile, patients are comfortable in using the V-LAP technology and, in the majority of cases, they can correctly and consistently acquire and transmit hemodynamic data. Although the overall patient/care provider satisfaction with the V-LAP system seems to be acceptable, improvements can be achieved after ameliorating the design of the measuring tools. Trial Registration ClincalTrials.gov NCT03775161; https://clinicaltrials.gov/ct2/show/NCT03775161
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Affiliation(s)
- Giuseppe D Ancona
- Department of Cardiology, Vivantes Hospital Am Urban, Berlin, Germany
| | | | | | - Hilmi Kaplan
- Department of Cardiology, Vivantes Hospital Am Urban, Berlin, Germany
| | - Alper Öner
- Department of Cardiology, Rostock University, Rostock, Germany
| | - Jasmin Ortak
- Department of Cardiology, Rostock University, Rostock, Germany
| | - Hueseyin Ince
- Department of Cardiology, Vivantes Hospital Am Urban, Berlin, Germany.,Department of Cardiology, Rostock University, Rostock, Germany
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46
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Deberneh HM, Kim I. Prediction of Type 2 Diabetes Based on Machine Learning Algorithm. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:3317. [PMID: 33806973 PMCID: PMC8004981 DOI: 10.3390/ijerph18063317] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/15/2021] [Accepted: 03/17/2021] [Indexed: 12/17/2022]
Abstract
Prediction of type 2 diabetes (T2D) occurrence allows a person at risk to take actions that can prevent onset or delay the progression of the disease. In this study, we developed a machine learning (ML) model to predict T2D occurrence in the following year (Y + 1) using variables in the current year (Y). The dataset for this study was collected at a private medical institute as electronic health records from 2013 to 2018. To construct the prediction model, key features were first selected using ANOVA tests, chi-squared tests, and recursive feature elimination methods. The resultant features were fasting plasma glucose (FPG), HbA1c, triglycerides, BMI, gamma-GTP, age, uric acid, sex, smoking, drinking, physical activity, and family history. We then employed logistic regression, random forest, support vector machine, XGBoost, and ensemble machine learning algorithms based on these variables to predict the outcome as normal (non-diabetic), prediabetes, or diabetes. Based on the experimental results, the performance of the prediction model proved to be reasonably good at forecasting the occurrence of T2D in the Korean population. The model can provide clinicians and patients with valuable predictive information on the likelihood of developing T2D. The cross-validation (CV) results showed that the ensemble models had a superior performance to that of the single models. The CV performance of the prediction models was improved by incorporating more medical history from the dataset.
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Affiliation(s)
| | - Intaek Kim
- Department of Information and Communications Engineering, Myongji University, 116 Myongji-ro, Yongin, Gyeonggi 17058, Korea;
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47
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Jabbar SI. Early prediction of diabetic type 2 based on fuzzy technique. Biomed Phys Eng Express 2021; 7. [DOI: 10.1088/2057-1976/abd688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 12/23/2020] [Indexed: 11/11/2022]
Abstract
Abstract
Intelligent analysis of present lifestyle may help to understand the development of the chronic diseases and the relationship of these diseases together. It is possible to reduce or prevent the development of these diseases. In this work, a novel intelligent method is introduced and applied for early detection of type 2 diabetic. Intelligent analysis depends mainly on evaluation life-threatening conditions (obesity, hypertension, smoking status, alcohol drinking status and low level of physical activities) to extract knowledge from linguistic variablesand design a new cognitive tool to assist in the prediction process.This method consists from three stages: in the first stage, data was collected from 100 healthy volunteers, which includes evaluations of life-threatening conditions. The second stage is implementation of fuzzy model for early prediction of type 2 diabetes. Predicted blood glucose values of proposal technique were compared with average fasting blood glucose values based on analysis of Bland-Altman plot. Furthermore, fuzzy system model presents superior results (accuracy = 81%, precision = 0.57% and recall = 0.83%).
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48
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Pham Q, Gamble A, Hearn J, Cafazzo JA. The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations. J Med Internet Res 2021; 23:e22320. [PMID: 33565982 PMCID: PMC7904401 DOI: 10.2196/22320] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 11/02/2020] [Accepted: 01/16/2021] [Indexed: 12/13/2022] Open
Abstract
There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities—foreign-born, immigrant, refugee, and culturally marginalized—are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled “Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.” Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants’ ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.
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Affiliation(s)
- Quynh Pham
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | - Anissa Gamble
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Jason Hearn
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Faculty of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Joseph A Cafazzo
- Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
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49
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Maeda-Gutiérrez V, Galván-Tejada CE, Cruz M, Valladares-Salgado A, Galván-Tejada JI, Gamboa-Rosales H, García-Hernández A, Luna-García H, Gonzalez-Curiel I, Martínez-Acuña M. Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach. Healthcare (Basel) 2021; 9:138. [PMID: 33535510 PMCID: PMC7912731 DOI: 10.3390/healthcare9020138] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 01/23/2021] [Accepted: 01/25/2021] [Indexed: 12/05/2022] Open
Abstract
The prevalence of diabetes mellitus is increasing worldwide, causing health and economic implications. One of the principal microvascular complications of type 2 diabetes is Distal Symmetric Polyneuropathy (DSPN), affecting 42.6% of the population in Mexico. Therefore, the purpose of this study was to find out the predictors of this complication. The dataset contained a total number of 140 subjects, including clinical and paraclinical features. A multivariate analysis was constructed using Boruta as a feature selection method and Random Forest as a classification algorithm applying the strategy of K-Folds Cross Validation and Leave One Out Cross Validation. Then, the models were evaluated through a statistical analysis based on sensitivity, specificity, area under the curve (AUC) and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model with this approach, presenting 67% of AUC with only three features as predictors. It is possible to conclude that this proposed methodology can classify patients with DSPN, obtaining a preliminary computer-aided diagnosis tool for the clinical area in helping to identify the diagnosis of DSPN.
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Affiliation(s)
- Valeria Maeda-Gutiérrez
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Carlos E. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Miguel Cruz
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI. Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Mexico City 06720, Mexico; (M.C.); (A.V.-S.)
| | - Adan Valladares-Salgado
- Unidad de Investigación Médica en Bioquímica, Hospital de Especialidades, Centro Médico Nacional Siglo XXI. Instituto Mexicano del Seguro Social, Av. Cuauhtémoc 330, Col. Doctores, Del. Cuauhtémoc, Mexico City 06720, Mexico; (M.C.); (A.V.-S.)
| | - Jorge I. Galván-Tejada
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Hamurabi Gamboa-Rosales
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Alejandra García-Hernández
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Huizilopoztli Luna-García
- Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, 98000 Zacatecas, Zac, Mexico; (V.M.-G.); (J.I.G.-T.); (H.G.-R.); (A.G.-H.); (H.L.-G.)
| | - Irma Gonzalez-Curiel
- Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (I.G.-C.); (M.M.-A.)
| | - Mónica Martínez-Acuña
- Unidad Académica de Ciencias Químicas, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Mexico; (I.G.-C.); (M.M.-A.)
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50
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Raj GM, Mathaiyan J. Precision medicine in diabetes-Finally some light at the end of the tunnel? Br J Clin Pharmacol 2020; 87:2625-2628. [PMID: 33284495 DOI: 10.1111/bcp.14674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 11/09/2020] [Accepted: 11/16/2020] [Indexed: 11/28/2022] Open
Affiliation(s)
- Gerard Marshall Raj
- Department of Pharmacology, All India Institute of Medical Sciences (AIIMS), Bibinagar, Hyderabad Metropolitan Region, Telangana, India
| | - Jayanthi Mathaiyan
- Department of Pharmacology, Jawaharlal Institute of Postgraduate Medical Education and Research (JIPMER), Puducherry, India
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