Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone.

BACKGROUND/AIMS: To validate a deep learning algorithm to diagnose glaucoma from fundus photography obtained with a smartphone. METHODS: A training dataset consisting of 1364 colour fundus photographs with glaucomatous indications and 1768 colour fundus photographs without glaucomatous features was obtained using an ordinary fundus camera. The testing dataset consisted of 73 eyes of 73 patients with glaucoma and 89 eyes of 89 normative subjects. In the testing dataset, fundus photographs were acquired using an ordinary fundus camera and a smartphone. A deep learning algorithm was developed to diagnose glaucoma using a training dataset. The trained neural network was evaluated by prediction result of the diagnostic of glaucoma or normal over the test datasets, using images from both an ordinary fundus camera and a smartphone. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC). RESULTS: The AROC with a fundus camera was 98.9% and 84.2% with a smartphone. When validated only in eyes with advanced glaucoma (mean deviation value < -12 dB, N=26), the AROC with a fundus camera was 99.3% and 90.0% with a smartphone. There were significant differences between these AROC values using different cameras. CONCLUSION: The usefulness of a deep learning algorithm to automatically screen for glaucoma from smartphone-based fundus photographs was validated. The algorithm had a considerable high diagnostic ability, particularly in eyes with advanced glaucoma.

Pressure accelerates the circadian clock of cyanobacteria.

Although organisms are exposed to various pressure and temperature conditions, information remains limited on how pressure affects biological rhythms. This study investigated how hydrostatic pressure affects the circadian clock (KaiA, KaiB, and KaiC) of cyanobacteria. While the circadian rhythm is inherently robust to temperature change, KaiC phosphorylation cycles that were accelerated from 22 h at 1 bar to 14 h at 200 bars caused the circadian-period length to decline. This decline was caused by the pressure-induced enhancement of KaiC ATPase activity and allosteric effects. Because ATPase activity was elevated in the CI and CII domains of KaiC, while ATP hydrolysis had negative activation volumes (ΔV≠), both domains played key roles in determining the period length of the KaiC phosphorylation cycle. The thermodynamic contraction of the structure of the active site during the transition state might have positioned catalytic residues and lytic water molecules favourably to facilitate ATP hydrolysis. Internal cavities might represent sources of compaction and structural rearrangement in the active site. Overall, the data indicate that pressure differences could alter the circadian rhythms of diverse organisms with evolved thermotolerance, as long as enzymatic reactions defining period length have a specific activation volume.

Validation of a Deep Learning Model to Screen for Glaucoma Using Images from Different Fundus Cameras and Data Augmentation.

PURPOSE: To validate a deep residual learning algorithm to diagnose glaucoma from fundus photography using different fundus cameras at different institutes. DESIGN: Cross-sectional study. PARTICIPANTS: A training dataset consisted of 1364 color fundus photographs with glaucomatous indications and 1768 color fundus photographs without glaucomatous features. Two testing datasets consisted of (1) 95 images of 95 glaucomatous eyes and 110 images of 110 normative eyes, and (2) 93 images of 93 glaucomatous eyes and 78 images of 78 normative eyes. METHODS: A deep learning algorithm known as Residual Network (ResNet) was used to diagnose glaucoma using a training dataset. The 2 testing datasets were obtained using different fundus cameras (different manufacturers) across multiple institutes. The size of the training data was artificially increased by adding minor alterations to the original data, known as "image augmentation." Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AROC). MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve. RESULTS: When image augmentation was not used, the AROC was 94.8% (90.3-96.8) in the first testing dataset and 99.7% (99.4-100.0) in the second dataset. These AROC values were significantly (P < 0.05) smaller without augmentation (87.7% [82.8-92.6] in the first testing dataset and 94.5% [91.3-97.6] in the second testing dataset). CONCLUSIONS: The previously developed deep residual learning algorithm achieved high diagnostic performance with different fundus cameras across multiple institutes, in particular when image augmentation was used.

Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.

The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundus photographs with glaucomatous indications and 1,768 color fundus photographs without glaucomatous features. A testing dataset consisted of 60 eyes of 60 glaucoma patients and 50 eyes of 50 normal subjects. Using the training dataset, a deep learning algorithm known as Deep Residual Learning for Image Recognition (ResNet) was developed to discriminate glaucoma, and its diagnostic accuracy was validated in the testing dataset, using the area under the receiver operating characteristic curve (AROC). The Deep Residual Learning for Image Recognition was constructed using the training dataset and validated using the testing dataset. The presence of glaucoma in the testing dataset was also confirmed by three Residents in Ophthalmology. The deep learning algorithm achieved significantly higher diagnostic performance compared to Residents in Ophthalmology; with ResNet, the AROC from all testing data was 96.5 (95% confidence interval [CI]: 93.5 to 99.6)% while the AROCs obtained by the three Residents were between 72.6% and 91.2%.