Comparative study of the glistening between four intraocular lens models assessed by OCT and deep learning.

PURPOSE: To evaluate the glistening in 4 different models of intraocular lenses (IOLs) using optical coherence tomography (OCT) and deep learning (DL). SETTING: Centro Internacional de Oftalmología Avanzada (Madrid, Spain). DESIGN: Cross-sectional study. METHODS: 325 eyes were assessed for the presence and severity of glistening in 4 IOL models: ReSTOR+3 SN6AD1 (n = 41), SN60WF (n = 110), PanOptix TFNT (n = 128) and Vivity DFT015 (n = 46). The presence of glistening was analyzed using OCT, identifying the presence of hyperreflective foci (HRF) in the central area of the IOL. A manual and an original DL-based quantification algorithm designed for this purpose was applied. RESULTS: Glistening was detected in 22 (53.7%) ReSTOR SN6AD1, 44 (40%) SN60WF, 49 (38.3%) PanOptix TFNT, and 4 (8.7%) Vivity DFT015 IOLs, when any grade was considered. In the comparison of the different types of IOLs, global glistening measured as total HRF was 17.3 ± 25.9 for the ReSTOR+3; 9.3 ± 15.7 for the SN60WF; 6.9 ± 10.5 for the PanOptix; and 1.2 ± 2.6 for the Vivity ( P < .05). There was excellent agreement between manual and DL-based quantification (≥0.829). CONCLUSIONS: It is possible to quantify, classify and compare the glistening severity in different IOL models using OCT images in a simple and objective manner with a DL algorithm. In the comparative study, the Vivity presented the lowest severity of glistening.

NEmecum: digital tool for assisting in the prescription and dispensing of enteral nutrition formulas and infant preparations / NEmecum: herramienta digital de ayuda a la prescripción y dispensación de fórmulas de nutrición enteral y preparados infantiles.

Introduction: Introduction: there is a wide variety of enteral nutrition and infant formulas preparations. When there is a need to find infomation on a product, only the infomation from industy is available. Comparison amomg products becomes then ardous. Objective: to describe the development of NEmecum as the first website that allows a directed and independent search for enteral nutrition products and infant formulas, currently available in Spain, and to evaluate the initial use of NEmecum. Methods: the structure of a database that unifies the parameters of all formulas was developed, and the nutritional composition of all formulas was analyzed. Subsequently, the main search algorithms were selected and the digital tool was codified. Intensive dissemination was performed and the impact was evaluated. The profile of users and registered centers and the use of the tool were analyzed, and its usability was evaluated using the System Usability Scale (SUS) questionnaire. Results: a free-access responsive website (http://nemecum.com) that allows searches based on pre-established search filters was obtained. This tool allows for a detailed analysis avalaible formulas in Spain by observing a wide variety of formulas with similar characteristics. The dissemination campaign managed to increase its use exponentially, currently reaching 1,370 users and 79 registered centers. Usability was rated as excellent. Conclusion: the development of the NEmecum represents a valuable tool in the search and consultation of the characteristics of enteral nutrition formulas and infant preparations.

Introducción: Introducción: existe una amplia variedad de fórmulas o preparados de nutrición enteral y fórmulas o preparados infantiles. La consulta de información relacionada debe hacerse en las herramientas propias de cada laboratorio, lo que dificulta la visión crítica y la comparativa entre las mismas. Objetivo: describir el desarrollo de NEmecum como la primera web que permite realizar una búsqueda dirigida e independiente de fórmulas de nutrición o preparados infantiles, analizar el abanico nutricional actual en España y evaluar los datos de uso de la herramienta. Métodos: se desarrolló la estructura de una base de datos que unifica los parámetros de todas las fórmulas y se analizó el abanico nutricional español. Posteriormente, se seleccionaron los principales algoritmos de búsqueda dirigida y se codificó la herramienta digital. Se llevó a cabo una intensa difusión y se evaluó el impacto obtenido. Se analizaron el perfil de usuarios y centros registrados y los datos de uso de la herramienta y se evaluó su usabilidad mediante el cuestionario System Usability Scale (SUS). Resultados: se obtuvo una web responsive de acceso gratuito (http://nemecum.com) que permite realizar búsquedas dirigidas en base a unos filtros de búsqueda preestablecidos. La herramienta permitió analizar detalladamente el abanico nutricional en España, observándose la gran variedad de fórmulas disponibles de similares características. La campaña de difusión consiguió incrementar su uso de forma exponencial y cuenta actualmente con 1.370 usuarios y 79 centros registrados. La usabilidad fue valorada como excelente. Conclusión: el desarrollo de NEmecum supone una herramienta valiosa en la búsqueda y consulta de datos de fórmulas o preparados de nutrición enteral y fórmulas o preparados infantiles.

Prediction of the response to photodynamic therapy in patients with chronic central serous chorioretinopathy based on optical coherence tomography using deep learning.

PURPOSE: To assess the prediction of the response to photodynamic therapy (PDT) in chronic central serous chorioretinopathy (CSCR) based on spectral-domain optical coherence tomography (SD-OCT) images using deep learning (DL). METHODS: Retrospective study including 216 eyes of 175 patients with CSCR and persistent subretinal fluid (SRF) who underwent half-fluence PDT. SD-OCT macular examination was performed before (baseline) and 3 months after treatment. Patients were classified into groups by experts based on the response to PDT: Group 1, complete SRF resorption (n = 100); Group 2, partial SRF resorption (n = 66); and Group 3, absence of any SRF resorption (n = 50). This work proposes different computational approaches: 1st approach compares all groups; 2nd compares groups 1 vs. 2 and 3 together; 3rd compares groups 2 vs. 3. RESULTS: The mean age was 55.6 ± 10.9 years and 70.3% were males. In the first approach, the algorithm showed a precision of up to 57% to detect the response to treatment in group 1 based on the initial scan, with a mean average accuracy of 0.529 ± 0.035. In the second model, the mean accuracy was higher (0.670 ± 0.046). In the third approach, the algorithm showed a precision of 0.74 ± 0.12 to detect the response to treatment in group 2 (partial SRF resolution) and 0.69 ± 0.15 in group 3 (absence of SRF resolution). CONCLUSION: Despite the high clinical variability in the response of chronic CSCR to PDT, this DL algorithm offers an objective and promising tool to predict the response to PDT treatment in clinical practice.

Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images.

The current COVID-19 pandemic, that has caused more than 100 million cases as well as more than two million deaths worldwide, demands the development of fast and accurate diagnostic methods despite the lack of available samples. This disease mainly affects the respiratory system of the patients and can lead to pneumonia and to severe cases of acute respiratory syndrome that result in the formation of several pathological structures in the lungs. These pathological structures can be explored taking advantage of chest X-ray imaging. As a recommendation for the health services, portable chest X-ray devices should be used instead of conventional fixed machinery, in order to prevent the spread of the pathogen. However, portable devices present several problems (specially those related with capture quality). Moreover, the subjectivity and the fatigue of the clinicians lead to a very difficult diagnostic process. To overcome that, computer-aided methodologies can be very useful even taking into account the lack of available samples that the COVID-19 affectation shows. In this work, we propose an improvement in the performance of COVID-19 screening, taking advantage of several cycle generative adversarial networks to generate useful and relevant synthetic images to solve the lack of COVID-19 samples, in the context of poor quality and low detail datasets obtained from portable devices. For validating this proposal for improved COVID-19 screening, several experiments were conducted. The results demonstrate that this data augmentation strategy improves the performance of a previous COVID-19 screening proposal, achieving an accuracy of 98.61% when distinguishing among NON-COVID-19 (i.e. normal control samples and samples with pathologies others than COVID-19) and genuine COVID-19 samples. It is remarkable that this methodology can be extrapolated to other pulmonary pathologies and even other medical imaging domains to overcome the data scarcity.