Clinically relevant factors associated with a binary outcome of diabetic macular ischaemia: an OCTA study

AIMS

We investigated the demographic, ocular, diabetes-related and systemic factors associated with a binary outcome of diabetic macular ischaemia (DMI) as assessed by optical coherence tomography angiography (OCTA) evaluation of non-perfusion at the level of the superficial capillary plexus (SCP) and deep capillary plexus (DCP) in a cohort of patients with diabetes mellitus (DM).

MATERIALS AND METHODS

617 patients with DM were recruited from July 2015 to December 2020 at the Chinese University of Hong Kong Eye Centre. Image quality assessment (gradable or ungradable for assessing DMI) and DMI evaluation (presence or absence of DMI) were assessed at the level of the SCP and DCP by OCTA.

RESULTS

1107 eyes from 593 subjects were included in the final analysis. 560 (50.59%) eyes had DMI at the level of SCP, and 647 (58.45%) eyes had DMI at the level of DCP. Among eyes without diabetic retinopathy (DR), DMI was observed in 19.40% and 24.13% of eyes at SCP and DCP, respectively. In the multivariable logistic regression models, older age, poorer visual acuity, thinner ganglion cell-inner plexiform layer thickness, worsened DR severity, higher haemoglobin A1c level, lower estimated glomerular filtration rate and higher low-density lipoprotein cholesterol level were associated with SCP-DMI. In addition to the aforementioned factors, presence of diabetic macular oedema and shorter axial length were associated with DCP-DMI.

CONCLUSION

We reported a series of associated factors of SCP-DMI and DCP-DMI. The binary outcome of DMI might promote a simplified OCTA-based DMI evaluation before subsequent quantitative analysis for assessing DMI extent and fulfil the urge for an updating diabetic retinal disease staging to be implemented with OCTA.

A MULTITASK DEEP-LEARNING SYSTEM FOR ASSESSMENT OF DIABETIC MACULAR ISCHEMIA ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGES

PURPOSE

We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical coherence tomography angiography (OCTA) images.

METHODS

This study included 7,194 OCTA images with diabetes mellitus for training and primary validation and 960 images from three independent data sets for external testing. A trinary classification for image quality assessment and the presence or absence of DMI for DMI assessment were labeled on all OCTA images. Two DenseNet-161 models were built for both tasks for OCTA images of superficial and deep capillary plexuses, respectively. External testing was performed on three unseen data sets in which one data set using the same model of OCTA device as of the primary data set and two data sets using another brand of OCTA device. We assessed the performance by using the area under the receiver operating characteristic curves with sensitivities, specificities, and accuracies and the area under the precision-recall curves with precision.

RESULTS

For the image quality assessment, analyses for gradability and measurability assessment were performed. Our deep-learning system achieved the area under the receiver operating characteristic curves >0.948 and area under the precision-recall curves >0.866 for the gradability assessment, area under the receiver operating characteristic curves >0.960 and area under the precision-recall curves >0.822 for the measurability assessment, and area under the receiver operating characteristic curves >0.939 and area under the precision-recall curves >0.899 for the DMI assessment across three external validation data sets. Grad-CAM demonstrated the capability of our deep-learning system paying attention to regions related to DMI identification.

CONCLUSION

Our proposed multitask deep-learning system might facilitate the development of a simplified assessment of DMI on OCTA images among individuals with diabetes mellitus at high risk for visual loss.

A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

OBJECTIVE

Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices.

RESEARCH DESIGN AND METHODS

We trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent data sets from Singapore, Hong Kong, the U.S., China, and Australia.

RESULTS

In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920-0.954), 0.958 (0.930-0.977), and 0.965 (0.948-0.977) for the primary data set obtained from CIRRUS, SPECTRALIS, and Triton OCTs, respectively, in addition to AUROCs >0.906 for the external data sets. For further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940-0.995), 0.951 (0.898-0.982), and 0.975 (0.947-0.991) for the primary data set and >0.894 for the external data sets.

CONCLUSIONS

We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.

Geothermal Gases--Community Experiences, Perceptions, and Exposures in Northern California

Lake County, California, is in a high geothermal-activity area. Over the past 30 years, the city of Clearlake has reported health effects and building evacuations related to geothermal venting. Previous investigations in Clearlake revealed hydrogen sulfide at levels known to cause health effects and methane at levels that can cause explosion risks. The authors conducted an investigation in multiple cities and towns in Lake County to understand better the risk of geothermal venting to the community. They conducted household surveys and outdoor air sampling of hydrogen sulfide and methane and found community members were aware of geothermal venting and some expressed concerns. The authors did not, however, find hydrogen sulfide above the California Environmental Protection Agency air quality standard of 30 parts per billion over one hour or methane above explosive thresholds. The authors recommend improving risk communication, continuing to monitor geothermal gas effects on the community, and using community reports and complaints to monitor and document geothermal venting incidents.