Detection & Monitoring: Emerging Technologies

In the context of ophthalmic conditions, glaucoma presents some unique challenges. This insidious disease is typically asymptomatic until later stages, hence is most commonly detected during a routine eye examination. Many people do not attend for regular eye and vision checks, hence despite increased understanding of glaucoma and its risk factors amongst eye care practitioners, recent Australian data reveals that only 52.4 per cent of non- Indigenous Australians and 28 per cent of Indigenous Australians with glaucoma were aware that they had the disease.¹

it is possible to detect patients with rapidly progressing glaucoma within 10 months using home monitoring of visual field compared to 2.5 years

For patients with known glaucoma, improvements in individually targeted monitoring are required. The key problem is that it is currently difficult to identify which patients with glaucoma are likely to progress rapidly and therefore which are in most need of treatment.

The proportion of people with glaucoma who are losing visual field rapidly – mean deviation (MD) changing between -1 to -2dB/year – is approximately 4.3 per cent and those with catastrophic visual field progression (defined as MD reducing by more than -2dB/year) is approximately 1.5 per cent.²

In Australia, the current status quo is to monitor approximately six monthly. This results in large numbers of stable patients occupying limited clinical resources. However, for those with rapid disease progression, six monthly visits are not frequent enough to convincingly identify those who are losing visual field rapidly due to the intrinsic variability in the test. Consequently, repeated visits are required to be certain of progressive loss of visual function. If the repeat test interval is every six months, a significant delay in treatment can ensue.

However, with the emerging technologies described here, the potential for early detection and targeted disease management is promising.

PORTABLE AND COMPACT DEVICES

The stage of glaucoma is typically determined by assessment of anatomical parameters (using direct visualisation, photography or optical coherence tomography, OCT), in addition to assessment of visual function using visual field assessment. Conventional devices for both ophthalmic imaging and visual field assessment have large footprints, are highly specialised, and not particularly portable. Recent developments in both portable OCT and visual field devices have the potential to significantly increase access to these platforms for both detection and monitoring of glaucoma. Several international research groups are developing handheld OCT devices,³ and portable versions of perimetry are available on tablet devices,^4,5 and via smartphones combined with virtual reality headsets.^6,7 Portable devices provide the opportunity to conduct testing in remote settings and may improve the clinical information available to be used in telemedicine. Inexpensive compact devices also open the door for home monitoring of glaucoma. A key advantage of home monitoring for glaucomatous progression is the potential for much more regular testing. A recent modelling showed that it is possible to detect patients with rapidly progressing glaucoma within 10 months using home monitoring of visual field compared to 2.5 years with standard six monthly clinic based testing.⁸ This would allow patients who are most in need of escalation of treatment to receive the sight preserving treatment earlier than they otherwise would have.

COMBINING STRUCTURE AND FUNCTION

Traditionally, structural and functional tests are conducted independently, and the results are evaluated by the clinician to determine whether the two modalities produce a consistent picture regarding the stage of disease or its progression. Future innovations are likely to integrate imaging and visual fields at the point of testing, and also in advanced statistical analysis. Fundus oriented perimetry is commercially available for glaucoma, combining retinal photography and real time eye tracking during perimetry in the one instrument.⁹ Algorithms are being developed to integrate OCT imaging data with visual field testing by customising both the spatial locations tested in the visual field,¹⁰ and the stimulus intensities tested,^11,12 according to an individual’s OCT data. Advanced statistical techniques are also being developed to combine information across platforms^13,14 with the goal of improving diagnostic accuracy and measurement precision of disease progression.

IOP remains the key modifiable risk factor for glaucoma and glaucomatous progression

A FUTURE WITH ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) has the potential to process large datasets quickly and efficiently, permitting automated decision making regarding the classification of images or visual field data. In the context of glaucoma, AI has the potential to enable the classification of large amounts of patient data collected in non-expert settings (for example screening or home monitoring) to select patients for more resource intensive, routine clinical care. With increasing availability of internet access in remote settings, AI also has potential to be an adjunct to telemedicine to assist in clinical decision making in remote communities where access to experts can be difficult. Applications of AI in ophthalmology are still in their infancy as documented¹⁵ with common issues being difficulties in determining the generality of the very large datasets required for training, in addition to the somewhat fuzzy nature of clinical expert classification of glaucoma and its progression.

MAINTAINING AND MONITORING IOP

IOP remains the key modifiable risk factor for glaucoma and glaucomatous progression. Accurate understanding of daily variations of IOP within individuals is currently limited by access to measurement. IOP measurement traditionally can only be performed in the clinic setting with specialised equipment and technical proficiency. Recent advances in handheld rebound tonometers create the situation where patients are able to monitor their IOP in a non-invasive way in the home. A recent Australian study demonstrates that many patients are able to successfully self measure IOP, enabling the measurement of diurnal fluctuation and response to treatment.¹⁶ Patient training and adherence to the correct measurement technique is required for accurate measurement, however the ability to home monitor with such devices is feasible for most people. In the study, researchers were able to show effective IOP lowering in participants with latanoprost within 24 hours of commencing treatment, demonstrating the power of home IOP monitoring to provide timely information to clinicians making future treatment decisions. Contact lens based devices and more invasive intraocular implantable devices for continuous 24 hour monitoring are also being developed, however the accuracy and biocompatibility of these devices are still being investigated, as evidenced in current literature.¹⁷

 Dr George Kong is an ophthalmologist and glaucoma subspecialist at Royal Victorian Eye and Ear Hospital, Victoria Australia. He has a research interest in developing disruptive technologies in ophthalmic care and has developed the Melbourne Rapid Fields perimetry software for portable tablet devices. 

Professor Allison McKendrick is Head of Department of Optometry & Vision Sciences at the University of Melbourne. She is a recognised international expert in visual functional assessment and ophthalmic imaging. Her research is supported by the NHMRC and Australian Research Council, in addition to industry support from the ophthalmic industry (Heidelberg Engineering, GmBH; Haag- Streit AG, CenterVue SpA). 

References 1. Keel S, Xie J, Foreman J, et al. Prevalence of glaucoma in the Australian National Eye Health Survey. Br J Ophthalmol 2018;Apr 26: e-pub ahead of print. 

2. Chauhan BC, Malik R, Shuba LM, Rafuse PE, Nicolela MT, Artes PH. Rates of glaucomatous visual field change in a large clinical population. Invest Ophthalmol Vis Sci 2014;55:4135-4143. 
3. Monroy GL, Won J, Spillman DR, Dsouza R, Boppart SA. Clinical translation of handheld optical coherence tomography: practical considerations and recent advancements. J Biomed Optics 2017;22:121715. 
4. Kong YXG, He M, Crowston JG, Vingrys AJ. A comparison of perimetric results from a tablet perimeter and Humphrey Field Analyzer in glaucoma patients. Trans Vis Sci Technol 2016;5:2. 
5. Johnson CA, Thapa S, Kong YXG, Robin AL. Performance of an iPad application to detect moderate and advanced visual field loss in Nepal. Am J Ophthalmol 2017;182:147-154. 
6. Sircar T, Mishra A, Bopardikar A, Tiwari VN. GearVision: Smartphone based head mounted perimeter for detection of visual field defects. Conf Proc IEEE Eng Med Biol Soc 2018;Jul:5402-5405.
7. Eizenman M, Shi R, Fee T, Mahsood Y, Buys Y, Trope G. Visual field testing on a personal smartphone. IOVS – ARVO Abstract 2018;Abstract no 6029. 
8. Anderson AJ, Bedggood PA, Kong YXG, Martin KR, Vingrys AJ. Can home monitoring allow earlier detection of rapid visual field progression in glaucoma? Ophthalmology 2017;124:1735-1742. 
9. Montesano G, Bryan SR, Crabb DP, et al. A comparison between the Compass Fundus perimeter and the Humphrey Field Analyzer. Ophthalmology 2018;Epub ahead of print: Aug 14. 
10. Ganeshrao SB, Turpin A, McKendrick AM. Sampling the visual field based on individual retinal nerve fiber layer thickness profile. Invest Ophthalmol Vis Sci 2018;59:1066-1074. 
11. Denniss J, McKendrick AM, Turpin A. Towards patienttailored perimetry: Automated perimetry can be improved by seeding procedures with patient-specific structural information. Trans Vis Sci Tech 2013;2(4):3. 
12. Montesano G, Rosseti LM, Allegrini D, Romano MR, Crabb DP. Improving visual field examination of the macula using structural information. Trans Vis Sci Tech 2018;7:36. 
13. Medeiros FA, Lisboa R, Weinreb RN, Girkin CA, Liebmann JM, Zangwill LM. A combined index of structure and function for staging glaucomatous damage. Arch Ophthalmol 2012;130:1107-1116. 
14. Shigueoka LS, Vasconcellos JPC, Schimiti RB, et al. Automated algorithms combining structure and function outperform general ophthalmologies in diagnosing glaucoma. PLoS One 2018;13:e0207784. 
15. Zheng C, Johnson TV, Garg A, Boland MV. Artificial intelligence in glaucoma. Curr Opin Ophthalmol 2018;2018. 
16. Huang J, Katalinic P, Kalloniatis M, Hennessy MP, Zangerl B. Diurnal intraocular pressure fluctuations with self-tonometry in glaucoma patients and suspects: a clinical trial. Optom Vis Sci 2018;95:88-95. 
17. Konstas AG, Kahook MY, Araie M, et al. Diurnal and 24-h intraocular pressures in glaucoma: monitoring strategies and impact on prognosis and treatment. Adv Ther 2018;35:1775-1804.