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.
Deep learning offers considerable promise for medical diagnostics. We aimed to evaluate the diagnostic accuracy of deep learning algorithms versus health-care professionals in classifying diseases ...using medical imaging.
In this systematic review and meta-analysis, we searched Ovid-MEDLINE, Embase, Science Citation Index, and Conference Proceedings Citation Index for studies published from Jan 1, 2012, to June 6, 2019. Studies comparing the diagnostic performance of deep learning models and health-care professionals based on medical imaging, for any disease, were included. We excluded studies that used medical waveform data graphics material or investigated the accuracy of image segmentation rather than disease classification. We extracted binary diagnostic accuracy data and constructed contingency tables to derive the outcomes of interest: sensitivity and specificity. Studies undertaking an out-of-sample external validation were included in a meta-analysis, using a unified hierarchical model. This study is registered with PROSPERO, CRD42018091176.
Our search identified 31 587 studies, of which 82 (describing 147 patient cohorts) were included. 69 studies provided enough data to construct contingency tables, enabling calculation of test accuracy, with sensitivity ranging from 9·7% to 100·0% (mean 79·1%, SD 0·2) and specificity ranging from 38·9% to 100·0% (mean 88·3%, SD 0·1). An out-of-sample external validation was done in 25 studies, of which 14 made the comparison between deep learning models and health-care professionals in the same sample. Comparison of the performance between health-care professionals in these 14 studies, when restricting the analysis to the contingency table for each study reporting the highest accuracy, found a pooled sensitivity of 87·0% (95% CI 83·0-90·2) for deep learning models and 86·4% (79·9-91·0) for health-care professionals, and a pooled specificity of 92·5% (95% CI 85·1-96·4) for deep learning models and 90·5% (80·6-95·7) for health-care professionals.
Our review found the diagnostic performance of deep learning models to be equivalent to that of health-care professionals. However, a major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample. Additionally, poor reporting is prevalent in deep learning studies, which limits reliable interpretation of the reported diagnostic accuracy. New reporting standards that address specific challenges of deep learning could improve future studies, enabling greater confidence in the results of future evaluations of this promising technology.
None.
Recent improvements in ophthalmic imaging have led to the identification of a thickened choroid or pachychoroid to be associated with a number of retinal diseases. The number of conditions linked to ...this phenotype has continued to widen with specific endophenotypes found within the pachychoroid spectrum. The spectrum includes choroidal features such as focal or diffuse choroidal thickening and thinning of the overlying inner choroid, and choroidal hyperpermeability as demonstrated by indocyanine green angiography. In addition, these diseases are associated with overlying retinal pigmentary changes and retinal pigment epithelial dysfunction and may also be associated with choroidal neovascularization. This article provides a comprehensive review of the literature looking at diseases currently described within the pachychoroid spectrum including central serous chorioretinopathy, pachychoroid pigment epitheliopathy, pachychoroid neovasculopathy, polypoidal choroidal vasculopathy/aneurysmal type 1 neovascularization, peripapillary pachychoroid disease and focal choroidal excavation. We particularly focus on clinical imaging, genetics and pathological findings in these conditions with the aim of updating evidence suggesting a common aetiology between diseases within the pachychoroid spectrum.
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.
Abstract
A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In ...this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.
The objective of this study was to evaluate the effects of corn hybrid and processing methods on intake and digestibility of nutrients, rumen fermentation and blood metabolites of steers fed ...no‐forage finishing diets. Four ruminally fistulated Nellore castrated steers (502 ± 15 kg initial body weight) were distributed in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement consisting of two corn hybrids (semi‐dent and flint) and two processing methods (dry milled and high moisture grain). Interactions of hybrid and processing methods were observed on intake of dry matter (DM), organic matter (OM) and crude protein (CP), as well as on digestibility of DM and CP, rumen pH and ammonia nitrogen (N‐NH3). There was no interaction between hybrid and processing for the volatile fatty acids (VFA) total, acetate (C2), propionate (C3), isobutyric (iC4) and valeric (nC5) concentrations. VFA total concentration shown an average of 103.4 mmol/L. The C2 and C3 concentrations had no effect of the hybrid or processing with averages of 58.7 mmol/L for C2, and 31.3 mmol/l for C3. There was an effect of the processing method on starch consumption and fecal pH, the highest values were observed in grains with high moisture content. Starch digestibility was 0.89 g/g in dry milled and 0.96 g/g in high moisture corn. The greatest digestibility of starch in high moisture corn, irrespective of the corn hybrid, provided evidence of an increase in the energy supply, which may improve the feed efficiency and growth performance of cattle fed no‐roughage finishing diets.
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.
Extruded urea could reduce true protein source in beef cattle diets Moraes, Gabriella J.; Ítavo, Luís Carlos V.; Ítavo, Camila Celeste B. F. ...
Journal of animal physiology and animal nutrition,
September 2019, 2019-Sep, 2019-09-00, 20190901, Letnik:
103, Številka:
5
Journal Article
Recenzirano
Rumen micro‐organisms are capable of producing microbial protein from ammonia and carbon skeleton, and non‐protein nitrogen (NPN) may be one of the sources of ammonia. Alternative source of NPN is ...the slow release of ammonia sources in which the product is the extrusion of starch with urea. This work aimed to determine the effects on nutrient intake, ingestive behaviour, digestibility, nitrogen balance, ruminal pH, rumen ammonia nitrogen, volatile fatty acids (VFA) and blood parameters with increased levels of extruded urea (50, 60, 70 and 80 g/100 kg of body weight BW) in beef cattle diet. Four rumen cannulated crossbred steers with initial mean weight of 336 ± 47 kg in a 4 × 4 Latin square design were distributed. Diets were formulated with 400:600 g/kg roughage:concentrate ratio on dry matter based and provided once per day, being used whole corn silage as roughage. There were no effects on nutrient intake (kg/day), ingestive behaviour, apparent digestibility, nitrogen balance, volatile fatty acid (VFA) and blood parameters in extruded urea treatment groups. Similar results were observed on time spent on feeding, rumination and idleness. There were positive linear effects (p = 0.022) on rumen pH in the time of 8 hr after feeding and also on plasma concentration of the extruded urea levels (p = 0.039); whereas a linear negative effect (p = 0.030) was observed on ammonia nitrogen for the collection time of 2 hr after feeding. Increased levels of extruded urea could maintain nutrient intake, digestibility, ingestive behaviour, rumen pH and blood parameters in normal conditions. In conclusion, we recommend the extruded urea use with values up to 80 g/100 kg BW in confined beef cattle that receive balanced diets with 140 g/kg of crude protein.
Protodioscin poisoning of
Brachiaria
spp. has been a serious problem for lambs in grazing systems. The defoliation process can stimulate the appearance of new leaves and, the younger leaves have a ...lower concentration of protodioscin. Thus, it was aimed to assess the effect of different leaf offerings on the protodioscin content in forage and if protodioscin can interfere with the dry matter intake (DMI) and on metabolic and productive parameters of lambs. Twelve tester lambs (average weight 17.5 ± 3.48 kg) were divided into four groups of different levels of dry leafy matter (60, 75, 90, or 105 g/kg body weight). In addition to these, 33 regulator lambs were used as needed to adjust the leaf offerings of pasture. The animals were divided and kept in 12 paddocks, each with a tester lamb. The DMI was estimated using chromic oxide as an external marker. In vitro digestibility and degradation kinetics and ruminal, blood, and urinary parameters were measured in the forage sampled by the hand plucking method. The protodioscin concentration in forage was determined by high-performance liquid chromatography. The DMI increased linearly as a result of the supply of leaves. However, the protodioscin content and its consumption showed a quadratic behavior. There was no effect of leaf supply on in vitro digestibility, in vitro degradation kinetics of forage, and on ruminal, blood, and urinary parameters of lambs. However, a negative correlation was observed between the DMI and the concentration of protodioscin at the highest level of leaf supply. This is due to the fact that in the most intense grazing, there is a stimulus for greater regrowth; therefore, there was an increase in the concentration of protodioscin in forages in older forages. Protodioscin poisoning was confirmed by urinary and blood parameters.
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