Novel artificial intelligence for diabetic retinopathy and diabetic macular edema: what is new in 2024?

被引：2

作者：

Vujosevic, Stela ^{[1
,2
]}

Limoli, Celeste ^{[3
]}

Nucci, Paolo ^{[1
]}

机构：

[1] Univ Milan, Dept Biomed Surg & Dent Sci, Via San Vittore 12, I-20123 Milan, MI, Italy

[2] Univ Milan, Eye Clin, IRCCS Multimed, Milan, Italy

[3] Univ Milan, Dept Ophthalmol, Milan, Italy

来源：

CURRENT OPINION IN OPHTHALMOLOGY | 2024年 / 35卷 / 06期

关键词：

artificial intelligence; deep learning; diabetic retinopathy; foundation model; retinal imaging; MODEL; AI; EYE;

D O I：

10.1097/ICU.0000000000001084

中图分类号：

R77 [眼科学];

学科分类号：

100212 ;

摘要：

Purpose of reviewGiven the increasing global burden of diabetic retinopathy and the rapid advancements in artificial intelligence, this review aims to summarize the current state of artificial intelligence technology in diabetic retinopathy detection and management, assessing its potential to improve care and visual outcomes in real-world settings.Recent findingsMost recent studies focused on the integration of artificial intelligence in the field of diabetic retinopathy screening, focusing on real-world efficacy and clinical implementation of such artificial intelligence models. Additionally, artificial intelligence holds the potential to predict diabetic retinopathy progression, enhance personalized treatment strategies, and identify systemic disease biomarkers from ocular images through 'oculomics', moving towards a more precise, efficient, and accessible care. The emergence of foundation model architectures and generative artificial intelligence, which more clearly reflect the clinical care process, may enable rapid advances in diabetic retinopathy care, research and medical education.SummaryThis review explores the emerging technology of artificial intelligence to assess the potential to improve patient outcomes and optimize personalized management in healthcare delivery and medical research. While artificial intelligence is expected to play an increasingly important role in diabetic retinopathy care, ongoing research and clinical trials are essential to address implementation issues and focus on long-term patient outcomes for successful real-world adoption of artificial intelligence in diabetic retinopathy.

引用

页码：472 / 479

页数：8

共 53 条

[1] Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial [J].

Abramoff, Michael D. ;

Whitestone, Noelle ;

Patnaik, Jennifer L. ;

Rich, Emily ;

Ahmed, Munir ;

Husain, Lutful ;

Hassan, Mohammad Yeadul ;

Tanjil, Md. Sajidul Huq ;

Weitzman, Dena ;

Dai, Tinglong ;

Wagner, Brandie D. ;

Cherwek, David H. ;

Congdon, Nathan ;

Islam, Khairul .

NPJ DIGITAL MEDICINE, 2023, 6 (01)

[2] Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices [J].

Abramoff, Michael D. ;

Lavin, Philip T. ;

Birch, Michele ;

Shah, Nilay ;

Folk, James C. .

NPJ DIGITAL MEDICINE, 2018, 1

[3] Vision-Language Models for Feature Detection of Macular Diseases on Optical Coherence Tomography [J].

Antaki, Fares ;

Chopra, Reena ;

Keane, Pearse A. .

JAMA OPHTHALMOLOGY, 2024, 142 (06) :573-576

[4]

Aristidou A, 2022, LANCET, V399, P620, DOI 10.1016/S0140-6736(22)00235-5

[5] A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study [J].

Babenko, Boris ;

Traynis, Ilana ;

Chen, Christina ;

Singh, Preeti ;

Uddin, Akib ;

Cuadros, Jorge ;

Daskivich, Lauren P. ;

Maa, April Y. ;

Kim, Ramasamy ;

Kang, Eugene Yu-Chuan ;

Matias, Yossi ;

Corrado, Greg S. ;

Peng, Lily ;

Webster, Dale R. ;

Semturs, Christopher ;

Krause, Jonathan ;

Varadarajan, Avinash V. ;

Hammel, Naama ;

Liu, Yun .

LANCET DIGITAL HEALTH, 2023, 5 (05) :E257-E264

[6] Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes [J].

Betzler, Bjorn Kaijun ;

Chee, Evelyn Yi Lyn ;

He, Feng ;

Lim, Cynthia Ciwei ;

Ho, Jinyi ;

Hamzah, Haslina ;

Tan, Ngiap Chuan ;

Liew, Gerald ;

Mckay, Gareth J. ;

Hogg, Ruth E. ;

Young, Ian S. ;

Cheng, Ching-Yu ;

Lim, Su Chi ;

Lee, Aaron Y. ;

Wong, Tien Yin ;

Lee, Mong Li ;

Hsu, Wynne ;

Tan, Gavin Siew Wei ;

Sabanayagam, Charumathi .

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2023, 30 (12) :1904-1914

[7] Classification of diabetic maculopathy based on optical coherence tomography images using a Vision Transformer model [J].

Cai, Liwei ;

Wen, Chi ;

Jiang, Jingwen ;

Liang, Congbi ;

Zheng, Hongmei ;

Su, Yu ;

Chen, Changzheng .

BMJ OPEN OPHTHALMOLOGY, 2023, 8 (01)

[8] Comparing generative and retrieval-based chatbots in answering patient questions regarding age-related macular degeneration and diabetic retinopathy [J].

Cheong, Kai Xiong ;

Zhang, Chenxi ;

Tan, Tien-En ;

Fenner, Beau J. ;

Wong, Wendy Meihua ;

Teo, Kelvin Yc ;

Wang, Ya Xing ;

Sivaprasad, Sobha ;

Keane, Pearse A. ;

Lee, Cecilia Sungmin ;

Lee, Aaron Y. ;

Cheung, Chui Ming Gemmy ;

Wong, Tien Yin ;

Cheong, Yun-Gyung ;

Song, Su Jeong ;

Tham, Yih Chung .

BRITISH JOURNAL OF OPHTHALMOLOGY, 2024, 108 (10) :1443-1449

[9] Validation of a deep learning system for the detection of diabetic retinopathy in Indigenous Australians [J].

Chia, Mark A. ;

Hersch, Fred ;

Sayres, Rory ;

Bavishi, Pinal ;

Tiwari, Richa ;

Keane, Pearse A. ;

Turner, Angus W. .

BRITISH JOURNAL OF OPHTHALMOLOGY, 2024, 108 (02) :268-273

[10] Artificial intelligence-supported diabetic retinopathy screening in Tanzania: rationale and design of a randomised controlled trial [J].

Cleland, Charles R. ;

Bascaran, Covadonga ;

Makupa, William ;

Shilio, Bernadetha ;

Sandi, Frank A. ;

Philippin, Heiko ;

Marques, Ana Patricia ;

Egan, Catherine ;

Tufail, Adnan ;

Keane, Pearse A. ;

Denniston, Alastair K. ;

Macleod, David ;

Burton, Matthew J. .

BMJ OPEN, 2024, 14 (01)

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