Integrating independent spatio-temporal replications to assess population trends in disease spread

Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions

Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness. Current challenges in the direct application of artificial intelligence to further our understating of this disease are also outlined. The article also discusses how the combined use of mathematical modeling and artificial intelligence may help to address these challenges, along with stronger communication between data scientists and clinicians.

Functional assessment of glaucoma: Uncovering progression

Clinicians who manage glaucoma patients carefully monitor the visual field to determine if treatments are effective or interventions are needed. Visual field tests may reflect disease progression or variability among examinations. We describe the approaches and perimetric tests used to evaluate glaucomatous visual field progression and factors that are important for identifying progression. These include stimulus size, which area of the visual field to assess (central versus peripheral), and the testing frequency, evaluating which is important to detect change early while minimizing patient testing burden. We also review the different statistical methods developed to identify change. These include trend- and event-based analyses, parametric and nonparametric tests, population-based versus individualized approaches, as well as pointwise and global analyses. We hope this information will prove useful and important to enhance the management of glaucoma patients. Overall, analysis procedures based on series of at least 5 to 6 examinations that require confirmation and persistence of changes, that are guided by the pattern and shape of the glaucomatous visual field deficits, and that are consistent with structural defects provide the best clinical performance.

Forecasting future Humphrey Visual Fields using deep learning

Purpose

To determine if deep learning networks could be trained to forecast future 24–2 Humphrey Visual Fields (HVFs).

Methods

All data points from consecutive 24–2 HVFs from 1998 to 2018 were extracted from a university database. Ten-fold cross validation with a held out test set was used to develop the three main phases of model development: model architecture selection, dataset combination selection, and time-interval model training with transfer learning, to train a deep learning artificial neural network capable of generating a point-wise visual field prediction. The point-wise mean absolute error (PMAE) and difference in Mean Deviation (MD) between predicted and actual future HVF were calculated.

Results

More than 1.7 million perimetry points were extracted to the hundredth decibel from 32,443 24–2 HVFs. The best performing model with 20 million trainable parameters, CascadeNet-5, was selected. The overall point-wise PMAE for the test set was 2.47 dB (95% CI: 2.45 dB to 2.48 dB), and deep learning showed a statistically significant improvement over linear models. The 100 fully trained models successfully predicted future HVFs in glaucomatous eyes up to 5.5 years in the future with a correlation of 0.92 between the MD of predicted and actual future HVF and an average difference of 0.41 dB.

Conclusions

Using unfiltered real-world datasets, deep learning networks show the ability to not only learn spatio-temporal HVF changes but also to generate predictions for future HVFs up to 5.5 years, given only a single HVF.