Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.

Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions

Synaptic interactome mining reveals p140Cap as a new hub for PSD proteins involved in psychiatric and neurological disorders

Altered synaptic function has been associated with neurological and psychiatric conditions including intellectual disability, schizophrenia and autism spectrum disorder (ASD). Amongst the recently discovered synaptic proteins is p140Cap, an adaptor that localizes at dendritic spines and regulates their maturation and physiology. We recently showed that p140Cap knockout mice have cognitive deficits, impaired long-term potentiation (LTP) and long-term depression (LTD), and immature, filopodia-like dendritic spines. Only a few p140Cap interacting proteins have been identified in the brain and the molecular complexes and pathways underlying p140Cap synaptic function are largely unknown. Here, we isolated and characterized the p140Cap synaptic interactome by co-immunoprecipitation from crude mouse synaptosomes, followed by mass spectrometry-based proteomics. We identified 351 p140Cap interactors and found that they cluster to sub complexes mostly located in the postsynaptic density (PSD). p140Cap interactors converge on key synaptic processes, including transmission across chemical synapses, actin cytoskeleton remodeling and cell-cell junction organization. Gene co-expression data further support convergent functions: the p140Cap interactors are tightly co-expressed with each other and with p140Cap. Importantly, the p140Cap interactome and its co-expression network show strong enrichment in genes associated with schizophrenia, autism, bipolar disorder, intellectual disability and epilepsy, supporting synaptic dysfunction as a shared biological feature in brain diseases. Overall, our data provide novel insights into the molecular organization of the synapse and indicate that p140Cap acts as a hub for postsynaptic complexes relevant to psychiatric and neurological disorders

Analisi automatica di immagini della retina acquisite con oftalmoscopio basato su smartphone

Il progetto di tesi consiste nello sviluppo di un algoritmo per l'analisi automatica di immagini retiniche acquisite con il dispositivo ottico sviluppato da D-EYE, start-up del gruppo M31 con sede a Padova. Questo sistema viene agganciato alla fotocamera di uno smartphone trasformandolo in un oftalmoscopio digitale diretto in grado di registrare video e immagini ad alta definizione del fondo oculare

Analisi automatica di immagini della retina acquisite con oftalmoscopio basato su smartphone

Il progetto di tesi consiste nello sviluppo di un algoritmo per l'analisi automatica di immagini retiniche acquisite con il dispositivo ottico sviluppato da D-EYE, start-up del gruppo M31 con sede a Padova. Questo sistema viene agganciato alla fotocamera di uno smartphone trasformandolo in un oftalmoscopio digitale diretto in grado di registrare video e immagini ad alta definizione del fondo oculare

3D Reconstruction and Analysis of Nevi for Melanoma Detection

Melanoma is the most aggressive skin cancer and its detection in the early stage is crucial for a successful medical treatment. Many algorithms have been developed to provide an objective examination, but none of them proved to be effective in clinical practice. We propose an innovative and automatic method that derives a 3D model of the patient body from multiple images through photogrammetry, identifies all nevi, and computes diagnostic indices related to asymmetry, border, color, diameter, and volume for each detected nevus. Good preliminary results were obtained from 15 subjects with both normal and melanoma suspicious nevi

Light Driven Design of Dynamical Thermosensitive Plasmonic Superstructures: A Bottom-Up Approach Using Silver Supercrystals

International audienceWhen narrowly distributed silver nanoparticles (NPs) are functionalized by dodecanethiol, they acquire the ability to self-organize in organic solvents into 3D supercrystals (SCs). The NP surface chemistry is shown to introduce a light-driven thermomigration effect, thermophoresis. Using a laser beam to heat the NPs and generate steep thermal gradients, the migration effect is triggered dynamically, leading to tailored structures with high density of plasmonic hot spots. This work describes how to manipulate the hot spots and monitor the effect by holography, thus providing a complete characterization of the migration process on a single object basis. Extensive single object tracking strategies are employed to measure the SCs trajectories, evaluate their size, drift velocity magnitude and direction, allowing the identification of the physical chemical origins of the migration. The phenomenon is shown to happen as a result of the combination of thermophoresis (at short length scales) and convection (long-range), and does not require a metallic substrate. This constitutes a fully optical method to dynamically generate plasmonic platforms in situ and on demand, without requiring substrate nanostructuration and with minimal interference on the chemistry of the system. The importance of the proof-of-concept herein described stems from the numerous potential applications, spanning over a variety of fields such as microfluidics and biosensing