Progression to exudative 'wet' age-related macular degeneration (exAMD) is a major cause of visual deterioration. In patients diagnosed with exAMD in one eye, we introduce an artificial intelligence ...(AI) system to predict progression to exAMD in the second eye. By combining models based on three-dimensional (3D) optical coherence tomography images and corresponding automatic tissue maps, our system predicts conversion to exAMD within a clinically actionable 6-month time window, achieving a per-volumetric-scan sensitivity of 80% at 55% specificity, and 34% sensitivity at 90% specificity. This level of performance corresponds to true positives in 78% and 41% of individual eyes, and false positives in 56% and 17% of individual eyes at the high sensitivity and high specificity points, respectively. Moreover, we show that automatic tissue segmentation can identify anatomical changes before conversion and high-risk subgroups. This AI system overcomes substantial interobserver variability in expert predictions, performing better than five out of six experts, and demonstrates the potential of using AI to predict disease progression.
Optical coherence tomography (OCT) is a paragon of success in the translation of biophotonics science to clinical practice. OCT systems have become ubiquitous in eye clinics but access beyond this is ...limited by their cost, size and the skill required to operate the devices. Remarkable progress has been made in the development of OCT technology to improve the speed of acquisition, the quality of images and into functional extensions of OCT such as OCT angiography. However, more needs to be done to radically improve the access to OCT by addressing its limitations and enable penetration outside of typical clinical settings and into underserved populations. Beyond high-income countries, there are 6.5 billion people with similar eye-care needs, which cannot be met by the current generation of bulky, expensive and complex OCT systems. In addition, advancing the portability of this technology to address opportunities in point-of-care diagnostics, telemedicine and remote monitoring may aid development of personalised medicine. In this review, we discuss the major milestones in OCT hardware development to reach those beyond the eye clinic.
The volume and complexity of diagnostic imaging is increasing at a pace faster than the availability of human expertise to interpret it. Artificial intelligence has shown great promise in classifying ...two-dimensional photographs of some common diseases and typically relies on databases of millions of annotated images. Until now, the challenge of reaching the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans has remained unsolved. Here, we apply a novel deep learning architecture to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients referred to a major eye hospital. We demonstrate performance in making a referral recommendation that reaches or exceeds that of experts on a range of sight-threatening retinal diseases after training on only 14,884 scans. Moreover, we demonstrate that the tissue segmentations produced by our architecture act as a device-independent representation; referral accuracy is maintained when using tissue segmentations from a different type of device. Our work removes previous barriers to wider clinical use without prohibitive training data requirements across multiple pathologies in a real-world setting.
To apply a deep learning algorithm for automated, objective, and comprehensive quantification of OCT scans to a large real-world dataset of eyes with neovascular age-related macular degeneration ...(AMD) and make the raw segmentation output data openly available for further research.
Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database.
A total of 2473 first-treated eyes and 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017.
A deep learning algorithm was used to segment all baseline OCT scans. Volumes were calculated for segmented features such as neurosensory retina (NSR), drusen, intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), retinal pigment epithelium (RPE), hyperreflective foci (HRF), fibrovascular pigment epithelium detachment (fvPED), and serous PED (sPED). Analyses included comparisons between first- and second-treated eyes by visual acuity (VA) and race/ethnicity and correlations between volumes.
Volumes of segmented features (mm3) and central subfield thickness (CST) (μm).
In first-treated eyes, the majority had both IRF and SRF (54.7%). First-treated eyes had greater volumes for all segmented tissues, with the exception of drusen, which was greater in second-treated eyes. In first-treated eyes, older age was associated with lower volumes for RPE, SRF, NSR, and sPED; in second-treated eyes, older age was associated with lower volumes of NSR, RPE, sPED, fvPED, and SRF. Eyes from Black individuals had higher SRF, RPE, and serous PED volumes compared with other ethnic groups. Greater volumes of the majority of features were associated with worse VA.
We report the results of large-scale automated quantification of a novel range of baseline features in neovascular AMD. Major differences between first- and second-treated eyes, with increasing age, and between ethnicities are highlighted. In the coming years, enhanced, automated OCT segmentation may assist personalization of real-world care and the detection of novel structure–function correlations. These data will be made publicly available for replication and future investigation by the AMD research community.
Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to ...develop medical image diagnostic classifiers by health-care professionals with no coding-and no deep learning-expertise.
We used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin lesions (Human Against Machine HAM 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou Medical University and Shiley Eye Institute, version 3 and the National Institute of Health NIH dataset, respectively) to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning model developed on a subset of the HAM10000 dataset, we did external validation using the Edinburgh Dermofit Library dataset.
Diagnostic properties and discriminative performance from internal validations were high in the binary classification tasks (sensitivity 73·3-97·0%; specificity 67-100%; AUPRC 0·87-1·00). In the multiple classification tasks, the diagnostic properties ranged from 38% to 100% for sensitivity and from 67% to 100% for specificity. The discriminative performance in terms of AUPRC ranged from 0·57 to 1·00 in the five automated deep learning models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model showed an AUPRC of 0·47, with a sensitivity of 49% and a positive predictive value of 52%.
All models, except the automated deep learning model trained on the multilabel classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms. The performance in the external validation study was low. The quality of the open-access datasets (including insufficient information about patient flow and demographics) and the absence of measurement for precision, such as confidence intervals, constituted the major limitations of this study. The availability of automated deep learning platforms provide an opportunity for the medical community to enhance their understanding in model development and evaluation. Although the derivation of classification models without requiring a deep understanding of the mathematical, statistical, and programming principles is attractive, comparable performance to expertly designed models is limited to more elementary classification tasks. Furthermore, care should be placed in adhering to ethical principles when using these automated models to avoid discrimination and causing harm. Future studies should compare several application programming interfaces on thoroughly curated datasets.
National Institute for Health Research and Moorfields Eye Charity.
Quantitative volumetric measures of retinal disease in optical coherence tomography (OCT) scans are infeasible to perform owing to the time required for manual grading. Expert-level deep learning ...systems for automatic OCT segmentation have recently been developed. However, the potential clinical applicability of these systems is largely unknown.
To evaluate a deep learning model for whole-volume segmentation of 4 clinically important pathological features and assess clinical applicability.
This diagnostic study used OCT data from 173 patients with a total of 15 558 B-scans, treated at Moorfields Eye Hospital. The data set included 2 common OCT devices and 2 macular conditions: wet age-related macular degeneration (107 scans) and diabetic macular edema (66 scans), covering the full range of severity, and from 3 points during treatment. Two expert graders performed pixel-level segmentations of intraretinal fluid, subretinal fluid, subretinal hyperreflective material, and pigment epithelial detachment, including all B-scans in each OCT volume, taking as long as 50 hours per scan. Quantitative evaluation of whole-volume model segmentations was performed. Qualitative evaluation of clinical applicability by 3 retinal experts was also conducted. Data were collected from June 1, 2012, to January 31, 2017, for set 1 and from January 1 to December 31, 2017, for set 2; graded between November 2018 and January 2020; and analyzed from February 2020 to November 2020.
Rating and stack ranking for clinical applicability by retinal specialists, model-grader agreement for voxelwise segmentations, and total volume evaluated using Dice similarity coefficients, Bland-Altman plots, and intraclass correlation coefficients.
Among the 173 patients included in the analysis (92 53% women), qualitative assessment found that automated whole-volume segmentation ranked better than or comparable to at least 1 expert grader in 127 scans (73%; 95% CI, 66%-79%). A neutral or positive rating was given to 135 model segmentations (78%; 95% CI, 71%-84%) and 309 expert gradings (2 per scan) (89%; 95% CI, 86%-92%). The model was rated neutrally or positively in 86% to 92% of diabetic macular edema scans and 53% to 87% of age-related macular degeneration scans. Intraclass correlations ranged from 0.33 (95% CI, 0.08-0.96) to 0.96 (95% CI, 0.90-0.99). Dice similarity coefficients ranged from 0.43 (95% CI, 0.29-0.66) to 0.78 (95% CI, 0.57-0.85).
This deep learning-based segmentation tool provided clinically useful measures of retinal disease that would otherwise be infeasible to obtain. Qualitative evaluation was additionally important to reveal clinical applicability for both care management and research.
With an estimated failure rate of about 90%, immunotherapies that are intended for the treatment of solid tumors have caused an anomalous rise in the mortality rate over the past decades. It is ...apparent that resistance towards such therapies primarily occurs due to elevated levels of HIF-1 (Hypoxia-induced factor) in tumor cells, which are caused by disrupted microcirculation and diffusion mechanisms. With the advent of nanotechnology, several innovative advances were brought to the fore; and, one such promising direction is the use of perfluorocarbon nanoparticles in the management of solid tumors. Perfluorocarbon nanoparticles enhance the response of hypoxia-based agents (HBAs) within the tumor cells and have been found to augment the entry of HBAs into the tumor micro-environment. The heightened penetration of HBAs causes chronic hypoxia, thus aiding in the process of cell quiescence. In addition, this technology has also been applied in photodynamic therapy, where oxygen self-enriched photosensitizers loaded perfluorocarbon nanoparticles are employed. The resulting processes initiate a cascade, depleting tumour oxygen and turning it into a reactive oxygen species eventually to destroy the tumour cell. This review elaborates on the multiple applications of nanotechnology based perfluorocarbon formulations that are being currently employed in the treatment of tumour hypoxia.
Polycystic ovary syndrome (PCOS) is associated with sympathetic nervous system activation, insulin resistance, and blood pressure elevation. Renal nerve ablation has been demonstrated to reduce ...sympathetic outflow and improve blood pressure control. Here we report on the effects of renal denervation on hemodynamic, metabolic, and renal parameters in two obese PCOS patients with hypertension.
Sympathetic nerve activity was assessed at baseline using microneurography and norepinephrine spillover measurements. Insulin sensitivity was assessed by euglycemic hyperinsulinemic clamp. Measurements of cystatin-C, creatinine clearance, and urinary albumin-creatinine ratio were also obtained. All measurements were repeated 3 months after bilateral renal denervation achieved via percutaneous endovascular radiofrequency ablation.
Muscle sympathetic nerve activity and whole body norepinephrine spillover were substantially elevated at baseline in both patients by approximately 2.5-3-fold. Bilateral renal nerve ablation reduced both indices of sympathetic nerve activity. This was associated with moderate reductions in blood pressure and a substantial improvement in insulin sensitivity by approximately 17.5% in the absence of weight changes at 3-month follow-up. Glomerular hyperfiltration and urinary albumin excretion were also reduced.
These findings corroborate the relevance of sympathetic activation in PCOS and suggest that renal denervation exerts beneficial effects not only on blood pressure control but also on insulin sensitivity, renal, and endocrine abnormalities characteristic of PCOS.
Considerable between-individual variation in retinal ganglion cell (RGC) density exists in healthy individuals, making identification of change from normal to glaucoma difficult. In ascertaining ...local cone-to-RGC density ratios in healthy individuals, we wished to investigate the usefulness of objective cone density estimates as a surrogate of baseline RGC density in glaucoma patients, and thus a more efficient way of identifying early changes.
Exploratory cohort study.
Twenty glaucoma patients (60% women) with a median age of 54 years and mean deviation (MD) in the visual field of -5 dB and 20 healthy controls (70% women) with a median age of 57 years and a mean MD of 0 dB were included.
Glaucoma patients and healthy participants underwent in vivo cone imaging at 4 locations of 8.8° eccentricity with a modified Heidelberg Retina Angiograph HRA2 (scan angle, 3°). Cones were counted using an automated program. Retinal ganglion cell density was estimated at the same test locations from peripheral grating resolution acuity thresholds.
Retinal cone density, estimated RGC density, and cone-to-RGC ratios in glaucoma patients and healthy controls.
Median cone-to-RGC density was 3.51:1 (interquartile range IQR, 2.59:1-6.81:1) in glaucoma patients compared with 2.35:1 (IQR, 1.83:1-2.82:1) in healthy participants. Retinal ganglion cell density was 33% lower in glaucoma patients than in healthy participants; however, cone density was very similar in glaucoma patients (7248 cells/mm
) and healthy controls (7242 cells/mm
). The area under the receiver operator characteristic curve was 0.79 (95% confidence interval CI, 0.71-0.86) for both RGC density and cone-to-RGC ratio and 0.49 (95% CI, 0.39-0.58) for cone density.
Local measurements of cone density do not differ significantly from normal in glaucoma patients despite large differences in RGC density. There was no statistically significant association between RGC density and cone density in the normal participants, and the range of cone-to-RGC density ratios was relatively large in healthy controls. These findings suggest that estimates of baseline RGC density from cone density are unlikely to be precise and offer little advantage over determination of RGC alone in the identification of early glaucomatous change.
ObjectivesTo analyse treatment outcomes and share clinical data from a large, single-centre, well-curated database (8174 eyes/6664 patients with 120 756 single entries) of patients with neovascular ...age-related macular degeneration (AMD) treated with anti-vascular endothelial growth factor (VEGF). By making our depersonalised raw data openly available, we aim to stimulate further research in AMD, as well as set a precedent for future work in this area.SettingRetrospective, comparative, non-randomised electronic medical record (EMR) database cohort study of the UK Moorfields AMD database with data extracted between 2008 and 2018.ParticipantsIncluding one eye per patient, 3357 eyes/patients (61% female). Extraction criteria were ≥1 ranibizumab or aflibercept injection, entry of ‘AMD’ in the diagnosis field of the EMR and a minimum of 1 year of follow-up. Exclusion criteria were unknown date of first injection and treatment outside of routine clinical care at Moorfields before the first recorded injection in the database.Main outcome measuresPrimary outcome measure was change in VA at 1 and 2 years from baseline as measured in Early Treatment Diabetic Retinopathy Study letters. Secondary outcomes were the number of injections and predictive factors for VA gain.ResultsMean VA gain at 1 year and 2 years were +5.5 (95% CI 5.0 to 6.0) and +4.9 (95% CI 4.2 to 5.6) letters, respectively. Fifty-four per cent of eyes gained ≥5 letters at 2 years, 63% had stable VA (±≤14 letters), 44% of eyes maintained good VA (≥70 letters). Patients received a mean of 7.7 (95% CI 7.6 to 7.8) injections during year 1 and 13.0 (95% CI 12.8 to 13.2) injections over 2 years. Younger age, lower baseline VA and more injections were associated with higher VA gain at 2 years.ConclusionThis study benchmarks high quality EMR study results of real life AMD treatment and promotes open science in clinical AMD research by making the underlying data publicly available.
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