BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.

INTRODUCTION: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. METHODS: Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used a ConvNext model trained during 50 epochs using a weighted cross entropy loss to avoid overfitting, with 70% training (20% validation), and 30% testing subsets. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Saliency maps were calculated for interpretability. RESULTS: BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 (± 18.26) years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. A ConvNext V2 model was trained and evaluated BRSET in four prediction tasks: "binary diabetic retinopathy diagnosis (Normal vs Diabetic Retinopathy)" (AUC: 97, F1: 89); "3 class diabetic retinopathy diagnosis (Normal, Proliferative, Non-Proliferative)" (AUC: 97, F1: 82); "diabetes diagnosis" (AUC: 91, F1: 83); "sex classification" (AUC: 87, F1: 70). DISCUSSION: BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.

Image quality comparison of AirDoc portable retina camera versus eyer in a diabetic retinopathy screening program.

BACKGROUND: Diabetic retinopathy (DR) stands as the foremost cause of preventable blindness in adults. Despite efforts to expand DR screening coverage in the Brazilian public healthcare system, challenges persist due to various factors including social, medical, and financial constraints. Our objective was to evaluate the quality of images obtained with the AirDoc, a novel device, compared to Eyer portable camera which has already been clinically validated. METHODS: Images were captured by two portable retinal devices: AirDoc and Eyer. The included patients had their fundus images obtained in a screening program conducted in Blumenau, Santa Catarina. Two retina specialists independently assessed image's quality. A comparison was performed between both devices regarding image quality and the presence of artifacts. RESULTS: The analysis included 129 patients (mean age of 61 years), with 29 (43.28%) male and an average disease duration of 11.1 ± 8 years. In Ardoc, 21 (16.28%) images were classified as poor quality, with 88 (68%) presenting artifacts; in Eyer, 4 (3.1%) images were classified as poor quality, with 94 (72.87%) presenting artifacts. CONCLUSIONS: Although both Eyer and AirDoc devices show potential as screening tools, the AirDoc images displayed higher rates of ungradable and low-quality images, that may directly affect the DR and DME grading. We must acknowledge the limitations of our study, including the relatively small sample size. Therefore, the interpretations of our analyses should be approached with caution, and further investigations with larger patient cohorts are warranted to validate our findings.

Retrospective case series of high-density silicone oil (Oxane HD) in severe proliferative vitreorretinal retinal detachment patients.

BACKGROUND: Describe complications and clinical outcomes of heavy silicone oil (HSO) Oxane HD® use as an alternative to overcome the challenges of performing vitrectomy to treat tractional and rhegmatogenous retinal detachments with proliferative vitreoretinopathy (PVR). METHODS: A retrospective, observational study was performed on patients from one center from August 2014 to Aug 2023. It was included patients who underwent surgery using HSO Oxane HD® to treat rhegmatogenous retinal detachment with PVR or mixed tractional and rhegmatogenous diabetic retinal detachment. Severely ill patients who could not attend to follow up were excluded. The primary outcome was successful retinal attachment at first postoperative month. A descriptive analysis was performed. RESULTS: Among the 31 patients, 29 (93.5%) underwent surgeries due to rhegmatogenous retinal detachment and two (6.5%) for diabetic retinal detachment. The primary anatomic success was achieved in 27 (87.1%) patients. At the final visit, 17 (56.6%) had vision better than 20/400 (range, 20/30 to light perception). The vision was stable or improved in 22 (76.8%) patients at the end of follow-up. Nineteen (61.3%) patients required hypotensive eye drops after HSO use and twelve (38.7%) still required hypotensive eye drops at the final follow-up; three (9.7%) patients required additional glaucoma surgeries. CONCLUSIONS: HSO is safe and useful for complex retinal detachments cases specially with inferior tears and PVR. Ocular hypertension is frequent and usually clinically controlled with hypotensive eyedrops. Close postoperatively follow-up is advised due to the ocular complications, particularly elevated intraocular pressure and emulsification.

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.

Introduction: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. Methods: Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used Dino V2 Base for feature extraction, with 70% training and 30% testing subsets. Support Vector Machines (SVM) and Logistic Regression (LR) were employed with weighted training. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Results: BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. Models were trained on BRSET in three prediction tasks: "diabetes diagnosis"; "sex classification"; and "diabetic retinopathy diagnosis". Discussion: BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.

An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience / Predição do influxo de atendimentos e casos de trauma em um serviço de urgência oftalmológico brasileiro realizado por um oftalmologista sem experiência em programação

ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients' volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists' experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

RESUMO Objetivo: Esse estudo tem como objetivo criar um modelo de Machine Learning por um oftalmologista sem experiência em programação utilizando auto Machine Learning predizendo influxo de pacientes em serviço de emergência e casos de trauma. Métodos: Um dataset de 366,610 visitas em Hospital Universitário da Universidade Federal de São Paulo de 01 de janeiro de 2014 até 31 de dezembro de 2019 foi incluído no treinamento do modelo, incluindo visitas/dia e código internacional de doenças. O treinamento e predição foram realizados com o Amazon Forecast por dois oftalmologistas sem experiência com programação. Resultados: O período de previsão estimou um volume de 206,37 pacientes/dia em p90, 180,75 em p50, 140,35 em p10 e média de 7,42 casos de trauma/dia em p90, 3,99 em p50 e 0,56 em p10. Janeiro de 2020 teve um total de 6.604 pacientes e média de 206,37 pacientes/dia, 13,5% menos do que a predição em p50. O período teve um total de 199 casos de trauma e média de 6,21 casos/dia, 55,77% mais casos do que a predição em p50. Conclusão: O desenvolvimento de modelos era restrito a cientistas de dados com experiencia em programação, porém a transferência de ensino com a tecnologia de auto Machine Learning permite o desenvolvimento de algoritmos por qualquer pessoa sem experiencia em programação. Esse estudo mostra um modelo com valores preditos próximos ao que ocorreram em janeiro de 2020. Fatores que podem ter influenciados no resultado foram feriados e tamanho do banco de dados. Esse é o primeiro estudo que aplicada auto Machine Learning em predição de visitas hospitalares com resultados próximos aos que ocorreram.

Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review.

BACKGROUND: Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study's characteristics, fairness and generalisability efforts. METHODS: Our review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones. RESULTS: All the article's authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients' sex was described, but none applied a bias control in their models. CONCLUSION: The reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments.

Ophthalmology emergency department visits in a Brazilian tertiary hospital over the last 11 years: data analysis.

PURPOSE: This study aimed to describe the visits profile to Hospital São Paulo's ophthalmology emergency department, a 24-hour public open-access tertiary-care service in São Paulo, Brazil, that belongs to Federal University of São Paulo, over the last 11 years. METHODS: A cross-sectional retrospective study was conducted, including all patients (n=634,726) admitted to the ophthalmology emergency department of Hospital São Paulo between January 2009 and December 2019. RESULTS: From 2009 to 2019, the number of patients' presentations increased to 39.2%, with considerable visits variation across the period. The median age was 38 ± 20.4 years. Males represented 53.3%, and single-visit patients represented 53.1%. A total of 79.5% of patients' presentations occurred from 7 am to 5 pm, and 80.8% of patients' presentations occurred during regular weekdays. The most frequent diagnoses were conjunctivitis, blepharitis, keratitis, hordeolum/chalazion, and corneal foreign body. CONCLUSIONS: Over the study period, presentations significantly increased in number, with nonurgent visits predominance, and a low number of single-visit patients. Our results demonstrate the ophthalmic visits profile and can lead to changes in the public health system to improve the quality of care and ophthalmology emergency access in São Paulo city.

Ophthalmology emergency department visits in a Brazilian tertiary hospital over the last 11 years: data analysis / Análise dos dados de atendimentos de um serviço de emergência oftalmológica em um hospital terciário brasileiro nos últimos 11 anos

ABSTRACT Purpose: This study aimed to describe the visits profile to Hospital São Paulo's ophthalmology emergency department, a 24-hour public open-access tertiary-care service in São Paulo, Brazil, that belongs to Federal University of São Paulo, over the last 11 years. Methods: A cross-sectional retrospective study was conducted, including all patients (n=634,726) admitted to the ophthalmology emergency department of Hospital São Paulo between January 2009 and December 2019. Results: From 2009 to 2019, the number of patients' presentations increased to 39.2%, with considerable visits variation across the period. The median age was 38 ± 20.4 years. Males represented 53.3%, and single-visit patients represented 53.1%. A total of 79.5% of patients' presentations occurred from 7 am to 5 pm, and 80.8% of patients' presentations occurred during regular weekdays. The most frequent diagnoses were conjunctivitis, blepharitis, keratitis, hordeolum/chalazion, and corneal foreign body. Conclusions: Over the study period, presentations significantly increased in number, with nonurgent visits predominance, and a low number of single-visit patients. Our results demonstrate the ophthalmic visits profile and can lead to changes in the public health system to improve the quality of care and ophthalmology emergency access in São Paulo city.

RESUMO Objetivos: O objetivo do estudo é avaliar o perfil das visitas ao Pronto-Socorro de Oftalmologia (PS) do Hospital São Paulo, serviço público de atendimento terciário aberto 24 horas em São Paulo - Brasil, pertencente à Universidade Federal de São Paulo, nos últimos 11 anos. Métodos: Foi realizado um estudo transversal retrospectivo, com base em todos os pacientes (n=634.726) admitidos no pronto-socorro de oftalmologia do Hospital São Paulo entre janeiro de 2009 e dezembro de 2019. Resultados: De 2009 a 2019, houve um aumento no influxo de 39,2% com importante variação nos atendimentos ao longo dos anos, a mediana de idade foi de 38 ± 20,4 anos, o sexo masculino representou 53,3% e os pacientes únicos representaram 53,1%. Verificou-se que 79,5% das visitas ocorreram das 7h às 17h e 80,8% nos dias da semana. Os diagnósticos mais frequentes foram conjuntivite aguda seguida de blefarite, ceratite, hordéolo / calázio e corpo estranho corneano. Conclusão: Ao longo do período de análise do estudo, houve importante aumento nas apresentações, com predominância de atendimentos não urgentes e baixa proporção de pacientes com uma única visita. Nossos resultados evidenciam o perfil das consultas oftalmológicas, podendo gerar mudanças no sistema público de saúde visando a melhoria da qualidade do atendimento e acesso às emergências oftalmológicas na cidade de São Paulo.

Impact of SARS-CoV-2 pandemic on ophthalmological emergency visits: 1 year of experience / Impacto da pandemia de SARS-CoV-2 nas visitas ao serviço oftalmológico de emergência: experiência de 1 ano

ABSTRACT Purpose: The COVID-19 pandemic began in March 2020 and changed the healthcare system overall. The pandemic led to resource allocation changes, overloading of intensive care units, apprehensiveness of patients to seek medical care not related to COVID-19, and an abrupt reduction in all nonurgent consultations and surgeries. This study evaluated the impact on an ophthalmological emergency room for one year by assessing the correlation between societal lockdown phases and COVID-19 mortality. Methods: An observational, retrospective study was conducted that included all patients admitted to the Ophthalmology Emergency Department between January 1, 2019, and March 28, 2021. The visits were classified into prepandemic and pandemic groups that were then compared. Results: In the prepandemic period, the hospital registered a total of 71,485 visits with a mean of 194.78 ± 49.74 daily visits. In the pandemic group, there was a total of 41,791 visits with a mean of 114.18 ± 43.12 daily visits, which was a 41.4% decrease. A significant decrea­se (16.4 p<0.001) was observed in the prevalence of acute conjunctivitis, and a significant increase (6.4%; p<0.01) was observed in the prevalence of corneal foreign body disorders. A negative correlation was identified between the COVID-19 death rate and the ophthalmological inflow rates. Conclusion: This one-year analysis showed a reduction of 41.4% in emergency department visits and a significant decrease in infectious conditions. A change in hygiene habits and social distancing could explain this reduction, and the increased prevalence of trauma consultations highlighted the need for preventive and educative measures during these types of restrictive periods.

RESUMO Objetivos: A pandemia de COVID-19 foi iniciada em março de 2020 e mudou o sistema de saúde. Mudanças na alocação de recursos, sobrecarga de unidades de terapia intensiva, apreensão dos pacientes em procurar atendimento médico não relacionado ao COVID-19 e redução abrupta de todas as consultas e cirurgias não urgentes. Este estudo avalia o impacto em um pronto-socorro oftalmológico após 1 ano de pandemia, avaliando a correlação entre as fases de lockdown, a mortalidade do COVID-19 e as visitas ao pronto-socorro. Métodos: Estudo observacional retrospectivo que incluiu todos os pacientes admitidos no serviço de emergência oftalmológica do Hospital São Paulo, vinculado a UNIFESP/EPM, entre 1º de janeiro de 2019 e 28 de março de 2021. As visitas foram classificadas e comparadas em um grupo pré-pandemia e pandemia. Resultados: No período pré-pandemia, o hospital registrou um total de 71.485 atendimentos com média de 194,78 ± 49,74 atendimentos diários, e no grupo pandemia, um total de 41.791 com média de 114,18 ± 43,12 atendimentos diários, redução de 41,4%. Uma diminuição significativa de 16,4% (p<0,001) foi observada na prevalência de conjuntivite aguda e um aumento significativo de 6,4% (p<0,01) na prevalência de corpo estranho da córnea. Foi identificada uma correlação negativa entre a taxa de mortalidade do COVID-19 e as taxas de visita ao pronto-socorro. Conclusão: Esta análise de um ano mostrou uma redução de 41,4% nas visitas ao pronto-socorro, e uma diminuição significativa nas conjuntivites agudas. A mudança nos hábitos de higiene e o distanciamento social poderiam explicar essa redução, e o aumento da prevalência de traumas corneanos. Achados destacam a necessidade de medidas preventivas e educativas durante os períodos restritivos.

An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience.

PURPOSE: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients' volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. METHODS: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. RESULTS: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. CONCLUSIONS: The development of models was previously restricted to data scientists' experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

Impact of SARS-CoV-2 pandemic on ophthalmological emergency visits: 1 year of experience.

PURPOSE: The COVID-19 pandemic began in March 2020 and changed the healthcare system overall. The pandemic led to resource allocation changes, overloading of intensive care units, apprehensiveness of patients to seek medical care not related to COVID-19, and an abrupt reduction in all nonurgent consultations and surgeries. This study evaluated the impact on an ophthalmological emergency room for one year by assessing the correlation between societal lockdown phases and COVID-19 mortality. METHODS: An observational, retrospective study was conducted that included all patients admitted to the Ophthalmology Emergency Department between January 1, 2019, and March 28, 2021. The visits were classified into prepandemic and pandemic groups that were then compared. RESULTS: In the prepandemic period, the hospital registered a total of 71,485 visits with a mean of 194.78 ± 49.74 daily visits. In the pandemic group, there was a total of 41,791 visits with a mean of 114.18 ± 43.12 daily visits, which was a 41.4% decrease. A significant decrea-se (16.4 p<0.001) was observed in the prevalence of acute conjunctivitis, and a significant increase (6.4%; p<0.01) was observed in the prevalence of corneal foreign body disorders. A negative correlation was identified between the COVID-19 death rate and the ophthalmological inflow rates. CONCLUSION: This one-year analysis showed a reduction of 41.4% in emergency department visits and a significant decrease in infectious conditions. A change in hygiene habits and social distancing could explain this reduction, and the increased prevalence of trauma consultations highlighted the need for preventive and educative measures during these types of restrictive periods.

Diabetic retinopathy classification for supervised machine learning algorithms.

BACKGROUND: Artificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness worldwide, and artificial intelligence technology provides precocious diagnosis, monitoring, and guide treatment. High-quality exams are fundamental in supervised artificial intelligence algorithms, but the lack of ground truth standards in retinal exams datasets is a problem. MAIN BODY: In this article, ETDRS, NHS, ICDR, SDGS diabetic retinopathy grading, and manual annotation are described and compared in publicly available datasets. The various DR labeling systems generate a fundamental problem for AI datasets. Possible solutions are standardization of DR classification and direct retinal-finding identifications. CONCLUSION: Reliable labeling methods also need to be considered in datasets with more trustworthy labeling.

Iniciação científica na graduação: experiência da Liga da Mama da Universidade Federal de Goiás / Scientific activities during undergraduate education: the experience of Breast League of the Federal University of Goiás

Objetivo: Descrever a experiência da Liga da Mama da Universidade Federal de Goiás no processo de iniciação científica na graduação e descrever a produção científica entre 2008 e 2015, com a participação direta dos acadêmicos dessa liga. Métodos: Trata-se de um estudo descritivo e retrospectivo que analisou a produção científica dos acadêmicos da Liga da Mama da Universidade Federal de Goiás entre 2008 e 2015. As seguintes variáveis foram analisadas: apresentações orais ou em pôster, em congressos científicos e estudantis e publicações em periódicos nacionais e internacionais. Resultados: Entre 2008 e 2015, aproximadamente 11 0 alunos de graduação participaram de algum projeto científico vinculado à Liga da Mama da Universidade Federal de Goiás. Nesse período, os acadêmicos da liga apresentaram 233 trabalhos em congressos científicos e estudantis, dos quais 196 (84,1%) foram em formato de pôster e 37 (15,9%) na forma de apresentação oral. Ainda, observou-se a publicação de 29 artigos científicos com participação direta de 1 ou mais discentes de graduação vinculados ao projeto. Conclusão: Na Universidade Federal de Goiás, os projetos de iniciação científica da Liga da Mama constituem uma oportunidade de desenvolvimento acadêmico para os discentes de graduação. As ligas acadêmicas, quando orientadas de forma adequada, podem contribuir para a produção científica nacional e a consolidação do currículo acadêmico.

Objective: To describe the experience of Breast League of the Federal University of Goiás, Brazil, in scientific research process in undergraduate and describe the scientific production between 2008 and 2015, with the direct participation of the students. Methods: This is a descriptive and retrospective study that analyzed the scientific production of Breast League of the Federal University of Goiás between 2008 and 2015. The following variables were analyzed: oral or poster presentations, scientific and student conferences and papers in national and international journals. Results: Between 2008 and 2015, about 110 undergraduate students participated in a scientific project linked to the Breast League of the Federal University of Goiás. During this period, the students of the league had presented 233 works in scientific conferences, of which 196 (84.1%) were in poster format and 37 (15.9%) were oral presentations. There was the publication of 29 scientific papers with direct participation of 1 or more undergraduate students linked to the project. Conclusion: At the Federal University of Goiás, the scientific projects of the Breast League are an academic development opportunity for undergraduate students. The academic leagues, when directed properly, can contribute to the national scientific production and the consolidation of the academic curriculum.