Are artificial intelligence chatbots a reliable source of information about contact lenses?

INTRODUCTION: Artificial Intelligence (AI) chatbots are able to explain complex concepts using plain language. The aim of this study was to assess the accuracy of three AI chatbots answering common questions related to contact lens (CL) wear. METHODS: Three open access AI chatbots were compared: Perplexity, Open Assistant and ChatGPT 3.5. Ten general CL questions were asked to all AI chatbots on the same day in two different countries, with the questions asked in Spanish from Spain and in English from the U.K. Two independent optometrists with experience working in each country assessed the accuracy of the answers provided. Also, the AI chatbots' responses were assessed if their outputs showed any bias towards (or against) any eye care professional (ECP). RESULTS: The answers obtained by the same AI chatbots were different in Spain and the U.K. Also, statistically significant differences were found between the AI chatbots for accuracy. In the U.K., ChatGPT 3.5 was the most and Open Assistant least accurate (p < 0.01). In Spain, Perplexity and ChatGPT were statistically more accurate than Open Assistant (p < 0.01). All the AI chatbots presented bias, except ChatGPT 3.5 in Spain. CONCLUSIONS: AI chatbots do not always consider local CL legislation, and their accuracy seems to be dependent on the language used to interact with them. Hence, at this time, although some AI chatbots might be a good source of information for general CL related questions, they cannot replace an ECP.

Multi-task localization of the hemidiaphragms and lung segmentation in portable chest X-ray images of COVID-19 patients.

Background: The COVID-19 can cause long-term symptoms in the patients after they overcome the disease. Given that this disease mainly damages the respiratory system, these symptoms are often related with breathing problems that can be caused by an affected diaphragm. The diaphragmatic function can be assessed with imaging modalities like computerized tomography or chest X-ray. However, this process must be performed by expert clinicians with manual visual inspection. Moreover, during the pandemic, the clinicians were asked to prioritize the use of portable devices, preventing the risk of cross-contamination. Nevertheless, the captures of these devices are of a lower quality. Objectives: The automatic quantification of the diaphragmatic function can determine the damage of COVID-19 on each patient and assess their evolution during the recovery period, a task that could also be complemented with the lung segmentation. Methods: We propose a novel multi-task fully automatic methodology to simultaneously localize the position of the hemidiaphragms and to segment the lung boundaries with a convolutional architecture using portable chest X-ray images of COVID-19 patients. For that aim, the hemidiaphragms' landmarks are located adapting the paradigm of heatmap regression. Results: The methodology is exhaustively validated with four analyses, achieving an 82.31% ± 2.78% of accuracy when localizing the hemidiaphragms' landmarks and a Dice score of 0.9688 ± 0.0012 in lung segmentation. Conclusions: The results demonstrate that the model is able to perform both tasks simultaneously, being a helpful tool for clinicians despite the lower quality of the portable chest X-ray images.

Intra- and Inter-expert Validation of an Automatic Segmentation Method for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Images.

Central Serous Chorioretinopathy (CSC) is a retinal disorder caused by the accumulation of fluid, resulting in vision distortion. The diagnosis of this disease is typically performed through Optical Coherence Tomography (OCT) imaging, which displays any fluid buildup between the retinal layers. Currently, these fluid regions are manually detected by visual inspection a time-consuming and subjective process that can be prone to errors. A series of six deep learning-based automatic segmentation architectural configurations of different levels of complexity were trained and compared in order to determine the best model intended for the automatic segmentation of CSC-related lesions in OCT images. The best performing models were then evaluated in an external validation study. Furthermore, an intra- and inter-expert analysis was conducted in order to compare the manual segmentation performed by expert ophthalmologists with the automatic segmentation provided by the models. Test results of the best performing configuration achieved a mean Dice of 0.868 ± 0.056 in the internal dataset. In the external validation set, these models achieved a level of agreement with human experts of up to 0.960 in terms of Kappa coefficient, contrasting with a value of 0.951 for agreement between human experts. Overall, the models reached a better agreement with either of the human experts than these experts with each other, suggesting that automatic segmentation models for the detection of CSC-related lesions in OCT imaging can be useful tools for assessing this disease, reducing the workload of manual inspection and leading to a more robust and objective diagnosis method.

Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios.

Retinal Optical Coherence Tomography (OCT) allows the non-invasive direct observation of the central nervous system, enabling the measurement and extraction of biomarkers from neural tissue that can be helpful in the assessment of ocular, systemic and Neurological Disorders (ND). Deep learning models can be trained to segment the retinal layers for biomarker extraction. However, the onset of ND can have an impact on the neural tissue, which can lead to the degraded performance of models not exposed to images displaying signs of disease during training. We present a fully automatic approach for the retinal layer segmentation in multiple neurodegenerative disorder scenarios, using an annotated dataset of patients of the most prevalent NDs: Alzheimer's disease, Parkinson's disease, multiple sclerosis and essential tremor, along with healthy control patients. Furthermore, we present a two-part, comprehensive study on the effects of ND on the performance of these models. The results show that images of healthy patients may not be sufficient for the robust training of automated segmentation models intended for the analysis of ND patients, and that using images representative of different NDs can increase the model performance. These results indicate that the presence or absence of patients of ND in datasets should be taken into account when training deep learning models for retinal layer segmentation, and that the proposed approach can provide a valuable tool for the robust and reliable diagnosis in multiple scenarios of ND.

Robust multi-view approaches for retinal layer segmentation in glaucoma patients via transfer learning.

Background: Glaucoma is the leading global cause of irreversible blindness. Glaucoma patients experience a progressive deterioration of the retinal nervous tissues that begins with a loss of peripheral vision. An early diagnosis is essential in order to prevent blindness. Ophthalmologists measure the deterioration caused by this disease by assessing the retinal layers in different regions of the eye, using different optical coherence tomography (OCT) scanning patterns to extract images, generating different views from multiple parts of the retina. These images are used to measure the thickness of the retinal layers in different regions. Methods: We present two approaches for the multi-region segmentation of the retinal layers in OCT images of glaucoma patients. These approaches can extract the relevant anatomical structures for glaucoma assessment from three different OCT scan patterns: circumpapillary circle scans, macular cube scans and optic disc (OD) radial scans. By employing transfer learning to take advantage of the visual patterns present in a related domain, these approaches use state-of-the-art segmentation modules to achieve a robust, fully automatic segmentation of the retinal layers. The first approach exploits inter-view similarities by using a single module to segment all of the scan patterns, considering them as a single domain. The second approach uses view-specific modules for the segmentation of each scan pattern, automatically detecting the suitable module to analyse each image. Results: The proposed approaches produced satisfactory results with the first approach achieving a dice coefficient of 0.85±0.06 and the second one 0.87±0.08 for all segmented layers. The first approach produced the best results for the radial scans. Concurrently, the view-specific second approach achieved the best results for the better represented circle and cube scan patterns. Conclusions: To the extent of our knowledge, this is the first proposal in the literature for the multi-view segmentation of the retinal layers of glaucoma patients, demonstrating the applicability of machine learning-based systems for aiding in the diagnosis of this relevant pathology.

Age-Related Effects of COVID-19 Pandemic on Mechanical Reperfusion and 30-Day Mortality for STEMI: Results of the ISACS-STEMI COVID-19 Registry.

BACKGROUND: The constraints in the management of patients with ST-segment elevation myocardial infarction (STEMI) during the COVID-19 pandemic have been suggested to have severely impacted mortality levels. The aim of the current analysis is to evaluate the age-related effects of the COVID-19 pandemic on mechanical reperfusion and 30-day mortality for STEMI within the registry ISACS-STEMI COVID-19. METHODS: This retrospective multicenter registry was performed in high-volume PPCI centers on four continents and included STEMI patients undergoing PPCI in March-June 2019 and 2020. Patients were divided according to age (< or ≥75 years). The main outcomes were the incidence and timing of PPCI, (ischemia time longer than 12 h and door-to-balloon longer than 30 min), and in-hospital or 30-day mortality. RESULTS: We included 16,683 patients undergoing PPCI in 109 centers. In 2020, during the pandemic, there was a significant reduction in PPCI as compared to 2019 (IRR 0.843 (95%-CI: 0.825-0.861, p < 0.0001). We found a significant age-related reduction (7%, p = 0.015), with a larger effect on elderly than on younger patients. Furthermore, we observed significantly higher 30-day mortality during the pandemic period, especially among the elderly (13.6% vs. 17.9%, adjusted HR (95% CI) = 1.55 [1.24-1.93], p < 0.001) as compared to younger patients (4.8% vs. 5.7%; adjusted HR (95% CI) = 1.25 [1.05-1.49], p = 0.013), as a potential consequence of the significantly longer ischemia time observed during the pandemic. CONCLUSIONS: The COVID-19 pandemic had a significant impact on the treatment of patients with STEMI, with a 16% reduction in PPCI procedures, with a larger reduction and a longer delay to treatment among elderly patients, which may have contributed to increase in-hospital and 30-day mortality during the pandemic.

Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.

COVID-19 is a global threat for the healthcare systems due to the rapid spread of the pathogen that causes it. In such situation, the clinicians must take important decisions, in an environment where medical resources can be insufficient. In this task, the computer-aided diagnosis systems can be very useful not only in the task of supporting the clinical decisions but also to perform relevant analyses, allowing them to understand better the disease and the factors that can identify the high risk patients. For those purposes, in this work, we use several machine learning algorithms to estimate the outcome of COVID-19 patients given their clinical information. Particularly, we perform 2 different studies: the first one estimates whether the patient is at low or at high risk of death whereas the second estimates if the patient needs hospitalization or not. The results of the analyses of this work show the most relevant features for each studied scenario, as well as the classification performance of the considered machine learning models. In particular, the XGBoost algorithm is able to estimate the need for hospitalization of a patient with an AUC-ROC of 0 . 8415 ± 0 . 0217 while it can also estimate the risk of death with an AUC-ROC of 0 . 7992 ± 0 . 0104 . Results have demonstrated the great potential of the proposal to determine those patients that need a greater amount of medical resources for being at a higher risk. This provides the healthcare services with a tool to better manage their resources.

Gender Difference in the Effects of COVID-19 Pandemic on Mechanical Reperfusion and 30-Day Mortality for STEMI: Results of the ISACS-STEMI COVID-19 Registry.

BACKGROUND: Several reports have demonstrated the impact of the COVID-19 pandemic on the management and outcome of patients with ST-segment elevation myocardial infarction (STEMI). The aim of the current analysis is to investigate the potential gender difference in the effects of the COVID-19 pandemic on mechanical reperfusion and 30-day mortality for STEMI patients within the ISACS-STEMI COVID-19 Registry. METHODS: This retrospective multicenter registry was performed in high-volume primary percutaneous coronary intervention (PPCI) centers on four continents and included STEMI patients undergoing PPCIs in March-June 2019 and 2020. Patients were divided according to gender. The main outcomes were the incidence and timing of the PPCI, (ischemia time ≥ 12 h and door-to-balloon ≥ 30 min) and in-hospital or 30-day mortality. RESULTS: We included 16683 STEMI patients undergoing PPCIs in 109 centers. In 2020 during the pandemic, there was a significant reduction in PPCIs compared to 2019 (IRR 0.843 (95% CI: 0.825-0.861, p < 0.0001). We did not find a significant gender difference in the effects of the COVID-19 pandemic on the numbers of STEMI patients, which were similarly reduced from 2019 to 2020 in both groups, or in the mortality rates. Compared to prepandemia, 30-day mortality was significantly higher during the pandemic period among female (12.1% vs. 8.7%; adjusted HR [95% CI] = 1.66 [1.31-2.11], p < 0.001) but not male patients (5.8% vs. 6.7%; adjusted HR [95% CI] = 1.14 [0.96-1.34], p = 0.12). CONCLUSIONS: The COVID-19 pandemic had a significant impact on the treatment of patients with STEMI, with a 16% reduction in PPCI procedures similarly observed in both genders. Furthermore, we observed significantly increased in-hospital and 30-day mortality rates during the pandemic only among females. Trial registration number: NCT 04412655.

Fully automatic segmentation and monitoring of choriocapillaris flow voids in OCTA images.

Optical coherence tomography angiography (OCTA) is a non-invasive ophthalmic imaging modality that is widely used in clinical practice. Recent technological advances in OCTA allow imaging of blood flow deeper than the retinal layers, at the level of the choriocapillaris (CC), where a granular image is obtained showing a pattern of bright areas, representing blood flow, and a pattern of small dark regions, called flow voids (FVs). Several clinical studies have reported a close correlation between abnormal FVs distribution and multiple diseases, so quantifying changes in FVs distribution in CC has become an area of interest for many clinicians. However, CC OCTA images present very complex features that make it difficult to correctly compare FVs during the monitoring of a patient. In this work, we propose fully automatic approaches for the segmentation and monitoring of FVs in CC OCTA images. First, a baseline approach, in which a fully automatic segmentation methodology based on local contrast enhancement and global thresholding is proposed to segment FVs and measure changes in their distribution in a straightforward manner. Second, a robust approach in which, prior to the use of our segmentation methodology, an unsupervised trained neural network is used to perform a deformable registration that aligns inconsistencies between images acquired at different time instants. The proposed approaches were tested with CC OCTA images collected during a clinical study on the response to photodynamic therapy in patients affected by chronic central serous chorioretinopathy (CSC), demonstrating their clinical utility. The results showed that both approaches are accurate and robust, surpassing the state of the art, therefore improving the efficacy of FVs as a biomarker to monitor the patient treatments. This gives great potential for the clinical use of our methods, with the possibility of extending their use to other pathologies or treatments associated with this type of imaging.

Multivendor fully automatic uncertainty management approaches for the intuitive representation of DME fluid accumulations in OCT images.

Diabetes represents one of the main causes of blindness in developed countries, caused by fluid accumulations in the retinal layers. The clinical literature defines the different types of diabetic macular edema (DME) as cystoid macular edema (CME), diffuse retinal thickening (DRT), and serous retinal detachment (SRD), each with its own clinical relevance. These fluid accumulations do not present defined borders that facilitate segmentational approaches (specially the DRT type, usually not taken into account by the state of the art for this reason) so a diffuse paradigm is used for its detection and visualization. In this paper, we propose three novel approaches for the representation and characterization of these types of DME. A baseline proposal, using a convolutional neural network as backbone, another based on transfer learning from a general domain, and a third approach exploiting information of regions without a defined label. Overall, our baseline proposal obtained an AUC of 0.9583 ± 0.0093, the approach pretrained with a general-domain dataset an AUC of 0.9603 ± 0.0087, and the approach pretrained in the domain taking advantage of uncertainty, an AUC of 0.9619 ± 0.0073.

A new generative approach for optical coherence tomography data scarcity: unpaired mutual conversion between scanning presets.

In optical coherence tomography (OCT), there is a trade-off between the scanning time and image quality, leading to a scarcity of high quality data. OCT platforms provide different scanning presets, producing visually distinct images, limiting their compatibility. In this work, a fully automatic methodology for the unpaired visual conversion of the two most prevalent scanning presets is proposed. Using contrastive unpaired translation generative adversarial architectures, low quality images acquired with the faster Macular Cube preset can be converted to the visual style of high visibility Seven Lines scans and vice-versa. This modifies the visual appearance of the OCT images generated by each preset while preserving natural tissue structure. The quality of original and synthetic generated images was compared using BRISQUE. The synthetic generated images achieved very similar scores to original images of their target preset. The generative models were validated in automatic and expert separability tests. These models demonstrated they were able to replicate the genuine look of the original images. This methodology has the potential to create multi-preset datasets with which to train robust computer-aided diagnosis systems by exposing them to the visual features of different presets they may encounter in real clinical scenarios without having to obtain additional data. Graphical Abstract Unpaired mutual conversion between scanning presets. Two generative adversarial models are trained for the conversion of OCT images into images of another scanning preset, replicating the visual features that characterise said preset.

SARS-CoV-2 Positivity, Stent Thrombosis, and 30-day Mortality in STEMI Patients Undergoing Mechanical Reperfusion.

SARS-Cov-2 has been suggested to promote thrombotic complications and higher mortality. The aim of the present study was to evaluate the impact of SARS-CoV-2 positivity on in-hospital outcome and 30-day mortality in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PCI) enrolled in the International Survey on Acute Coronary Syndromes ST-segment elevation Myocardial Infarction (ISACS-STEMI COVID-19 registry. The 109 SARS-CoV-2 positive patients were compared with 2005 SARS-CoV-2 negative patients. Positive patients were older (P = .002), less often active smokers (P = .002), and hypercholesterolemic (P = .006), they presented more often later than 12 h (P = .037), more often to the hub and were more often in cardiogenic shock (P = .02), or requiring rescue percutaneous coronary intervention after failed thrombolysis (P < .0001). Lower postprocedural Thrombolysis in Myocardial Infarction 3 flow (P = .029) and more thrombectomy (P = .046) were observed. SARS-CoV-2 was associated with a significantly higher in-hospital mortality (25.7 vs 7%, adjusted Odds Ratio (OR) [95% Confidence Interval] = 3.2 [1.71-5.99], P < .001) in-hospital definite in-stent thrombosis (6.4 vs 1.1%, adjusted Odds Ratio [95% CI] = 6.26 [2.41-16.25], P < .001) and 30-day mortality (34.4 vs 8.5%, adjusted Hazard Ratio [95% CI] = 2.16 [1.45-3.23], P < .001), confirming that SARS-CoV-2 positivity is associated with impaired reperfusion, with negative prognostic consequences.

Impact of chronic obstructive pulmonary disease on short-term outcome in patients with ST-elevation myocardial infarction during COVID-19 pandemic: insights from the international multicenter ISACS-STEMI registry.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is projected to become the third cause of mortality worldwide. COPD shares several pathophysiological mechanisms with cardiovascular disease, especially atherosclerosis. However, no definite answers are available on the prognostic role of COPD in the setting of ST elevation myocardial infarction (STEMI), especially during COVID-19 pandemic, among patients undergoing primary angioplasty, that is therefore the aim of the current study. METHODS: In the ISACS-STEMI COVID-19 registry we included retrospectively patients with STEMI treated with primary percutaneous coronary intervention (PCI) between March and June of 2019 and 2020 from 109 high-volume primary PCI centers in 4 continents. RESULTS: A total of 15,686 patients were included in this analysis. Of them, 810 (5.2%) subjects had a COPD diagnosis. They were more often elderly and with a more pronounced cardiovascular risk profile. No preminent procedural dissimilarities were noticed except for a lower proportion of dual antiplatelet therapy at discharge among COPD patients (98.9% vs. 98.1%, P = 0.038). With regards to short-term fatal outcomes, both in-hospital and 30-days mortality occurred more frequently among COPD patients, similarly in pre-COVID-19 and COVID-19 era. However, after adjustment for main baseline differences, COPD did not result as independent predictor for in-hospital death (adjusted OR [95% CI] = 0.913[0.658-1.266], P = 0.585) nor for 30-days mortality (adjusted OR [95% CI] = 0.850 [0.620-1.164], P = 0.310). No significant differences were detected in terms of SARS-CoV-2 positivity between the two groups. CONCLUSION: This is one of the largest studies investigating characteristics and outcome of COPD patients with STEMI undergoing primary angioplasty, especially during COVID pandemic. COPD was associated with significantly higher rates of in-hospital and 30-days mortality. However, this association disappeared after adjustment for baseline characteristics. Furthermore, COPD did not significantly affect SARS-CoV-2 positivity. TRIAL REGISTRATION NUMBER: NCT04412655 (2nd June 2020).

COVID-19 pandemic, mechanical reperfusion and 30-day mortality in ST elevation myocardial infarction.

OBJECTIVE: The initial data of the International Study on Acute Coronary Syndromes - ST Elevation Myocardial Infarction COVID-19 showed in Europe a remarkable reduction in primary percutaneous coronary intervention procedures and higher in-hospital mortality during the initial phase of the pandemic as compared with the prepandemic period. The aim of the current study was to provide the final results of the registry, subsequently extended outside Europe with a larger inclusion period (up to June 2020) and longer follow-up (up to 30 days). METHODS: This is a retrospective multicentre registry in 109 high-volume primary percutaneous coronary intervention (PPCI) centres from Europe, Latin America, South-East Asia and North Africa, enrolling 16 674 patients with ST segment elevation myocardial infarction (STEMI) undergoing PPPCI in March/June 2019 and 2020. The main study outcomes were the incidence of PPCI, delayed treatment (ischaemia time >12 hours and door-to-balloon >30 min), in-hospital and 30-day mortality. RESULTS: In 2020, during the pandemic, there was a significant reduction in PPCI as compared with 2019 (incidence rate ratio 0.843, 95% CI 0.825 to 0.861, p<0.0001). This reduction was significantly associated with age, being higher in older adults (>75 years) (p=0.015), and was not related to the peak of cases or deaths due to COVID-19. The heterogeneity among centres was high (p<0.001). Furthermore, the pandemic was associated with a significant increase in door-to-balloon time (40 (25-70) min vs 40 (25-64) min, p=0.01) and total ischaemia time (225 (135-410) min vs 196 (120-355) min, p<0.001), which may have contributed to the higher in-hospital (6.5% vs 5.3%, p<0.001) and 30-day (8% vs 6.5%, p=0.001) mortality observed during the pandemic. CONCLUSION: Percutaneous revascularisation for STEMI was significantly affected by the COVID-19 pandemic, with a 16% reduction in PPCI procedures, especially among older patients (about 20%), and longer delays to treatment, which may have contributed to the increased in-hospital and 30-day mortality during the pandemic. TRIAL REGISTRATION NUMBER: NCT04412655.

Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images.

Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact in the patients, critical issue to prevent further complications caused by this highly infectious disease. In this work, we propose 4 fully automatic approaches for the classification of chest X-ray images under the analysis of 3 different categories: Covid-19, pneumonia and healthy cases. Given the similarity between the pathological impact in the lungs between Covid-19 and pneumonia, mainly during the initial stages of both lung diseases, we performed an exhaustive study of differentiation considering different pathological scenarios. To address these classification tasks, we evaluated 6 representative state-of-the-art deep network architectures on 3 different public datasets: (I) Chest X-ray dataset of the Radiological Society of North America (RSNA); (II) Covid-19 Image Data Collection; (III) SIRM dataset of the Italian Society of Medical Radiology. To validate the designed approaches, several representative experiments were performed using 6,070 chest X-ray radiographs. In general, satisfactory results were obtained from the designed approaches, reaching a global accuracy values of 0.9706 ± 0.0044, 0.9839 ± 0.0102, 0.9744 ± 0.0104 and 0.9744 ± 0.0104, respectively, thus helping the work of clinicians in the diagnosis and consequently in the early treatment of this relevant pandemic pathology.

Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19.

One of the main challenges in times of sanitary emergency is to quickly develop computer aided diagnosis systems with a limited number of available samples due to the novelty, complexity of the case and the urgency of its implementation. This is the case during the current pandemic of COVID-19. This pathogen primarily infects the respiratory system of the afflicted, resulting in pneumonia and in a severe case of acute respiratory distress syndrome. This results in the formation of different pathological structures in the lungs that can be detected by the use of chest X-rays. Due to the overload of the health services, portable X-ray devices are recommended during the pandemic, preventing the spread of the disease. However, these devices entail different complications (such as capture quality) that, together with the subjectivity of the clinician, make the diagnostic process more difficult and suggest the necessity for computer-aided diagnosis methodologies despite the scarcity of samples available to do so. To solve this problem, we propose a methodology that allows to adapt the knowledge from a well-known domain with a high number of samples to a new domain with a significantly reduced number and greater complexity. We took advantage of a pre-trained segmentation model from brain magnetic resonance imaging of a unrelated pathology and performed two stages of knowledge transfer to obtain a robust system able to segment lung regions from portable X-ray devices despite the scarcity of samples and lesser quality. This way, our methodology obtained a satisfactory accuracy of 0.9761 ± 0.0100 for patients with COVID-19, 0.9801 ± 0.0104 for normal patients and 0.9769 ± 0.0111 for patients with pulmonary diseases with similar characteristics as COVID-19 (such as pneumonia) but not genuine COVID-19.

Renin-angiotensin system inhibitors and mortality among diabetic patients with STEMI undergoing mechanical reperfusion during the COVID-19 pandemic.

BACKGROUND: During the coronavirus disease 2019 (COVID-19) pandemic, concerns have been arisen on the use of renin-angiotensin system inhibitors (RASI) due to the potentially increased expression of Angiotensin-converting-enzyme (ACE)2 and patient's susceptibility to SARS-CoV2 infection. Diabetes mellitus have been recognized favoring the coronavirus infection with consequent increase mortality in COVID-19. No data have been so far reported in diabetic patients suffering from ST-elevation myocardial infarction (STEMI), a very high-risk population deserving of RASI treatment. METHODS: The ISACS-STEMI COVID-19 registry retrospectively assessed STEMI patients treated with primary percutaneous coronary intervention (PPCI) in March/June 2019 and 2020 in 109 European high-volume primary PCI centers. This subanalysis assessed the prognostic impact of chronic RASI therapy at admission on mortality and SARS-CoV2 infection among diabetic patients. RESULTS: Our population is represented by 3812 diabetic STEMI patients undergoing mechanical reperfusion, 2038 in 2019 and 1774 in 2020. Among 3761 patients with available data on chronic RASI therapy, between those ones with and without treatment there were several differences in baseline characteristics, (similar in both periods) but no difference in the prevalence of SARS-CoV2 infection (1.6% vs 1.3%, respectively, p = 0.786). Considering in-hospital medication, RASI therapy was overall associated with a significantly lower in-hospital mortality (3.3% vs 15.8%, p < 0.0001), consistently both in 2019 and in 2010. CONCLUSIONS: This is first study to investigate the impact of RASI therapy on prognosis and SARS-CoV2 infection of diabetic patients experiencing STEMI and undergoing PPCI during the COVID-19 pandemic. Both pre-admission chronic RASI therapy and in-hospital RASI did not negatively affected patients' survival during the hospitalization, neither increased the risk of SARS-CoV2 infection. TRIAL REGISTRATION NUMBER: NCT04412655.

Joint Diabetic Macular Edema Segmentation and Characterization in OCT Images.

The automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the disease severity. According to clinical knowledge, the DME disorder can be categorized into three main pathological types: serous retinal detachment (SRD), cystoid macular edema (CME), and diffuse retinal thickening (DRT). The implementation of computational systems for their automatic extraction and characterization may help the clinicians in their daily clinical practice, adjusting the diagnosis and therapies and consequently the life quality of the patients. In this context, this paper proposes a fully automatic system for the identification, segmentation and characterization of the three ME types using optical coherence tomography (OCT) images. In the case of SRD and CME edemas, different approaches were implemented adapting graph cuts and active contours for their identification and precise delimitation. In the case of the DRT edemas, given their fuzzy regional appearance that requires a complex extraction process, an exhaustive analysis using a learning strategy was designed, exploiting intensity, texture, and clinical-based information. The different steps of this methodology were validated with a heterogeneous set of 262 OCT images, using the manual labeling provided by an expert clinician. In general terms, the system provided satisfactory results, reaching Dice coefficient scores of 0.8768, 0.7475, and 0.8913 for the segmentation of SRD, CME, and DRT edemas, respectively.

Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images.

Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is currently one of the main means of identification and characterization of intraretinal cystoid regions, a crucial task in the diagnosis of exudative macular disease or macular edema, among the main causes of blindness in developed countries. This work presents an exhaustive analysis of intensity and texture-based descriptors for its identification and classification, using a complete set of 510 texture features, three state-of-the-art feature selection strategies, and seven representative classifier strategies. The methodology validation and the analysis were performed using an image dataset of 83 OCT scans. From these images, 1609 samples were extracted from both cystoid and non-cystoid regions. The different tested configurations provided satisfactory results, reaching a mean cross-validation test accuracy of 92.69%. The most promising feature categories identified for the issue were the Gabor filters, the Histogram of Oriented Gradients (HOG), the Gray-Level Run-Length matrix (GLRL), and the Laws' texture filters (LAWS), being consistently and considerably selected along all feature selector algorithms in the top positions of different relevance rankings.

Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices.

The recent human coronavirus disease (COVID-19) is a respiratory infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the effects of COVID-19 in pulmonary tissues, chest radiography imaging plays an important role in the screening, early detection, and monitoring of the suspected individuals. Hence, as the pandemic of COVID-19 progresses, there will be a greater reliance on the use of portable equipment for the acquisition of chest X-ray images due to its accessibility, widespread availability, and benefits regarding to infection control issues, minimizing the risk of cross-contamination. This work presents novel fully automatic approaches specifically tailored for the classification of chest X-ray images acquired by portable equipment into 3 different clinical categories: normal, pathological, and COVID-19. For this purpose, 3 complementary deep learning approaches based on a densely convolutional network architecture are herein presented. The joint response of all the approaches allows to enhance the differentiation between patients infected with COVID-19, patients with other diseases that manifest characteristics similar to COVID-19 and normal cases. The proposed approaches were validated over a dataset specifically retrieved for this research. Despite the poor quality of the chest X-ray images that is inherent to the nature of the portable equipment, the proposed approaches provided global accuracy values of 79.62%, 90.27% and 79.86%, respectively, allowing a reliable analysis of portable radiographs to facilitate the clinical decision-making process.