Validation of Smartphone-Based Retinal Photography for Diabetic Retinopathy Screening

被引:21
|
作者
Bilong, Yannick [1 ]
Katte, Jean-Claude [2 ]
Koki, Godefroy [1 ]
Kagmeni, Giles [1 ]
Obama, Odile Pascale Nga [4 ]
Fofe, Hermann Rossi Ngoufo [5 ]
Mvilongo, Caroline [1 ]
Nkengfack, Oliver [6 ]
Bimbai, Andre Michel [7 ]
Sobngwi, Eugene [3 ]
Mbacham, Wilfred [8 ]
Mbanya, Jean Claude [3 ]
Bella, Lucienne Assumpta [1 ]
Sharma, Ashish [9 ]
机构
[1] Univ Yaounde I, Fac Med & Sci, Dept Ophthalmol, POB 1364, Yaounde, Cameroon
[2] Univ Yaounde I, Dept Publ Hlth, Yaounde, Cameroon
[3] Univ Yaounde I, Dept Internal Med & Specialties, Yaounde, Cameroon
[4] Minist Publ Hlth, Mbalmayo Dist Hosp, Yaounde, Cameroon
[5] Minist Publ Hlth, Ebolowa Reg Hosp, Yaounde, Cameroon
[6] Minist Publ Hlth, Tibati Dist Hosp, Yaounde, Cameroon
[7] Paris IV Univ, Fac Med, Dept Publ Hlth, Paris, France
[8] Univ Yaounde I, Biotechnol Ctr, Lab Publ Hlth Res & Biotechnol, Yaounde, Cameroon
[9] Lotus Eye Hosp & Inst, Coimbatore, Tamil Nadu, India
来源
关键词
FUNDUS; PREVALENCE;
D O I
10.3928/23258160-20190108-05
中图分类号
R77 [眼科学];
学科分类号
100212 ;
摘要
BACKROUND AND OBJECTIVE: Screening for diabetic retinopathy (DR) is cost-effective when compared with disability loss for those who go blind in the absence of a screening program. We aimed to evaluate the sensitivity and specificity of a smartphone-based device for the screening and detection of DR. PATIENTS AND METHODS: A cross-sectional study of 220 patients with diabetes (440 eyes, all patients age 25 years or older) was completed. Tropicamide 0.5% was used for iris dilation followed by an indirect ophthalmoscopy using a 20-D lens. Retinal images were later obtained using a smartphone attached to an adaptable camera device. Retinal images permitted the visualization of the macular and papillary regions and were sent without compression via the internet to a retinal specialist for interpretation. Sensitivity and specificity were calculated for all cases and stages of DR. RESULTS: Using our standard examination method, the prevalence of DR and macular edema were 13.6% and 6.4%, respectively. With the smartphone-based retinal camera, the prevalence of DR and macular edema were 18.2% and 8.2%, respectively. Sensitivity and specificity for the detection of all stages of DR was 73.3% and 90.5%, respectively. For the detection of macular edema, sensitivity was 77.8%, and specificity was 95%. For severe nonproliferative DR (NPDR), sensitivity and specificity were 80% and 99%, respectively; for proliferative DR (PDR), they were both 100%. In the early stages of DR, specificity was 89.8% for mild NPDR and 97.1% for moderate NPDR. Sensitivity was 57.1% and 42.9%, respectively. CONCLUSION: Screening for DR using a smartphone-based retinal camera has a satisfactory specificity at all DR stages. Its sensitivity seems to be high only in the stages of DR necessitating a specific therapeutic decision (eg, macular edema, severe NPDR, and PDR). A smartphone-based retinal camera may be a useful device to screen for DR in resource-limited settings.
引用
收藏
页码:S18 / S22
页数:5
相关论文
共 50 条
  • [21] Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence
    Ramachandran Rajalakshmi
    Radhakrishnan Subashini
    Ranjit Mohan Anjana
    Viswanathan Mohan
    Eye, 2018, 32 : 1138 - 1144
  • [22] Sensitivity and Specificity of Smartphone-Based Retinal Imaging for Diabetic Retinopathy A Comparative Study
    Sengupta, Sabyasachi
    Sindal, Manavi D.
    Baskaran, Prabu
    Pan, Utsab
    Venkatesh, Rengaraj
    OPHTHALMOLOGY RETINA, 2019, 3 (02): : 146 - 153
  • [23] Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence
    Rajalakshmi, Ramachandran
    Subashini, Radhakrishnan
    Anjana, Ranjit Mohan
    Mohan, Viswanathan
    EYE, 2018, 32 (06) : 1138 - 1144
  • [24] Smartphone-based Retinal Imaging to Characterize Early Functional Retinal Vascular Changes in Diabetic Retinopathy
    Cheung, Carol Yim-lui
    Wang, Yu Meng
    Liu, John H. K.
    Lai, Kenny H. W.
    Chang, Robert
    Wong, Tien Yin
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2019, 60 (09)
  • [25] Screening for Diabetic Retinopathy Using Artificial Intelligence and Smartphone-Based Fundus Images
    Kalavar, Meghana
    Watane, Arjun
    Sridhar, Jayanth
    INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2020, 61 (09)
  • [26] Medios-A Smartphone-Based Artificial Intelligence Algorithm in Screening for Diabetic Retinopathy
    Sosale, Bhavana
    Sosale, Aravind R.
    Murthy, Hemanth
    Narayana, Srikanth
    Sharma, Usha
    Gowda, Sahana G. V.
    Naveenam, Muralidhar
    DIABETES, 2019, 68
  • [27] Validation of smartphone-based screening for retinopathy of prematurity in a low-resource setting
    Adhikari, Srijana
    Bajimaya, Sanyam
    Ranjitkar, Eli Pradhan
    JOURNAL OF AAPOS, 2021, 25 (05): : 274.e1 - 274.e5
  • [28] Deep learning frameworks for diabetic retinopathy detection with smartphone-based retinal imaging systems
    Hacisoftaoglu, Recep E.
    Karakaya, Mahmut
    Sallam, Ahmed B.
    PATTERN RECOGNITION LETTERS, 2020, 135 : 409 - 417
  • [29] High Frequency of Asteroid Hyalosis Precludes Diabetic Retinopathy Screening With Smartphone-Based Retinal Camera in Brazilian Xavante Indians
    Malerbi, Fernando Korn
    Dal Fabbro, Amaury Lelis
    Santiago Moises, Regina Celia
    Botelho Vieira Filho, Joao Paulo
    Franco, Laercio Joel
    JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, 2020, 14 (05): : 974 - 975
  • [30] Diabetic Retinopathy Screening Using Smartphone-Based Fundus Photography and Deep-Learning Artificial Intelligence in the Yucatan Peninsula: A Field Study
    Wroblewski, John J.
    Sanchez-Buenfil, Ermilo
    Inciarte, Miguel
    Berdia, Jay
    Blake, Lewis
    Wroblewski, Simon
    Patti, Alexandria
    Suter, Gretchen
    Sanborn, George E.
    JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, 2025, 19 (02): : 370 - 376