Multitask Learning for Activity Detection in Neovascular Age-Related Macular Degeneration.

Purpose: The purpose of this study was to provide a comparison of performance and explainability of a multitask convolutional deep neuronal network to single-task networks for activity detection in neovascular age-related macular degeneration (nAMD).

Methods: From 70 patients (46 women and 24 men) who attended the University Eye Hospital Tübingen, 3762 optical coherence tomography B-scans (right eye = 2011 and left eye = 1751) were acquired with Heidelberg Spectralis, Heidelberg, Germany. B-scans were graded by a retina specialist and an ophthalmology resident, and then used to develop a multitask deep learning model to predict disease activity in neovascular age-related macular degeneration along with the presence of sub- and intraretinal fluid.

Clinical validation of saliency maps for understanding deep neural networks in ophthalmology.

Deep neural networks (DNNs) have achieved physician-level accuracy on many imaging-based medical diagnostic tasks, for example classification of retinal images in ophthalmology. However, their decision mechanisms are often considered impenetrable leading to a lack of trust by clinicians and patients. To alleviate this issue, a range of explanation methods have been proposed to expose the inner workings of DNNs leading to their decisions.

Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection.

Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practice, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decisions. However, deep neural networks (DNNs) are being often overconfident in their predictions, and are not amenable to a straightforward probabilistic treatment.

[Potential of methods of artificial intelligence for quality assurance].

Background: Procedures with artificial intelligence (AI), such as deep neural networks, show promising results in automatic analysis of ophthalmological imaging data.

Objective: This article discusses to what extent the application of AI algorithms can contribute to quality assurance in the field of ophthalmology.

Methods: Relevant aspects from the literature are discussed.

Leveraging uncertainty information from deep neural networks for disease detection.

Deep learning (DL) has revolutionized the field of computer vision and image processing. In medical imaging, algorithmic solutions based on DL have been shown to achieve high performance on tasks that previously required medical experts. However, DL-based solutions for disease detection have been proposed without methods to quantify and control their uncertainty in a decision.

Exploitation of 3D stereotactic surface projection for predictive modelling of Alzheimer's disease.

Alzheimer's Disease (AD) is one major cause of dementia. Previous studies have indicated that the use of features derived from Positron Emission Tomography (PET) scans lead to more accurate and earlier diagnosis of AD, compared to the traditional approaches that use a combination of clinical assessments. In this study, we compare Naive Bayes (NB) with variations of Support Vector Machines (SVMs) for the automatic diagnosis of AD.