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Hyperreflective foci on OCT may be a potential biomarker for treatment response and disease progression in diabetic retinal disease. In this study of Type 1 diabetics, associations were found between hyperreflective foci and disease severity and with both morphological and clinical characteristics., Purpose: To investigate hyperreflective foci (HF) on spectral-domain optical coherence tomography in patients with Type 1 diabetes mellitus across different stages of diabetic retinopathy (DR) and diabetic macular edema (DME) and to study clinical and morphological characteristics associated with HF. Methods: Spectral-domain optical coherence tomography scans and color fundus photographs were obtained of 260 patients. Spectral-domain optical coherence tomography scans were graded for the number of HF and other morphological characteristics. The distribution of HF across different stages of DR and DME severity were studied. Linear mixed-model analysis was used to study associations between the number of HF and clinical and morphological parameters. Results: Higher numbers of HF were found in patients with either stage of DME versus patients without DME (P < 0.001). A trend was observed between increasing numbers of HF and DR severity, although significance was only reached for moderate nonproliferative DR (P = 0.001) and proliferative DR (P = 0.019). Higher numbers of HF were associated with longer diabetes duration (P = 0.029), lower high-density lipoprotein cholesterol (P = 0.005), and the presence of microalbuminuria (P = 0.005). In addition, HF were associated with morphological characteristics on spectral-domain optical coherence tomography, including central retinal thickness (P = 0.004), cysts (P < 0.001), subretinal fluid (P = 0.001), and disruption of the external limiting membrane (P = 0.018). Conclusion: The number of HF was associated with different stages of DR and DME severity. The associations between HF and clinical and morphological characteristics can be of use in further studies evaluating the role of HF as a biomarker for disease progression and treatment response.

Contains fulltext : 282663.pdf (Publisher’s version ) (Open Access) An increasing number of artificial intelligence (AI) systems are being proposed in ophthalmology, motivated by the variety and amount of clinical and imaging data, as well as their potential benefits at the different stages of patient care. Despite achieving close or even superior performance to that of experts, there is a critical gap between development and integration of AI systems in ophthalmic practice. This work focuses on the importance of trustworthy AI to close that gap. We identify the main aspects or challenges that need to be considered along the AI design pipeline so as to generate systems that meet the requirements to be deemed trustworthy, including those concerning accuracy, resiliency, reliability, safety, and accountability. We elaborate on mechanisms and considerations to address those aspects or challenges, and define the roles and responsibilities of the different stakeholders involved in AI for ophthalmic care, i.e., AI developers, reading centers, healthcare providers, healthcare institutions, ophthalmological societies and working groups or committees, patients, regulatory bodies, and payers. Generating trustworthy AI is not a responsibility of a sole stakeholder. There is an impending necessity for a collaborative approach where the different stakeholders are represented along the AI design pipeline, from the definition of the intended use to post-market surveillance after regulatory approval. This work contributes to establish such multi-stakeholder interaction and the main action points to be taken so that the potential benefits of AI reach real-world ophthalmic settings.

The purpose of this study was to develop and validate a deep learning (DL) framework for the detection and quantification of reticular pseudodrusen (RPD) and drusen on optical coherence tomography (OCT) scans.A DL framework was developed consisting of a classification model and an out-of-distribution (OOD) detection model for the identification of ungradable scans; a classification model to identify scans with drusen or RPD; and an image segmentation model to independently segment lesions as RPD or drusen. Data were obtained from 1284 participants in the UK Biobank (UKBB) with a self-reported diagnosis of age-related macular degeneration (AMD) and 250 UKBB controls. Drusen and RPD were manually delineated by five retina specialists. The main outcome measures were sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), kappa, accuracy, intraclass correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curves.The classification models performed strongly at their respective tasks (0.95, 0.93, and 0.99 AUC, respectively, for the ungradable scans classifier, the OOD model, and the drusen and RPD classification models). The mean ICC for the drusen and RPD area versus graders was 0.74 and 0.61, respectively, compared with 0.69 and 0.68 for intergrader agreement. FROC curves showed that the model's sensitivity was close to human performance.The models achieved high classification and segmentation performance, similar to human performance.Application of this robust framework will further our understanding of RPD as a separate entity from drusen in both research and clinical settings.

Adversarial attacks are considered a potentially serious security threat for machine learning systems. Medical image analysis (MedIA) systems have recently been argued to be vulnerable to adversarial attacks due to strong financial incentives and the associated technological infrastructure. In this paper, we study previously unexplored factors affecting adversarial attack vulnerability of deep learning MedIA systems in three medical domains: ophthalmology, radiology, and pathology. We focus on adversarial black-box settings, in which the attacker does not have full access to the target model and usually uses another model, commonly referred to as surrogate model, to craft adversarial examples. We consider this to be the most realistic scenario for MedIA systems. Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model. Secondly, we study the influence of differences in development data between target and surrogate models. We further study the interaction of weight initialization and data differences with differences in model architecture. All experiments were done with a perturbation degree tuned to ensure maximal transferability at minimal visual perceptibility of the attacks. Our experiments show that pre-training may dramatically increase the transferability of adversarial examples, even when the target and surrogate's architectures are different: the larger the performance gain using pre-training, the larger the transferability. Differences in the development data between target and surrogate models considerably decrease the performance of the attack; this decrease is further amplified by difference in the model architecture. We believe these factors should be considered when developing security-critical MedIA systems planned to be deployed in clinical practice., Comment: First three authors contributed equally

Retinal swelling due to the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) is the current standard of care in assessing the presence and quantity of retinal fluid and image-guided treatment management. Deep learning methods have made their impact across medical imaging, and many retinal OCT analysis methods have been proposed. However, it is currently not clear how successful they are in interpreting the retinal fluid on OCT, which is due to the lack of standardized benchmarks. To address this, we organized a challenge RETOUCH in conjunction with MICCAI 2017, with eight teams participating. The challenge consisted of two tasks: fluid detection and fluid segmentation. It featured for the first time: all three retinal fluid types, with annotated images provided by two clinical centers, which were acquired with the three most common OCT device vendors from patients with two different retinal diseases. The analysis revealed that in the detection task, the performance on the automated fluid detection was within the inter-grader variability. However, in the segmentation task, fusing the automated methods produced segmentations that were superior to all individual methods, indicating the need for further improvements in the segmentation performance.

Importance: Treatments for geographic atrophy (GA), a late stage of age-related macular degeneration (AMD), are currently under development. Understanding the natural course is needed for optimal trial design. Although enlargement rates of GA and visual acuity (VA) in the short term are known from clinical studies, knowledge of enlargement in the long term, life expectancy, and visual course is lacking. Objective: To determine long-term enlargement of GA. Design, Setting, and Participants: In this study, participant data were collected from 4 population-based cohort studies, with up to 25 years of follow-up and eye examinations at 5-year intervals: the Rotterdam Study cohorts 1, 2, and 3 and the Blue Mountains Eye Study. Data were collected from 1990 to 2015, and data were analyzed from January 2019 to November 2020. Main Outcomes and Measures: Area of GA was measured pixel by pixel using all available imaging. Area enlargement and enlargement of the square root-transformed area, time until GA reached the central fovea, and time until death were assessed, and best-corrected VA, smoking status, macular lesions according to the Three Continent AMD Consortium classification, a modified version of the Wisconsin age-related maculopathy grading system, and AMD genetic variants were covariates in Spearman, Pearson, or Mann-Whitney analyses. Results: Of 171 included patients, 106 (62.0%) were female, and the mean (SD) age at inclusion was 82.6 (7.1) years. A total of 147 of 242 eyes with GA (60.7%) were newly diagnosed in our study. The mean area of GA at first presentation was 3.74 mm2(95% CI, 3.11-4.67). Enlargement rate varied widely between persons (0.02 to 4.05 mm2per year), with a mean of 1.09 mm2per year (95% CI, 0.89-1.30). Stage of AMD in the other eye was correlated with GA enlargement (Spearman ρ = 0.34; P =.01). Foveal involvement was already present in incident GA in 55 of 147 eyes (37.4%); 23 of 42 eyes (55%) developed this after a mean (range) period of 5.6 (3-12) years, and foveal involvement did not develop before death in 11 of 42 eyes (26%). After first diagnosis, 121 of 171 patients with GA (70.8%) died after a mean (SD) period of 6.4 (5.4) years. Visual function was visually impaired (less than 20/63) in 47 of 107 patients (43.9%) at last visit before death. Conclusions and Relevance: In this study, enlargement of GA appeared to be highly variable in the general population. More than one-third of incident GA was foveal at first presentation; those with extrafoveal GA developed foveal GA after a mean of 5.6 years. Future intervention trials should focus on recruiting those patients who have a high chance of severe visual decline within their life expectancy..

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

Technological developments in ophthalmic imaging and artificial intelligence (AI) create new possibilities for diagnostics in eye care. AI has already been applied in ophthalmic diabetes care. AI-systems currently detect diabetic retinopathy in general practice with a high sensitivity and specificity. AI-systems for the screening, monitoring and treatment of age-related macular degeneration and glaucoma are promising and are still being developed. AI-algorithms, however, only perform tasks for which they have been specifically trained and highly depend on the data and reference-standard that were used to train the system in identifying a certain abnormality or disease. How the data and the gold standard were established and determined, influences the performance of the algorithm. Furthermore, interpretability of deep learning algorithms is still an ongoing issue. By highlighting on images the areas that were critical for the decision of the algorithm, users can gain more insight into how algorithms come to a particular result.

RESUMEN Objetivos Este estudio buscó comprender la problemática sanitaria asociada con la presencia de plomo y mercurio en el agua del río Bogotá. Materiales y Métodos Se realizó un estudio mixto con entrevistas, encuesta y mediciones de zinc protoporfirina (ZPP) y mercurio en sangre a 172 individuos. Se identificaron variables asociadas con las mayores concentraciones, y mediante análisis comparativo cualitativo los atributos de los casos con mayor exposición. Resultados Las entrevistas reportaron que la contaminación del río se asocia con enfermedades, pese a que la exposición directa al agua no es frecuente. El agua del río es usada ampliamente para riego de cultivos que se venden en otros municipios. Las comunidades con mayor exposición al agua del río son de Tocaima y Girardot. Los mayores niveles de ZPP se observaron entre quienes vivían en estos municipios, con ocupación hogar y que realizaban actividades extralaborales con metales. Los niveles elevados de ZPP se asociaron con depresión, sangrado nasal y pérdida del apetito. Las mayores concentraciones de mercurio se presentaron entre habitantes de Girardot. Estas se asociaron con sudoración, desorientación, náuseas y diarrea. Conclusión Se presentaron bajos niveles de plomo y mercurio en la población estudiada, aunque hay casos específicos con alta vulnerabilidad social que requieren atención especial. ABSTRACT Objectives This study sought to understand the health problems associated with the presence of lead and mercury in the water of the Bogotá River. Materials and Methods A mixed methods study was conducted using interviews, surveys and measurements of zinc protoporphyrin (ZPP) and mercury in blood from 172 individuals. Variables associated with the highest concentrations were identified, as well as the attributes of the cases with increased exposure by means of a qualitative comparative analysis. Results Interviews reported that river pollution is associated with diseases, although direct exposure to water is rare. River water is widely used for irrigation of crops that are sold in other municipalities. Communities with greater exposure to river water are Tocaima and Girardot. The highest levels of ZPP were observed among those living in these municipalities, did activities at home and performed non-work activities using metals. Increased ZPP levels were associated with depression, nasal bleeding and loss of appetite. The highest concentrations of mercury were found in the inhabitants of Girardot, and were associated with sweating, disorientation, nausea and diarrhea. Conclusion Low levels of lead and mercury were observed in the study population, although there are specific cases with high social vulnerability that require special attention.

Contains fulltext : 235480.pdf (Publisher’s version ) (Open Access) PURPOSE: We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD). DESIGN: Development and validation of a deep-learning model for feature segmentation. METHODS: Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve. RESULTS: On 11 of 13 features, the model obtained a mean Dice score of 0.63 +/- 0.15, compared with 0.61 +/- 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 +/- 0.22, compared with 0.62 +/- 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers. CONCLUSIONS: The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.

Purpose: To develop and validate a deep learning model for automatic segmentation of geographic atrophy (GA) in color fundus images (CFIs) and its application to study growth rate of GA. Participants: 409 CFIs of 238 eyes with GA from the Rotterdam Study (RS) and the Blue Mountain Eye Study (BMES) for model development, and 5,379 CFIs of 625 eyes from the Age-Related Eye Disease Study (AREDS) for analysis of GA growth rate. Methods: A deep learning model based on an ensemble of encoder-decoder architectures was implemented and optimized for the segmentation of GA in CFIs. Four experienced graders delineated GA in CFIs from RS and BMES. These manual delineations were used to evaluate the segmentation model using 5-fold cross-validation. The model was further applied to CFIs from the AREDS to study the growth rate of GA. Linear regression analysis was used to study associations between structural biomarkers at baseline and GA growth rate. A general estimate of the progression of GA area over time was made by combining growth rates of all eyes with GA from the AREDS set. Results: The model obtained an average Dice coefficient of 0.72 $\pm$ 0.26 on the BMES and RS. An intraclass correlation coefficient of 0.83 was reached between the automatically estimated GA area and the graders' consensus measures. Eight automatically calculated structural biomarkers (area, filled area, convex area, convex solidity, eccentricity, roundness, foveal involvement and perimeter) were significantly associated with growth rate. Combining all growth rates indicated that GA area grows quadratically up to an area of around 12 mm$^{2}$, after which growth rate stabilizes or decreases. Conclusion: The presented deep learning model allowed for fully automatic and robust segmentation of GA in CFIs. These segmentations can be used to extract structural characteristics of GA that predict its growth rate., Comment: 22 pages, 3 tables, 4 figures, 1 supplemental figure

The pretreatment of biomass is a critical requirement of bio-renewable fuel production from lignocellulose. Although current processes primarily involve chemical and physical approaches, the biological breakdown of lignin using enzymes and microorganisms is quickly becoming an interesting eco-friendly alternative to classical processes. As a result, bioprospection of wild fungi from naturally occurring lignin-rich sources remains a suitable method to uncover and isolate new species exhibiting ligninolytic activity. In this study, wild species of white rot fungi were collected from Colombian forests based on their natural wood decay ability and high capacity to secrete oxidoreductases with high affinity for phenolic polymers such as lignin. Based on high activity obtained from solid-state fermentation using a lignocellulose source from oil palm as matrix, we describe the isolation and whole-genome sequencing ofDictyopanus pusillus, a wild basidiomycete fungus exhibiting ABTS oxidation as an indication of laccase activity. Functional characterization of a crude enzymatic extract identified laccase activity as the main enzymatic contributor to fungal extracts, an observation supported by the identification of 13 putative genes encoding for homologous laccases in the genome. To the best of our knowledge, this represents the first report of an enzymatic extract exhibiting laccase activity in theDictyopanusgenera, offering means to exploit this species and its enzymes for the delignification process of lignocellulosic by-products from oil palm.

espanolComercialmente las enzimas de sacarificacion son mas costosas que las de licuefaccion, por tal razon se buscan estrategias que permitan el suministro de estas enzimas a bajo costo. Los objetivos de este estudio fueron evaluar extractos enzimaticos sacaroliticos a partir de cepas nativas, seleccionar un extracto y determinar las mejores variables para la produccion de jarabes de glucosa. Para ello, en trece hongos filamentosos aislados de yuca amarga Manihot sp. fueron evaluadas la actividad sacarolitica, la hidrolisis de maltosa, la produccion de glucosa y la formacion de biomasa bajo condiciones de fermentacion sumergida. El aislamiento identificado como Aspergillus A1 fue seleccionado por presentar la mas alta actividad volumetrica (0,09UI.L-1). Durante la fermentacion en estado solido se corroboro la presencia de proteinas mediante el metodo de electroforesis SDS-PAGE y se evidencio una banda de mayor intensidad con peso molecular entre 60 y 70 kDa. Para el extracto enzimatico de Aspergillus A1 se determino que las mejores condiciones experimentales de sacarificacion, con el uso de maltosa como sustrato, fueron pH 4,0, temperatura 55,0 °C, y sin agitacion. Igualmente en la evaluacion del efecto de los cofactores Cu2+, K+, Mg2+ y Na+ en concentraciones de 1 mM, se observo que todos incrementan la actividad enzimatica especialmente el Mg2+ , el cual la aumenta en 1,32 UI.mg-1 (32,4%). La estabilidad termica de la proteina a 55,0°C fue de 120 minutos. La capacidad sacarolitica del extracto enzimatico fue confirmada usando como sustrato almidon de yuca amarga licuado. EnglishThe saccharification enzymes are more expensive than liquefaction enzymes for that reason are sought strategies that allow the supply of these enzymes at low cost. The objectives of this study were to evolve saccharolytic enzymatic extracts from native strains, to select an extract and to determine the best variables for the production of glucose syrups from liquefied bitter cassava starch.Thirteen mushrooms isolated from sour cassava (Manihot sp). were evaluated for their saccharolytic activity, hydrolysis of maltose, glucose production and biomass formation in submerged fermentation. Aspergillus A1 was selected because it had the highest volumetric activity (0.09UI.L-1). During solid-state fermentation, the presence of proteins was corroborated by electrophoresis SDS-PAGE. Through various experiments, the best experimental condition were pH 4.0, agitation 0r.p.m and temperature 55.0°C; the effect of cofactors Cu2+, K+, Mg2+ and Na+ was evaluated, Mg2+ increases the activity in 1.32UImg-1 (32.4%). The thermal stability at 55.0°C is 120 minutes. Finally, the saccharolytic capacity of the enzymatic extract was confirmed using liquefied cassava starch. portuguesComercialmente as enzimas de sacarificacao sao mais custosas do que as de liquefacao, por esta razao procuram-se estrategias que permitam fornecer este tipo de enzimas a baixo custo. Os objetivos deste estudio foram avaliar extratos enzimaticos sacarolīticos de cepas nativas, selecionar um extrato e determinar as melhores variaveis para a producao de xaropes de glicose. Para isto, de treze fungos filamentosos isolados de mandioca amarga Manihot sp., foram avaliadas a atividade sacarolitica, a hidrolise de maltose, a producao de glicose e a formacao de biomassa em condicoes de fermentacao submersa. O isolamento Aspergillus A1 foi selecionado por apresentar a maior atividade volumetrica (0,091UI.L-1). Durante a fermentacao em superficie a presenca de proteinas foi confirmada pelo metodo de eletroforese de SDS-PAGE junto com uma banda de maior intensidade com peso molecular entre os 60 e 70kDa. Para os preparados enzimaticos de Aspergillus A1 determinou-se que as melhores condicoes experimentais de sacarificacao usando maltose como substrato foram pH 4,0, temperatura de 55,0°C, sem agitacao. Igualmente na avaliacao do efeito dos cofatores Cu2+, K+, Mg2+ e Na+ em concentracoes de 1 mM, observou-se que todos incrementam a atividade enzimatica principalmente o Mg2+ em 1,32UI.mg-1 (32,4%) em relacao ao controle. A estabilidade termica da proteina de 55,0°C e de 120 minutos. A capacidade sacarolitica do extrato enzimatico foi confirmada usando substrato hidrolisado de mandioca amarga.

Contains fulltext : 191291.pdf (Publisher’s version ) (Open Access) We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.

Crystal violet is a hazardous organic triphenylmethane dye used for various dyeing processes as well as for bacterial identification. A strategy for dye pre-treatment is the use of macromycetes from Basidiomycota to achieve mineralization of hazardous compounds trough ligninolytic enzymes. Biocatalysis of hazardous compounds offers a biotechnological alternative over traditional physicochemical processes. Crystal violet degradation by the native ligninolytic isolate Dictyopanus pusillus LMB4 was assessed. Single and combined effects of enzymatic variables in crystal violet degradation were evaluated for a crude enzymatic extract from D. pusillus LMB4 recovered by solid-state fermentation. Enzymatic variables optimization improved the crystal violet degradation up to 81%. Results showed reduction of intermediate compounds from crystal violet degradation and a hypothetical degradation pathway was proposed based on GS-MS analyses. To the best of our knowledge, this represents the first report that evaluates crystal violet degradation by crude enzymatic extracts from Dictyopanus genera.

Interpretability of deep learning (DL) systems is gaining attention in medical imaging to increase experts' trust in the obtained predictions and facilitate their integration in clinical settings. We propose a deep visualization method to generate interpretability of DL classification tasks in medical imaging by means of visual evidence augmentation. The proposed method iteratively unveils abnormalities based on the prediction of a classifier trained only with image-level labels. For each image, initial visual evidence of the prediction is extracted with a given visual attribution technique. This provides localization of abnormalities that are then removed through selective inpainting. We iteratively apply this procedure until the system considers the image as normal. This yields augmented visual evidence, including less discriminative lesions which were not detected at first but should be considered for final diagnosis. We apply the method to grading of two retinal diseases in color fundus images: diabetic retinopathy (DR) and age-related macular degeneration (AMD). We evaluate the generated visual evidence and the performance of weakly-supervised localization of different types of DR and AMD abnormalities, both qualitatively and quantitatively. We show that the augmented visual evidence of the predictions highlights the biomarkers considered by experts for diagnosis and improves the final localization performance. It results in a relative increase of 11.2± 2.0% per image regarding sensitivity averaged at 10 false positives/image on average, when applied to different classification tasks, visual attribution techniques and network architectures. This makes the proposed method a useful tool for exhaustive visual support of DL classifiers in medical imaging.

Several prediction models for progression of age-related macular degeneration (AMD) have been developed, but the added value of using genetic information in those models in addition to clinical characteristics is ambiguous. In this prospective cohort study, we explored the added value of genetics using a genetic risk score (GRS) based on 52 AMD-associated variants, in addition to the clinical severity grading at baseline as quantified by validated drusen detection software, to predict disease progression in 177 AMD patients after 6.5 years follow-up. The GRS was strongly associated with the drusen coverage at baseline (P 2 increased from 0.46 to 0.56. This improvement by the GRS was predominantly seen in patients with a drusen coverage

Purpose To investigate intersibling phenotypic concordance in Stargardt disease (STGD1). Design Retrospective cohort study. Participants Siblings with genetically confirmed STGD1 and at least 1 available fundus autofluorescence (FAF) image of both eyes. Methods We compared age at onset within families. Disease duration was matched to investigate differences in best-corrected visual acuity (BCVA) and compared the survival time for reaching severe visual impairment ( 1.0 logarithm of the minimum angle of resolution [logMAR]). Central retinal atrophy area was quantified independently by 2 experienced graders using semiautomated software and compared between siblings. Both graders performed qualitative assessment of FAF and spectral-domain (SD) OCT images to identify phenotypic differences. Main Outcome Measures Differences in age at onset, disease duration-matched BCVA, time to severe visual impairment development, FAF atrophy area, FAF patterns, and genotypes. Results Substantial differences in age at onset were present in 5 of 17 families, ranging from 13 to 39 years. Median BCVA at baseline was 0.60 logMAR (range, –0.20 to 2.30 logMAR; Snellen equivalent, 20/80 [range, 20/12–hand movements]) in the right eye and 0.50 logMAR (range, –0.20 to 2.30 logMAR; Snellen equivalent, 20/63 [range, 20/12–hand movements]) in the left eye. Disease duration-matched BCVA was investigated in 12 of 17 families, and the median difference was 0.41 logMAR (range, 0.00–1.10 logMAR) for the right eye and 0.41 logMAR (range, 0.00–1.08 logMAR) for the left eye. We observed notable differences in time to severe visual impairment development in 7 families, ranging from 1 to 29 years. Median central retinal atrophy area was 11.38 mm2 in the right eye (range, 1.98–44.78 mm2) and 10.59 mm2 in the left eye (range, 1.61–40.59 mm2) and highly comparable between siblings. Similarly, qualitative FAF and SD OCT phenotypes were highly comparable between siblings. Conclusions Phenotypic discordance between siblings with STGD1 carrying the same ABCA4 variants is a prevalent phenomenon. Although the FAF phenotypes are highly comparable between siblings, functional outcomes differ substantially. This complicates both sibling-based prognosis and genotype-phenotype correlations and has important implications for patient care and management.

Contains fulltext : 220771.pdf (Publisher’s version ) (Open Access) PURPOSE: To validate the performance of a commercially available, CE-certified deep learning (DL) system, RetCAD v.1.3.0 (Thirona, Nijmegen, The Netherlands), for the joint automatic detection of diabetic retinopathy (DR) and age-related macular degeneration (AMD) in colour fundus (CF) images on a dataset with mixed presence of eye diseases. METHODS: Evaluation of joint detection of referable DR and AMD was performed on a DR-AMD dataset with 600 images acquired during routine clinical practice, containing referable and non-referable cases of both diseases. Each image was graded for DR and AMD by an experienced ophthalmologist to establish the reference standard (RS), and by four independent observers for comparison with human performance. Validation was furtherly assessed on Messidor (1200 images) for individual identification of referable DR, and the Age-Related Eye Disease Study (AREDS) dataset (133 821 images) for referable AMD, against the corresponding RS. RESULTS: Regarding joint validation on the DR-AMD dataset, the system achieved an area under the ROC curve (AUC) of 95.1% for detection of referable DR (SE = 90.1%, SP = 90.6%). For referable AMD, the AUC was 94.9% (SE = 91.8%, SP = 87.5%). Average human performance for DR was SE = 61.5% and SP = 97.8%; for AMD, SE = 76.5% and SP = 96.1%. Regarding detection of referable DR in Messidor, AUC was 97.5% (SE = 92.0%, SP = 92.1%); for referable AMD in AREDS, AUC was 92.7% (SE = 85.8%, SP = 86.0%). CONCLUSION: The validated system performs comparably to human experts at simultaneous detection of DR and AMD. This shows that DL systems can facilitate access to joint screening of eye diseases and become a quick and reliable support for ophthalmological experts.

Glaucoma detection in color fundus images is a challenging task that requires expertise and years of practice. In this study we exploited the application of different Convolutional Neural Networks (CNN) schemes to show the influence in the performance of relevant factors like the data set size, the architecture and the use of transfer learning vs newly defined architectures. We also compared the performance of the CNN based system with respect to human evaluators and explored the influence of the integration of images and data collected from the clinical history of the patients. We accomplished the best performance using a transfer learning scheme with VGG19 achieving an AUC of 0.94 with sensitivity and specificity ratios similar to the expert evaluators of the study. The experimental results using three different data sets with 2313 images indicate that this solution can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs. info:eu-repo/semantics/acceptedVersion

Contains fulltext : 167797.pdf (Publisher’s version ) (Open Access) Convolutional neural networks (CNNs) are deep learning network architectures that have pushed forward the state-of-the-art in a range of computer vision applications and are increasingly popular in medical image analysis. However, training of CNNs is time-consuming and challenging. In medical image analysis tasks, the majority of training examples are easy to classify and therefore contribute little to the CNN learning process. In this paper, we propose a method to improve and speed-up the CNN training for medical image analysis tasks by dynamically selecting misclassified negative samples during training. Training samples are heuristically sampled based on classification by the current status of the CNN. Weights are assigned to the training samples and informative samples are more likely to be included in the next CNN training iteration. We evaluated and compared our proposed method by training a CNN with (SeS) and without (NSeS) the selective sampling method. We focus on the detection of hemorrhages in color fundus images. A decreased training time from 170 epochs to 60 epochs with an increased performance-on par with two human experts-was achieved with areas under the receiver operating characteristics curve of 0.894 and 0.972 on two data sets. The SeS CNN statistically outperformed the NSeS CNN on an independent test set.

Contains fulltext : 168320.pdf (Publisher’s version ) (Open Access) We developed an automatic system to identify and differentiate color fundus images containing no lesions, drusen or exudates. Drusen and exudates are lesions with a bright appearance, associated with age-related macular degeneration and diabetic retinopathy, respectively. The system consists of three lesion detectors operating at pixel-level, combining their outputs using spatial pooling and classification with a random forest classifier. System performance was compared with ratings of two independent human observers using human-expert annotations as reference. Kappa agreements of 0.89, 0.97 and 0.92 and accuracies of 0.93, 0.98 and 0.95 were obtained for the system and observers, respectively.

Tuberculosis (TB) is a common disease with high mortality and morbidity rates worldwide. Automatic systems to detect TB on chest radiographs (CXRs) can improve the efficiency of diagnostic algorithms for pulmonary TB. The diverse manifestation of TB on CXRs from different populations requires a system that can be adapted to deal with different types of abnormalities. A computer aided detection (CAD) system was developed which combines several subscores of supervised subsystems detecting textural, shape, and focal abnormalities into one TB score. A general framework was developed to combine an arbitrary number of subscores: subscores were normalized, collected in a feature vector and then combined using a supervised classifier into one combined score. The method was evaluated on two databases, both consisting of 200 digital CXRs, from: (A) Western high-risk group screening, (B) TB suspect screening in Africa. The subscores and combined score were compared to (1) an external, non-radiological, reference and (2) a radiological reference determined by a human expert. Performance was measured using Receiver Operator Characteristic (ROC) analysis. Different subscores performed best in the two databases. The combined TB score performed better than the individual subscores, except for the external reference in database B. The performances of the independent observer were slightly higher than the combined TB score. Compared to the external reference, differences in performance between the combined TB score and the independent observer were not significant in both databases. Supervised combination to compute an overall TB score allows for a necessary adaptation of the CAD system to different settings or different operational requirements.

The pretreatment of biomass remains a critical requirement for bio-renewable fuel production from lignocellulose. Although current processes primarily involve chemical and physical approaches, the biological breakdown of lignin using enzymes and microorganisms is quickly becoming an interesting eco-friendly alternative to classical processes. As a result, bioprospection of wild fungi from naturally occurring lignin-rich sources remains a suitable method to uncover and isolate new species exhibiting ligninolytic activity. In this study, wild species of white rot fungi were collected from Colombian forests based on their natural wood decay ability and high capacity to secrete oxidoreductases with high affinity for phenolic polymers such as lignin. Based on high activity obtained from solid-state fermentation using a lignocellulose source from oil palm as matrix, we describe the isolation and whole-genome sequencing of Dictyopanus pusillus, a wild basidiomycete fungus exhibiting ABTS oxidation as an indication of laccase activity. Functional characterization of a crude enzymatic extract identified laccase activity as the main enzymatic contributor to fungal extracts, an observation supported by the identification of 13 putative genes encoding for homologous laccases in the genome. To the best of our knowledge, this represents the first report of an enzymatic extract exhibiting laccase activity in the Dictyopanus genera, offering means to exploit this species and its enzymes for the delignification process of lignocellulosic by-products from oil palm.

Purpose: Currently, no outcome measures are clinically validated and accepted as clinical endpoints by regulatory agencies for drug development in intermediate age-related macular degeneration (iAMD). The MACUSTAR Consortium, a public-private research group funded by the European Innovative Medicines Initiative intends to close this gap. Procedures: Development of study protocol and statistical analysis plan including predictive modelling of multimodal endpoints based on a review of the literature and expert consensus. Results: This observational study consists of a cross-sectional and a longitudinal part. Functional outcome measures assessed under low contrast and low luminance have the potential to detect progression of visual deficit within iAMD and to late AMD. Structural outcome measures will be multimodal and investigate topographical relationships with function. Current patient-reported outcome measures (PROMs) are not acceptable to regulators and may not capture the functional deficit specific to iAMD with needed precision, justifying development of novel PROMs for iAMD. The total sample size will be n = 750, consisting mainly of subjects with iAMD (n = 600). Conclusions: As clinical endpoints currently accepted by regulators cannot detect functional loss or patient-relevant impact in iAMD, we will clinically validate novel candidate endpoints for iAMD.

This study sought to understand the health problems associated with the presence of lead and mercury in the water of the Bogotá River.A mixed methods study was conducted using interviews, surveys and measurements of zinc protoporphyrin (ZPP) and mercury in blood from 172 individuals. Variables associated with the highest concentrations were identified, as well as the attributes of the cases with increased exposure by means of a qualitative comparative analysis.Interviews reported that river pollution is associated with diseases, although direct exposure to water is rare. River water is widely used for irrigation of crops that are sold in other municipalities. Communities with greater exposure to river water are Tocaima and Girardot. The highest levels of ZPP were observed among those living in these municipalities, did activities at home and performed non-work activities using metals. Increased ZPP levels were associated with depression, nasal bleeding and loss of appetite. The highest concentrations of mercury were found in the inhabitants of Girardot, and were associated with sweating, disorientation, nausea and diarrhea.Low levels of lead and mercury were observed in the study population, although there are specific cases with high social vulnerability that require special attention.Este estudio buscó comprender la problemática sanitaria asociada con la presencia de plomo y mercurio en el agua del río Bogotá.Se realizó un estudio mixto con entrevistas, encuesta y mediciones de zinc protoporfirina (ZPP) y mercurio en sangre a 172 individuos. Se identificaron variables asociadas con las mayores concentraciones, y mediante análisis comparativo cualitativo los atributos de los casos con mayor exposición.Las entrevistas reportaron que la contaminación del río se asocia con enfermedades, pese a que la exposición directa al agua no es frecuente. El agua del río es usada ampliamente para riego de cultivos que se venden en otros municipios. Las comunidades con mayor exposición al agua del río son de Tocaima y Girardot. Los mayores niveles de ZPP se observaron entre quienes vivían en estos municipios, con ocupación hogar y que realizaban actividades extralaborales con metales. Los niveles elevados de ZPP se asociaron con depresión, sangrado nasal y pérdida del apetito. Las mayores concentraciones de mercurio se presentaron entre habitantes de Girardot. Estas se asociaron con sudoración, desorientación, náuseas y diarrea.Se presentaron bajos niveles de plomo y mercurio en la población estudiada, aunque hay casos específicos con alta vulnerabilidad social que requieren atención especial.

Purpose: Currently, no outcome measures are clinically validated and accepted as clinical endpoints by regulatory agencies for drug development in intermediate age-related macular degeneration (iAMD). The MACUSTAR Consortium, a public-private research group funded by the European Innovative Medicines Initiative intends to close this gap. Procedures: Development of study protocol and statistical analysis plan including predictive modelling of multimodal endpoints based on a review of the literature and expert consensus. Results: This observational study consists of a cross-sectional and a longitudinal part. Functional outcome measures assessed under low contrast and low luminance have the potential to detect progression of visual deficit within iAMD and to late AMD. Structural outcome measures will be multimodal and investigate topographical relationships with function. Current patient-reported outcome measures (PROMs) are not acceptable to regulators and may not capture the functional deficit specific to iAMD with needed precision, justifying development of novel PROMs for iAMD. The total sample size will be n = 750, consisting mainly of subjects with iAMD (n = 600). Conclusions: As clinical endpoints currently accepted by regulators cannot detect functional loss or patient-relevant impact in iAMD, we will clinically validate novel candidate endpoints for iAMD.

Contains fulltext : 153295.pdf (Publisher’s version ) (Open Access) Automated quantitative analysis systems for medical images often lack the capability to successfully process images from multiple sources. Normalization of such images prior to further analysis is a possible solution to this limitation. This work presents a general method to normalize medical images and thoroughly investigates its effectiveness for chest radiography (CXR). The method starts with an energy decomposition of the image in different bands. Next, each band's localized energy is scaled to a reference value and the image is reconstructed. We investigate iterative and local application of this technique. The normalization is applied iteratively to the lung fields on six datasets from different sources, each comprising 50 normal CXRs and 50 abnormal CXRs. The method is evaluated in three supervised computer-aided detection tasks related to CXR analysis and compared to two reference normalization methods. In the first task, automatic lung segmentation, the average Jaccard overlap significantly increased from 0:720:30 and 0:870:11 for both reference methods to 0:89 0:09 (p < 0:01) with normalization. The second experiment was aimed at segmentation of the clavicles. The reference methods had an average Jaccard index of 0:570:26 and 0:530:26; with normalization this significantly increased to 0:68 0:23 (p < 0:01). The third experiment was detection of tuberculosis related abnormalities in the lung fields. The average area under the Receiver Operating Curve increased significantly from 0:720:14 and 0:790:06 using the reference methods to 0:85 0:05 (p < 0:01) with normalization. We conclude that the normalization can be successfully applied in chest radiography and makes supervised systems more generally applicable to data from different sources.