Association of microaneurysms with retinal vascular alterations in patients with retinal vein occlusion.

Objective: To investigate the localization, distribution, and type of central microaneurysms (MAs) and their relationship with retinal vascular alterations in patients with retinal vein occlusion (RVO).

Methods: In this cross-sectional study, ultra-widefield color fundus photography (UWF-CF), standard and single-capture 65° widefield (WF) optical coherence tomography angiography (OCTA) were performed in consecutive patients with RVO treated at the Department of Ophthalmology and Optometry, Medical University of Vienna. UWF-CF, en face and B-Scans in 6 mm × 6 mm OCTA were examined for detection of MAs.

A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning.

Background And Objective: Due to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time.

Association of Diabetic Lesions and Retinal Nonperfusion Using Widefield Multimodal Imaging.

Purpose: To evaluate the association of microvascular lesions on ultrawidefield (UWF) color fundus (CF) images with retinal nonperfusion (RNP) up to the midperiphery on single-capture widefield (WF) OCT angiography (OCTA) in patients with diabetic retinopathy (DR).

Design: Cross-sectional study.

Subjects: Seventy-five eyes of 50 patients with mild to severe nonproliferative DR (NPDR) and proliferative DR (PDR) were included in this analysis.

Live 4D-OCT denoising with self-supervised deep learning.

By providing three-dimensional visualization of tissues and instruments at high resolution, live volumetric optical coherence tomography (4D-OCT) has the potential to revolutionize ophthalmic surgery. However, the necessary imaging speed is accompanied by increased noise levels. A high data rate and the requirement for minimal latency impose major limitations for real-time noise reduction.

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography.

Background: Automatically detecting and quantifying pneumothorax on chest computed tomography (CT) may impact clinical decision-making. Machine learning methods published so far struggle with the heterogeneity of technical parameters and the presence of additional pathologies, highlighting the importance of stable algorithms.

Methods: A deep residual UNet was developed and evaluated for automated, volume-level pneumothorax grading (i.

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT.

Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical images. Although supervised deep learning can perform accurate segmentation of pathological areas, it is limited by requiring a priori definitions of these regions, large-scale annotations, and a representative patient cohort in the training set. In contrast, anomaly detection is not limited to specific definitions of pathologies and allows for training on healthy samples without annotation.

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised learning yields good results if expert labeled training data is available, the visual variability, and thus the vocabulary of findings, we can detect and exploit, is limited to the annotated lesions.

Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data.

The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the marker vocabulary to known entities.

Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.

Purpose: To evaluate the potential of machine learning to predict best-corrected visual acuity (BCVA) outcomes from structural and functional assessments during the initiation phase in patients receiving standardized ranibizumab therapy for neovascular age-related macular degeneration (AMD).

Design: Post hoc analysis of a randomized, prospective clinical trial.

Participants: Data of 614 evaluable patients receiving intravitreal ranibizumab monthly or pro re nata according to protocol-specified criteria in the HARBOR trial.

Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning.

Purpose: Development and validation of a fully automated method to detect and quantify macular fluid in conventional OCT images.

Design: Development of a diagnostic modality.

Participants: The clinical dataset for fluid detection consisted of 1200 OCT volumes of patients with neovascular age-related macular degeneration (AMD, n = 400), diabetic macular edema (DME, n = 400), or retinal vein occlusion (RVO, n = 400) acquired with Zeiss Cirrus (Carl Zeiss Meditec, Dublin, CA) (n = 600) or Heidelberg Spectralis (Heidelberg Engineering, Heidelberg, Germany) (n = 600) OCT devices.

Analyzing and Predicting Visual Acuity Outcomes of Anti-VEGF Therapy by a Longitudinal Mixed Effects Model of Imaging and Clinical Data.

Purpose: We develop a longitudinal statistical model describing best-corrected visual acuity (BCVA) changes in anti-VEGF therapy in relation to imaging data, and predict the future BCVA outcome for individual patients by combining population-wide trends and initial subject-specific time points.

Methods: Automatic segmentation algorithms were used to measure intraretinal (IRF) and subretinal (SRF) fluid volume on monthly spectral-domain optical coherence tomography scans of eyes with central retinal vein occlusion (CRVO) receiving standardized anti-VEGF treatment. The trajectory of BCVA over time was modeled as a multivariable repeated-measure mixed-effects regression model including fluid volumes as covariates.

Prediction of Anti-VEGF Treatment Requirements in Neovascular AMD Using a Machine Learning Approach.

Purpose: The purpose of this study was to predict low and high anti-VEGF injection requirements during a pro re nata (PRN) treatment, based on sets of optical coherence tomography (OCT) images acquired during the initiation phase in neovascular AMD.

Methods: Two-year clinical trial data of subjects receiving PRN ranibizumab according to protocol specified criteria in the HARBOR study after three initial monthly injections were included. OCT images were analyzed at baseline, month 1, and month 2.

Computational image analysis for prognosis determination in DME.

In this pilot study, we evaluated the potential of computational image analysis of optical coherence tomography (OCT) data to determine the prognosis of patients with diabetic macular edema (DME). Spectral-domain OCT scans with fully automated retinal layer segmentation and segmentation of intraretinal cystoid fluid (IRC) and subretinal fluid of 629 patients receiving anti-vascular endothelial growth factor therapy for DME in a randomized prospective clinical trial were analyzed. The results were used to define 312 potentially predictive features at three timepoints (baseline, weeks 12 and 24) for best-corrected visual acuity (BCVA) at baseline and after one year used in a random forest prediction path.

Musculoskeletal imaging in preventive medicine.

The aim is to review the modalities in musculoskeletal imaging with view on the prognostic impact for the patient's and for social outcome and with view on three major fields of preventive medicine: nutrition and metabolism, sports, and patient education. The added value provided by preventive imaging is (1) to monitor bone health and body composition with a broad spectrum of biomarkers, (2) to detect and quantify variants or abnormalities of nerves, muscles, tendons, bones, and joints with a risk of overuse, rupture, or fracture, and (3) to develop radiology reports from the widely used narrative format to structured text and multimedia datasets. The awareness problem is a term for describing the underreporting and the underdiagnosis of fragility fractures in osteoporosis.

Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks.

Learning representative computational models from medical imaging data requires large training data sets. Often, voxel-level annotation is unfeasible for sufficient amounts of data. An alternative to manual annotation, is to use the enormous amount of knowledge encoded in imaging data and corresponding reports generated during clinical routine.

The relationship between eye movement and vision develops before birth.

While the visuomotor system is known to develop rapidly after birth, studies have observed spontaneous activity in vertebrates in visually excitable cortical areas already before extrinsic stimuli are present. Resting state networks and fetal eye movements were observed independently in utero, but no functional brain activity coupled with visual stimuli could be detected using fetal fMRI. This study closes this gap and links in utero eye movement with corresponding functional networks.