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Dos Reis MA, Künas CA, da Silva Araújo T, Schneiders J, de Azevedo PB, Nakayama LF, Rados DRV, Umpierre RN, Berwanger O, Lavinsky D, Malerbi FK, Navaux POA, Schaan BD. Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population. Diabetol Metab Syndr 2024; 16:209. [PMID: 39210394 PMCID: PMC11360296 DOI: 10.1186/s13098-024-01447-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 08/12/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND In healthcare systems in general, access to diabetic retinopathy (DR) screening is limited. Artificial intelligence has the potential to increase care delivery. Therefore, we trained and evaluated the diagnostic accuracy of a machine learning algorithm for automated detection of DR. METHODS We included color fundus photographs from individuals from 4 databases (primary and specialized care settings), excluding uninterpretable images. The datasets consist of images from Brazilian patients, which differs from previous work. This modification allows for a more tailored application of the model to Brazilian patients, ensuring that the nuances and characteristics of this specific population are adequately captured. The sample was fractionated in training (70%) and testing (30%) samples. A convolutional neural network was trained for image classification. The reference test was the combined decision from three ophthalmologists. The sensitivity, specificity, and area under the ROC curve of the algorithm for detecting referable DR (moderate non-proliferative DR; severe non-proliferative DR; proliferative DR and/or clinically significant macular edema) were estimated. RESULTS A total of 15,816 images (4590 patients) were included. The overall prevalence of any degree of DR was 26.5%. Compared with human evaluators (manual method of diagnosing DR performed by an ophthalmologist), the deep learning algorithm achieved an area under the ROC curve of 0.98 (95% CI 0.97-0.98), with a specificity of 94.6% (95% CI 93.8-95.3) and a sensitivity of 93.5% (95% CI 92.2-94.9) at the point of greatest efficiency to detect referable DR. CONCLUSIONS A large database showed that this deep learning algorithm was accurate in detecting referable DR. This finding aids to universal healthcare systems like Brazil, optimizing screening processes and can serve as a tool for improving DR screening, making it more agile and expanding care access.
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Affiliation(s)
- Mateus A Dos Reis
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil.
- Universidade Feevale, Novo Hamburgo, RS, Brazil.
| | - Cristiano A Künas
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Thiago da Silva Araújo
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Josiane Schneiders
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | | | - Luis F Nakayama
- Department of Ophthalmology and Visual Sciences, Universidade Federal de São Paulo, São Paulo, Brazil
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dimitris R V Rados
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- TelessaúdeRS Project, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Roberto N Umpierre
- TelessaúdeRS Project, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Department of Social Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Otávio Berwanger
- The George Institute for Global Health, Imperial College London, London, UK
| | - Daniel Lavinsky
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Department of Ophthalmology, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Fernando K Malerbi
- Department of Ophthalmology and Visual Sciences, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Philippe O A Navaux
- Institute of Informatics, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Beatriz D Schaan
- Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Institute for Health Technology Assessment (IATS) - CNPq, Porto Alegre, Brazil
- Endocrinology Unit, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS, Brazil
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Oliveira GMMD, Brant LCC, Polanczyk CA, Malta DC, Biolo A, Nascimento BR, Souza MDFMD, Lorenzo ARD, Fagundes Júnior AADP, Schaan BD, Silva CGDSE, Castilho FMD, Cesena FHY, Soares GP, Xavier Junior GF, Barreto Filho JAS, Passaglia LG, Pinto Filho MM, Machline-Carrion MJ, Bittencourt MS, Pontes Neto OM, Villela PB, Teixeira RA, Stein R, Sampaio RO, Gaziano TA, Perel P, Roth GA, Ribeiro ALP. Cardiovascular Statistics - Brazil 2023. Arq Bras Cardiol 2024; 121:e20240079. [PMID: 38896747 PMCID: PMC11185831 DOI: 10.36660/abc.20240079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024] Open
Affiliation(s)
- Gláucia Maria Moraes de Oliveira
- Instituto do Coração Edson Saad da Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ - Brasil
- Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ - Brasil
| | - Luisa Campos Caldeira Brant
- Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
- Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
| | - Carisi Anne Polanczyk
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS - Brasil
- Hospital Moinhos de Vento, Porto Alegre, RS - Brasil
- Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS - Brasil
| | | | - Andreia Biolo
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS - Brasil
- Hospital Moinhos de Vento, Porto Alegre, RS - Brasil
- Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS - Brasil
| | - Bruno Ramos Nascimento
- Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
- Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
- Hospital Madre Teresa, Belo Horizonte, MG - Brasil
| | | | - Andrea Rocha De Lorenzo
- Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ - Brasil
- Instituto Nacional de Cardiologia, Rio de Janeiro, RJ - Brasil
| | | | - Beatriz D Schaan
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS - Brasil
- Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS - Brasil
| | - Christina Grüne de Souza E Silva
- Instituto do Coração Edson Saad da Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ - Brasil
- Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ - Brasil
| | - Fábio Morato de Castilho
- Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
- Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
| | | | - Gabriel Porto Soares
- Instituto do Coração Edson Saad da Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ - Brasil
- Curso de Medicina da Universidade de Vassouras, Vassouras, RJ - Brasil
| | | | | | - Luiz Guilherme Passaglia
- Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
- Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
| | - Marcelo Martins Pinto Filho
- Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
- Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
| | | | | | - Octavio M Pontes Neto
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (USP), São Paulo, SP - Brasil
| | - Paolo Blanco Villela
- Instituto do Coração Edson Saad da Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ - Brasil
- Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ - Brasil
| | | | - Ricardo Stein
- Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, RS - Brasil
| | - Roney Orismar Sampaio
- Faculdade de Medicina da Universidade de São Paulo (USP), São Paulo, SP - Brasil
- Instituto do Coração (Incor) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HCFMUSP), São Paulo, SP - Brasil
| | - Thomaz A Gaziano
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston - EUA
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston - EUA
| | - Pablo Perel
- World Heart Federation, Geneva - Suíça
- Centre for Global Chronic Conditions, London School of Hygiene & Tropical Medicine, Londres - Inglaterra
| | - Gregory A Roth
- Division of Cardiology, Department of Medicine, University of Washington, Washington - EUA
| | - Antonio Luiz Pinho Ribeiro
- Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
- Hospital das Clínicas da Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, MG - Brasil
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Schneiders J, Telo GH, Lavinsky D, Dos Reis MA, Corrêa BG, Schaan BD. Organizational intervention to improve access to retinopathy screening for patients with diabetes mellitus: health care service improvement project in a tertiary public hospital. Prim Care Diabetes 2023:S1751-9918(23)00101-8. [PMID: 37328386 DOI: 10.1016/j.pcd.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 03/27/2023] [Accepted: 05/28/2023] [Indexed: 06/18/2023]
Abstract
INTRODUCTION The aim of this study was to improve the access to diabetic retinopathy (DR) screening with retinographies in a tertiary care center through a process conducted by an interdisciplinary group driven by a nurse. MATERIALS AND METHODS This is a quality improvement study that evaluated the flow for DR screening conducted by an interdisciplinary group using the Plan-Do-Study-Act methodology. As a result measure, we assessed the quantity of retinographies performed after the project implementation, the proportion of patients who presented abnormal retinographies, and the proportion of patients referred to a specialist. RESULTS The new patient screening flow and the reinforcement of available human resources led to an increase in the number of retinographies performed and screened patients. A total of 1184 retinographies were conducted, and it was possible to observe DR alterations in 37.8 % patients, of which only 6 % needed to be referred to the DR reference center. CONCLUSIONS This study showed a significant increase in the number of retinographies performed. The Plan-Do-Study-Act methodology was an important tool to improve the flow of patient access to fundus images, helping to consistently and continuously improve these processes.
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Affiliation(s)
| | - Gabriela H Telo
- Graduate Program, School of Medicine, Pontifícia Universidade Católica do Rio Grande do Sul, Internal Medicine Department, Hospital São Lucas, Porto Alegre, Brazil
| | - Daniel Lavinsky
- Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | | | | | - Beatriz D Schaan
- Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
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Oliveira GMMD, Brant LCC, Polanczyk CA, Malta DC, Biolo A, Nascimento BR, Souza MDFMD, Lorenzo ARD, Fagundes AADP, Schaan BD, Castilho FMD, Cesena FHY, Soares GP, Xavier GF, Barreto JAS, Passaglia LG, Pinto MM, Machline-Carrion MJ, Bittencourt MS, Pontes OM, Villela PB, Teixeira RA, Sampaio RO, Gaziano TA, Perel P, Roth GA, Ribeiro ALP. Estatística Cardiovascular – Brasil 2021. Arq Bras Cardiol 2022; 118:115-373. [PMID: 35195219 PMCID: PMC8959063 DOI: 10.36660/abc.20211012] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 11/10/2021] [Indexed: 02/07/2023] Open
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Fenalti Salla R, de David J, Schneider L, Tschiedel B, Teló GH, Schaan BD. Predictors of traffic events due to hypoglycemia in adults with type 1 diabetes: A Brazilian prospective cohort study. Diabetes Res Clin Pract 2021; 178:108954. [PMID: 34273455 DOI: 10.1016/j.diabres.2021.108954] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 06/29/2021] [Accepted: 07/08/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Individuals with type 1 diabetes (T1D) are exposed to an elevated risk of automobile accidents especially because of hypoglycemia that impairs physiological and defense responses. OBJECTIVES To assess local risk factors for traffic events in T1D adult Brazilian patients. METHODS This is a prospective study and 12-month follow-up to assess predictors for traffic events on a cohort of drivers with T1D (n = 168) in Brazil. The inclusion criteria for participants were Brazilian nationality, age ≥ 18 years-old, diagnosis of T1D for more than one year, driving license B, C or D categories (four-wheel vehicles), driving three-times per week or more, and checking blood glucose twice-daily or more. The primary outcome was hypoglycemia driving mishaps assessed by a seven-query questionnaire about the past 30 days. Secondary outcomes included driving mishaps not related to hypoglycemia. Statistical analysis was performed through Poisson regression models with robust variance estimarion, in which the measure of association is the relative risk. RESULTS A total of 109 participants completed the 12-month follow-up. Most of them were men (66%) and 37 ± 11 years-old, and had a mean HbA1c of 8.2% (66 mmol/mol). In the follow up, the incidence of traffic events was high (70.6%); however, only a minority was attributed to hypoglycemia as the cause of the reported event (19.3%). The best predictors for new traffic events due to hypoglycemia were those related to driving characteristics. The best of them was a history of episodes of hypoglycemia while driving [RR 3.40 (1.22-9.43); p < 0.05]. CONCLUSIONS We found that previous episodes of hypoglycemia while driving significantly increase the risk of new traffic events and are the best predictor for it. This highlights the need to assess the risks of traffic accidents especially in people who have had experienced episodes of hypoglycemia while driving.
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Affiliation(s)
- Rafaela Fenalti Salla
- Programa de Pós-Graduação em Endocrinologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
| | - Julia de David
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Larissa Schneider
- Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | | | - Gabriela H Teló
- Escola de Medicina, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Beatriz D Schaan
- Programa de Pós-Graduação em Endocrinologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Faculdade de Medicina, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, Brazil
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Ramalho A, Castro P, Lobo M, Souza J, Santos P, Freitas A. Integrated quality assessment for diabetes care in Portuguese primary health care using prevention quality indicators. Prim Care Diabetes 2021; 15:507-512. [PMID: 33441264 DOI: 10.1016/j.pcd.2021.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/02/2021] [Accepted: 01/04/2021] [Indexed: 01/25/2023]
Abstract
AIMS This study evaluates the prevention quality indicators (PQI) for Diabetes Mellitus (DM) in Portugal using contemporary data and explores their variability according to Primary Health Care (PHC) quality indicators. METHODS We conducted a retrospective observational analysis of secondary data comprising Portuguese PHC indicators by health centres group (ACES) and the National Hospital Morbidity Database. We calculated and analysed age-sex-adjusted rates for each PQI. Worse-performing ACES were identified using the 2017 median PQI values as an assessment cut-off. A multivariate logistic analysis was carried to find variables associated with the likelihood of being a worse-performing ACES for the biennium. RESULTS The median values of the indicator PQI93 - Prevention Quality Diabetes Composite were 79 and 65.2 hospitalizations per 100 000 pop, in 2016 and 2017 respectively. Diabetes long term complications (PQI 03) accounted for most of the hospitalizations. The quality indicator in PHC with greater influence on PQI93 was the proportion of DM patients with <65 years with test results for HbA1c < = 6.5%. CONCLUSIONS This study shows that some PHC quality indicators are closely related to DM care, and so their monitoring is of high importance. Diabetes long term complications (PQI 03) demand greater attention from PHC professionals.
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Affiliation(s)
- A Ramalho
- MEDCIDS - Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; CINTESIS - Centre for Health Technology and Services Research, Porto, Portugal; ACHE - American College of Healthcare Executives, Chicago, IL, USA.
| | - P Castro
- CINTESIS - Centre for Health Technology and Services Research, Porto, Portugal; USF Camélias, ACeS Gaia - Grande Porto VII (ARS Norte) - Vila Nova de Gaia, Portugal
| | - M Lobo
- MEDCIDS - Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; CINTESIS - Centre for Health Technology and Services Research, Porto, Portugal
| | - J Souza
- CINTESIS - Centre for Health Technology and Services Research, Porto, Portugal
| | - P Santos
- MEDCIDS - Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; CINTESIS - Centre for Health Technology and Services Research, Porto, Portugal
| | - A Freitas
- MEDCIDS - Department of Community Medicine, Information and Health Decision Sciences, Faculty of Medicine, University of Porto, Porto, Portugal; CINTESIS - Centre for Health Technology and Services Research, Porto, Portugal
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