Diagnosis Of Retinopathy Of Prematurity Research Articles

Multinational External Validation of Autonomous Retinopathy of Prematurity Screening

Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis. Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.

OVERVIEW OF PATIENTS SCREENED FOR AND DIAGNOSED WITH ROP IN PEDIATRIC OPHTHALMOLOGY OUTPATIENT CLINIC BEFORE, DURING, AND AFTER THE COVID-19 PANDEMIC

Introduction & Objectives : The COVID-19 pandemic has profoundly impacted healthcare systems worldwide, leading to significant changes in the delivery of medical services. Non-urgent ophthalmology visits were canceled. However, premature infants at risk of retinopathy of prematurity (ROP) require timely and in-person care to prevent vision loss. This study aims to report on the high-risk babies screened and diagnosed with ROP before, during, and after the COVID-19 pandemic
 Methods : This is a descriptive study conducted on premature infants who visited the pediatric ophthalmology outpatient clinic at Cipto Mangunkusumo Hospital Jakarta between January 2019 and December 2022. Data was collected using medical records and presented using tables and charts
 Results : A total of 368 babies were screened for ROP in 2019, and 75% were diagnosed with ROP. During the lockdown in 2020, the number of babies screened for ROP decreased to 286, and the incidence of ROP was 38.5%. In 2021 and 2022, ROP screening increased to 458 and 515 babies per year, respectively. After the COVID-19 pandemic subsided, ROP screenings increased significantly from 2020 to 2021. The number of babies screened in 2021 and 2022 was similar, but the incidence of ROP decreased from 45.4% in 2021 to 20.3% in 2022
 Conclusion : This study highlights the impact of the COVID-19 pandemic on ROP screening and diagnosis in a pediatric ophthalmology outpatient clinic. After the COVID-19 pandemic, the number of babies screened for ROP increased due to the end of the lockdown

Automatic zoning for retinopathy of prematurity with a key area location system.

Retinopathy of prematurity (ROP) usually occurs in premature or low birth weight infants and has been an important cause of childhood blindness worldwide. Diagnosis and treatment of ROP are mainly based on stage, zone and disease, where the zone is more important than the stage for serious ROP. However, due to the great subjectivity and difference of ophthalmologists in the diagnosis of ROP zoning, it is challenging to achieve accurate and objective ROP zoning diagnosis. To address it, we propose a new key area location (KAL) system to achieve automatic and objective ROP zoning based on its definition, which consists of a key point location network and an object detection network. Firstly, to achieve the balance between real-time and high-accuracy, a lightweight residual heatmap network (LRH-Net) is designed to achieve the location of the optic disc (OD) and macular center, which transforms the location problem into a pixel-level regression problem based on the heatmap regression method and maximum likelihood estimation theory. In addition, to meet the needs of clinical accuracy and real-time detection, we use the one-stage object detection framework Yolov3 to achieve ROP lesion location. Finally, the experimental results have demonstrated that the proposed KAL system has achieved better performance on key point location (6.13 and 17.03 pixels error for OD and macular center location) and ROP lesion location (93.05% for AP50), and the ROP zoning results based on it have good consistency with the results manually labeled by clinicians, which can support clinical decision-making and help ophthalmologists correctly interpret ROP zoning, reducing subjective differences of diagnosis and increasing the interpretability of zoning results.

Image-free recognition of moderate ROP from mild with machine learning algorithm on plasma Raman spectrum

The retinopathy of prematurity (ROP) can cause serious clinical consequences and, fortunately, it is remediable while the time window for treatment is relatively narrow. Therefore, it is urgent to screen all premature infants and diagnose ROP degree timely, which has become a large workload for pediatric ophthalmologists. We developed a retinal image-free procedure using small amount of blood samples based on the plasma Raman spectrum with the machine learning model to automatically classify ROP cases before medical intervention was performed. Statistical differences in infrared Raman spectra of plasma samples were found among the control, mild (ZIIIS1), moderate (ZIIIS2 & ZIIS1), and advanced (ZIIS2) ROP groups. With the different wave points of Raman spectra as the inputs, the outputs of our support vector machine showed that the area under the curves in the receiver operating characteristic (AUC) were 0.763 for the pair comparisons of the control with the mild groups, 0.821 between moderate and advanced groups (ZIIS2), while more than 90% in comparisons of the other four pairs: control vs. moderate (0.981), control vs. advanced (0.963), mild vs. moderate (0.936), and mild vs. advanced (0.953), respectively. Our study could advance principally the ROP diagnosis in two dimensions: the moderate ROPs have been classified remarkably from the mild ones, which leaves more time for the medical treatments, and the procedure of Raman spectrum with a machine learning model based on blood samples can be conveniently promoted to those hospitals lacking of the pediatric ophthalmologists with experience in reading retinal images.

Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning.

Background: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis. Methods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs. Results: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment. Conclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed.

Vascular imaging findings in retinopathy of prematurity.

Retinopathy of prematurity (ROP) is a vascular disease among preterm infants involving incomplete or abnormal retinal vascularization and is a leading cause of preventable blindness globally. Measurements of ocular blood flow originating from a variety of imaging modalities, including colour Doppler imaging (CDI), fluorescein angiography (FA) and ocular coherence tomography angiography (OCTA), have been associated with changes in ROP patients. Herein, we discuss and summarize the relevant current literature on vascular imaging and ROP reviewed through December 2022. Differences in vascular imaging parameters between ROP patients and healthy controls are reviewed and summarized. The available data identify significantly increased peak systolic velocity (PSV) in the central retinal artery and ophthalmic artery as measured by CDI, increased vascular tortuosity as measured by FA, smaller foveal avascular zone (FAZ) as measured by FA and OCTA, and increased foveal vessel density (VD) and reduced parafoveal VD as measured by OCTA in ROP patients compared with controls. None of the above findings appear to reliably correlate with visual acuity. The studies currently available, however, are inconclusive and lack robust longitudinal data. Vascular imaging demonstrates the potential to aid in the diagnosis, management and monitoring of ROP, alongside retinal examination via indirect ophthalmoscopy and fundus photography.

Vessels characteristics in familial exudative vitreoretinopathy and retinopathy of prematurity based on deep convolutional neural networks.

The purpose of this study was to investigate the quantitative retinal vascular morphological characteristics of Retinopathy of Prematurity (ROP) and Familial Exudative Vitreoretinopathy (FEVR) in the newborn by the application of a deep learning network with artificial intelligence. Standard 130-degree fundus photographs centered on the optic disc were taken in the newborns. The deep learning network provided segmentation of the retinal vessels and the optic disc (OD). Based on the vessel segmentation, the vascular morphological characteristics, including avascular area, vessel angle, vessel density, fractal dimension (FD), and tortuosity, were automatically evaluated. 201 eyes of FEVR, 289 eyes of ROP, and 195 eyes of healthy individuals were included in this study. The deep learning system of blood vessel segmentation had a sensitivity of 72% and a specificity of 99%. The vessel angle in the FEVR group was significantly smaller than that in the normal group and ROP group (37.43 ± 5.43 vs. 39.40 ± 5.61, 39.50 ± 5.58, P = 0.001, < 0.001 respectively). The normal group had the lowest vessel density, the ROP group was in between, and the FEVR group had the highest (2.64 ± 0.85, 2.97 ± 0.92, 3.37 ± 0.88 respectively). The FD was smaller in controls than in the FEVR and ROP groups (0.984 ± 0.039, 1.018 ± 0.039 and 1.016 ± 0.044 respectively, P < 0.001). The ROP group had the most tortuous vessels, while the FEVR group had the stiffest vessels, the controls were in the middle (11.61 ± 3.17, 8.37 ± 2.33 and 7.72 ± 1.57 respectively, P < 0.001). The deep learning technology used in this study has good performance in the quantitative analysis of vascular morphological characteristics in fundus photography. Vascular morphology was different in the newborns of FEVR and ROP compared to healthy individuals, which showed great clinical value for the differential diagnosis of ROP and FEVR.

Deep Learning-assisted Retinopathy of Prematurity (ROP) Screening

Retinopathy of prematurity (ROP) is a leading cause of blindness in premature infants worldwide, particularly in developing countries. In this research, we propose a Deep Convolutional Neural Network (DCNN) and image processing-based approach for the automatic detection of retinal features, including the optical disc (OD) and retinal blood vessels (BV), as well as disease classification using a rule-based method for ROP patients. Our DCNN model uses YOLO-v5 for OD detection and either Pix2Pix or a U-Net for BV segmentation. We trained our DCNN models on publicly available fundus image datasets of size 1,117 and 288 for OD detection and BV segmentation, respectively. We evaluated our approach on a dataset of 439 preterm neonatal retinal images, testing for ROP Zone and 6 BV masks. Our proposed system achieved excellent results, with the OD detection module achieving an overall accuracy of 98.94% (when IoU 0.5) and the BV segmentation module achieving an accuracy of 96.69% and a Dice coefficient between 0.60 and 0.64. Moreover, our system accurately diagnosed ROP in Zone-1 with 88.23% accuracy. Our approach offers a promising solution for accurate ROP screening and diagnosis, particularly in low-resource settings, where it has the potential to improve healthcare outcomes.

Vitreoretinal biomarkers of retinopathy of prematurity using handheld optical coherence tomography: a review.

Retinopathy of prematurity (ROP) is caused by abnormal retinal vascularization in premature infants that has the potential for severe long-term vision impairment. Recent advancements in handheld optical coherence tomography (OCT) have enabled noninvasive, high-resolution, cross-sectional imaging of the infant eye at the bedside. The use of handheld OCT devices in the diagnosis of ROP in premature infants has furthered our understanding of disease state and progression. This review discusses the known and novel biomarkers of ROP severity in premature infants identified through handheld OCT and potential for future directions.

Most Up-to-Date Analysis of Epidemiological Data on the Screening Guidelines and Incidence of Retinopathy of Prematurity in Europe-A Literature Review.

Global predictions indicate that the incidence of retinopathy of prematurity (ROP) is increasing, but the lack of current epidemiological data on the occurrence of ROP in Europe prompted the authors to update these data. European studies related to the presence of ROP were analyzed, and the reason for the differences in the percentage of ROP and different screening criteria were checked. The study presents results from individual and multiple centers. Current ROP incidence data varies from a low of 9.3% in Switzerland to the highest values of 64.1% in Portugal and 39.5% in Norway. The national screening criteria are used in the Netherlands, Germany, Norway, Poland, Portugal, Switzerland, and Sweden. Uniform criteria-Royal College of Paediatrics and Child Health guidelines are used in England and Greece. American Academy of Pediatrics screening guidelines are used in France and Italy. The epidemiology of ROP in European countries varies significantly. The increase in the diagnosis and treatment rate of ROP in recent years correlates with the narrowing of diagnostic criteria in newly issued guidelines (which include the WINROP and G-ROP algorithms), a higher number of less developed preterm infants, and a lower percentage of live births.

Prediction of ROP Zones Using Deep Learning Algorithms and Voting Classifier Technique

Retinopathy of prematurity (ROP) can cause blindness in premature neonates. It is diagnosed when new blood vessels form abnormally in the retina. However, people at high risk of ROP might benefit significantly from early detection and treatment. Therefore, early diagnosis of ROP is vital in averting visual impairment. However, due to a lack of medical experience in detecting this condition, many people refuse treatment; this is especially troublesome given the rising cases of ROP. To deal with this problem, we trained three transfer learning models (VGG-19, ResNet-50, and EfficientNetB5) and a convolutional neural network (CNN) to identify the zones of ROP in preterm newborns. The dataset to train the model contains 1365 fundus images from the ROP screening. This dataset was gathered from the Private Clinic Al-Amal Eye center in Baghdad, Iraq. The models above are ensemble through voting classifier techniques to increase the performance. The proposed method had an overall accuracy of 88.82 percent when employing the voting classifier. On the other hand, EfficientNetB5 has outperformed other models in terms of accuracy with 87.27%.

TRENDS OF RETINOPATHY OF PREMATURITY IN A TERTIARY CARE CENTRE IN URBAN INDIA

Introduction: Retinopathy of prematurity is the leading cause of preventable blindness in premature infants. Our study aims at nding the Incidence and severity of Retinopathy of prematurity (ROP) and its association with various risk factors like low gestational age, low birth weight, oxygen exposure, respiratory distress syndrome, sepsis, blood transfusion and intraventricular hemorrhage. This is a prospective observational study conducted in a tertiary ca Methods: re teaching hospital in Western Maharashtra. Patient data including systemic and ocular ndings were documented. Incidence and severity of ROP was calculated and the association of various risk factors with ROP was calculated using Chi-square test. Among the 266 babies i Results: ncluded in study, the Incidence of ROP was found to be 13.50% and Type I disease was found in 1.31%. The mean birth weight of neonates with ROP was 1230.2 ± 479.5 g and the mean gestational age was 30.27 ± 2.87 weeks. Development of ROP was signicantly associated with low gestational age (GA), low birth weight (BW) and Respiratory Distress Syndrome (RDS). Early screening of preterm babies is crucial Conclusion: for early diagnosis and treatment of ROP to prevent its sight threatening complications

Опыт использования моно и комбинированного лечения ретинопатии недоношенных с применением лазерной коагуляции сетчатки и введения ингибиторов сосудистого эндотелиального фактора роста

Retinopathy of prematurity (ROP) is one of the main causes of blindness and visual impairment in young children. Laser coagulation of the avascular retina is a generally recognized method of treating active progressive ROP. The efficiency of laser coagulation depends on the type of ROP. Combined treatment is used when one of the types of monotherapy is ineffective. Purpose. To evaluate the effectiveness of mono and combined treatment of ROP using laser coagulation and introduction of vascular endothelial growth factor inhibitors. Material and methods. In the period 2020–2022, 403 babies born prematurely were under observation. The diagnosis of ROP was made to 170 (42 %) children. 47 (27 %) patients needed treatment. Mono laser coagulation was performed in 32 (68 %) patients, and 9 (19.1 %) were administered introduction of vascular endothelial growth factor inhibitors. Conclusions. Combined treatment was carried out on 6 (12.7 %). In all cases of PH treatment, a positive result was achieved with the preservation of vision. If we consider the PH of the scar phase of the 3rd degree as an unfavorable outcome, then success is achieved only in 95.6 % of cases. Combined treatment proved to be justified and effective – in our observation in 6.4 % of cases. The results of our study confirm the expediency of monitoring children after the introduction of vascular endothelial growth factor inhibitors until they reach the postconceptual age of 70–80 weeks. Keywords: retinopathy of prematurity, laser photocoagulation of the retina, inhibitors of vascular endothelial growth factor, combined treatment, monotherapy

Retinopathy of Prematurity Outcomes of Neonates Meeting Only a Single Screening Criterion: Proposal of the TWO-ROP Algorithm

To assess the rates of retinopathy of prematurity (ROP) and treatment-warranted ROP in a modern set of patients meeting 0 or 1 of the current ROP screening criteria. Retrospective cohort study. Single-center study of 9350 infants screened for ROP from 2009 to 2019. Rates of ROP and treatment-warranted ROP were evaluated in group 1 (birth weight [BW] <1500 g and gestational age [GA] ≥30 weeks), group 2 (BW ≥1500 g and GA <30 weeks), and group 3 (BW ≥1500 g and GA ≥30 weeks). Of 7520 patients with reported BW and GA, 1612 (21.4%) patients met the inclusion criteria. The number of patients in groups 1, 2, and 3 was 466 (6.19%), 23 (0.31%), and 1123 (14.93%), respectively. The number of patients diagnosed with ROP was 20 (4.29%) in group 1, 1 (4.35%) in group 2, and 12 (1.07%) in group 3 (P < .001). The mean interval between birth and ROP diagnosis was 36.25 days (range 12-75 days) in group 1, 47 days in group 2, and 23.33 days (range 10-39 days) in group 3 (P=.05). No cases of stage 3, zone 1, or plus disease were recorded. No patients met the treatment criteria. Patients meeting 1 screening criterion had a low rate of ROP (<5%), with no stage 3, zone 1, or plus disease. No patients required treatment. We propose a possible algorithm (TWO-ROP) in appropriate neonatal intensive care units, with an amendment in screening protocol for this low-risk population to include only an outpatient screening examination within 1 week of discharge, or at 40 weeks if inpatient, to decrease the inpatient ROP screening burden while maintaining safety. Further external validation of this protocol would be required.

A Multicenter Cross-Sectional Study of the Awareness and Screening for Retinopathy of Prematurity Among NICU Pediatricians in Makkah and Jeddah, Saudi Arabia.

Purpose This study aims to evaluate neonatal intensive care unit (NICU) pediatricians' knowledge about retinopathy of prematurity (ROP) in the major tertiary centers in Makkah and Jeddah, Saudi Arabia. Methods This cross-sectional study uses a self-administered electronic questionnaire completed by NICU pediatricians at the main hospitals of Makkah and Jeddah cities. Based on the participants' correctly selected responses to the validated questionnaire, a scoring system was used in the data analysis to show their level of ROP knowledge. Results Seventy-seven responses were analyzed. The male gender was 49.4%. The majority were recruited from the ministry of health hospitals (63.6%). A small proportion (28.6%) correctly identified who performs the examination. Around three-quarters of the participants have correctly stated that ROP therapy is a very good option to prevent blindness (72.7%). The treatment should generally begin within 72 hours after diagnosis of sight-threatening ROP (79.2%). The requirements for ROP screening were unknown to more than half of our participants (53.2%). With the lowest score of 4.0 and a maximum score of 17.0, the median knowledge score was 13.0 (IQR = 11.0 to 14.0). Based on pediatricians' clinical qualifications, knowledge scores varied significantly. Residents had a significantly lower knowledge score than specialists and consultants (median = 7.0, IQR = 6.0 to 9.0, p = 0.001). Additionally, pediatricians with less experience (<5 years) performed significantly lower on the knowledge score (median = 10.0, IQR = 6.2 to 12.8) than those with more experience (median = 13.0, IQR = 11.0 to 15.0) for participants with 5-10 years of experience, and (median = 13.0, IQR = 11.0 to 14.0) for participants with >10 years of experience). Conclusion Our study showed that NICU pediatricians understood ROP risk factors and treatment options. Nevertheless, they needed to understand the ROP screening inclusion criteria and when the screening could be stopped. Residents scored substantially lower in knowledge overall. Accordingly, we emphasized the need for NICU pediatricians to increase their level of awareness by having regular educational sessions and standardizing one guideline to be strictly followed.

Latest Trends in Retinopathy of Prematurity: Research on Risk Factors, Diagnostic Methods and Therapies.

Retinopathy of prematurity (ROP) is a vasoproliferative disorder with an imminent risk of blindness, in cases where early diagnosis and treatment are not performed. The doctors' constant motivation to give these fragile beings a chance at life with optimal visual acuity has never stopped, since Terry first described this condition. Thus, throughout time, several specific advancements have been made in the management of ROP. Apart from the most known risk factors, this narrative review brings to light the latest research about new potential risk factors, such as: proteinuria, insulin-like growth factor 1 (IGF-1) and blood transfusions. Digital imaging has revolutionized the management of retinal pathologies, and it is more and more used in identifying and staging ROP, particularly in the disadvantaged regions by the means of telescreening. Moreover, optical coherence tomography (OCT) and automated diagnostic tools based on deep learning offer new perspectives on the ROP diagnosis. The new therapeutical trend based on the use of anti-VEGF agents is increasingly used in the treatment of ROP patients, and recent research sustains the theory according to which these agents do not interfere with the neurodevelopment of premature babies.

Blood metabolites in preterm infants with retinopathy of prematurity based on tandem mass spectrometry: a preliminary study

To study new biomarkers for the early diagnosis of retinopathy of prematurity (ROP) by analyzing the differences in blood metabolites based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) and metabolomics. Dried blood spots were collected from 21 infants with ROP (ROP group) and 21 infants without ROP (non-ROP group) who were hospitalized in the Sixth Affiliated Hospital of Sun Yat-sen University from January 2013 to December 2016. LC-MS/MS was used to measure the metabolites, and orthogonal partial least squares-discriminant analysis was used to search for differentially expressed metabolites and biomarkers. There was a significant difference in blood metabolic profiles between the ROP and non-ROP groups. The pattern recognition analysis, Score-plot, and weight analysis obtained 10 amino acids with a relatively large difference. Further statistical analysis showed that the ROP group had significant increases in blood levels of glutamic acid, leucine, aspartic acid, ornithine, and glycine compared with the non-ROP group (P<0.05). The receiver operating characteristic curve analysis showed that glutamic acid and ornithine had the highest value in diagnosing ROP. Blood metabolites in preterm infants with ROP are different from those without ROP. Glutamic acid and ornithine are the metabolic markers for diagnosing ROP. LC-MS/MS combined with metabolomics analysis has a potential application value in the early identification and diagnosis of ROP.

GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.

One of the most serious and dangerous ocular problems in premature infants is retinopathy of prematurity (ROP), a proliferative vascular disease. Ophthalmologists can use automatic computer-assisted diagnostic (CAD) tools to help them make a safe, accurate, and low-cost diagnosis of ROP. All previous CAD tools for ROP diagnosis use the original fundus images. Unfortunately, learning the discriminative representation from ROP-related fundus images is difficult. Textural analysis techniques, such as Gabor wavelets (GW), can demonstrate significant texture information that can help artificial intelligence (AI) based models to improve diagnostic accuracy. In this paper, an effective and automated CAD tool, namely GabROP, based on GW and multiple deep learning (DL) models is proposed. Initially, GabROP analyzes fundus images using GW and generates several sets of GW images. Next, these sets of images are used to train three convolutional neural networks (CNNs) models independently. Additionally, the actual fundus pictures are used to build these networks. Using the discrete wavelet transform (DWT), texture features retrieved from every CNN trained with various sets of GW images are combined to create a textural-spectral-temporal demonstration. Afterward, for each CNN, these features are concatenated with spatial deep features obtained from the original fundus images. Finally, the previous concatenated features of all three CNN are incorporated using the discrete cosine transform (DCT) to lessen the size of features caused by the fusion process. The outcomes of GabROP show that it is accurate and efficient for ophthalmologists. Additionally, the effectiveness of GabROP is compared to recently developed ROP diagnostic techniques. Due to GabROP's superior performance compared to competing tools, ophthalmologists may be able to identify ROP more reliably and precisely, which could result in a reduction in diagnostic effort and examination time.

Perfil epidemiológico de recém-nascidos com retinopatia da prematuridade do Hospital Universitário da Universidade Federal do Maranhão

OBJECTIVE: To describe the profile of premature newborns and exploratory analysis of factors associated with the development of retinopathy of prematurity (ROP) at the University Hospital of the Federal University of Maranhão (HU-UFMA). METHODS: This is a cross-sectional study based on data from the medical records of premature newborns treated at the University Hospital of the Federal University of Maranhão (HU-UFMA), from 2015 to 2020. Possible statically significant associations for the development of ROP were evaluated: sex, gestational age, birth weight, length, head circumference, length of hospital stay, APGAR and others. The significance level adopted was 5% (p&lt;0.05). RESULTS: It was found that, in children with a confirmed diagnosis of ROP, the most prevalent sex was female (50.95%), the mean gestational age was 29.08 weeks, the most frequent outcome was hospital discharge (70, 71%), mean birth weight was 1,089 grams, mean hospital stay was 45.44 days, mean length was 35.73 cm, mean head circumference was 26.06, Apgar most prevalent in the first minute was in the range from 4 to 7 (43.32%) and in the fifth minute from 8 to 10 (56.40%). CONCLUSIONS: The prevalence of ROP cases was 61.40% among newborns followed up at a reference service in the state of Maranhão. It was observed that the lower the gestational age and birth weight, the greater the risk of developing ROP.

PERFIL EPIDEMIOLÓGICO DE RECÉM NASCIDOS COM RETINOPATIA DA PREMATURIDADE DO HOSPITAL UNIVERSITÁRIO DA UNIVERSIDADE FEDERAL DO MARANHÃO

Objective: To describe the profile of premature newborns and exploratory analysis of factors associated with the development of retinopathy of prematurity (ROP) at the University Hospital of the Federal University of Maranhão (HUUFMA). Methods: This is a cross-sectional study based on data from the medical records of premature newborns treated at the University Hospital of the Federal University of Maranhão (HU-UFMA), from 2015 to 2020. Possible statically significant associations for the development of ROP were evaluated: sex, gestational age, birth weight, length, head circumference, length of hospital stay, APGAR and others. The significance level adopted was 5% (p&lt;0.05). Results: It was found that, in children with a confirmed diagnosis of ROP, the most prevalent sex was female (50.95%), the mean gestational age was 29.08 weeks, the most frequent outcome was hospital discharge (70, 71%), mean birth weight was 1,089 grams, mean hospital stay was 45.44 days, mean length was 35.73 cm, mean head circumference was 26.06, Apgar most prevalent in the first minute was in the range from 4 to 7 (43.32%) and in the fifth minute from 8 to 10 (56.40%). Conclusions: The prevalence of ROP cases was 61.40% among newborns followed up at a reference service in the state of Maranhão. It was observed that the lower the gestational age and birth weight, the greater the risk of developing ROP.