Performance of classification systems for age-related macular degeneration in the rotterdam study

Purpose: To compare frequently used classification systems for age-related macular degeneration(AMD) in their abilty to predictlate AMD. Methods:Intotal,9066participantsfromthepopulation-basedRotterdamStudywere followedupforprogressionofAMDduringastudyperiodupto30years.AMDlesions weregradedoncolorfundusphotographsafterconfirmationonotherimagemodalities andgroupedatbaselineaccordingtosixclassificationsystems.LateAMDwasdefinedas geographicatrophyorchoroidalneovascularization.Incidencerate(IR)andcumulative incidence(CuI)oflateAMDwerecalculated,andKaplan-Meierplotsandareaunderthe operating characteristics curves(AUCs)wereconstructed. Results: A total of 186 persons developed incident late AMD during a mean follow-up timeof8.7years.TheAREDSsimplifiedscaleshowedthehighestIRforlateAMDat104 cases/1000 py for ages 75 years. The 3-Continent harmonization classification provided the most stable progression. Drusen area >10% ETDRS grid (hazard ratio 30.05, 95% confidence interval [CI] 19.25–46.91) was most prognostic of progression. The highest AUC of late AMD (0.8372, 95% CI: 0.8070-0.8673) was achieved when all AMD features present at base line were included. Conclusions: Highest turnover rates from intermediate to late AMD were provided by the AREDS simplified scale and the Rotterdam classification. The 3-Continent harmonization classification showed the most stable progression. All features, especially drusenarea,contribute to late AMD prediction. Translational Relevance: Findings will help stakeholders select appropriate classification systems for screening,deep learning algorithms, or trials

Material dependent differences in inflammatory gene expression by giant cells during the foreign body reaction

Multinucleated giant cells (GCs) are often observed in the foreign body reaction against implanted materials. The in vivo function of GCs in this inflammatory process remains to be elucidated. GCs degrade collagen implants in rats and may also orchestrate the inflammatory process via the expression and secretion of modulators, such as cytokines and chemokines. In this study, we show that the gene expression of PMN chemoattractants, CXCL1/KC and CXCL2/MIP-2, is high in GCs micro-dissected from explanted Dacron, cross-linked collagen (HDSC), and bioactive ureido-pyrimidinone functionalized oligocaprolactone (bioactive PCLdiUPy). Conversely, the gene expression levels of TGF beta and pro-angiogenic mediators VEGF and FGF were found to be low in these GCs as compared with the expression levels in total explants. GCs in bioactive PCLdiUPy displayed high cytokine and angiogenic mediator expression compared with GCs isolated from the two other studied materials, whereas chemokine gene expression in GCs isolated form HDSC was low. Thus, GCs adopt their expression profile in response to the material that is encountered. (c) 2007 Wiley Periodicals, In

[Artificial intelligence for eye care]

Contains fulltext : 229079.pdf (Publisher’s version ) (Closed access)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

Performance of Classification Systems for Age-Related Macular Degeneration in the Rotterdam Study

Contains fulltext : 225990.pdf (publisher's version ) (Open Access)PURPOSE: To compare frequently used classification systems for age-related macular degeneration (AMD) in their abilty to predict late AMD. METHODS: In total, 9066 participants from the population-based Rotterdam Study were followed up for progression of AMD during a study period up to 30 years. AMD lesions were graded on color fundus photographs after confirmation on other image modalities and grouped at baseline according to six classification systems. Late AMD was defined as geographic atrophy or choroidal neovascularization. Incidence rate (IR) and cumulative incidence (CuI) of late AMD were calculated, and Kaplan-Meier plots and area under the operating characteristics curves (AUCs) were constructed. RESULTS: A total of 186 persons developed incident late AMD during a mean follow-up time of 8.7 years. The AREDS simplified scale showed the highest IR for late AMD at 104 cases/1000 py for ages 75 years. The 3-Continent harmonization classification provided the most stable progression. Drusen area >10% ETDRS grid (hazard ratio 30.05, 95% confidence interval [CI] 19.25-46.91) was most prognostic of progression. The highest AUC of late AMD (0.8372, 95% CI: 0.8070-0.8673) was achieved when all AMD features present at baseline were included. CONCLUSIONS: Highest turnover rates from intermediate to late AMD were provided by the AREDS simplified scale and the Rotterdam classification. The 3-Continent harmonization classification showed the most stable progression. All features, especially drusen area, contribute to late AMD prediction. TRANSLATIONAL RELEVANCE: Findings will help stakeholders select appropriate classification systems for screening, deep learning algorithms, or trials

Modulation of the Inflammatory Response for Enhanced Bone Tissue Regeneration

Proinflammatory cytokines are infamous for their catabolic effects on tissues and joints in both inflammatory diseases and following the implantation of biomedical devices. However, recent studies indicate that many of these same molecules are critical for triggering tissue regeneration following injury. This review will discuss the role of inflammatory signals in regulating bone regeneration and the impact of both immunomodulatory and antiinflammatory pharmacologic agents on fracture healing, to demonstrate the importance of incorporating rational control of inflammation into the design of tissue engineering strategies

Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration

OBJECTIVE: Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autofluorescence (FAF). The objective was to develop and evaluate the performance of a novel multimodal, multitask, multiattention (M3) deep learning framework on RPD detection. MATERIALS AND METHODS: A deep learning framework (M3) was developed to detect RPD presence accurately using CFP alone, FAF alone, or both, employing >8000 CFP-FAF image pairs obtained prospectively (Age-Related Eye Disease Study 2). The M3 framework includes multimodal (detection from single or multiple image modalities), multitask (training different tasks simultaneously to improve generalizability), and multiattention (improving ensembled feature representation) operation. Performance on RPD detection was compared with state-of-the-art deep learning models and 13 ophthalmologists; performance on detection of 2 other AMD features (geographic atrophy and pigmentary abnormalities) was also evaluated. RESULTS: For RPD detection, M3 achieved an area under the receiver-operating characteristic curve (AUROC) of 0.832, 0.931, and 0.933 for CFP alone, FAF alone, and both, respectively. M3 performance on CFP was very substantially superior to human retinal specialists (median F1 score = 0.644 vs 0.350). External validation (the Rotterdam Study) demonstrated high accuracy on CFP alone (AUROC, 0.965). The M3 framework also accurately detected geographic atrophy and pigmentary abnormalities (AUROC, 0.909 and 0.912, respectively), demonstrating its generalizability. CONCLUSIONS: This study demonstrates the successful development, robust evaluation, and external validation of a novel deep learning framework that enables accessible, accurate, and automated AMD diagnosis and prognosis