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Review Article

Smartphone-Based Fundus Imaging–Where Are We Now?

Wintergerst, Maximilian W.M. MD; Jansen, Linus G.; Holz, Frank G. MD; Finger, Robert P. PhD

Author Information
Asia-Pacific Journal of Ophthalmology: July-August 2020 - Volume 9 - Issue 4 - p 308-314
doi: 10.1097/APO.0000000000000303
  • Open


With the ubiquity of smartphones in daily life, it is not surprising that their presence is increasing also in the health space. Ten years ago, Lord et al1 estimated that “1 out of every 2 physicians uses a personal digital assistant or smartphone.” This number has increased considerably since then. Smartphones offer a wide variety of applications: they allow for mobile access to patient relevant information and medical databases, can be used for the education of both patients and health care professionals, and of course offer smartphone-based diagnostics.2 Although there are a number of basic ways to use smartphones in medical diagnostics, smartphone-based fundus imaging (SBFI) is one of the more advanced applications requiring additional smartphone modifications.1 When first tried and documented, the smartphone was used with a handheld lens for indirect ophthalmoscopy and as both needed to be as perfectly aligned as possible to achieve sufficient image quality, the examination was quite challenging.1,3,4 Using video mode and extracting still frames and applications to manually adjust the image settings improved the quality of image acquisition.5 During its onset, SBFI already showed potential as an easy to learn option for inexpensive image acquisition with a built-in option for use in telemedical applications. These might be particularly attractive for low- and middle-income settings. Although authors did raise safety concerns regarding utilized light sources (compliance with international standards on ophthalmic instruments, eg, ISO 10940 and ISO 15004) and possible reduced hygiene as the smartphone cannot be disinfected like other medical devices, many classified SBFI as a remarkable innovation in fundus imaging.2,3,5 Today, with the swift advancements in smartphone technology (including continuously improving smartphone camera quality) and a variety of devices available, SBFI is as promising as ever to make eye care more accessible and affordable.


There are several advantages of SBFI over conventional digital fundus imaging, which might render it preferable in low- and middle-resources settings. SBFI can make eye care more accessible due to it being more affordable, portable, coming with built-in connectivity and ease of use.5–13 Its mobility also allows for examination of immobilized patients (Fig. 1) and its connectivity offers the potential to be used in telemedical settings. The computing abilities of smartphones in addition enable post-processing and further editing such as image stitching.3,14,15 Using smartphone cameras enables capturing both videos and single images.16,17 Image acquisition can be accomplished by nonexpert examiners creating comparable results to the reference standard.5,15,18–21 Even though image quality is—dependent on the comparator—in general inferior to conventional digital fundus photography, SBFI can be used to sufficiently document various fundus pathologies.16,21–24

Smartphone-based fundus imaging of an immobilized patient (iC2, HEINE Optotechnik GmbH).


As mentioned above, SBFI originates from do-it-yourself solutions starting with approaches based on indirect ophthalmoscopy using handheld funduscopy lenses and external light sources until newer smartphones came with a built-in flash, and solutions for direct funduscopy adding a light source closely to the smartphone camera.1–3,5,25 Although some authors reported sufficient image quality and improved handling, others concluded that so far SBFI was an alternative that could not keep up with reference standards regarding image quality and sensitivity in detecting certain diseases.4,25 Aside from those minimalist approaches, different adapters connecting smartphones and lenses have been introduced recently.2,4,6,7,10,14,15,26–44

Most of the available SBFI devices require pupil dilation to achieve acceptable image quality, yet there are devices that are built for image acquisition with an undilated pupil.10,28,29 Although in undilated pupils devices achieve an approximate 20-degree field of view, image acquisition in mydriasis can capture up to 56 degrees (Fig. 2) or even more depending on the indirect ophthalmoscopy lens used.10,27–29 Most devices are designed to image the macula and the optic disc; however, there are some that also capture more peripheral images in satisfying quality.15,30

Smartphone-based fundus imaging with an approximately 56 degree field-of-view (Paxos Scope, Verana Health, Inc., equipped with a Pan Retinal 2.2, Volk Optical Inc.).

Apart from that there are significant discrepancies in applicability for disease screening. For example, in diabetic retinopathy, sensitivity of detecting disease ranges from 50% to >90%.4,7,10,14,15,42 Hence, there is a strong need for a comparison of different SBFI approaches and the diseases they are supposed to screen for.43 Furthermore, image quality for SBFI varies substantially and a reference standard for grading of SBFI image quality is warranted.

The possibility of delegating examinations to nonexpert operators, for example, in a telemedical setting, are of particular interest. There have been attempts for training medical students or health care workers in performing SBFI. Most of them conclude that it is easy and fast to learn while still achieving acceptable outcomes.2,6 Depending less on ophthalmologists, who are a scarce resource in low- and middle-income settings, SBFI would make fundus examinations for patients in these settings more accessible.

Accessibility also depends on which smartphones can be used for which devices. A number of SBFI adapters is limited to only 1 or 2 smartphone models.27,36 This restricts their accessibility and it is also problematic in a time where a large number of smartphones with improved features is released every year and older models are outdated quickly. Other SBFI devices either offer casings for a larger number of models and different operating systems or are built in a modular way so that almost any smartphone is compatible with these devices.10,28,29,31

SBFI could potentially improve cost-effectiveness of screening-programs that involve fundus imaging. Obviously, the cost-effectiveness of different devices needs to be evaluated. With do-it-yourself solutions causing no additional cost but for the smartphone and ophthalmoscopy lens, which every ophthalmologist carries, the costs for dedicated SBFI adapters cover a wide range from less than one hundred to several thousand US dollars.10,25,26,32–35,37–41,44,45 Against this background there is a variety of different applications of SBFI from disease screening to use as a digital surrogate for direct ophthalmoscopy in clinical practice (Fig. 3).

Overview on the current applications for smartphone-based fundus imaging.


Diabetic retinopathy is the leading cause of visual impairment among working-aged adults worldwide and its prevalence is increasing particularly rapidly in middle-income countries.46–50 Nearly 80% of all persons with diabetes and associated retinopathy live in low- and middle-income countries48,51,52; however, in most of these countries no established screening programs exist.49,51,53–58

There is a significant number of publications on the feasibility of SBFI for the screening of diabetic retinopathy. However, results are conflicting with one study stating that SBFI has “no role in screening for diabetic retinopathy at this time” and others doubting that SBFI can replace mainstream imaging devices like the conventional fundus camera.4,25 There are also reports of inappropriate sensitivity and poor image quality.14 Yet more recent studies present more promising results regarding the use of SBFI for detection of diabetic retinopathy. In those, SBFI presented satisfactory sensitivity and specificity when compared with conventional digital fundus photography or clinical examination.7,10,24,59


Glaucoma is a leading global cause of irreversible visual loss and blindness and a delayed diagnosis is one of the main obstacles to visual outcomes in low- and middle-income countries. SBFI can be utilized in the acquisition of fundus images to evaluate the optic nerve head, for example, to screen for patients at risk of having glaucoma. Images from SBFI can for example be evaluated for optic disc pallor and cup-to-disc ratio.8 Similar to the screening for diabetic retinopathy affordability, connectivity and portability make this an attractive approach, especially when the access to preventive health care is limited.8,9,18,29 No substantial differences were found comparing SBFI with clinical examinations via ophthalmoscopy and a digital retinal camera in previous studies.18,29 However, one study compared undilated and dilated SBFI and found that cup-to-disc evaluation on SBFI after dilation achieved a higher accordance with the reference standard and complete visualization of the optic disc was more often achieved with dilation.8 Yet, overall image quality was still superior with conventional digital fundus imaging.8 Bastawrous et al18 found no difference in image quality when comparing images acquired by experienced ophthalmological photographers to unexperienced personnel.

SBFI shows promise in glaucoma screening and monitoring; however, further development of devices, a better contextualization and inclusion of additional tests are still needed.8,9


Retinopathy of prematurity (ROP) is an important cause of childhood visual impairment. This is particularly the case in middle-income countries, with a high overall premature birth rate and sparse and still-developing neonatal care.60–64 No retinopathy of prematurity screening programs are available in most middle- and low-income countries.64

Many advantages of SBFI also apply to ROP imaging/screening; however, some specific requirements need to be considered. The light sources provided by the smartphones used in the reviewed studies are considered safe for the human eye and when compared with conventional retinal imaging devices used in ROP imaging, SBFI does not require contact with the cornea, which makes it less invasive and potentially also less stressful for the patients (Fig. 4).12,16,21–23 The time needed for the examination and image acquisition by experienced ophthalmologists differs from 1 minute, which is just slightly longer than with a conventional retinal imaging device, to 5 minutes.21–23 SBFI offers promising results when compared with the clinical reference standard, yet there are some conflicting results regarding image quality. Although Goyal et al state that the image quality is not equivalent to conventional digital retinal imaging, another study found that the images taken with SBFI had a higher level of detail and better resolution.21,23 However, SBFI has a smaller field of view with a range of 45 to 90 degree compared with, for example, 130 degree of conventional digital retinal cameras.22,23 It should be mentioned that a larger field of view in SBFI requires a higher power in the lens used, which can compromise feasibility and image quality.21 To achieve larger field of views the computing abilities of the smartphone can be utilized to stitch several images achieving, for example, 90° degree images via post-processing.12 As stated before SBFI can be superior in taking peripheral images.16,22

Smartphone-based fundus imaging for retinopathy of prematurity screening and documentation (Paxos Scope, Verana Health, Inc., equipped with a Pan Retinal 2.2, Volk Optical Inc.; left image copyright Boris Airo).

Despite substantial differences in image acquisition and post-processing, image quality obtained was sufficient and SBFI based ROP staging has been reported to reach substantial agreement with the used reference standards and its sensitivity was comparable with indirect ophthalmoscopy.11,16,21–23,65,12,17


Day et al66 acquired images with undilated pupils in an emergency room setting. The device utilized seemed more suitable for imaging of the disc than the macula.66 For medical students SBFI appeared to be superior to indirect ophthalmoscopy when detecting an abnormal fundus and it took a shorter time to examine patients with SBFI. In this setting it should be noted once more that the examination can be performed by a nonophthalmologist and still deliver sufficient results.67 The examined patients mostly presented with papilledema, with SBFI having a tendency to overestimate the severity of the swelling.67 Even though this is only a first pilot study and more evidence needs to be created, it shows that SBFI could make fundus examinations accessible in settings in which they are needed, but are currently unavailable due to a lack of resources.


SBFI may facilitate easier ocular imaging and examination in infants and children (Fig. 5). The casings of the devices can be made more children-friendly and attention-grabbing, in particular those which are 3D-printed could be easily altered.68 Changing the color of the light-source to red can reduce photophobia in infants and the examination can be carried out without the use of eye speculums making the examination less stressful for patients.68 Using SBFI in a pediatric setting is, similar to the use in an emergency setting, still in an early stage. The overall features of SBFI do make it a potentially promising approach, but more studies need to be conducted in this field.

Smartphone-based fundus imaging in a 4-year-old child (D-EYE, D-EYE S.r.l.).


Aside from being useful in a clinical setting, SBFI might also be utilized in teaching. Medical students need to be taught ophthalmological diagnostics during their clinical courses. Direct ophthalmoscopy can be difficult to learn and to teach because the instructor does not share the view with the students, whereas in SBFI both share the same view.69,70

Students examined volunteers, mannequins, or fellow students both with and without pupil dilation and SBFI was shown to have a steeper learning curve, provided better results and students felt more comfortable and confident when using it.20,69,70 The participants felt that SBFI also slightly improved their abilities in direct ophthalmoscopy afterwards.70 Mamtora et al20 propose that eventually SBFI could even replace the direct ophthalmoscope in clinical medicine. Using SBFI for teaching is still in its beginnings, although it appears that teaching funduscopy can be made easier and more understandable through SBFI.


Conventional retinal cameras can acquire images of eg, 130 degree and thus outperform SBFI. However, there are approaches aiming at generating SBFI wide-field images based on post-processing. A montage of 5 single SBFI images can create a high-quality 100-degree image.15 The examination itself takes only 1 minute; however, the automated image stitching is more time-consuming and may take up to 5 minutes.15 A different approach is presented by Toslak et al71: instead of transpupillary illumination, they use a warm white LED with 1.5 Lumen for transpalpebral illumination. Using this approach they were able to create a 152-degree image of the retina in 1 single shot without pupil dilation.71


Telemedicine is the missing link to provide accessible and affordable screenings, where an expert's opinion is needed for the diagnosis but not for the examination itself.4,11,72 Hence, in a world which is becoming more and more connected and where a majority of people are familiar with the use of smartphones, the combination of SBFI and telemedicine presents a promising approach to involve nonexpert examiners in providing quality health care on a larger scale.4,16 However, there is a need for further evidence, notably when it comes to the use of telemedicine and the acquisition of images through nonexpert operators.4,11,21


The use of machine learning for analysis of SBFI could make screenings even more accessible. Its application for detection of retinopathy of prematurity and diabetic retinopathy achieved first encouraging results.73–77 Online- and offline-based machine learning algorithms are conceivable, the offline version being more suitable for rural areas in which no stable internet connection might be available.75–77 Machine learning brought together with SBFI may be one way to further reduce the burden of providing affordable and accessible screenings. So far, the presented data are limited to retinopathy of prematurity and diabetic retinopathy with the software typically only being available in combination with specific devices, but with software engineering and smartphone technology advancing rapidly, the prospects are auspicious.


In summary, there are a large number of different SBFI adapters available to date. All of the presented SBFI approaches are relatively low-cost compared with conventional retinal imaging devices. Furthermore, SBFI is delegable to paramedical staff/technicians and can be implemented in telemedical health service provision. Against this background and because of promising results reviewed herein, it is likely for the smartphone to play an essential role in screening for diabetic retinopathy, glaucoma, and retinopathy of prematurity in low- and middle-income settings.33 In addition, combination with machine learning algorithms might further facilitate applicability in these settings.

However, with significant discrepancies in diagnostic accuracy for intended disease screening and substantially varying SBFI image quality, more studies on the comparison of SBFI devices and a reference standard for grading of SBFI image quality are warranted.

Smartphones have become an integral part of our daily life. It is highly likely that they will continue to do so and emerge even more for purposes beyond including medicine. Technology is progressing at a high pace and with that pace increasing, opportunities and also challenges arise. SBFI is a very promising approach making health care more accessible worldwide at lower costs and hence can be considered a great opportunity. In the coming years it should be made sure that upcoming technology in hardware and software is modular, easily accessible, and thoroughly reviewed to maximize the benefits that can be generated out of these advancements globally.


1. Lord RK, Shah VA, San Filippo AN, Krishna R. Novel uses of smartphones in ophthalmology. Ophthalmology 2010; 117:1274–1274.
2. Chhablani J, Kaja S, Shah VA. Smartphones in ophthalmology. Indian J Ophthalmol 2012; 60:127–131.
3. Bastawrous A. Smartphone fundoscopy. Ophthalmology 2012; 119:432–433.
4. Ryan ME, Rajalakshmi R, Prathiba V, et al. Comparison Among Methods of Retinopathy Assessment (CAMRA) Study smartphone, nonmydriatic, and mydriatic photography. Ophthalmology 2015; 122:2038–2043.
5. Haddock LJ, Kim DY, Mukai S. Simple, inexpensive technique for high-quality smartphone fundus photography in human and animal eyes. J Ophthalmol 2013; 2013:518479.
6. Lodhia V, Karanja S, Lees S, Bastawrous A. Acceptability, usability, and views on deployment of peek, a mobile phone mhealth intervention for eye care in Kenya: qualitative study. JMIR MHealth UHealth 2016; 4:e30.
7. Toy BC, Myung DJ, He L, et al. Smartphone-based dilated fundus photography and near visual acuity testing as inexpensive screening tools to detect referral warranted diabetic eye disease. Retina 2016; 36:1000–1008.
8. Wintergerst MWM, Brinkmann CK, Holz FG, Finger RP. Undilated versus dilated monoscopic smartphone-based fundus photography for optic nerve head evaluation. Sci Rep 2018; 8:10228.
9. Bilong Y, Domngang CN, Nwanlih Gimma G, et al. Smartphone-assisted glaucoma screening in patients with type 2 diabetes: a pilot study. Med Hypothesis Discov Innov Ophthalmol 2020; 9:61–65.
10. Russo A, Morescalch F, Costagliola C, Delcassi L, Semeraro F. Comparison of smartphone ophthalmoscopy with slit-lamp biomicroscopy for grading diabetic retinopathy. Am J Ophthalmol 2015; 159:360–364.
11. Sharma A, Goyal A, Bilong Y, et al. Comparison of a smartphone-based photography method with indirect ophthalmoscopic assessment in referable retinopathy of prematurity: a smart retinopathy of prematurity model pilot study. Ophthalmol Retina 2019; 3:911–912.
12. Patel TP, Aaberg MT, Paulus YM, et al. Smartphone-based fundus photography for screening of plus-disease retinopathy of prematurity. Graefes Arch Clin Exp Ophthalmol 2019; 257:2579–2585.
13. Fu L. Use of smartphone funduscopy to aid diagnosis of chorioretinitis after donor-recipient mismatched cardiac transplantation. BMJ Case Rep 2017; doi:10.1136/bcr-2017-221981.
14. Rajalakshmi R, Arunmalar S, Usha M, et al. Validation of smartphone based retinal photography for diabetic retinopathy screening. PLoS One 2015; 10:e0138285.
15. Kim TN, Myers F, Reber C, et al. A smartphone-based tool for rapid, portable, and automated wide-field retinal imaging. Transl Vis Sci Technol 2018; 7:21.
16. Lin S-J, Yang C-M, Yeh P-T, Ho T-C. Smartphone fundoscopy for retinopathy of prematurity. Taiwan J Ophthalmol 2014; 4:82–85.
17. Oluleye TS, Rotimi-Samuel A, Adenekan A. Mobile phones for retinopathy of prematurity screening in Lagos, Nigeria, sub-Saharan Africa. Eur J Ophthalmol 2016; 26:92–94.
18. Bastawrous A, Giardini ME, Bolster NM, et al. Clinical validation of a smartphone-based adapter for optic disc imaging in Kenya. JAMA Ophthalmol 2016; 134:151–158.
19. Wu AR, Fouzdar-Jain S, Suh DW. Comparison study of funduscopic examination using a smartphone-based digital ophthalmoscope and the direct ophthalmoscope. J Pediatr Ophthalmol Strabismus 2018; 55:201–206.
20. Mamtora S, Sandinha MT, Ajith A, Song A, Steel DHW. Smart phone ophthalmoscopy: a potential replacement for the direct ophthalmoscope. Eye (Lond) 2018; 32:1766–1771.
21. Wintergerst MWM, Petrak M, Li JQ, et al. Non-contact smartphone-based fundus imaging compared to conventional fundus imaging: a low-cost alternative for retinopathy of prematurity screening and documentation. Sci Rep 2019; 9:19711.
22. Lekha T, Ramesh S, Sharma A, Abinaya G. MII RetCam assisted smartphone based fundus imaging for retinopathy of prematurity. Indian J Ophthalmol 2019; 67:834–839.
23. Goyal A, Gopalakrishnan M, Gopalakrishnan G, Chandrashekharan DP, Thachil T, Sharma A. Smartphone guided wide-field imaging for retinopathy of prematurity in neonatal intensive care unit—a Smart ROP (SROP) initiative. Indian J Ophthalmol 2019; 67:840–845.
24. Sengupta S, Sindal MD, Baskaran P, Pan U, Venkatesh R. Sensitivity and specificity of smartphone-based retinal imaging for diabetic retinopathy: a comparative study. Ophthalmol Retina 2019; 3:146–153.
25. Shanmugam MP, Mishra DK, Madhukumar R, et al. Fundus imaging with a mobile phone: a review of techniques. Indian J Ophthalmol 2014; 62:960–962.
26. Bolster NM, Giardini ME, Livingstone IA, Bastawrous A. How the smartphone is driving the eye-health imaging revolution. Expert Rev Ophthalmol 2014; 9:475–485.
27. Maamari RN, Keenan JD, Fletcher DA, Margolis TP. A mobile phone-based retinal camera for portable wide field imaging. Br J Ophthalmol 2014; 98:438–441.
28. Russo A, Morescalchi F, Costagliola C, Delcassi L, Semeraro F. A novel device to exploit the smartphone camera for fundus photography. J Ophthalmol 2015; 2015:823139.
29. Russo A, Mapham W, Turano R, et al. Comparison of smartphone ophthalmoscopy with slit-lamp biomicroscopy for grading vertical cup-to-disc ratio. J Glaucoma 2016; 25:e777–e781.
30. Sharma A, Subramaniam SD, Ramachandran KI, et al. Smartphone-based fundus camera device (MII Ret Cam) and technique with ability to image peripheral retina. Eur J Ophthalmol 2016; 26:142–144.
31. Ludwig CA, Murthy SI, Pappuru RR, et al. A novel smartphone ophthalmic imaging adapter: user feasibility studies in Hyderabad, India. Indian J Ophthalmol 2016; 64:191–200.
32. Panwar N, Huang P, Lee J, et al. Fundus photography in the 21st century-a review of recent technological advances and their implications for worldwide healthcare. Telemed J E Health 2016; 22:198–208.
33. Micheletti JM, Hendrick AM, Khan FN, Ziemer DC, Pasquel FJ. Current and next generation portable screening devices for diabetic retinopathy. J Diabetes Sci Technol 2016; 10:295–300.
34. DeBuc DC. The role of retinal imaging and portable screening devices in tele-ophthalmology applications for diabetic retinopathy management. Curr Diab Rep 2016; 16:132.
35. Bolster NM, Giardini ME, Bastawrous A. The diabetic retinopathy screening workflow: potential for smartphone imaging. J Diabetes Sci Technol 2015; 10:318–324.
36. Xu X, Ding W, Wang X, et al. Smartphone-based accurate analysis of retinal vasculature towards point-of-care diagnostics. Sci Rep 2016; 6:34603.
37. Mohammadpour M, Heidari Z, Mirghorbani M, Hashemi H. Smartphones, tele-ophthalmology, and VISION 2020. Int J Ophthalmol 2017; 10:1909–1918.
38. Nazari Khanamiri H, Nakatsuka A, El-Annan J. Smartphone fundus photography. J Vis Exp 2017; 55958.
39. Bifolck E, Fink A, Pedersen D, Gregory T. Smartphone imaging for the ophthalmic examination in primary care. JAAPA 2018; 31:34–38.
40. Rodenbeck SJ, Mackay DD. Examining the ocular fundus in neurology. Curr Opin Neurol 2019; 32:105–110.
41. Vilela MA, Valenca FM, Barreto PK, Amaral CE, Pellanda LC. Agreement between retinal images obtained via smartphones and images obtained with retinal cameras or fundoscopic exams—systematic review and meta-analysis. Clin Ophthalmol 2018; 12:2581–2589.
42. Bilong Y, Katte JC, Koki G, et al. Validation of smartphone-based retinal photography for diabetic retinopathy screening. Ophthalmic Surg Lasers Imaging Retina 2019; 50:S18–S22.
43. Tan CH, Quah WH, Tan CSH, Smith H, Tudor Car L. Use of smartphones for detecting diabetic retinopathy: a protocol for a scoping review of diagnostic test accuracy studies. BMJ Open 2019; 9:e028811.
44. Hogarty DT, Hogarty JP, Hewitt AW. Smartphone use in ophthalmology: what is their place in clinical practice? Surv Ophthalmol 2020; 65:250–262.
45. Shanmugam MP, Mishra DK, Rajesh R, Madhukumar R. Unconventional techniques of fundus imaging: a review. Indian J Ophthalmol 2015; 63:582–585.
46. Cheung N, Mitchell P, Wong TY. Diabetic retinopathy. Lancet 2010; 376:124–136.
47. Yau JW, Rogers SL, Kawasaki R, et al. Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 2012; 35:556–564.
48. IDF Diabetes Atlas, International Diabetes Federation. 8th ed2017.
49. World Health Organization. Global Report on Diabetes 2016.
50. Flaxman SR, Bourne RRA, Resnikoff S, et al. Global causes of blindness and distance vision impairment 1990-2020: a systematic review and meta-analysis. Lancet Glob health 2017; 5:e1221–e1234.
51. International Diabetes Federation. The Diabetic Retinopathy Barometer Report: Global Findings 2017.
52. Sabanayagam C, Banu R, Chee ML, et al. Incidence and progression of diabetic retinopathy: a systematic review. Lancet Diabetes Endocrinol 2018; 7:140–149.
53. Ruta LM, Magliano DJ, Lemesurier R, et al. Prevalence of diabetic retinopathy in Type 2 diabetes in developing and developed countries. Diabet Med 2013; 30:387–398.
54. Ramasamy K, Raman R, Tandon M. Current state of care for diabetic retinopathy in India. Curr Diab Rep 2013; 13:460–468.
55. Sasongko MB, Widyaputri F, Agni AN, et al. Prevalence of diabetic retinopathy and blindness in indonesian adults with type 2 diabetes. Am J Ophthalmol 2017; 181:79–87.
56. Song P, Yu J, Chan KY, Theodoratou E, Rudan I. Prevalence, risk factors and burden of diabetic retinopathy in China: a systematic review and meta-analysis. J Glob Health 2018; 8:010803.
57. Murthy KR, Murthy PR, Kapur A, Owens DR. Mobile diabetes eye care: experience in developing countries. Diabetes Res Clin Pract 2012; 97:343–349.
58. Sabanayagam C, Yip W, Ting DS, Tan G, Wong TY. Ten emerging trends in the epidemiology of diabetic retinopathy. Ophthalmic Epidemiol 2016; 23:209–222.
59. Prathiba V, Rajalakshmi R, Arunmalar S, et al. Accuracy of the smartphone-based nonmydriatic retinal camera in the detection of sight-threatening diabetic retinopathy. Indian J Ophthalmol 2020; 68:S42–S46.
60. Gilbert C, Rahi J, Eckstein M, O'sullivan J, Foster A. Retinopathy of prematurity in middle-income countries. Lancet 1997; 350:12–14.
61. Chen J, Smith LEH. Retinopathy of prematurity. Angiogenesis 2007; 10:133–140.
62. Hellström A, Smith LEH, Dammann O. Retinopathy of prematurity. Lancet 2013; 382:1445–1457.
63. Hartnett ME. Advances in understanding and management of retinopathy of prematurity. Surv Ophthalmol 2017; 62:257–276.
64. Gilbert C, Fielder A, Gordillo L, et al. Characteristics of infants with severe retinopathy of prematurity in countries with low, moderate, and high levels of development: implications for screening programs. Pediatrics 2005; 115:e518–e525.
65. Raju B, Raju NSD, Akkara JD, Pathengay A. Smartphone-based fundus documentation in retinopathy of prematurity. Indian J Ophthalmol 2019; 67:1909.
66. Day LM, Wang SX, Huang CJ. Nonmydriatic fundoscopic imaging using the Pan Optic iExaminer System in the pediatric emergency department. Acad Emerg Med 2017; 24:587–594.
67. Muiesan ML, Salvetti M, Paini A, et al. Ocular fundus photography with a smartphone device in acute hypertension. J Hypertens 2017; 35:1660–1665.
68. Patel TP, Kim TN, Yu G, et al. Smartphone-based, rapid, wide-field fundus photography for diagnosis of pediatric retinal diseases. Transl Vis Sci Technol 2019; 8:29.
69. Hakimi AA, Lalehzarian SP, Lalehzarian AS, et al. The utility of a smartphone-enabled ophthalmoscope in pre-clinical fundoscopy training. Acta Ophthalmol 2018; 97:e327–e328.
70. Kim Y, Chao DL. Comparison of smartphone ophthalmoscopy vs conventional direct ophthalmoscopy as a teaching tool for medical students: the COSMOS study. Clin Ophthalmol 2019; 13:391–401.
71. Toslak D, Thapa D, Chen Y, et al. Trans-palpebral illumination: an approach for wide-angle fundus photography without the need for pupil dilation. Opt Lett 2016; 41:2688–2691.
72. Collon S, Chang D, Tabin G, et al. Utility and feasibility of teleophthalmology using a smartphone-based ophthalmic camera in screening camps in Nepal. Asia Pac J Ophthalmol (Phila) 2020; 9:54–58.
73. Brown JM, Campbell JP, Beers A, et al. Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. JAMA Ophthalmol 2018; 136:803–810.
74. Redd TK, Campbell JP, Brown JM, et al. Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br J Ophthalmol 2018; doi:10.1136/bjophthalmol-2018-313156.
75. Rajalakshmi R, Subashini R, Anjana RM, Mohan V. Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence. Eye (Lond) 2018; 32:1138–1144.
76. Natarajan S, Jain A, Krishnan R, Rogye A, Sivaprasad S. Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone. JAMA Ophthalmol 2019; 137:1182–1188.
77. Sosale B, Sosale AR, Murthy H, Sengupta S, Naveenam M. Medios—an offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy. Indian J Ophthalmol 2020; 68:391–395.

smartphone funduscope; smartphone funduscopy; smartphone ophthalmoscope; smartphone ophthalmoscopy; smartphone-based fundus imaging

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