Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection.

Automated lesion detection in retinal optical coherence tomography (OCT) scans has shown promise for several clinical applications, including diagnosis, monitoring and guidance of treatment decisions. However, segmentation models still struggle to achieve the desired results for some complex lesions or datasets that commonly occur in real-world, e.g. due to variability of lesion phenotypes, image quality or disease appearance. While several techniques have been proposed to improve them, one line of research that has not yet been investigated is the incorporation of additional semantic context through the application of anomaly detection models. In this study we experimentally show that incorporating weak anomaly labels to standard segmentation models consistently improves lesion segmentation results. This can be done relatively easy by detecting anomalies with a separate model and then adding these output masks as an extra class for training the segmentation model. This provides additional semantic context without requiring extra manual labels. We empirically validated this strategy using two in-house and two publicly available retinal OCT datasets for multiple lesion targets, demonstrating the potential of this generic anomaly guided segmentation approach to be used as an extra tool for improving lesion detection models.

Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation.

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.

Synergistic In Vitro Antiviral Effect of Combinations of Ivermectin, Essential Oils, and 18-(Phthalimid-2-yl)ferruginol against Arboviruses and Herpesvirus.

Combining antiviral drugs with different mechanisms of action can help prevent the development of resistance by attacking the infectious agent through multiple pathways. Additionally, by using faster and more economical screening methods, effective synergistic drug candidates can be rapidly identified, facilitating faster paths to clinical testing. In this work, a rapid method was standardized to identify possible synergisms from drug combinations. We analyzed the possible reduction in the antiviral effective concentration of drugs already approved by the FDA, such as ivermectin (IVM), ribavirin (RIBA), and acyclovir (ACV) against Zika virus (ZIKV), Chikungunya virus (CHIKV), and herpes virus type 2 (HHV-2). Essential oils (EOs) were also included in the study since they have been reported for more than a couple of decades to have broad-spectrum antiviral activity. We also continued studying the antiviral properties of one of our patented molecules with broad-spectrum antiviral activity, the ferruginol analog 18-(phthalimid-2-yl)ferruginol (phthFGL), which presented an IC99 of 25.6 µM for the three types of virus. In general, the combination of IVM, phthFGL, and oregano EO showed the greatest synergism potential against CHIKV, ZIKV, and HHV-2. For instance, this combination achieved reductions in the IC99 value of each component up to ~8-, ~27-, and ~12-fold for CHIKV, respectively. The ternary combination of RIBA, phthFGL, and oregano EO was slightly more efficient than the binary combination RIBA/phthFGL but much less efficient than IVM, phthFGL, and oregano EO, which indicates that IVM could contribute more to the differentiation of cell targets (for example via the inhibition of the host heterodimeric importin IMP α/ß1 complex) than ribavirin. Statistical analysis showed significant differences among the combination groups tested, especially in the HHV-2 and CHIKV models, with p = 0.0098. Additionally, phthFGL showed a good pharmacokinetic profile that should encourage future optimization studies.

Assessment of Detoxification Strategies for Used Dental Implant Healing Abutments: Macroscopic and Biological Implications.

PURPOSE: Dental implant manufacturers recommend healing abutments (HA) be used for single-patient use; however, reuse on multiple patients following decontamination and sterilization is common. This study aims to evaluate four decontamination strategies utilizing enzymatic agents, available in most clinical settings, to determine the level to which biomaterial can be removed in a group of previously used HA (uHA). Secondly, to determine the degree to which the decontaminated HA are capable of inducing an inflammatory response in-vitro compared to new, never used HA. MATERIALS AND METHODS: Fifty HA were collected following 2-4 weeks of intraoral use and distributed randomly into 5 test groups (Group A-E; n = 10/group). Group A: Enzymatic cleaner foam + Autoclave; Group B: Ultrasonic bath with enzymatic cleaner + Autoclave; Group C: Prophy jet + Enzymatic cleaner foam + Autoclave; Group D: Prophy jet + ultrasonic bath with enzymatic cleaner + Autoclave; Group E: Prophy jet + Autoclave. Ten new, sterile HA served as controls (Group "Control"). Residual protein concentration was determined by a Micro BCA protein assay while HA from each group were stained with Phloxine B and macroscopically examined for the presence of debris. To examine the inflammatory potential, human primary macrophages were exposed to HA and supernatant levels of 9 cytokines/chemokines profiles were analyzed using a multiplex bead assay. RESULTS: All test groups presented with differences in the degree of visual decontamination compared to Controls, with Groups D and E displaying the most effective surface debris removaland reduced protein concentration. Of the detoxification strategies, Groups D and E removed the greatest biomaterial while least effective was Group A. However, compared to Controls, multiplex assays revealed high levels of inflammatory cytokine secretion up to 5 days from all Test Groups (A-E) irrespective of the decontamination method used. CONCLUSION: Our study found that compared to new, never used HA, decontamination of uHA utilizing enzymatic cleaners failed to reestablish inert HA surfaces and prevent an inflammatory immune response in-vitro. Clinicians should not reuse HA even after attempts to decontaminate and sterilize HA surfaces.

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Colour fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfil the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus colour photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyse their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniques and strategies that could be of practical value for the clinical diagnosis of glaucoma. As the first in-depth study of fundus & OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will serve as an essential guideline and benchmark for future research.

NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features.

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer's Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.

Postoperative vault prediction for phakic implantable collamer lens surgery: LASSO formulas.

PURPOSE: To develop and evaluate reliable formulas for predicting postoperative vault more accurately after implantable collamer lens (ICL) surgery in a White patient population with varying degrees of ametropia. SETTING: Private clinical practice. DESIGN: Retrospective analysis on dataset split into a separate training and test set. METHODS: 115 eyes of 59 patients were used to train regression models predicting postoperative vault based on anterior segment optical coherence tomography (OCT) parameters (Least Absolute Shrinkage and Selection Operator [LASSO]-OCT formula), ocular biometry data (LASSO-Biometry formula), or data from both devices (LASSO-Full formula). The performance of these models was evaluated against the manufacturer's nomogram (Online Calculation and Ordering System [OCOS]) and Nakamura 1 (NK1) and 2 (NK2) formulas on a matched separate test set of 37 eyes of 19 patients. RESULTS: The mean preoperative spherical equivalent was -5.32 ± 3.37 (range: +3.75 to -17.375 diopters). The mean absolute errors of the estimated vs achieved postoperative vault for the LASSO-Biometry, LASSO-OCT, and LASSO-Full formulas were 144.1 ± 107.9 µm, 145.6 ± 100.6 µm, and 132.0 ± 86.6 µm, respectively. These results were significantly lower compared with the OCOS, NK1, and NK2 formulas ( P < .006). Postoperative vault could be estimated within 500 µm in 97.3% (LASSO-Biometry) to 100% of cases (LASSO-OCT and LASSO-Full). CONCLUSIONS: The LASSO suite provided a set of powerful, reproducible yet convenient ICL sizing formulas with state-of-the-art performance in White patients, including those with low to moderate degrees of myopia. The calculator can be accessed at http://icl.emmetropia.be .

Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures.

In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.

ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images.

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

Disnea secundaria a ticagrelor posterior a arteriografía coronaria. Reporte de caso / Dyspnea secondary to ticagrelor posterior to coronary arteriography. Case report

Resumen Los antiagregantes plaquetarios son medicamentos ampliamente utilizados para la prevención y tratamiento de patologías aterotrombóticas, como lo es el síndrome coronario agudo. A pesar de tener un efecto benéfico, no están exentos de ocasionar múltiples alteraciones a nivel sistémico, como lo es la disnea en pacientes sometidos a manejo con ticagrelor. Se expone el caso de un paciente de 66 años con antecedente de cardiopatía isquémico-hipertensiva, tabaquismo pesado y alergia al ácido acetilsalicílico (ASA), con requerimiento de 2 arteriografías coronarias, quien presenta disnea en reposo en menos de 24 horas posterior al inicio de manejo antiagregante tromboprofiláctico con ticagrelor, que resuelve de forma satisfactoria tras la suspensión del medicamento. Al ser un efecto secundario relativamente frecuente en el marco del uso del ticagrelor, se hace relevante revisar los hallazgos en la literatura actual sobre la aparición de disnea en pacientes tratados con dicho fármaco, para así tener en cuenta posibles recomendaciones acerca del manejo de la disnea asociada a ticagrelor, basadas en el conocimiento actual. MÉD.UIS.2022;35(1): 9-15.

Abstract Antiplatelet agents are widely used drugs for the prevention and treatment of atherothrombotic pathologies such as acute coronary syndrome, however, despite having a beneficial effect, they're not exempt from causing multiple systemic alterations, such as dyspnea in patients undergoing management with ticagrelor. We will now present the case of a 66-year-old patient with a history of hypertensive ischemic heart disease requiring 2 cardiac catheterizations, heavy smoking and allergic to Acetyl Salicylic Acid (ASA) who presented dyspnea at rest in less than 24 hours after the start of thromboprophylaxis management with ticagrelor, that resolves satisfactorily after discontinuation of the drug. Because it is a frequent side effect in the framework of the use of ticagrelor, it's relevant to review the current literature on the appearance of dyspnea in patients treated with ticagrelor, to highlight recommendations for the management of dyspnea associated with ticagrelor based on current knowledge. MÉD.UIS.2022;35(1): 9-15.

Linking Function and Structure with ReSensNet: Predicting Retinal Sensitivity from OCT using Deep Learning.

PURPOSE: The currently used measures of retinal function are limited by being subjective, nonlocalized, or taxing for patients. To address these limitations, we sought to develop and evaluate a deep learning (DL) method to automatically predict the functional end point (retinal sensitivity) based on structural OCT images. DESIGN: Retrospective, cross-sectional study. SUBJECTS: In total, 714 volumes of 289 patients were used in this study. METHODS: A DL algorithm was developed to automatically predict a comprehensive retinal sensitivity map from an OCT volume. Four hundred sixty-three spectral-domain OCT volumes from 174 patients and their corresponding microperimetry examinations (Nidek MP-1) were used for development and internal validation, with a total of 15 563 retinal sensitivity measurements. The patients presented with a healthy macula, early or intermediate age-related macular degeneration, choroidal neovascularization, or geographic atrophy. In addition, an external validation was performed using 251 volumes of 115 patients, comprising 3 different patient populations: those with diabetic macular edema, retinal vein occlusion, or epiretinal membrane. MAIN OUTCOME MEASURES: We evaluated the performance of the algorithm using the mean absolute error (MAE), limits of agreement (LoA), and correlation coefficients of point-wise sensitivity (PWS) and mean sensitivity (MS). RESULTS: The algorithm achieved an MAE of 2.34 dB and 1.30 dB, an LoA of 5.70 and 3.07, a Pearson correlation coefficient of 0.66 and 0.84, and a Spearman correlation coefficient of 0.68 and 0.83 for PWS and MS, respectively. In the external test set, the method achieved an MAE of 2.73 dB and 1.66 dB for PWS and MS, respectively. CONCLUSIONS: The proposed approach allows the prediction of retinal function at each measured location directly based on an OCT scan, demonstrating how structural imaging can serve as a surrogate of visual function. Prospectively, the approach may help to complement retinal function measures, explore the association between image-based information and retinal functionality, improve disease progression monitoring, and provide objective surrogate measures for future clinical trials.

Evaluación médico ocupacional como práctica en la identificación de la hipertensión arterial silenciosa / Medical-Occupational Evaluation as a Practice for the Identification of Silent Arterial Hypertension

Introducción: La hipertensión arterial representa un problema de salud que, si no se identifica a tiempo, puede conducir a enfermedades cardiovasculares graves. Objetivo: Describir la evaluación médico ocupacional como práctica en la identificación de la hipertensión arterial silenciosa. Métodos: Estudio descriptivo transversal, retro-prospectivo, con enfoque cuantitativo, diseño no experimental, realizado en una población de 1245 trabajadores que solicitaron los servicios de evaluación médico ocupacional, en una Institución prestadora de Servicios de Salud. La información se obtuvo de las evaluaciones clínicas, realizadas durante el primer trimestre de 2020 y registros condensados en el software de evaluaciones médico ocupacionales del último trimestre de 2019. La medición de la tensión arterial siguió los parámetros establecidos por la guía de la Sociedad Europea de Hipertensión y Cardiología. El índice de masa corporal siguió las orientaciones de la Organización Mundial de la Salud. El análisis de la información fue hecho en hoja de cálculo Excel y el software PAST, mediante estadísticos descriptivos. Resultados: Durante las evaluaciones médico ocupacionales se identificó 22,26 por ciento de trabajadores con cifras tensionales altas, distribuidas de la siguiente manera: 13,97 por ciento normales altas; 5,22 por ciento hipertensión sistólica aislada, 1,28 por ciento hipertensión grado I, 1,44 por ciento hipertensión grado II y 0,32 por ciento hipertensión grado III; 97,9 por ciento manifestó no percibir sintomatología relacionada con hipertensión. Conclusiones: La evaluación médico ocupacional es una práctica apropiada para la identificación de la hipertensión arterial silenciosa, hallazgos que contribuyen favorablemente al fortalecimiento de intervenciones tempranas conducentes a la prevención de complicaciones cardiovasculares, a corto, mediano o largo plazo(AU)

Introduction: Arterial hypertension represents a health concern that, if not identified on time, can lead to serious cardiovascular diseases. Objective: To describe medical-occupational evaluation as a practice in the identification of silent arterial hypertension. Methods: Descriptive, cross-sectional and retroprospective study with a quantitative approach and nonexperimental design carried out in a population of 1245 workers who requested medical-occupational evaluation services from a healthcare institution. The information was obtained from the clinical evaluations carried out during the first trimester of 2020 and the condensed records of the medical-occupational evaluation software in the last trimester of 2019. Blood pressure measurement followed the parameters established in the guidelines of the European Society of Cardiology/European Society of Hypertension. Body mass index followed the guidelines of the World Health Organization. The information analysis was presented in an Excel spreadsheet and the PAST software, using descriptive statistics. Results: During the medical-occupational evaluations, 22.26 percent of the workers with high blood pressure figures were identified: 13.97 percent was normal high, 5.22 percent had isolated systolic hypertension, 1.28 percent had grade I hypertension, 1.44 percent had grade II hypertension, and 0.32 percent had grade III hypertension; while 97.9 percent stated that they did not perceive symptoms related to hypertension. Conclusions: Medical-occupational evaluation is an appropriate practice for the identification of silent arterial hypertension, findings that contribute favorably to strengthening early interventions leading to the prevention of cardiovascular complications, in the short, medium or long terms(AU)

Early Extubation in a Pediatric Cardiac Surgery Program Located at High Altitude.

BACKGROUND: Early extubation is performed either in the operating room or in the cardiovascular intensive care unit during the first 24 postoperative hours; however, altitude might possibly affect the process. The aim of this study is the evaluation of early extubation feasibility of patients undergoing congenital heart surgery in a center located at 2,691 m (8,828 ft.) above sea level. MATERIAL AND METHODS: Patients undergoing congenital heart surgery, from August 2012 through December 2018, were considered for early extubation. The following variables were recorded: weight, serum lactate, presence or not of Down syndrome, optimal oxygenation and acid-base status according to individual physiological condition (biventricular or univentricular), age, bypass time, and ventricular function. Standardized anesthetic management with dexmedetomidine-fentanyl-rocuronium and sevoflurane was used. If extubation in the operating room was considered, 0.08 mL/kg of 0.5% ropivacaine was injected into the parasternal intercostal spaces bilaterally before closing the sternum. RESULTS: Four hundred seventy-eight patients were operated and 81% were early extubated. Mean pre- and postoperative SaO2 was 92% and 98%; postoperative SaO2 for Glenn and Fontan procedures patients was 82% and 91%, respectively. Seventy-three percent of patients who underwent Glenn procedure, 89% of those who underwent Fontan procedure (all nonfenestrated), and 85% with Down syndrome were extubated in the operating room. Reintubation rate in early extubated patients was 3.6%. CONCLUSION: Early extubation is feasible, with low reintubation rates, at 2,691 m (8,828 ft.) above sea level, even in patients with single ventricle physiology.

Pielonefritis enfisematosa. Reporte de dos casos con manejo exitoso / Emphysematous pyelonephritis: two cases with successful management

Resumen La pielonefritis enfisematosa es una enfermedad grave y de alta mortalidad, pero de baja frecuencia, que suele presentarse en población con factores de riesgo, dentro de los que destacan la diabetes mellitus tipo 2, la uropatía obstructiva, el etilismo y la inmunosupresión. La clasificación radiológica de la pielonefritis enfisematosa va desde clase 1, que comprende el gas que compromete el sistema colector, hasta clase 4, que es la afección de un solo riñón o bilateral. El tratamiento de la pielonefritis enfisematosa depende del grado de severidad: los casos más leves pueden tratarse con catéter o drenaje percutáneo más terapia antibiótica, mientras que los más graves pueden necesitar intervención quirúrgica para nefrectomía. Aquellos pacientes con choque séptico, trombocitopenia, insuficiencia renal aguda e hipoalbuminemia generalmente tienen pronóstico desfavorable. A continuación, se presentan dos casos de pacientes diabéticos mal controlados mayores de 50 años de edad, quienes fueron diagnosticados a través de estudios imagenológicos. Uno de los pacientes tenía uropatía obstructiva y el otro, riñón en herradura; ambos fueron tratados exitosamente con manejo médico y procedimiento mínimamente invasivo.

Abstract Emphysematous pyelonephritis is a serious disease with an infrequent presentation and high mortality. It tends to occur more frequently in the population with risk factors, among which the following stand out: type 2 diabetes mellitus, obstructive uropathy, alcoholism, immunosuppression. The radiological classification of emphysematous pyelonephritis ranges from type one, which comprises gas that involves the collecting system, to type 4, which is a single or bilateral kidney disease. Treatment will depend on the degree of severity, milder cases can be treated with catheter or percutaneous drainage plus antibiotic therapy, while more severe cases may require paranephrectomy surgery. Patients with septic shock, thrombocytopenia, acute renal failure, and hypoalbuminemia generally have a poor prognosis. We present two poorly controlled diabetic patients over 50 years of age diagnosed through computed tomography. One of the patients with obstructive uropathy and the other with horseshoe kidney, both patients successfully treated with medical management and minimally invasive procedure.

Machine learning for filtering out false positive grey matter atrophies in single subject voxel based morphometry: A simulation based study.

Single subject VBM (SS-VBM), has been used as an alternative tool to standard VBM for single case studies. However, it has the disadvantage of producing an excessively large number of false positive detections. In this study we propose a machine learning technique widely used for automated data classification, namely Support Vector Machine (SVM), to refine the findings produced by SS-VBM. A controlled set of experiments was conducted to evaluate the proposed approach using three-dimensional T1 MRI scans from control subjects collected from the publicly available IXI dataset. The scans were artificially atrophied at different locations and with different sizes to mimic the behavior of neurological disorders. Results empirically demonstrated that the proposed method is able to significantly reduce the amount of false positive clusters (p < 0.05), with no statistical differences in the true positive findings (p > 0.05). This evidence was observed to be consistent for different atrophied areas and sizes of atrophies. This approach could be potentially be applied to alleviate the intensive manual analysis that radiologists and clinicians have to perform to filter out miss-detections of SS-VBM, increasing its usability for image reading.

The analysis of alendronate action in bone fracture healing in rats.

INTRODUCTION: Osteoporosis is defined as a systemic skeletal disease characterized by reduced bone mass and degeneration of bone tissue microarchitecture which leads to bone fragility and fracture risk. Annually, 100 to 200 million people around the world are at risk for osteoporotic fractures. One way to prevent osteoporosis fracture is by using medications such as bisphosphonates. Alendronate is the most prescribed bisphosphonate in the world. The objective of this article is to evaluate the effect of alendronate on bone fracture healing. MATERIAL AND METHODS: 15 adult male rats that were 60 days old were used, divided into three groups: A or Control, B (non-osteoporotic bones plus alendronate application) and C (osteoporotic bones plus alendronate application). Osteoporotic bones were compared with non-osteoporotic bones that underwent bone window creation and administration of alendronate sodium. These bones were submitted to radiographic and histological analysis. RESULTS: All of Group A had complete bone healing, reaching the phase of bone remodeling. While in groups B and C, the rats were in the repair phase. CONCLUSIONS: The drug alendronate interferes with delayed fracture healing and delayed bone remodeling. The article advises that studies in humans are needed in order to assess whether the alendronate interferes with bone healing.

AGE challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography.

Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually lead to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a uniform way, which limits progress in the development of automated techniques for angle closure detection and assessment. To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019. The AGE challenge consisted of two tasks: scleral spur localization and angle closure classification. For this challenge, we released a large dataset of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models. During the AGE challenge, over 200 teams registered online, and more than 1100 results were submitted for online evaluation. Finally, eight teams participated in the onsite challenge. In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results for the two tasks. We further discuss limitations and future directions. In the AGE challenge, the top-performing approach had an average Euclidean Distance of 10 pixels (10 µm) in scleral spur localization, while in the task of angle closure classification, all the algorithms achieved satisfactory performances, with two best obtaining an accuracy rate of 100%. These artificial intelligence techniques have the potential to promote new developments in AS-OCT image analysis and image-based angle closure glaucoma assessment in particular.

Correction to "Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT".

The authors of "Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT" which appeared in the January 2020 issue of this journal [1] would like to provide an updated Fig. 3 because there was an error in the published version. The output of the last convolutional layers says "2" in the number of channels but it should be "11" (10 retinal layer and the background).

Efectos adversos a nivel cardiovascular y neurológico asociados al consumo de modafinilo / Adverse effects at the cardiovascular and neurological level associated with the consumption of modafinil

Resumen Introducción: el modafinilo es un fármaco neuroestimulante utilizado principalmente para promover estados de vigilia atención y disminuir la fatiga ante ciertos comportamientos que propician la somnolencia diurna excesiva. Objetivo: identificar en la literatura científica los efectos adversos neurológicos y cardiovasculares causados por el consumo del modafinilo. Materiales y Métodos: revisión bibliográfica de los artículos encontrados entre los meses de abril y julio de 2019 en las bases de datos PUBMED, SCOPUS, DIALNET. 51 artículos superaron la evaluación de calidad metodológica y se incluyeron en la revisión. Resultados: se identificaron que los principales efectos adversos a nivel cardiovascular son la cardiomiopatía Tako-Tsubo y la taquicardia ventricular polimórfica, mientras que a nivel neurológico puede generar insomnio y distonías. Conclusiones: El consumo del modafinilo genera repercusiones en las funciones cognitivas y cardiovasculares por lo cual no es aconsejable su uso a largo plazo en personas sanas. MÉD. UIS.2020;33(1):31-8.

Abstract Introduction: modafinil is a neurostimulant drug used mainly to promote wakefulness, attention and decrease fatigue in certain behaviors that cause excessive daytime sleepiness. Objective: identify in the scientific literature the neurological and cardiovascular adverse effects caused by the consumption of modafinil. Materials and Methods: bibliographic review of the articles found between the months of April and July of 2019 in the PUBMED, SCOPUS, DIALNET databases. 51 articles passed the methodological quality assessment and were included in the review. Results: the main adverse effects at the cardiovascular level were identified as Tako-Tsubo cardiomyopathy and polymorphic ventricular tachycardia, while at the neurological level it can generate insomnia and dystonia. Conclusions: the consumption of modafinil generates repercussions on cognitive and cardiovascular functions, so its long-term use in healthy people is not advisable. MÉD.UIS.2020;33(1):31-8.

Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning.

Diabetic macular edema (DME) and retina vein occlusion (RVO) are macular diseases in which central photoreceptors are affected due to pathological accumulation of fluid. Optical coherence tomography allows to visually assess and evaluate photoreceptor integrity, whose alteration has been observed as an important biomarker of both diseases. However, the manual quantification of this layered structure is challenging, tedious and time-consuming. In this paper we introduce a deep learning approach for automatically segmenting and characterising photoreceptor alteration. The photoreceptor layer is segmented using an ensemble of four different convolutional neural networks. En-face representations of the layer thickness are produced to characterize the photoreceptors. The pixel-wise standard deviation of the score maps produced by the individual models is also taken to indicate areas of photoreceptor abnormality or ambiguous results. Experimental results showed that our ensemble is able to produce results in pair with a human expert, outperforming each of its constitutive models. No statistically significant differences were observed between mean thickness estimates obtained from automated and manually generated annotations. Therefore, our model is able to reliable quantify photoreceptors, which can be used to improve prognosis and managment of macular diseases.