The evidence for automated grading in diabetic retinopathy screening

Peer reviewedPostprin

Can a single image processing algorithm work equally well across all phases of DCE-MRI?

Image segmentation and registration are said to be challenging when applied to dynamic contrast enhanced MRI sequences (DCE-MRI). The contrast agent causes rapid changes in intensity in the region of interest and elsewhere, which can lead to false positive predictions for segmentation tasks and confound the image registration similarity metric. While it is widely assumed that contrast changes increase the difficulty of these tasks, to our knowledge no work has quantified these effects. In this paper we examine the effect of training with different ratios of contrast enhanced (CE) data on two popular tasks: segmentation with nnU-Net and Mask R-CNN and registration using VoxelMorph and VTN. We experimented further by strategically using the available datasets through pretraining and fine tuning with different splits of data. We found that to create a generalisable model, pretraining with CE data and fine tuning with non-CE data gave the best result. This interesting find could be expanded to other deep learning based image processing tasks with DCE-MRI and provide significant improvements to the models performance

Quality assurance in diabetic retinal screening in South Africa

Background. Diabetic retinopathy (DR) is an important biomarker for microvascular disease and blindness. Digital fundus photography is a cost-effective way of screening for DR. Access to DR screening is difficult for many South Africans with diabetes.Objective. To perform external quality assurance (EQA) on graders registered in the Ophthalmological Society of South Africa DR screening programme.Methods. Graders registered on the South African (SA) Diabetic Register website were invited to participate in the study. The Scottish EQA software system was used to enable on-line grading of 100 retinal photographs. Expert National Health Service graders provided the consensus expert grading for the image set.Results. Two hundred and sixty-one participants completed the EQA process, including nine ophthalmologists, 243 optometrists, and nine other graders. A wide range of outcomes were demonstrated, with a mean sensitivity of 0.905 (range 0.286 - 1.000) and mean specificity of 0.507 (0.000 - 0.935). The mean diagnostic odds ratio was calculated to be 12.3 (range 0.147 - 148.2).Conclusions. This is the first quality assurance study conducted with SA healthcare professionals. The outcomes are of interest to all stakeholders dealing with the diabetes epidemic. The disparity in grader performance indicates room for improvement. The results demonstrate a high referral rate to ophthalmology, suggesting that on average graders are performing safely, but with a high number of inappropriate referrals

Effects of oxidized and reduced forms of methylthioninium in two transgenic mouse tauopathy models

Acknowledgements The authors acknowledge the contributions of Bettina Seelhorst (histological analysis), Anna Thoma (animal care), Marlene Arthur (animal dosing) and Pierre-Henri Moreau (experimental discussions). This work was supported by TauRx Therapeutics Ltd., Singapore.Peer reviewedPublisher PD

The role of noise in denoising models for anomaly detection in medical images

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research.Comment: Submitted to Medical Image Analysis special issue for MIDL 202

Assessment of Automated Disease Detection in Diabetic Retinopathy Screening Using Two-Field Photography

Aim: To assess the performance of automated disease detection in diabetic retinopathy screening using two field mydriatic photography. Methods: Images from 8,271 sequential patient screening episodes from a South London diabetic retinopathy screening service were processed by the Medalytix iGrading TM automated grading system. For each screening episode macularcentred and disc-centred images of both eyes were acquired and independently graded according to the English national grading scheme. Where discrepancies were found between the automated result and original manual grade, internal and external arbitration was used to determine the final study grades. Two versions of the software were used: one that detected microaneurysms alone, and one that detected blot haemorrhages and exudates in addition to microaneurysms. Results for each version were calculated once using both fields and once using the macula-centred field alone. Results: Of the 8,271 episodes, 346 (4.2%) were considered unassessable. Referable disease was detected in 587 episodes (7.1%). The sensitivity of the automated system for detecting unassessable images ranged from 97.4 % to 99.1 % depending on configuration. The sensitivity of the automated system for referable episodes ranged from 98.3 % to 99.3%. All the episodes that included proliferative or pre-proliferative retinopathy were detected by the automated system regardless of configuration (192/192, 95 % confidence interval 98.0 % to 100%). If implemented as the first step in grading, the automate

The role of automated grading of diabetic retinopathy in a primary care screening programme

Systematic screening for diabetic eye disease has been identified as a cost-effective use of resources. With the prevalence of diabetes increasing steadily, it is important that efficient screening services are in place to meet the increasing demand. One-field automated grading was compared with manual disease/no disease grading using reference graded photographic retinal images from 6,722 consecutive patients in Grampian. The costs and cost savings to the NHS associated with both approaches were estimated and compared