浏览全部资源
扫码关注微信
1. Eye Center, The Second Affiliated Hospital of Zhejiang University School of Medicine
2. Zhejiang Provincial Key Laboratory of Ophthalmology
3. Zhejiang Provincial Clinical Research Center for Eye Diseases
4. Zhejiang Provincial Engineering Institute on Eye Diseases,Hangzhou,China
5. Centre for Innovation and Precision Eye Health, National University of Singapore,Singapore
6. Department of Ophthalmology, National University of Singapore,Singapore
7. Singapore Eye Research Institute, Singapore National Eye Centre,Singapore
8. Ophthalmology and Visual Science Academic Clinical Program, Duke-NUS Medical School,Singapore
9. Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences,Ningbo,China
10. Ningbo Eye Hospital,Ningbo,China
11. Zhejiang International Scientific and Technological Cooperative Base of Biomedical Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences,Ningbo,China
12. Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences,Ningbo,China
13. Division of Public Health, Department of Social Medicine, Graduate School of Medicine, Osaka University,Osaka,Japan
14. Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital,Osaka,Japan
15. ,Poznan,Poland
Published:2024,
移动端阅览
KAI JIN, YINGYU LI, HONGKANG WU, et al. Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review. [J]. Aopr, 2024, 4(3): 120-127.
KAI JIN, YINGYU LI, HONGKANG WU, et al. Integration of smartphone technology and artificial intelligence for advanced ophthalmic care: A systematic review. [J]. Aopr, 2024, 4(3): 120-127. DOI: 10.1016/j.aopr.2024.03.003.
BackgroundThe convergence of smartphone technology and artificial intelligence (AI) has revolutionized the landscape of ophthalmic care
offering unprecedented opportunities for diagnosis
monitoring
and management of ocular conditions. Nevertheless
there is a lack of systematic studies on discussing the integration of smartphone and AI in this field.Main textThis review includes 52 studies
and explores the integration of smartphones and AI in ophthalmology
delineating its collective impact on screening methodologies
disease detection
telemedicine initiatives
and patient management. The collective findings from the curated studies indicate promising performance of the smartphone-based AI screening for various ocular diseases which encompass major retinal diseases
glaucoma
cataract
visual impairment in children and ocular surface diseases. Moreover
the utilization of smartphone-based imaging modalities
coupled with AI algorithms
is able to provide timely
efficient and cost-effective screening for ocular pathologies. This modality can also facilitate patient self-monitoring
remote patient monitoring and enhancing accessibility to eye care services
particularly in underserved regions. Challenges involving data privacy
algorithm validation
regulatory frameworks and issues of trust are still need to be addressed. Furthermore
evaluation on real-world implementation is imperative as well
and real-world prospective studies are currently lacking.ConclusionsSmartphone ocular imaging merged with AI enables earlier
precise diagnoses
personalized treatments
and enhanced service accessibility in eye care. Collaboration is crucial to navigate ethical and data security challenges while responsibly leveraging these innovations
promising a potential revolution in care access and global eye health equity.
Artificial intelligenceSmartphone imagingOphthalmologyTelemedicineOcular disease management
1 M.A.P. Vilela, A. Arrigo, M.B. Parodi, et al.Smartphone eye examination: artificial intelligence and telemedicine Telemedicine and e-Health (Aug 16 2023), 10.1089/tmj.2023.0041
2 S.P. Bhavnani, J. Narula, P.P. SenguptaMobile technology and the digitization of healthcare Eur Heart J, 37 (18) (May 7 2016), pp. 1428-1438, 10.1093/eurheartj/ehv770
3 K. Jin, J. YeArtificial intelligence and deep learning in ophthalmology: current status and future perspectives Advances in Ophthalmology Practice and Research, 2 (3) (Aug 24 2022), Article 100078, 10.1016/j.aopr.2022.100078
4 Blindness and Vision Impairment (Aug 10 2023) https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment
5 R. Kumari, K. Pratap Singh, G. Dubey, et al.Chronic impediment in utilization of eye-care services Journal of Ophthalmology and Research, 3 (2) (2020), 10.26502/fjor.2644-00240020
6 M. Baskaran, R.C. Foo, C.Y. Cheng, et al.The prevalence and types of glaucoma in an urban Chinese population: the Singapore Chinese eye study JAMA Ophthalmol, 133 (8) (Aug 2015), pp. 874-880, 10.1001/jamaophthalmol.2015.1110
7 R. Li, W. Chen, M. Li, et al.LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality Nat Commun, 14 (1) (Nov 6 2023), p. 7126, 10.1038/s41467-023-42934-8
8 S. Natarajan, A. Jain, R. Krishnan, et al.Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone JAMA ophthalmology, 137 (10) (Oct 2019), pp. 1182-1188, 10.1001/jamaophthalmol.2019.2923
9 N. Cheung, P. Mitchell, T.Y. WongDiabetic retinopathy Lancet (London, England), 376 (9735) (Jul 10 2010), pp. 124-136, 10.1016/S0140-6736(09)62124-3
10 S. Vujosevic, S.J. Aldington, P. Silva, et al.Screening for diabetic retinopathy: new perspectives and challenges Lancet Diabetes Endocrinol, 8 (4) (Apr 2020), pp. 337-347, 10.1016/S2213-8587(19)30411-5
11 M.E. Ryan, R. Rajalakshmi, V. Prathiba, et al.Comparison among methods of retinopathy assessment (CAMRA) study: smartphone, nonmydriatic, and mydriatic photography Ophthalmology, 122 (10) (Oct 2015), pp. 2038-2043, 10.1016/j.ophtha.2015.06.011
12 R. Rajalakshmi, R. Subashini, R.M. Anjana, et al.Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence Eye, 32 (6) (Jun 2018), pp. 1138-1144, 10.1038/s41433-018-0064-9
13 A. Al-Karawi, E. AvşarA deep learning framework with edge computing for severity level detection of diabetic retinopathy Multimed Tool Appl (Mar 22 2023), pp. 1-22, 10.1007/s11042-023-15131-4
14 B. Sosale, A.R. Sosale, H. Murthy, et al.Medios–An offline, smartphone-based artificial intelligence algorithm for the diagnosis of diabetic retinopathy Indian J. Ophthalmol., 68 (2) (Feb 2020), p. 391, 10.4103/ijo.IJO_1203_19
15 D.K. Hwang, W.K. Yu, T.C. Lin, et al.Smartphone-based diabetic macula edema screening with an offline artificial intelligence J Chin Med Assoc, 83 (12) (Dec 2020), pp. 1102-1106, 10.1097/JCMA.0000000000000355
16 F.K. Malerbi, G. Mendes, N. Barboza, et al.Diabetic macular edema screened by handheld smartphone-based retinal camera and artificial intelligence J. Med. Syst., 46 (1) (Dec 11 2021), p. 8, 10.1007/s10916-021-01795-8
17 H. Naz, R. Nijhawan, N.J. AhujaClinical utility of handheld fundus and smartphone-based camera for monitoring diabetic retinal diseases: a review study Int Ophthalmol, 44 (1) (Feb 9 2024), p. 41, 10.1007/s10792-024-02975-4
18 U. Qidwai, U. Qidwai, M. Raja, et al.Smart AMD prognosis through cellphone: an innovative localized AI-based prediction system for anti-VEGF treatment prognosis in nonagenarians and centenarians Int. Ophthalmol., 42 (6) (Jun 2022), pp. 1749-1762, 10.1007/s10792-021-02171-8
19 B.K. Young, E.D. Cole, P.K. Shah, et al.Efficacy of smartphone-based telescreening for retinopathy of prematurity with and without artificial intelligence in India JAMA ophthalmology, 141 (6) (Jun 1 2023), pp. 582-588, 10.1001/jamaophthalmol.2023.1466
20 J.B. Jonas, T. Aung, R.R. Bourne, et al.Glaucoma Lancet (London, England), 390 (10108) (Nov 11 2017), pp. 2183-2193, 10.1016/S0140-6736(17)31469-1
21 K. Nakahara, R. Asaoka, M. Tanito, et al.Deep learning-assisted (automatic) diagnosis of glaucoma using a smartphone Br J Ophthalmol, 106 (4) (Apr 2022), pp. 587-592, 10.1136/bjophthalmol-2020-318107
22 F. Li, D. Song, H. Chen, et al.Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection NPJ digital medicine, 3 (2020), p. 123, 10.1038/s41746-020-00329-9
23 Y. Wu, I. Luttrell, S. Feng, et al.Development and validation of a machine learning, smartphone-based tonometer Br J Ophthalmol, 104 (10) (Oct 2020), pp. 1394-1398, 10.1136/bjophthalmol-2019-315446
24 S. Hu, X. Wang, H. Wu, et al.Unified diagnosis framework for automated nuclear cataract grading based on smartphone slit-lamp images IEEE Access, 8 (2020), pp. 174169-174178, 10.1109/ACCESS.2020.3025346
25 C.S. Vasan, S. Gupta, M. Shekhar, et al.Accuracy of an artificial intelligence-based mobile application for detecting cataracts: results from a field study Indian J Ophthalmol, 71 (8) (Aug 2023), pp. 2984-2989, 10.4103/IJO.IJO_3372_22
26 S. Keil, A. Fielder, J. SargentManagement of children and young people with vision impairment: diagnosis, developmental challenges and outcomes Arch Dis Child, 102 (6) (Jun 2017), pp. 566-571, 10.1136/archdischild-2016-311775
27 W.A. LagrèzeVision screening in preschool children: do the data support universal screening? Deutsches Arzteblatt International, 107 (28-29) (Jul 2010), pp. 495-499, 10.3238/arztebl.2010.0495
28 W. Chen, R. Li, Q. Yu, et al.Early detection of visual impairment in young children using a smartphone-based deep learning system Nat Med, 29 (2) (Feb 2023), pp. 493-503, 10.1038/s41591-022-02180-9
29 S. Ma, Y. Guan, Y. Yuan, et al.A one-step, streamlined children's vision screening solution based on smartphone imaging for resource-limited areas: design and preliminary field evaluation JMIR mHealth and uHealth, 8 (7) (Jul 13 2020), Article e18226, 10.2196/18226
30 K. Murali, V. Krishna, V. Krishna, et al.Application of deep learning and image processing analysis of photographs for amblyopia screening Indian J. Ophthalmol., 68 (7) (Jun 25 2020), p. 1407, 10.4103/ijo.IJO_1399_19
31 Y. Liu, C. Xu, S. Wang, et al.Accurate detection and grading of pterygium through smartphone by a fusion training model Br J Ophthalmol (Mar 1 2023), 10.1136/bjo-2022-322552 bjo-2022-322552
32 L. Wang, K. Chen, H. Wen, et al.Feasibility assessment of infectious keratitis depicted on slit-lamp and smartphone photographs using deep learning Int J Med Inf, 155 (Nov 2021), Article 104583, 10.1016/j.ijmedinf.2021.104583
33 A. Zhang, J.S. Pratap, J.R. Young, et al.Pilot clinical validation of a machine learning platform for noninvasive smartphone-based assessment of corneal epithelial integrity medRxiv (Aug 2023), 10.1101/2023.08.29.23293788v1
34 H.-C. Chen, S.-S. Tzeng, Y.-C. Hsiao, et al.Smartphone-based artificial intelligence–assisted prediction for eyelid measurements: algorithm development and observational validation study JMIR mHealth and uHealth, 9 (10) (Oct 2021), Article e32444, 10.2196/32444
35 H. Tabuchi, D. Nagasato, H. Masumoto, et al.Developing an iOS Application that Uses Machine Learning for the Automated Diagnosis of Blepharoptosis Graefe's Archive for Clinical and Experimental Ophthalmology (Apr 2022), pp. 1-7, 10.1007/s00417-021-05475-8
36 U. Schmidt-Erfurth, A. Sadeghipour, B.S. Gerendas, et al.Artificial intelligence in retina Prog. Retin. Eye Res., 67 (Nov 2018), pp. 1-29, 10.1016/j.preteyeres.2018.07.004
37 D.T. Hogarty, J.P. Hogarty, A.W. HewittSmartphone use in ophthalmology: what is their place in clinical practice? Surv Ophthalmol, 65 (2) (2020), pp. 250-262, 10.1016/j.survophthal.2019.09.001
38 J.-W. Wasmann, L. Pragt, R. Eikelboom, et al.Digital approaches to automated and machine learning assessments of hearing: scoping review J. Med. Internet Res., 24 (2) (Feb 2 2022), Article e32581, 10.2196/32581
39 J. He, S.L. Baxter, J. Xu, et al.The practical implementation of artificial intelligence technologies in medicine Nat Med, 25 (1) (Jan 2019), pp. 30-36, 10.1038/s41591-018-0307-0
40 J. Gomez Rossi, N. Rojas-Perilla, J. Krois, F. SchwendickeCost-effectiveness of artificial intelligence as a decision-support system applied to the detection and grading of melanoma, dental caries, and diabetic retinopathy JAMA Netw Open, 5 (3) (Mar 1 2022), Article e220269, 10.1001/jamanetworkopen.2022.0269
41 E.R. Dow, T.D.L. Keenan, E.M. Lad, et al.From data to deployment: the collaborative community on ophthalmic imaging roadmap for artificial intelligence in age-related macular degeneration Ophthalmology, 129 (5) (May 2022), pp. e43-e59, 10.1016/j.ophtha.2022.01.002
42 P. Gooding, T. KariotisEthics and law in research on algorithmic and data-driven technology in mental health care: scoping review JMIR mental health, 8 (6) (Jun 10 2021), Article e24668, 10.2196/24668
43 R.M.W.W. Tseng, D.V. Gunasekeran, S.S.H. Tan, et al.Considerations for artificial intelligence real-world implementation in ophthalmology: providers' and patients' perspectives Asia-Pacific Journal of Ophthalmology, 10 (3) (May 2021), pp. 299-306, 10.1097/APO.0000000000000400
0
Views
0
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution