Guía itinerario formativo tipo (GIFT) Oftalmología

Guía del itinerario formativo de los residentes en el servicio de Oftalmología del Complejo Hospitalario Universitario de FerrolGuía do itinerario formativo dos residentes no servizo de Oftalmoloxía do Complexo Hospitalario Universitario de Ferro

Innovaci?n en oftalmolog?a

Ponencia: "Innovaci?n en oftalmolog?a" presentada por Nuria Olivier Pascual en la Mesa Redonda 2 sobre "Proyectos innovadores en el ?rea Sanitaria de Ferrol", en la 1? Jornada de Innovaci?n en Salud del ?rea Sanitaria de Ferrol, celebrada en Ferrol el 22 de septiembre de 2022.Ponencia: "Innovaci?n en oftalmolox?a" presentada por Nuria Olivier Pascual na Mesa Redonda 2 sobre "Proxectos innovadores na ?rea Sanitaria de Ferrol", na 1? Xornada de Innovaci?n en Sa?de da ?rea Sanitaria de Ferrol, celebrada en Ferrol o 22 de setembro de 2022

Enhancing Pathological Detection and Monitoring in OCT Volumes with Limited Slices using Convolutional Neural Networks and 3D Visualization Techniques

Cursos e Congresos, C-155[Abstract] Optical Coherence Tomography (OCT) is a non-invasive imaging technique with a crucial role in the monitoring of a wide range of diseases. In order to make a good diagnosis it is essential that clinicians can observe any subtle changes that appear in the multiple ocular structures, so it is imperative that the 3D OCT volumes have good resolution in each axis. Unfortunately, there is a trade-off between image quality and the number of volume slices. In this work, we use a convolutional neural network to generate the intermediate synthetic slices of the OTC volumes and we propose a few variants of a 3D reconstruction algorithm to create visualizations that emphasize the changes present in multiple retinal structures to aid clinicians in the diagnostic processXunta de Galicia; ED431C 2020/24This research was funded by Government of Spain, Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research projects with reference PID2019-108435RB-I00, PDC2022-133132-I00 and TED2021-131201B-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, as Research Center accredited by Galician University System, is funded by ”Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by ”Secretaría Xeral de Universidades”, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project

Visualization of pathological changes in retinal layer thickness using optical coherence tomography

[Abstract]: Optical Coherence Tomography (OCT) is a non-invasive imaging technique that provides high-resolution cross-sectional images of biological tissues. Biomarkers such as the thickness of retinal layers can be used for the evaluation of several diseases such as glaucoma or multiple sclerosis. In this work, we create the thickness profiles of healthy and pathological eyes using a statistical model and developed a methodology to compare the thickness profiles of OCT images with the models and visually inspect the abnormal areas. This approach allows for a quick assessment of the retinal layers health, assisting clinicians and easing the diagnostic burden.This research was funded by Government of Spain, Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research projects with reference PID2019-108435RB-I00, reference PDC2022-133132-I00 and TED2021-131201B-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Emilio López Varela acknowledges its support under FPI PID2019-108435RB-I00 project.Xunta de Galicia; ED431C 2020/2

Fully automatic segmentation of the choroid in non-EDI OCT images of patients with multiple sclerosis

Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project.[Abstract]: Multiple Sclerosis (MS) is a chronic neurological disease, in which immune-mediated mechanisms lead to pathological processes of neurodegeneration. Optical coherence tomography (OCT) has recently begun to be used to diagnose and monitor patients with MS. Morphological changes in the choroid have been linked to the onset of MS, so an accurate segmentation of this layer is critical. Conventional OCT has several limitations in obtaining accurate images of the choroid, which has been improved through the use of systems such as Enhanced Depth Imaging (EDI) OCT. Unfortunately, many longitudinal studies that have collected samples over the years in the past have been performed using highly variable settings and without the use of the EDI protocol (or similar variants). For these reasons, in this work we propose a series of fully automatic approaches, based on convolutional neural networks, capable of robustly segmenting the choroid in OCT images without using the EDI protocol. To test the robustness and efficiency of our method, we performed experiments on a public dataset and a collected one. The Dice score obtained by the best proposed architecture is 89.7 for the public dataset, and 93.7 for the collected dataset.Instituto de Salud Carlos III; DTS18/00136Ministerio de Ciencia e Innovación y Universidades; RTI2018-095894-B-I00Xunta de Galicia; ED431C 2020/24Ministerio de Ciencia e Innovación; PID2019-108435RB-I00Axencia Galega de Innovación (GAIN); IN845D 2020/38Xunta de Galicia; ED431G 2019/0

Fully-Automatic 3D Intuitive Visualization of Age-Related Macular Degeneration Fluid Accumulations in OCT Cubes

[Abstract]: Age-related macular degeneration is the leading cause of vision loss in developed countries, and wet-type AMD requires urgent treatment and rapid diagnosis because it causes rapid irreversible vision loss. Currently, AMD diagnosis is mainly carried out using images obtained by optical coherence tomography. This diagnostic process is performed by human clinicians, so human error may occur in some cases. Therefore, fully automatic methodologies are highly desirable adding a layer of robustness to the diagnosis. In this work, a novel computer-aided diagnosis and visualization methodology is proposed for the rapid identification and visualization of wet AMD. We adapted a convolutional neural network for segmentation of a similar domain of medical images to the problem of wet AMD segmentation, taking advantage of transfer learning, which allows us to work with and exploit a reduced number of samples. We generate a 3D intuitive visualization where the existence, position and severity of the fluid were represented in a clear and intuitive way to facilitate the analysis of the clinicians. The 3D visualization is robust and accurate, obtaining satisfactory 0.949 and 0.960 Dice coefficients in the different evaluated OCT cube configurations, allowing to quickly assess the presence and extension of the fluid associated to wet AMD.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Funding for open access charge: Universidade da Coruña/CISUG. The research was funded by Instituto de Salud Carlos III, Government of Spain through the PI17/00940 and DTS18/00136 research projects, Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project, Ayudas para la formación de profesorado universitario (FPU), grant ref. FPU18/02271; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, unta de Galicia, Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2019/01). Emilio López Varela acknowledges its support under FPI Grant Program through the PID2019-108435RB-I00 project.Financiado para publicación en acceso aberto: Universidade da Coruña/CISUGXunta de Galicia; ED431C 2020/24Xunta de Galicia; ED431G 2019/0

Generation of synthetic intermediate slices in 3D OCT cubes for improving pathology detection and monitoring

Funding for open access charge: Universidade da Coruña/CISUG[Absctract]: OCT is a non-invasive imaging technique commonly used to obtain 3D volumes of the ocular structure. These volumes allow the monitoring of ocular and systemic diseases through the observation of subtle changes in the different structures present in the eye. In order to observe these changes it is essential that the OCT volumes have a high resolution in all axes, but unfortunately there is an inverse relationship between the quality of the OCT images and the number of slices of the cube. This results in routine clinical examinations using cubes that generally contain high-resolution images with few slices. This lack of slices complicates the monitoring of changes in the retina hindering the diagnostic process and reducing the effectiveness of 3D visualizations. Therefore, increasing the cross-sectional resolution of OCT cubes would improve the visualization of these changes aiding the clinician in the diagnostic process. In this work we present a novel fully automatic methodology to perform the synthesis of intermediate slices of OCT image volumes in an unsupervised manner. To perform this synthesis, we propose a fully convolutional neural network architecture that uses information from two adjacent slices to generate the intermediate synthetic slice. We also propose a training methodology, where we use three adjacent slices to train the network by contrastive learning and image reconstruction. We test our methodology with three different types of OCT volumes commonly used in the clinical setting and validate the quality of the synthetic slices created with several medical experts and using an expert system.This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the postdoctoral, grant ref. ED481B-2021-059; and Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia”, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by “Secretaría Xeral de Universidades”, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project. Funding for open access charge: Universidade da Coruña/CISUG .Xunta de Galicia; ED481B-2021-059Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/0

Efficient semi-supervised hierarchical training for segmenting choroidal vessels and other structures on OCT images of multiple sclerosis patients

[Abstract]: Optical coherence tomography (OCT) is a non-invasive imaging technique used to diagnose ocular and systemic diseases. Recently, several clinical studies have linked changes in different ocular layers to the development of multiple sclerosis (MS), so accurate segmentation of these structures has become an essential task. Unfortunately, segmenting the entire set of structures involved is a very difficult task, due to their large number and variability. These two factors hinder the labeling of images and therefore severely restrict the ability to achieve a large dataset with all structures manually annotated, limiting the use of a standard supervised approach. In this paper, we propose a semi-supervised learning methodology to robustly segment ocular structures in OCT images using a limited number of partially labeled images. Our methodology maximizes the information we can extract from labeled images through hierarchical learning, where multiple decoders are used to extract segmented structures progressively. We use a reconstruction loss function to provide structural coherence to the segmentation and a teacher–student strategy to effectively leverage the information present in the set of unlabeled images. In addition to the segmentation of labeled structures, this hierarchical approach allows segmenting structures that are not labeled in the dataset such as the choroidal vessels. To validate the proposed methodology, we have carried out extensive experimentation using two datasets with different characteristics. These experiments have demonstrated a great potential of this methodology to train networks efficiently with partially labeled images, which allows to accurately extract the main biomarkers linked to the development of MS.This research was funded by Government of Spain, Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovación, Government of Spain through the research projects with reference PID2019-108435RB-I00, PDC2022-133132-I00 and TED2021-1312 01B-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia through the Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; CITIC, as Research Center accredited by Galician University System, is funded by ‘‘Consellería de Cultura, Educación e Universidade from Xunta de Galicia, Spain’’, supported in an 80% through ERDF Funds, ERDF Operational Programme Galicia 2014–2020, and the remaining 20% by ‘‘Secretaría Xeral de Universidades, Spain’’, grant ref. ED431G 2019/01. Emilio López Varela acknowledges its support under FPI, Spain Grant Program through PID2019-108435RB-I00 project.Xunta de Galicia; ED431C 2020/24Xunta de Galicia; ED431G 2019/0

Choroid segmentation in non-EDI OCT images of multiple sclerosis patients

[Abstract]: Optical coherence tomography (OCT) is a non-invasive diagnostic technique that can image ocular structures. Recently, this imaging technique has been used to diagnose and monitor patients with multiple sclerosis (MS), as several clinical studies have linked the development of MS to various changes in the eye. Among the different structures, one of the relevant biomarkers for MS analysis is the choroid. Systems such as Enhanced Depth Imaging (EDI) provide detailed images of the choroid region. However, OCT images are not routinely captured using this technology unless the study is specifically focused on choroidal analysis. In this work we propose a robust approach, based on convolutional neural networks to segment the choroid in non-EDI OCT images. The results obtained show that the proposed network manages to delimit the inferior contour of the choroid in a similar way to the experts.Xunta de Galicia; ED431C 2020/24Xunta de Galicia; IN845D 2020/38Xunta de Galicia; ED431G 2019/01This research was funded by Instituto de Salud Carlos III, Government of Spain, DTS18/00136 research project; Ministerio de Ciencia e Innovación y Universidades, Government of Spain, RTI2018-095894-B-I00 research project; Ministerio de Ciencia e Innovaciónn, Government of Spain through the research project with reference PID2019-108435RB-I00; Consellería de Cultura, Educación e Universidade, Xunta de Galicia, Grupos de Referencia Competitiva, grant ref. ED431C 2020/24; Axencia Galega de Innovación (GAIN), Xunta de Galicia, grant ref. IN845D 2020/38; CITIC, Centro de Investigación de Galicia ref. ED431G 2019/01, receives financial support from Consellería de Educación, Universidade e Formación Profesional, Xunta de Galicia, through the ERDF (80%) and Secretaría Xeral de Universidades (20%). Emilio López Varela acknowledges its support under FPI Grant Program through PID2019-108435RB-I00 project

Global disparities in surgeons’ workloads, academic engagement and rest periods: the on-calL shIft fOr geNEral SurgeonS (LIONESS) study

: The workload of general surgeons is multifaceted, encompassing not only surgical procedures but also a myriad of other responsibilities. From April to May 2023, we conducted a CHERRIES-compliant internet-based survey analyzing clinical practice, academic engagement, and post-on-call rest. The questionnaire featured six sections with 35 questions. Statistical analysis used Chi-square tests, ANOVA, and logistic regression (SPSS® v. 28). The survey received a total of 1.046 responses (65.4%). Over 78.0% of responders came from Europe, 65.1% came from a general surgery unit; 92.8% of European and 87.5% of North American respondents were involved in research, compared to 71.7% in Africa. Europe led in publishing research studies (6.6 ± 8.6 yearly). Teaching involvement was high in North America (100%) and Africa (91.7%). Surgeons reported an average of 6.7 ± 4.9 on-call shifts per month, with European and North American surgeons experiencing 6.5 ± 4.9 and 7.8 ± 4.1 on-calls monthly, respectively. African surgeons had the highest on-call frequency (8.7 ± 6.1). Post-on-call, only 35.1% of respondents received a day off. Europeans were most likely (40%) to have a day off, while African surgeons were least likely (6.7%). On the adjusted multivariable analysis HDI (Human Development Index) (aOR 1.993) hospital capacity > 400 beds (aOR 2.423), working in a specialty surgery unit (aOR 2.087), and making the on-call in-house (aOR 5.446), significantly predicted the likelihood of having a day off after an on-call shift. Our study revealed critical insights into the disparities in workload, access to research, and professional opportunities for surgeons across different continents, underscored by the HDI