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Deep Learning in the Retina

Deep learning (DL) has been touted as a paradigm-shifting field of research with potential to transform the provision of eye care. With the dramatic rise of complex clinical data, DL solutions have been proposed to automate the data analysis process with an eye to decrease clinician burden. More recently, innovative applications of DL aim to assist clinicians with patient management. Jason Charng explores current and novel applications of DL in retinal diseases.

Deep learning, a term often erroneously used interchangeably with artificial intelligence or machine learning, aims to imitate the way humans process information. This is primarily done by exposing the DL algorithm to correctly annotated data and letting the program develop its own rationale to complete the task at hand. For example, if one was to develop a DL algorithm to classify birds from mammals, one would first input correctly labelled images of both animal classes into the algorithm. The DL algorithm would then identify and learn features it deems to differentiate these two groups by itself. One such feature may be that birds have beaks.

Novel applications include predicting DR development using fundus photographs and clinical risk factors,12 and estimating DR progression using fundus photographs alone

Once the algorithm is trained, new images can be fed into it to identify whether the animal is avian or mammalian.

DL has been extensively used in image analysis due to its ability to process and extract features directly from the input image. The trained algorithm can then be applied to the identification, classification and segmentation of images.1

Image segmentation, which classifies different portions of the image into groups or categories that share similar characteristics, is a time-consuming task in the clinic that can be streamlined with DL solutions. Examples of image segmentation include delineating diseased area in an en face image2 and sectioning different layers of an optical coherence tomography (OCT) B-scan.3

Diabetic Retinopathy

Early detection of diabetic retinopathy (DR) is essential in the prevention of vision loss. One effective public health strategy in early DR detection is an efficient screening program, which requires evaluating fundus images from patients. The current gold standard in telemedicine-based DR programs relies on human grading of fundus images taken from satellite sites.4-6 This time-consuming task draws clinicians away from precious chair time.

DL-based algorithms, designed to detect DR in fundus images, have been shown to have very similar accuracy compared to human graders.7,8 They have subsequently received regulatory approval for deployment into the health system (i.e. IDx-DR, United States; RetCad, European Union; SELENA+, Singapore).

Beyond disease detection, DL systems have also been developed to grade DR severity of fundus images,9 which may assist clinical decision making. Furthermore, investigators have deployed DL to analyse other multimodal images, such as optical coherence tomography (OCT)10 and OCT angiography (OCT-A),11 which can further alleviate the time-consuming demand of human grading. One exciting area of research is the potential to use DL to create personalized treatment, which should translate to better patient outcomes. Novel applications include predicting DR development using fundus photographs and clinical risk factors,12 and estimating DR progression using fundus photographs alone.13 Figure 1 summarises the current applications of DL in DR.

Figure 1. Current applications of deep learning in diabetic retinopthy.

Age-Related Macular Degeneration

Currently, the majority of DL applications in age-related macular degeneration (AMD) centre on the disease diagnosis using multimodal images. DL algorithms have been developed to determine whether a patient requires ophthalmology referral, based on assessing their fundus colour photograph, with the accuracy of these algorithms comparable to human graders.14

Additionally, algorithms have been developed to diagnose the presence of AMD using OCTs.15,16 Importantly, it has been shown that an AMD diagnosis algorithm using both fundus photographs and OCTs was more accurate than using one image modality alone,17 which is more reflective of everyday practice.

Investigators have also explored more complicated applications of DL beyond disease detection. Algorithms have been developed to classify AMD fundus images into clinical diagnoses of normal, nonexudative, or exudative AMD,18,19 which can automatically flag cases of interest to the practitioner. Further advancements in image classification include categorizing fundus images into both the nine-step20 and the simplified four-step21 Age-Related Eye Disease Study (AREDS) scales. These applications provide the ability to process large amounts of data while freeing up clinician time.

DL has also been used in more profound AMD applications. It has been employed to perform the time-consuming task of segmenting retinal layers in AMD OCT images.22 More interesting applications include identifying novel OCT-based disease biomarkers, such as pachychoroid-related changes23 and hyperreflective foci.24

An exciting field of research is using DL to predict AMD progression. Currently, mathematical models derived from population-based longitudinal data are used to predict the risk of AMD progression to ensure appropriate review periods and timely treatment.25,26 DL has been employed to both process the large amount of clinical data as well as develop the model itself.27,28 Impressively, it has been shown that a DL-based prediction model provided higher prognostic accuracy in predicting the risk of developing late-AMD than existing non-DL clinical standards.29

Furthermore, a DL-based prediction model combining both genetic and fundus data showed a better prediction of patients developing AMD than using fundus data alone.30 This opens the exciting possibility of using DL to amalgamate large amounts of diverse clinical data to better triage patients and enhance patient management.

Inherited Retinal Diseases

Similar to DR and AMD, to date, the majority of DL applications in inherited retinal diseases (IRD) have focused on image analysis. Examples include detecting the presence of pigmentary changes in colour fundus photographs of patients with retinitis pigmentosa31 and identifying patients with Stargardt disease in OCTs.32 Advancements have been made in developing DL algorithms delineating retinal layers in eyes with IRD,33 which can process large amounts of data efficiently while eliminating human bias. Furthermore, DL algorithms have been shown to analyse disease progression in IRD eyes by quantifying retinal changes over time in achromatopsia,34 retinitis pigmentosa,34,35 and Stargardt disease.2,34

Given that novel gene therapy options are in the pipeline to treat IRD, one question pertinent in choosing patients for future clinical trials is what the potential functional improvement will be following treatment Investigators have shown it is possible to predict visual acuity using confocal scanning laser ophthalmoscopy in retinitis pigmentosa eyes36 and retinal sensitivity from OCT scans in blue cone monochromacy and Leber congenital amaurosis.37,38 Hence these algorithms can assist investigators in identifying appropriate patients (i.e., those with greatest potential for vision improvement) for treatment as well as counselling patient expectations.

Rise of the Machines?

A major concern for clinicians is the notion of DL taking over human-based consultation. However, it is evident from current literature that DL algorithms are designed to streamline and/or assist clinical decision making and free up clinician time.

We note that DL algorithms face two major obstacles before being adapted into everyday practice.

Firstly, the features that a DL algorithm extracts from the data to reach its decision are mainly unknown to the human operator, hence human interpretation of the logic derived by the algorithm is complex. This differs from traditional clinical diagnosis, which is based on a logical compilation of clinical data. Therefore, clinicians are wary of clinical decision making based on DL algorithms alone.

Secondly, it has been shown that variability in algorithm designing can lead to difference in performance. Sources of variability include the classification of disease made by human graders, the differences in population demographics, the clinical data used, and the algorithm architecture. Therefore, there is a need to benchmark algorithms before clinical deployment.

Research is underway to address the two aforementioned obstacles to increase the acceptance of the technology.
In conclusion, DL is a fast-evolving area of study with widespread applications that shows promise to transform eye care. With aims to decrease clinician load and improve patient management, the technology offers revolutionary advances that will invariably be integrated into everyday practice.

Jason Charng is a senior lecturer at the Department of Optometry, University of Western Australia and Research Fellow at Lions Eye Institute. He has a keen interest in technological advances assessing structure and function of the retina, in particular as outcome measures for clinical trials.

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