The current state of artificial intelligence in ophthalmology

Kapoor, Rahul; Walters, Stephen; Al-Aswad, Lama A.

doi:10.1016/j.survophthal.2018.09.002

Cited by 140 publications

(92 citation statements)

References 57 publications

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“…3 Another important development particularly for screening is the automated grading of retinal images using deep learning algorithms. [4][5][6] Both developments require accurate classification systems: trials need clear criteria for inclusion with relatively high rates of outcome events, whereas automated algorithms need validation by a "ground truth. "…”

Section: Introductionmentioning

confidence: 99%

Performance of Classification Systems for Age-Related Macular Degeneration in the Rotterdam Study

Thee

Meester-Smoor

Luttikhuizen

et al. 2020

Trans. Vis. Sci. Tech.

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To compare frequently used classification systems for age-related macular degeneration (AMD) in their abilty to predict late AMD. Methods: In total, 9066 participants from the population-based Rotterdam Study were followed up for progression of AMD during a study period up to 30 years. AMD lesions were graded on color fundus photographs after confirmation on other image modalities and grouped at baseline according to six classification systems. Late AMD was defined as geographic atrophy or choroidal neovascularization. Incidence rate (IR) and cumulative incidence (CuI) of late AMD were calculated, and Kaplan-Meier plots and area under the operating characteristics curves (AUCs) were constructed. Results: A total of 186 persons developed incident late AMD during a mean follow-up time of 8.7 years. The AREDS simplified scale showed the highest IR for late AMD at 104 cases/1000 py for ages <75 years. The Rotterdam classification showed the highest IR at 89 cases/1000 py >75 years. The 3-Continent harmonization classification provided the most stable progression. Drusen area >10% ETDRS grid (hazard ratio 30.05, 95% confidence interval [CI] 19.25-46.91) was most prognostic of progression. The highest AUC of late AMD (0.8372, 95% CI: 0.8070-0.8673) was achieved when all AMD features present at baseline were included. Conclusions: Highest turnover rates from intermediate to late AMD were provided by the AREDS simplified scale and the Rotterdam classification. The 3-Continent harmonization classification showed the most stable progression. All features, especially drusen area, contribute to late AMD prediction.

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Section: Introductionmentioning

confidence: 99%

Performance of Classification Systems for Age-Related Macular Degeneration in the Rotterdam Study

Thee

Meester-Smoor

Luttikhuizen

et al. 2020

Trans. Vis. Sci. Tech.

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“…More recently, deep learning, where CBIA systems have been trained to automatically recognize and evaluate images, has been used for ROP screening. 59,60 Deep learning allows the system to continually learn and re-evaluate its process autonomously and consists of multiple layers of algorithms that data flow through to form a neural networks. 60 Convolutional neural networks have to be trained through exposure to a large number and variety of pathological and normal images to then apply a series of filters to produce the desired output, which in this case would be diagnosis or classification of ROP.…”

Section: Automated Image Analysismentioning

confidence: 99%

“…59,60 Deep learning allows the system to continually learn and re-evaluate its process autonomously and consists of multiple layers of algorithms that data flow through to form a neural networks. 60 Convolutional neural networks have to be trained through exposure to a large number and variety of pathological and normal images to then apply a series of filters to produce the desired output, which in this case would be diagnosis or classification of ROP. 60,61 The i-ROP consortium has developed a deep learning algorithm, which has shown a high accuracy for identifying plus disease.…”

Section: Automated Image Analysismentioning

confidence: 99%

“…60 Convolutional neural networks have to be trained through exposure to a large number and variety of pathological and normal images to then apply a series of filters to produce the desired output, which in this case would be diagnosis or classification of ROP. 60,61 The i-ROP consortium has developed a deep learning algorithm, which has shown a high accuracy for identifying plus disease. 61 The system was trained using a set of 5,511 retinal images that had been obtained as part of the i-ROP study and a reference standard diagnosis established by three trained graders and one expert clinical examiner.…”

Section: Automated Image Analysismentioning

confidence: 99%

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Telemedicine for Retinopathy of Prematurity

Brady

D'Amico

Campbell

2020

Telemedicine and e-Health

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Background: Retinopathy of prematurity (ROP) is a disease of the retinal vasculature that remains a leading cause of childhood blindness worldwide despite improvements in the systemic care of premature newborns. Screening for ROP is effective and cost-effective, but in many areas, access to skilled examiners to conduct dilated examinations is poor. Remote screening with retinal photography is an alternative strategy that may allow for improved ROP care. Methods: The current literature was reviewed to find clinical trials and expert consensus documents on the state-of-the-art of telemedicine for ROP. Results: Several studies have confirmed the utility of telemedicine for ROP. In addition, several clinical studies have reported favorable long-term results. Many investigators have reinforced the need for detailed protocols on image acquisition and image interpretation. Conclusions: Telemedicine for ROP appears to be a viable alternative to live ophthalmoscopic examinations in many circumstances. Standardization and documentation afforded by telemedicine may provide additional benefits to providers and their patients. With continued improvements in image quality and affordability of imaging systems as well as improved automated image interpretation tools anticipated in the near future, telemedicine for ROP is expected to play an expanding role for a uniquely vulnerable patient population.

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“…With the support of remote screening technique, AI is able to detect eye diseases in their early stages for the people living in rural areas. Moreover, AI adapted in smartphones plus basic ophthalmology equipment allow diagnosis to expand beyond the confines of clinic offices 32 . In other words, AI can assist the management of patients both in office and those in remote areas as well.…”

Section: Ai In Radiologymentioning

confidence: 99%

Insight of Artificial Intelligence Application in Healthcare

Kuang¹

2019

ijSciences

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Ever since "Deep Blue" defeated the human champion chess player in 1997, artificial intelligence has caused extensive attention and discussion. With the development of computing and data storage technologies, artificial intelligence has been made great progress and applied in a wide range of fields. Here, we focus on several creative and novel applications of artificial intelligence in the healthcare field and also discuss their future trends.

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The current state of artificial intelligence in ophthalmology

Cited by 140 publications

References 57 publications

Performance of Classification Systems for Age-Related Macular Degeneration in the Rotterdam Study

Performance of Classification Systems for Age-Related Macular Degeneration in the Rotterdam Study

Telemedicine for Retinopathy of Prematurity

Insight of Artificial Intelligence Application in Healthcare

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