International Agency for the Prevention of Blindness , 2021. IAPB vision atlas. Available at: https://www.iapb.org/learn/vision-atlas/. Accessed August 3, 2023.
Pascolini D, Mariotti SP, 2012. Global estimates of visual impairment: 2010. Br J Ophthalmol 96: 614–618.
World Health Organization , 2019. World Report on Vision. Geneva, Switzerland: WHO.
Nkanga D, Adenuga O, Okonkwo O, Ovienria W, Ibanga A, Agweye C, Oyekunle I, Akanbi T, 2020. Profile, visual presentation and burden of retinal diseases seen in ophthalmic clinics in sub-Saharan Africa. Clin Ophthalmol 14: 679–687.
Magan T, Pouncey A, Gadhvi K, Katta M, Posner M, Davey C, 2019. Prevalence and severity of diabetic retinopathy in patients attending the endocrinology diabetes clinic at Mulago Hospital in Uganda. Diabetes Res Clin Pract 152: 65–70.
Khachatryan T, Mozaffar T, Mnatsakanyan L, 2022. Utility of video-fundoscopy and prospects of portable stereo-photography of the ocular fundus in neurological patients. BMC Neurol 22: 61.
Muiesan ML et al., 2017. Ocular fundus photography with a smartphone device in acute hypertension. J Hypertens 35: 1660–1665.
Cheung CY et al., 2022. A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health 4: e806–e815.
MacCormick IJ, Beare NA, Taylor TE, Barrera V, White VA, Hiscott P, Molyneux ME, Dhillon B, Harding SP, 2014. Cerebral malaria in children: using the retina to study the brain. Brain 137: 2119–2142.
Beare NAV, 2023. Cerebral malaria – using the retina to study the brain. Eye 37: 2379–2384.
Heiden D et al., 2007. Cytomegalovirus retinitis: the neglected disease of the AIDS pandemic. PLoS Med 4: e334.
Ford N et al., 2013. Burden of HIV-related cytomegalovirus retinitis in resource-limited settings: a systematic review. Clin Infect Dis 57: 1351–1361.
Bechange S, Jolley E, Virendrakumar B, Pente V, Milgate J, Schmidt E, 2020. Strengths and weaknesses of eye care services in sub-Saharan Africa: a meta-synthesis of eye health system assessments. BMC Health Serv Res 20: 381.
Quellec G, Bazin L, Cazuguel G, Delafoy I, Cochener B, Lamard M, 2016. Suitability of a low-cost, handheld, nonmydriatic retinograph for diabetic retinopathy diagnosis. Transl Vis Sci Technol 5: 16.
Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M, 2016. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57: 5200–5206.
Bastawrous A et al., 2016. Clinical validation of a smartphone-based adapter for optic disc imaging in Kenya. JAMA Ophthalmol 134: 151–158.
Rajalakshmi R, Prathiba V, Arulmalar S, Usha M, 2021. Review of retinal cameras for global coverage of diabetic retinopathy screening. Eye 35: 162–172.
Sengupta S, Sindal MD, Baskaran P, Pan U, Venkatesh R, 2019. Sensitivity and specificity of smartphone-based retinal imaging for diabetic retinopathy: a comparative study. Ophthalmol Retina 3: 146–153.
Chalam KV, Chamchikh J, Gasparian S, 2022. Optics and utility of low-cost smartphone-based portable digital fundus camera system for screening of retinal diseases. Diagnostics (Basel) 12: 1499.
Kim TN et al., 2021. Comparison of automated and expert human grading of diabetic retinopathy using smartphone-based retinal photography. Eye 35: 334–342.
Kaur R et al., 2020. MII RetCam assisted smartphone-based fundus imaging (MSFI) – a boon for paediatric retinal imaging. Eye 34: 1307–1309.
Malerbi FK et al., 2022. Diabetic retinopathy screening using artificial intelligence and handheld smartphone-based retinal camera. J Diabetes Sci Technol 16: 716–723.
Chandrakanth P, Gosalia H, Verghese S, Narendran K, Narendran V, 2022. The Gimbalscope – a novel smartphone-assisted retinoscopy technique. Indian J Ophthalmol 70: 3112–3115.
International Centre for Eye Health, Ministry of Health, Mozambique (MoHM), Olhos do Mundo (Mozambique), University of Cape Town , 2016. Mozambique—Inhambane Rapid Assessment of Avoidable Blindness Survey 2016. Grootebroek, the Netherlands: RAAB Repository.
Nhacolo A et al., 2021. Cohort profile update: Manhiça Health and Demographic Surveillance System (HDSS) of the Manhiça Health Research Centre (CISM). Int J Epidemiol 50: 395.
Kemp S, 2021. Digital 2021: Global Overview Report. DataReportal. Available at: https://datareportal.com/reports/digital-2021-global-overview-report. Accessed August 3, 2023.
International Telecommunications Union, Development Sector , 2021. Digital Development: Facts and Figures 2021. Available at: https://www.itu.int/en/ITU-D/Statistics/Documents/facts/FactsFigures2021.pdf. Accessed August 3, 2023.
Nkomazana O, Tshitswana D, 2008. Ocular complications of HIV infection in sub-Sahara Africa. Curr HIV/AIDS Rep 5: 120–125.
Ekoru K et al., 2019. Type 2 diabetes complications and comorbidity in sub-Saharan Africans. EClinicalMedicine 16: 30–41.
Medeiros FA, Jammal AA, Mariottoni EB, 2021. Detection of progressive glaucomatous optic nerve damage on fundus photographs with deep learning. Ophthalmology 128: 383–392.
Huang X et al., 2022. Detecting glaucoma from multi-modal data using probabilistic deep learning. Front Med (Lausanne) 9: 923096.
Font O, Torrents-Barrena J, Royo D, García SB, Zarranz-Ventura J, Bures A, Salinas C, Zapata M, 2022. Validation of an autonomous artificial intelligence-based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context. Graefes Arch Clin Exp Ophthalmol 260: 3255–3265.
Panchal S, Naik A, Kokare M, Pachade S, Naigaonkar R, Phadnis P, Bhange A, 2023. Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: a dataset of frequently and rarely identified diseases. Data (Basel) 8: 29.
Nakahara K et al., 2022. Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone. Br J Ophthalmol 106: 587–592.
Past two years | Past Year | Past 30 Days | |
---|---|---|---|
Abstract Views | 1677 | 1677 | 175 |
Full Text Views | 30 | 30 | 7 |
PDF Downloads | 39 | 39 | 13 |
Low-income countries carry approximately 90% of the global burden of visual impairment, and up to 80% of this could be prevented or cured. However, there are only a few studies on the prevalence of retinal disease in these countries. Easier access to retinal information would allow differential diagnosis and promote strategies to improve eye health, which are currently scarce. This pilot study aims to evaluate the functionality and usability of a tele-retinography system for the detection of retinal pathology, based on a low-cost portable retinal scanner, manufactured with 3D printing and controlled by a mobile phone with an application designed ad hoc. The study was conducted at the Manhiça Rural Hospital in Mozambique. General practitioners, with no specific knowledge of ophthalmology or previous use of retinography, performed digital retinographies on 104 hospitalized patients. The retinographies were acquired in video format, uploaded to a web platform, and reviewed centrally by two ophthalmologists, analyzing the image quality and the presence of retinal lesions. In our sample there was a high proportion of exudates and hemorrhages—8% and 4%, respectively. In addition, the presence of lesions was studied in patients with known underlying risk factors for retinal disease, such as HIV, diabetes, and/or hypertension. Our tele-retinography system based on a smartphone coupled with a simple and low-cost 3D printed device is easy to use by healthcare personnel without specialized ophthalmological knowledge and could be applied for the screening and initial diagnosis of retinal pathology.
Disclosure: The study protocol, which followed the Declaration of Helsinki Ethical Principles, was reviewed and approved by the Mozambican National Bioethics Committee (CNBS) (Ref. 149/CNBS/17) and the Clinical Research Ethics Committee of the Centro de Investigação em Saúde de Manhiça (CISM; Ref. CIBS-CISM/135/2016). All participants were given detailed oral and written information about the study and signed a written informed consent document before participation in any study activity.
Authors’ addresses: Rosauro Varo, ISGlobal, Hospital Clínic—Universitat de Barcelona, Barcelona, Spain, and Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique, E-mail: rosauro.varo@isglobal.org. María Postigo, Elena Dacal, Jaime García-Villena, Daniel Cuadrado, Alexander Vladimirov, Nuria Díez, Ramón Vallés-López, and Miguel Luengo-Oroz, SpotLab, Madrid, Spain, E-mails: maria@spotlab.org, elena@spotlab.org, jaime@spotlab.org, danielcuadrado@spotlab.org, alex@spotlab.org, nuria@spotlab.org, ramon@spotlab.org, and miguel@spotlab.org. Rubao Bila, Hélio Chiconela, Antonio Sitoe, Pio Vitorino, and Campos Mucasse, Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique, E-mails: rubaobila92@gmail.com, hm.chiconela@gmail.com, antonio.sitoe@manhica.net, pio.vitorino@manhica.net, and campos.mucasse@manhica.net. Laura Beltran-Agullo and Virginia García, Institut Català de Retina, Barcelona, Spain, E-mails: laurabeltragullo@gmail.com and vgarcia@icrcat.com. Olivia Pujol, Institut Català de Retina, Barcelona, Spain, and Hospital Vall d´Hebron, Barcelona, Spain, E-mail: olpuca@hotmail.com. Mariamo Abdala, Departamento de Oftalmologia, Hospital Central de Maputo, Maputo, Mozambique, and Faculdade de Medicina, Universidade Eduardo Mondlane, Maputo, Mozambique, E-mail: mariamoabdala@yahoo.com.br. Lucía Sallé, Biomedical Image Technologies Group, Departamento de Ingeniería Electrónica, Escuela Técnica Superior de Ingenieros Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain, E-mail: lucia@spotlab.org. Alfonso Anton, Institut Català de Retina, Barcelona, Spain, and CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain, E-mail: aanton@uic.es. Andrés Santos and María J. Ledesma-Carbayo, Biomedical Image Technologies Group, Departamento de Ingeniería Electrónica, Escuela Técnica Superior de Ingenieros Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain, and CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain, E-mails: andres@die.upm.es and mj.ledesma@upm.es. Quique Bassat, ISGlobal, ISGlobal, Hospital Clínic—Universitat de Barcelona, Barcelona, Spain, Centro de Investigação em Saúde de Manhiça, Maputo, Mozambique, Institut Català de Recerca i Estudis Avançats, Barcelona, Spain, Pediatrics Department, Hospital Sant Joan de Déu, Universitat de Barcelona, Esplugues, Barcelona, Spain, and CIBER de Epidemiología y Salud Pública, Instituto de Salud Carlos III, Madrid, Spain, E-mail: quique.bassat@isglobal.org.
International Agency for the Prevention of Blindness , 2021. IAPB vision atlas. Available at: https://www.iapb.org/learn/vision-atlas/. Accessed August 3, 2023.
Pascolini D, Mariotti SP, 2012. Global estimates of visual impairment: 2010. Br J Ophthalmol 96: 614–618.
World Health Organization , 2019. World Report on Vision. Geneva, Switzerland: WHO.
Nkanga D, Adenuga O, Okonkwo O, Ovienria W, Ibanga A, Agweye C, Oyekunle I, Akanbi T, 2020. Profile, visual presentation and burden of retinal diseases seen in ophthalmic clinics in sub-Saharan Africa. Clin Ophthalmol 14: 679–687.
Magan T, Pouncey A, Gadhvi K, Katta M, Posner M, Davey C, 2019. Prevalence and severity of diabetic retinopathy in patients attending the endocrinology diabetes clinic at Mulago Hospital in Uganda. Diabetes Res Clin Pract 152: 65–70.
Khachatryan T, Mozaffar T, Mnatsakanyan L, 2022. Utility of video-fundoscopy and prospects of portable stereo-photography of the ocular fundus in neurological patients. BMC Neurol 22: 61.
Muiesan ML et al., 2017. Ocular fundus photography with a smartphone device in acute hypertension. J Hypertens 35: 1660–1665.
Cheung CY et al., 2022. A deep learning model for detection of Alzheimer’s disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health 4: e806–e815.
MacCormick IJ, Beare NA, Taylor TE, Barrera V, White VA, Hiscott P, Molyneux ME, Dhillon B, Harding SP, 2014. Cerebral malaria in children: using the retina to study the brain. Brain 137: 2119–2142.
Beare NAV, 2023. Cerebral malaria – using the retina to study the brain. Eye 37: 2379–2384.
Heiden D et al., 2007. Cytomegalovirus retinitis: the neglected disease of the AIDS pandemic. PLoS Med 4: e334.
Ford N et al., 2013. Burden of HIV-related cytomegalovirus retinitis in resource-limited settings: a systematic review. Clin Infect Dis 57: 1351–1361.
Bechange S, Jolley E, Virendrakumar B, Pente V, Milgate J, Schmidt E, 2020. Strengths and weaknesses of eye care services in sub-Saharan Africa: a meta-synthesis of eye health system assessments. BMC Health Serv Res 20: 381.
Quellec G, Bazin L, Cazuguel G, Delafoy I, Cochener B, Lamard M, 2016. Suitability of a low-cost, handheld, nonmydriatic retinograph for diabetic retinopathy diagnosis. Transl Vis Sci Technol 5: 16.
Abràmoff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M, 2016. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest Ophthalmol Vis Sci 57: 5200–5206.
Bastawrous A et al., 2016. Clinical validation of a smartphone-based adapter for optic disc imaging in Kenya. JAMA Ophthalmol 134: 151–158.
Rajalakshmi R, Prathiba V, Arulmalar S, Usha M, 2021. Review of retinal cameras for global coverage of diabetic retinopathy screening. Eye 35: 162–172.
Sengupta S, Sindal MD, Baskaran P, Pan U, Venkatesh R, 2019. Sensitivity and specificity of smartphone-based retinal imaging for diabetic retinopathy: a comparative study. Ophthalmol Retina 3: 146–153.
Chalam KV, Chamchikh J, Gasparian S, 2022. Optics and utility of low-cost smartphone-based portable digital fundus camera system for screening of retinal diseases. Diagnostics (Basel) 12: 1499.
Kim TN et al., 2021. Comparison of automated and expert human grading of diabetic retinopathy using smartphone-based retinal photography. Eye 35: 334–342.
Kaur R et al., 2020. MII RetCam assisted smartphone-based fundus imaging (MSFI) – a boon for paediatric retinal imaging. Eye 34: 1307–1309.
Malerbi FK et al., 2022. Diabetic retinopathy screening using artificial intelligence and handheld smartphone-based retinal camera. J Diabetes Sci Technol 16: 716–723.
Chandrakanth P, Gosalia H, Verghese S, Narendran K, Narendran V, 2022. The Gimbalscope – a novel smartphone-assisted retinoscopy technique. Indian J Ophthalmol 70: 3112–3115.
International Centre for Eye Health, Ministry of Health, Mozambique (MoHM), Olhos do Mundo (Mozambique), University of Cape Town , 2016. Mozambique—Inhambane Rapid Assessment of Avoidable Blindness Survey 2016. Grootebroek, the Netherlands: RAAB Repository.
Nhacolo A et al., 2021. Cohort profile update: Manhiça Health and Demographic Surveillance System (HDSS) of the Manhiça Health Research Centre (CISM). Int J Epidemiol 50: 395.
Kemp S, 2021. Digital 2021: Global Overview Report. DataReportal. Available at: https://datareportal.com/reports/digital-2021-global-overview-report. Accessed August 3, 2023.
International Telecommunications Union, Development Sector , 2021. Digital Development: Facts and Figures 2021. Available at: https://www.itu.int/en/ITU-D/Statistics/Documents/facts/FactsFigures2021.pdf. Accessed August 3, 2023.
Nkomazana O, Tshitswana D, 2008. Ocular complications of HIV infection in sub-Sahara Africa. Curr HIV/AIDS Rep 5: 120–125.
Ekoru K et al., 2019. Type 2 diabetes complications and comorbidity in sub-Saharan Africans. EClinicalMedicine 16: 30–41.
Medeiros FA, Jammal AA, Mariottoni EB, 2021. Detection of progressive glaucomatous optic nerve damage on fundus photographs with deep learning. Ophthalmology 128: 383–392.
Huang X et al., 2022. Detecting glaucoma from multi-modal data using probabilistic deep learning. Front Med (Lausanne) 9: 923096.
Font O, Torrents-Barrena J, Royo D, García SB, Zarranz-Ventura J, Bures A, Salinas C, Zapata M, 2022. Validation of an autonomous artificial intelligence-based diagnostic system for holistic maculopathy screening in a routine occupational health checkup context. Graefes Arch Clin Exp Ophthalmol 260: 3255–3265.
Panchal S, Naik A, Kokare M, Pachade S, Naigaonkar R, Phadnis P, Bhange A, 2023. Retinal Fundus Multi-Disease Image Dataset (RFMiD) 2.0: a dataset of frequently and rarely identified diseases. Data (Basel) 8: 29.
Nakahara K et al., 2022. Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone. Br J Ophthalmol 106: 587–592.
Past two years | Past Year | Past 30 Days | |
---|---|---|---|
Abstract Views | 1677 | 1677 | 175 |
Full Text Views | 30 | 30 | 7 |
PDF Downloads | 39 | 39 | 13 |