FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading.

OBJECTIVE: Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale. METHODS: A total of 1245 images were graded for quality by two ophthalmologists within the range 1-10, with a resolution of 0.5. A DL regression model was trained for fundus image quality assessment. The architecture used was Inception-V3. The model was developed using a total of 89,947 images from 6 databases, of which 1245 were labeled by the specialists and the remaining 88,702 images were used for pre-training and semi-supervised learning. The final DL model was evaluated on an internal test set (n=209) as well as an external test set (n=194). RESULTS: The final DL model, denoted FundusQ-Net, achieved a mean absolute error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary classification model on the public DRIMDB database as an external test set the model obtained an accuracy of 99%. SIGNIFICANCE: the proposed algorithm provides a new robust tool for automated quality grading of fundus images.

Remote health diagnosis and monitoring in the time of COVID-19.

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.

Distinct mechanisms mediate pacemaker dysfunction associated with catecholaminergic polymorphic ventricular tachycardia mutations: Insights from computational modeling.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a stress-induced ventricular arrhythmia associated with rhythm disturbance and impaired sinoatrial node cell (SANC) automaticity (pauses). Mutations associated with dysfunction of Ca2+-related mechanisms have been shown to be present in CPVT. These dysfunctions include impaired Ca2+ release from the ryanodine receptor (i.e., RyR2R4496C mutation) or binding to calsequestrin 2 (CASQ2). In SANC, Ca2+ signaling directly and indirectly mediates pacemaker function. We address here the following research questions: (i) what coupled-clock mechanisms and pathways mediate pacemaker mutations associated with CPVT in basal and in response to ß-adrenergic stimulation? (ii) Can different mechanisms lead to the same CPVT-related pacemaker pauses? (iii) Can the mutation-induced deteriorations in SANC function be reversed by drug intervention or gene manipulation? We used a numerical model of mice SANC that includes membrane and intracellular mechanisms and their interconnected signaling pathways. In the basal state of RyR2R4496C SANC, the model predicted that the Na+-Ca2+ exchanger current (INCX) and T-type Ca2+ current (ICaT) mediate between changes in Ca2+ signaling and SANC dysfunction. Under ß-adrenergic stimulation, changes in cAMP-PKA signaling and the sodium currents (INa), in addition to INCX and ICaT, mediate between changes in Ca2+ signaling and SANC automaticity pauses. Under basal conditions in Casq2-/-, the same mechanisms drove changes in Ca2+ signaling and subsequent pacemaker dysfunction. However, SANC automaticity pauses in response to ß-AR stimulation were mediated by ICaT and INa. Taken together, distinct mechanisms can lead to CPVT-associated SANC automaticity pauses. In addition, we predict that specifically increasing SANC cAMP-PKA activity by either a pharmacological agent (IBMX, a phosphodiesterase (PDE) inhibitor), gene manipulation (overexpression of adenylyl cyclase 1/8) or direct manipulation of the SERCA phosphorylation target through changes in gene expression, compensate for the impairment in SANC automaticity. These findings suggest new insights for understanding CPVT and its therapeutic approach.