An interactive two-dimensional approach to query aspects rewriting in systematic reviews. IMS unipd at CLEF eHealth task 2

International audienc

The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology

Fully automatized parallel segmentation of the optic disc in retinal fundus images

This paper presents a fully automatic parallel software for the localization of the optic disc (OD) in retinal fundus color images. A new method has been implemented with the Graphics Processing Units (GPU) technology. Image edges are extracted using a new operator, called AGP-color segmentator. The resulting image is binarized with Hamadani’s technique and, finally, a new algorithm called Hough circle cloud is applied for the detection of the OD. The reliability of the tool has been tested with 129 images from the public databases DRIVE and DIARETDB1 obtaining an average accuracy of 99.6% and a mean consumed time per image of 7.6 and 16.3 s respectively. A comparison with several state-of-the-art algorithms shows that our algorithm represents a significant improvement in terms of accuracy and efficiency.Ministerio de Economía y Competitividad TIN2012-3743

A Machine Learning System for Glaucoma Detection using Inexpensive Machine Learning

This thesis presents a neural network system which segments images of the retina to calculate the cup-to-disc ratio, one of the diagnostic indicators of the presence or continuing development of glaucoma, a disease of the eye which causes blindness. The neural network is designed to run on commodity hardware and to be run with minimal skill required from the user by packaging the software required to run the network into a Singularity image. The RIGA dataset used to train the network provides images of the retina which have been annotated with the location of the optic cup and disc by six ophthalmologists, and six separate models have been trained, one for each ophthalmologist. Previous work with this dataset has combined the annotations into a consensus annotation, or taken all annotations together as a group to create a model, as opposed to creating individual models by annotator. The interannotator disagreements in the data are large and the method implemented in this thesis captures their differences rather than combining them together. The mean error of the pixel label predictions across the six models is 10.8%; the precision and recall for the predictions of the cup-to-disc ratio across the six models are 0.920 and 0.946, respectively

Interobserver and Intertest Agreement in Telemedicine Glaucoma Screening with Optic Disk Photos and Optical Coherence Tomography

Glaucoma; Retinògrafs; TelemedicinaGlaucoma; Retinógrafos; TelemedicinaGlaucoma; Retinographs; TelemedicinePurpose: To evaluate interobserver and intertest agreement between optical coherence tomography (OCT) and retinography in the detection of glaucoma through a telemedicine program. Methods: A stratified sample of 4113 individuals was randomly selected, and those who accepted underwent examination including visual acuity, intraocular pressure (IOP), non-mydriatic retinography, and imaging using a portable OCT device. Participants’ data and images were uploaded and assessed by 16 ophthalmologists on a deferred basis. Two independent evaluations were performed for all participants. Agreement between methods was assessed using the kappa coefficient and the prevalence-adjusted bias-adjusted kappa (PABAK). We analyzed potential factors possibly influencing the level of agreement. Results: The final sample comprised 1006 participants. Of all suspected glaucoma cases (n = 201), 20.4% were identified in retinographs only, 11.9% in OCT images only, 46.3% in both, and 21.4% were diagnosed based on other data. Overall interobserver agreement outcomes were moderate to good with a kappa coefficient of 0.37 and a PABAK index of 0.58. Higher values were obtained by experienced evaluators (kappa = 0.61; PABAK = 0.82). Kappa and PABAK values between OCT and photographs were 0.52 and 0.82 for the first evaluation. Conclusion: In a telemedicine screening setting, interobserver agreement on diagnosis was moderate but improved with greater evaluator expertise.The study has been funded by the Fondo de InvestigacionesSanitarias of the Spanish Ministry of Health (PI15/00412)

Interobserver and Intertest Agreement in Telemedicine Glaucoma Screening with Optic Disk Photos and Optical Coherence Tomography

t: Purpose: To evaluate interobserver and intertest agreement between optical coherence tomography (OCT) and retinography in the detection of glaucoma through a telemedicine program. Methods: A stratified sample of 4113 individuals was randomly selected, and those who accepted underwent examination including visual acuity, intraocular pressure (IOP), non-mydriatic retinography, and imaging using a portable OCT device. Participants' data and images were uploaded and assessed by 16 ophthalmologists on a deferred basis. Two independent evaluations were performed for all participants. Agreement between methods was assessed using the kappa coefficient and the prevalence-adjusted bias-adjusted kappa (PABAK). We analyzed potential factors possibly influencing the level of agreement. Results: The final sample comprised 1006 participants. Of all suspected glaucoma cases (n = 201), 20.4% were identified in retinographs only, 11.9% in OCT images only, 46.3% in both, and 21.4% were diagnosed based on other data. Overall interobserver agreement outcomes were moderate to good with a kappa coefficient of 0.37 and a PABAK index of 0.58. Higher values were obtained by experienced evaluators (kappa = 0.61; PABAK = 0.82). Kappa and PABAK values between OCT and photographs were 0.52 and 0.82 for the first evaluation. Conclusion: In a telemedicine screening setting, interobserver agreement on diagnosis was moderate but improved with greater evaluator expertise