Using generative AI to investigate medical imagery models and datasets

BACKGROUND

AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren't yet known to experts.

METHODS

In this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model's predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier ("StylEx"); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e., what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g., whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries).

FINDINGS

To demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities-retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown).

INTERPRETATION

Our approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we will release code to help researchers train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes.

FUNDING

Google.

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study

BACKGROUND

Photographs of the external eye were recently shown to reveal signs of diabetic retinal disease and elevated glycated haemoglobin. This study aimed to test the hypothesis that external eye photographs contain information about additional systemic medical conditions.

METHODS

We developed a deep learning system (DLS) that takes external eye photographs as input and predicts systemic parameters, such as those related to the liver (albumin, aspartate aminotransferase [AST]); kidney (estimated glomerular filtration rate [eGFR], urine albumin-to-creatinine ratio [ACR]); bone or mineral (calcium); thyroid (thyroid stimulating hormone); and blood (haemoglobin, white blood cells [WBC], platelets). This DLS was trained using 123 130 images from 38 398 patients with diabetes undergoing diabetic eye screening in 11 sites across Los Angeles county, CA, USA. Evaluation focused on nine prespecified systemic parameters and leveraged three validation sets (A, B, C) spanning 25 510 patients with and without diabetes undergoing eye screening in three independent sites in Los Angeles county, CA, and the greater Atlanta area, GA, USA. We compared performance against baseline models incorporating available clinicodemographic variables (eg, age, sex, race and ethnicity, years with diabetes).

FINDINGS

Relative to the baseline, the DLS achieved statistically significant superior performance at detecting AST >36·0 U/L, calcium <8·6 mg/dL, eGFR <60·0 mL/min/1·73 m2, haemoglobin <11·0 g/dL, platelets <150·0 × 103/μL, ACR ≥300 mg/g, and WBC <4·0 × 103/μL on validation set A (a population resembling the development datasets), with the area under the receiver operating characteristic curve (AUC) of the DLS exceeding that of the baseline by 5·3-19·9% (absolute differences in AUC). On validation sets B and C, with substantial patient population differences compared with the development datasets, the DLS outperformed the baseline for ACR ≥300·0 mg/g and haemoglobin <11·0 g/dL by 7·3-13·2%.

INTERPRETATION

We found further evidence that external eye photographs contain biomarkers spanning multiple organ systems. Such biomarkers could enable accessible and non-invasive screening of disease. Further work is needed to understand the translational implications.

FUNDING

Google.

Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study

PURPOSE

To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus photographs (CFP), for which the reference standard for retinal thickness and fluid presence is derived from 3-dimensional OCT.

DESIGN

Retrospective validation of a DLS across international datasets.

PARTICIPANTS

Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics. The DLS was developed using data sets from Thailand, the United Kingdom, and the United States and validated using 3060 unique eyes from 1582 patients across screening populations in Australia, India, and Thailand. The DLS was separately validated in 698 eyes from 537 screened patients in the United Kingdom with mild DR and suspicion of DME based on CFP.

METHODS

The DLS was trained using DME labels from OCT. The presence of DME was based on retinal thickening or intraretinal fluid. The DLS's performance was compared with expert grades of maculopathy and to a previous proof-of-concept version of the DLS. We further simulated the integration of the current DLS into an algorithm trained to detect DR from CFP.

MAIN OUTCOME MEASURES

The superiority of specificity and noninferiority of sensitivity of the DLS for the detection of center-involving DME, using device-specific thresholds, compared with experts.

RESULTS

The primary analysis in a combined data set spanning Australia, India, and Thailand showed the DLS had 80% specificity and 81% sensitivity, compared with expert graders, who had 59% specificity and 70% sensitivity. Relative to human experts, the DLS had significantly higher specificity (P = 0.008) and noninferior sensitivity (P < 0.001). In the data set from the United Kingdom, the DLS had a specificity of 80% (P < 0.001 for specificity of >50%) and a sensitivity of 100% (P = 0.02 for sensitivity of > 90%).

CONCLUSIONS

The DLS can generalize to multiple international populations with an accuracy exceeding that of experts. The clinical value of this DLS to reduce false-positive referrals, thus decreasing the burden on specialist eye care, warrants a prospective evaluation.

Detection of signs of disease in external photographs of the eyes via deep learning

Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.

Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology

Genome-wide association studies (GWASs) require accurate cohort phenotyping, but expert labeling can be costly, time intensive, and variable. Here, we develop a machine learning (ML) model to predict glaucomatous optic nerve head features from color fundus photographs. We used the model to predict vertical cup-to-disc ratio (VCDR), a diagnostic parameter and cardinal endophenotype for glaucoma, in 65,680 Europeans in the UK Biobank (UKB). A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; p ≤ 5 × 10-8) hits in 156 loci. The ML-based GWAS replicated 62 of 65 GWS loci from a recent VCDR GWAS in the UKB for which two ophthalmologists manually labeled images for 67,040 Europeans. The ML-based GWAS also identified 93 novel loci, significantly expanding our understanding of the genetic etiologies of glaucoma and VCDR. Pathway analyses support the biological significance of the novel hits to VCDR: select loci near genes involved in neuronal and synaptic biology or harboring variants are known to cause severe Mendelian ophthalmic disease. Finally, the ML-based GWAS results significantly improve polygenic prediction of VCDR and primary open-angle glaucoma in the independent EPIC-Norfolk cohort.

Predicting the risk of developing diabetic retinopathy using deep learning

BACKGROUND

Diabetic retinopathy screening is instrumental to preventing blindness, but scaling up screening is challenging because of the increasing number of patients with all forms of diabetes. We aimed to create a deep-learning system to predict the risk of patients with diabetes developing diabetic retinopathy within 2 years.

METHODS

We created and validated two versions of a deep-learning system to predict the development of diabetic retinopathy in patients with diabetes who had had teleretinal diabetic retinopathy screening in a primary care setting. The input for the two versions was either a set of three-field or one-field colour fundus photographs. Of the 575 431 eyes in the development set 28 899 had known outcomes, with the remaining 546 532 eyes used to augment the training process via multitask learning. Validation was done on one eye (selected at random) per patient from two datasets: an internal validation (from EyePACS, a teleretinal screening service in the USA) set of 3678 eyes with known outcomes and an external validation (from Thailand) set of 2345 eyes with known outcomes.

FINDINGS

The three-field deep-learning system had an area under the receiver operating characteristic curve (AUC) of 0·79 (95% CI 0·77-0·81) in the internal validation set. Assessment of the external validation set-which contained only one-field colour fundus photographs-with the one-field deep-learning system gave an AUC of 0·70 (0·67-0·74). In the internal validation set, the AUC of available risk factors was 0·72 (0·68-0·76), which improved to 0·81 (0·77-0·84) after combining the deep-learning system with these risk factors (p<0·0001). In the external validation set, the corresponding AUC improved from 0·62 (0·58-0·66) to 0·71 (0·68-0·75; p<0·0001) following the addition of the deep-learning system to available risk factors.

INTERPRETATION

The deep-learning systems predicted diabetic retinopathy development using colour fundus photographs, and the systems were independent of and more informative than available risk factors. Such a risk stratification tool might help to optimise screening intervals to reduce costs while improving vision-related outcomes.

FUNDING

Google.

Author Correction: Detection of anaemia from retinal fundus images via deep learning

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Detection of anaemia from retinal fundus images via deep learning

Owing to the invasiveness of diagnostic tests for anaemia and the costs associated with screening for it, the condition is often undetected. Here, we show that anaemia can be detected via machine-learning algorithms trained using retinal fundus images, study participant metadata (including race or ethnicity, age, sex and blood pressure) or the combination of both data types (images and study participant metadata). In a validation dataset of 11,388 study participants from the UK Biobank, the fundus-image-only, metadata-only and combined models predicted haemoglobin concentration (in g dl-1) with mean absolute error values of 0.73 (95% confidence interval: 0.72-0.74), 0.67 (0.66-0.68) and 0.63 (0.62-0.64), respectively, and with areas under the receiver operating characteristic curve (AUC) values of 0.74 (0.71-0.76), 0.87 (0.85-0.89) and 0.88 (0.86-0.89), respectively. For 539 study participants with self-reported diabetes, the combined model predicted haemoglobin concentration with a mean absolute error of 0.73 (0.68-0.78) and anaemia an AUC of 0.89 (0.85-0.93). Automated anaemia screening on the basis of fundus images could particularly aid patients with diabetes undergoing regular retinal imaging and for whom anaemia can increase morbidity and mortality risks.

Remote Tool-Based Adjudication for Grading Diabetic Retinopathy

Purpose

To present and evaluate a remote, tool-based system and structured grading rubric for adjudicating image-based diabetic retinopathy (DR) grades.

Methods

We compared three different procedures for adjudicating DR severity assessments among retina specialist panels, including (1) in-person adjudication based on a previously described procedure (Baseline), (2) remote, tool-based adjudication for assessing DR severity alone (TA), and (3) remote, tool-based adjudication using a feature-based rubric (TA-F). We developed a system allowing graders to review images remotely and asynchronously. For both TA and TA-F approaches, images with disagreement were reviewed by all graders in a round-robin fashion until disagreements were resolved. Five panels of three retina specialists each adjudicated a set of 499 retinal fundus images (1 panel using Baseline, 2 using TA, and 2 using TA-F adjudication). Reliability was measured as grade agreement among the panels using Cohen's quadratically weighted kappa. Efficiency was measured as the number of rounds needed to reach a consensus for tool-based adjudication.

Results

The grades from remote, tool-based adjudication showed high agreement with the Baseline procedure, with Cohen's kappa scores of 0.948 and 0.943 for the two TA panels, and 0.921 and 0.963 for the two TA-F panels. Cases adjudicated using TA-F were resolved in fewer rounds compared with TA (P < 0.001; standard permutation test).

Conclusions

Remote, tool-based adjudication presents a flexible and reliable alternative to in-person adjudication for DR diagnosis. Feature-based rubrics can help accelerate consensus for tool-based adjudication of DR without compromising label quality.

Translational Relevance

This approach can generate reference standards to validate automated methods, and resolve ambiguous diagnoses by integrating into existing telemedical workflows.

Association of severity of primary open-angle glaucoma with serum vitamin D levels in patients of African descent

Purpose

To study the relationship between primary open-angle glaucoma (POAG) in a cohort of patients of African descent (AD) and serum vitamin D levels.

Methods

A subset of the AD and glaucoma evaluation study III (ADAGES III) cohort, consisting of 357 patients with a diagnosis of POAG and 178 normal controls of self-reported AD, were included in this analysis. Demographic information, family history, and blood samples were collected from all the participants. All the subjects underwent clinical evaluation, including visual field (VF) mean deviation (MD), central cornea thickness (CCT), intraocular pressure (IOP), and height and weight measurements. POAG patients were classified into early and advanced phenotypes based on the severity of their visual field damage, and they were matched for age, gender, and history of hypertension and diabetes. Serum 25-Hydroxy (25-OH) vitamin D levels were measured by enzyme-linked immunosorbent assay (ELISA). The association of serum vitamin D levels with the development and severity of POAG was tested by analysis of variance (ANOVA) and the paired t-test.

Results

The 178 early POAG subjects had a visual field MD of better than -4.0 dB, and the 179 advanced glaucoma subjects had a visual field MD of worse than -10 dB. The mean (95% confidence interval [CI]) levels of vitamin D of the subjects in the control (8.02 ± 6.19 pg/ml) and early phenotype (7.56 ± 5.74 pg/ml) groups were significantly or marginally significantly different from the levels observed in subjects with the advanced phenotype (6.35 ± 4.76 pg/ml; p = 0.0117 and 0.0543, respectively). In contrast, the mean serum vitamin D level in controls was not significantly different from that of the subjects with the early glaucoma phenotype (p = 0.8508).

Conclusions

In this AD cohort, patients with advanced glaucoma had lower serum levels of vitamin D compared with early glaucoma and normal subjects.

The African Descent and Glaucoma Evaluation Study (ADAGES) III: Contribution of Genotype to Glaucoma Phenotype in African Americans: Study Design and Baseline Data

PURPOSE

To describe the study protocol and baseline characteristics of the African Descent and Glaucoma Evaluation Study (ADAGES) III.

DESIGN

Cross-sectional, case-control study.

PARTICIPANTS

Three thousand two hundred sixty-six glaucoma patients and control participants without glaucoma of African or European descent were recruited from 5 study centers in different regions of the United States.

METHODS

Individuals of African descent (AD) and European descent (ED) with primary open-angle glaucoma (POAG) and control participants completed a detailed demographic and medical history interview. Standardized height, weight, and blood pressure measurements were obtained. Saliva and blood samples to provide serum, plasma, DNA, and RNA were collected for standardized processing. Visual fields, stereoscopic disc photographs, and details of the ophthalmic examination were obtained and transferred to the University of California, San Diego, Data Coordinating Center for standardized processing and quality review.

MAIN OUTCOME MEASURES

Participant gender, age, race, body mass index, blood pressure, history of smoking and alcohol use in POAG patients and control participants were described. Ophthalmic measures included intraocular pressure, visual field mean deviation, central corneal thickness, glaucoma medication use, or past glaucoma surgery. Ocular conditions, including diabetic retinopathy, age-related macular degeneration, and past cataract surgery, were recorded.

RESULTS

The 3266 ADAGES III study participants in this report include 2146 AD POAG patients, 695 ED POAG patients, 198 AD control participants, and 227 ED control participants. The AD POAG patients and control participants were significantly younger (both, 67.4 years) than ED POAG patients and control participants (73.4 and 70.2 years, respectively). After adjusting for age, AD POAG patients had different phenotypic characteristics compared with ED POAG patients, including higher intraocular pressure, worse visual acuity and visual field mean deviation, and thinner corneas (all P < 0.001). Family history of glaucoma did not differ between AD and ED POAG patients.

CONCLUSIONS

With its large sample size, extensive specimen collection, and deep phenotyping of AD and ED glaucoma patients and control participants from different regions in the United States, the ADAGES III genomics study will address gaps in our knowledge of the genetics of POAG in this high-risk population.

A Longitudinal Analysis of Peripapillary Choroidal Thinning in Healthy and Glaucoma Subjects

PURPOSE

To evaluate the rate of peripapillary choroidal thinning in glaucoma patients and healthy controls using spectral domain optical coherence tomography.

DESIGN

Cohort study.

METHODS

Participants from the multicenter African Descent and Glaucoma Evaluation Study and Diagnostic Innovations in Glaucoma Study were included. The San Diego Automated Segmentation Algorithm was used to automatically segment and measure peripapillary choroidal thickness (PCT) from circle scans centered on the optic nerve head. The rate of PCT thinning was calculated using mixed effects models.

RESULTS

Two hundred ninety-seven eyes with a median follow-up of 2.6 years were included. At baseline, the global mean PCT was significantly thinner in glaucoma patients than healthy control subjects (141.7 ± 66.3 μm vs 155.7 ± 64.8 μm, respectively; P < .001). However, when age was included in the model, this difference was no longer significant (P = .38). Both healthy controls and glaucoma patients had a significant decrease in mean (95% confidence interval) PCT change over time (-2.18 [-2.97 to -1.40 μm/year] and -1.88 [-3.08 to -0.67 μm/year], respectively) and mean PCT percent change over time (-3.32% [-4.36 to -2.27 μm/year] and -2.85% [-4.64 to -0.99 μm/year], respectively). No significant difference was found between healthy control subjects and glaucoma patients in the mean rate of PCT change (P = .28) or PCT percentage change over time (P = .23).

CONCLUSIONS

The rate of peripapillary choroidal thinning was not significantly different between healthy and glaucoma eyes during this relatively short follow-up period. Longer follow-up is needed to determine whether monitoring the rate of PCT change has a role in glaucoma management.

Comparing optical coherence tomography radial and cube scan patterns for measuring Bruch’s membrane opening minimum rim width (BMO-MRW) in glaucoma and healthy eyes: cross-sectional and longitudinal analysis

Aim

To compare the cube and radial scan patterns of the spectral domain optical coherence tomography (SD-OCT) for quantifying the Bruch’s membrane opening minimum rim width (BMO-MRW).

Methods

Sixty healthy eyes and 189 glaucomatous eyes were included. The optic nerve head cube and radial pattern scans were acquired using Spectralis SD-OCT. BMO-MRWs were automatically delineated using the San Diego Automated Layer Segmentation Algorithm. The BMO-MRW diagnostic accuracy for glaucoma detection and rates of change derived from the two scan patterns were compared.

Results

There was a significant difference between the baseline global BMO-MRW measurements of cube and radial scans for healthy (301.9±57.8 µm and 334.7±61.8 µm, respectively, p<0.003) and glaucoma eyes (181.2±63.0 µm and 210.2±67.2 µm, respectively, p<0.001). The area under the receiver operating characteristic curve for differentiating between healthy and glaucoma eyes was 0.90 for both the radial scan-based and cube scan-based BMO-MRW. No significant difference in the rate of BMO-MRW change (mean follow-up years) by scan pattern was found among both healthy (cube: −1.47 µm/year, radial: −1.53 µm/year; p=0.48) (1.6 years) and glaucoma eyes (cube: −2.37 µm/year, radial: −2.28 µm/year; p=0.45) (2.6 years).

Conclusion

Although the cube scan-based BMO-MRW was significantly smaller than the radial scan-based BMO-MRW, we found no significant difference between the two scan patterns for detecting glaucoma, identifying BMO location and measuring the rate of BMO-MRW change. These results suggest that although BMO-MRW estimates are not interchangeable, both scan patterns can be used for monitoring BMO-MRW changes over time.

'Blue bubble' technique: an ab interno approach for Descemet separation in deep anterior lamellar keratoplasty using trypan blue stained viscoelastic device

BACKGROUND

In this study, we examined a novel variant of 'big-bubble' deep anterior lamellar keratoplasty using trypan-blue-stained viscoelastic device for the creation of a pre-descemetic bubble.

METHODS

Ten corneoscleral rims were mounted on an artificial anterior chamber (AC). The AC was filled with air through a limbal paracentesis. A Melles' triangulated spatula was inserted through the paracentesis, with its tip penetrating the AC, was then slightly retracted and pushed into the deep stroma above the roof of the paracentesis. A mixture of trypan blue and viscoelastic device (Healon, Abbott Medical Optics, Abbott Park, Illinois) was injected into this intra-stromal pocket using a 27-G cannula to create a pre-descemetic separation bubble. Bubble type and visualization of dyed viscoelastic device were noted. The method was later employed in three cases.

RESULTS

In all 10 corneoscleral rims, the technique successfully created a visible pre-descemetic (type 1) bubble that could be expanded up to the predicted diameter of trephination. Subsequent trephination and the removal of corneal stroma were uneventful. In two out of four clinical cases, a type 1 bubble was created, while in two others, visco-dissection failed and dyed viscoelastic was seen in the AC.

CONCLUSIONS

The presented technique holds promise of being a relatively easy to perform, predictable and well-controlled alternative for achieving a type 1 bubble during deep anterior lamellar keratoplasty surgery. The trypan-blue-stained viscoelastic device facilitates proper visualization and control of the separation bubble and assists in identifying the penetrance to the separation bubble prior to removal of the stromal cap.

Does the Location of Bruch's Membrane Opening Change Over Time? Longitudinal Analysis Using San Diego Automated Layer Segmentation Algorithm (SALSA)

Purpose

We determined if the Bruch's membrane opening (BMO) location changes over time in healthy eyes and eyes with progressing glaucoma, and validated an automated segmentation algorithm for identifying the BMO in Cirrus high-definition coherence tomography (HD-OCT) images.

Methods

We followed 95 eyes (35 progressing glaucoma and 60 healthy) for an average of 3.7 ± 1.1 years. A stable group of 50 eyes had repeated tests over a short period. In each B-scan of the stable group, the BMO points were delineated manually and automatically to assess the reproducibility of both segmentation methods. Moreover, the BMO location variation over time was assessed longitudinally on the aligned images in 3D space point by point in x, y, and z directions.

Results

Mean visual field mean deviation at baseline of the progressing glaucoma group was −7.7 dB. Mixed-effects models revealed small nonsignificant changes in BMO location over time for all directions in healthy eyes (the smallest P value was 0.39) and in the progressing glaucoma eyes (the smallest P value was 0.30). In the stable group, the overall intervisit–intraclass correlation coefficient (ICC) and coefficient of variation (CV) were 98.4% and 2.1%, respectively, for the manual segmentation and 98.1% and 1.9%, respectively, for the automated algorithm

Conclusions

Bruch's membrane opening location was stable in normal and progressing glaucoma eyes with follow-up between 3 and 4 years indicating that it can be used as reference point in monitoring glaucoma progression. The BMO location estimation with Cirrus HD-OCT using manual and automated segmentation showed excellent reproducibility.

Diagnostic Accuracy of the Spectralis and Cirrus Reference Databases in Differentiating between Healthy and Early Glaucoma Eyes

PURPOSE

To evaluate and compare the diagnostic accuracy of global and sector analyses for detection of early visual field (VF) damage using the retinal nerve fiber layer (RNFL) reference databases of the Spectralis (Heidelberg Engineering, Heidelberg, Germany) and Cirrus (Carl Zeiss Meditec, Dublin, CA) spectral-domain optical coherence tomography (SD OCT) devices.

METHODS

Healthy subjects and glaucoma suspects from the Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES) with at least 2 years of follow-up were included. Global and sectoral RNFL measures were classified as within normal limits, borderline (BL), and outside normal limits (ONL) on the basis of the device reference databases. The sensitivity of ONL classification was estimated in glaucoma suspect eyes that developed repeatable VF damage.

RESULTS

A total of 353 glaucoma suspect eyes and 279 healthy eyes were included. A total of 34 (9.6%) of the glaucoma suspect eyes developed VF damage. In glaucoma suspect eyes, Spectralis and Cirrus ONL classification was present in 47 eyes (13.3%) and 24 eyes (6.8%), respectively. The sensitivity of the global RNFL ONL classification among eyes that developed VF damage was 23.5% for Cirrus and 32.4% for Spectralis. The specificity of within-normal-limits global classification in healthy eyes was 100% for Cirrus and 99.6% for Spectralis. There was moderate to substantial agreement between Cirrus and Spectralis classification as ONL.

CONCLUSIONS

The Spectralis and Cirrus reference databases have a high specificity for identifying healthy eyes and good agreement for detection of eyes with early glaucoma damage.

Intestinal perforation in very-low-birth-weight infants with necrotizing enterocolitis

PURPOSE

To identify risk factors for intestinal perforation in very-low-birth-weight (VLBW) infants with necrotizing enterocolitis (NEC).

METHODS

Retrospective case-control study over a 10-year period, using univariate and multivariate logistic regression analyses to compare all VLBW infants treated for perforated NEC, with two age and weight-matched groups: infants with non-perforated NEC and infants without NEC.

RESULTS

Twenty infants with perforated NEC were matched to 20 infants with non-perforated NEC and 38 infants without NEC. Infants with perforated NEC were younger (p<0.01) and had higher rates of abdominal distention, metabolic acidosis, hyperglycemia and elevated liver enzymes (p<0.05). On logistic regression analysis, abdominal distention was associated with an increased risk of intestinal perforation (OR 39.8, 95% CI 2.71-585) and late onset of NEC (one-day increments) was associated with a decreased risk (OR 0.93, 95% CI 0.87-1.0).

CONCLUSION

Identification of abdominal distention at an early age in VLBW infants should lead to increased vigilance for signs of perforated NEC and may enable early intervention.

The effect of riboflavin-ultraviolet A-induced collagen cross-linking on intraocular pressure measurement: an experimental study

PURPOSE

To investigate the effect of corneal collagen cross-linking (CCL) on tonopen measurements of intraocular pressure (IOP).

METHODS

CCL with 0.1% riboflavin solution and 30 min of ultraviolet A radiation was performed on the right eye of 15 New Zealand albino adult rabbits (1.8-2.4 kg) (15 eyes). The left eye served as control. IOP was measured by a pressure transducer system (true IOP) and by the tonopen hand-held device (corneal applanation tonometer) before treatment, at 1 week, 1 month and 3 months following CCL. Reference pressure in the globe was increased by increments of 10 mm Hg from 10 to 40 mm Hg, using an anterior chamber infusion on a stand with variable height, and tonopen IOP measurements were recorded for each reference pressure in both eyes.

RESULTS

Before CCL, tonopen readings were similar between the two eyes (p>0.05). Tonopen underestimated the true IOP in all cases. Following CCL treatment, IOP measurements were significantly higher in the treated eye, at all time intervals (0.005<p<0.03). The most significant difference between true and measured IOP was noted at a reference pressure of 20 mm Hg.

CONCLUSIONS

IOP measurements following CCL are overestimated by the tonopen, probably due to increased stiffness of the treated cornea.

Intravenous immunoglobulin in recurrent-relapsing inflammatory optic neuropathy

OBJECTIVE

Recurrent-relapsing inflammatory optic neuropathy, including chronic relapsing inflammatory and autoimmune optic neuropathies, is rare, but can cause severe visual loss. Long-term steroids may preserve vision, yet side effects are frequent. We describe our experience with intravenous immunoglobulins (IVIg).

DESIGN

A semi-prospective case series from 4 medical centres.

PARTICIPANTS

Patients with steroid responsive recurrent-relapsing optic neuropathy.

METHODS

Semiprospective case series of IVIg treatment in steroid-responsive recurrent-relapsing optic neuropathy at 4 medical centres. Outcome measures included visual outcome; time to, and duration of, remission; duration of corticosteroid use; and adverse events.

RESULTS

Vision stabilized in all 6 patients treated with IVIg without steroids for extended periods of time. None improved and none worsened. One adverse event occurred during an IVIg infusion after 3 uneventful years of IVIg maintenance. Average steroid use prior to IVIg was 12 months. After IVIg treatment, 5/6 patients no longer required corticosteroids. Two patients experienced late relapses on IVIg, one of whom was treated with cyclosporine, the other with steroids.

CONCLUSIONS

IVIg can be considered an effective steroid-sparing agent in selected cases with steroid-dependent recurrent-relapsing autoimmune optic neuropathy.