Updates in deep learning research in ophthalmology

Zhang, Shihao; Wang, Zhaoran; Ong, Charles; Gunasekeran, Dinesh Visva; Lim, Gilbert; Zheng, Feihui; Tan, Shaun Chern Yuan; Tan, Gavin Siew Wei; Rim, Tyler Hyungtaek; Schmetterer, Leopold; Ting, Daniel Shu Wei

doi:10.1042/cs20210207

Cited by 30 publications

(21 citation statements)

References 170 publications

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“…Despite the several advantages of using AI in ophthalmology, some limitations have been reported that create a serious challenge. These include higher accuracy in the training set than in the test set, which is called "overfitting" [1,80]; unfavorable results due to the use of irrelevant or inappropriate inputs, called "rubbish in and rubbish out" [1]; and the lack of transparency of decision-making and data analysis methods by the model, which is identified as a "black box" [1,80].…”

Section: Limitations Of Artificial Intelligencementioning

confidence: 99%

“…Therefore, it is necessary to validate a large dataset from a heterogeneous population that reflects real-world settings while observing medicolegal issues and ensuring data security [1,4]. Although there is a large amount of worldwide data available to design various AI-based models, data validity is an important issue [8,80]. Data should be verified by a specialist for quality and specific details related to the ocular structure, and manual data sorting is time-consuming.…”

Section: Limitations Of Artificial Intelligencementioning

confidence: 99%

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A review of artificial intelligence applications in anterior segment ocular diseases

Heidari¹,

Baharinia²,

Ebrahimi-Besheli³

et al. 2022

Med Hypothesis Discov Innov Optom

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Background: Artificial intelligence (AI) has great potential for interpreting and analyzing images and processing large amounts of data. There is a growing interest in investigating the applications of AI in anterior segment ocular diseases. This narrative review aims to assess the use of different AI-based algorithms for diagnosing and managing anterior segment entities. Methods: We reviewed the applications of different AI-based algorithms in the diagnosis and management of anterior segment entities, including keratoconus, corneal dystrophy, corneal grafts, corneal transplantation, refractive surgery, pterygium, infectious keratitis, cataracts, and disorders of the corneal nerves, conjunctiva, tear film, anterior chamber angle, and iris. The English-language databases PubMed/MEDLINE, Scopus, and Google Scholar were searched using the following keywords: artificial intelligence, deep learning, machine learning, neural network, anterior eye segment diseases, corneal disease, keratoconus, dry eye, refractive surgery, pterygium, infectious keratitis, anterior chamber, and cataract. Relevant articles were compared based on the use of AI models in the diagnosis and treatment of anterior segment diseases. Furthermore, we prepared a summary of the diagnostic performance of the AI-based methods for anterior segment ocular entities. Results: Various AI methods based on deep and machine learning can analyze data obtained from corneal imaging modalities with acceptable diagnostic performance. Currently, complicated and time-consuming manual methods are available for diagnosing and treating eye diseases. However, AI methods could save time and prevent vision impairment in eyes with anterior segment diseases. Because many anterior segment diseases can cause irreversible complications and even vision loss, sufficient confidence in the results obtained from the designed model is crucial for decision-making by experts. Conclusions: AI-based models could be used as surrogates for analyzing manual data with improveddiagnostic performance. These methods could be reliable tools for diagnosing and managing anterior segmentocular diseases in the near future in remote areas. It is expected that future studies can design algorithms thatuse less data in a multitasking manner for the detection and management of anterior segment diseases.

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Section: Limitations Of Artificial Intelligencementioning

confidence: 99%

Section: Limitations Of Artificial Intelligencementioning

confidence: 99%

A review of artificial intelligence applications in anterior segment ocular diseases

Heidari¹,

Baharinia²,

Ebrahimi-Besheli³

et al. 2022

Med Hypothesis Discov Innov Optom

View full text Add to dashboard Cite

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“…In the United Kingdom (UK), around 1 in 9 people live with cardiovascular disease (CVD) [ 1 , 2 ]. A formal risk assessment tool, QRISK3, was developed using data from a cohort of 1.28 million individuals, and it is currently recommended by the National Institute for Health and Care Excellence (NICE) for CVD risk assessment and guidance for primary prevention strategies [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank

Tseng

Rim

Shantsila

et al. 2023

BMC Med

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Background Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5–10% (termed as borderline-QRISK3 group) using the UK Biobank. Methods Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups. Results Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30–1.52) with a 13.1% (95% CI, 11.7–14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010–0.017) in non-statin cohort, 0.013 (0.007–0.019) in stage 1 hypertension cohort, and 0.023 (0.018–0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3. Conclusions Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD.

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“…Artificial intelligence is capable of adapting to the rich ophthalmic medical image data characteristics [6] . The use of AI image recognition technology for ophthalmic medical image recognition, data extraction, and analysis [7] can significantly improve the efficiency of ophthalmic medical image recognition and reduce the workload of medical staff [8] . For pixel levels that ophthalmologists cannot recognize with naked eyes, artificial intelligence recognition technology can be used to carry out in-depth analysis [9] , improve the accuracy of ophthalmic medical diagnosis, and improve the efficiency and accuracy of ophthalmic medical image data identification and analysis work.…”

Section: Introductionmentioning

confidence: 99%

Cultivation Path of Compound Talents in Ophthalmic Diagnosis, Treatment, and Nursing Based on Artificial Intelligence

Chen

Tang

et al. 2022

JCNR

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Artificial intelligence can effectively improve the efficiency and accuracy of medical diagnosis, clinical data analysis, medical image recognition, treatment plan decision-making, etc. It has broad application prospects in the ophthalmic diagnosis, treatment, and nursing industry. However, the application of artificial intelligence in the ophthalmic diagnosis and nursing industry in China started relatively late, and there are insufficient ophthalmic diagnosis and nursing personnel who are familiar with artificial intelligence technologies. In order to promote the modernization of ophthalmic medicine in China and accelerate the development of a high-quality and modern medical education system, it is necessary to train a new generation of compound ophthalmic medical talents who are skilled in artificial intelligence and develop an advanced talent training model that meets the needs of the ophthalmic profession and the society. Based on the application status and development prospects of artificial intelligence in the ophthalmology industry, this paper analyzes the current medical education model in ophthalmology, examines the path of cultivating compound talents in ophthalmic diagnosis, treatment, and nursing, as well as proposes suggestions for developing a high-quality and modern medical education system.

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Updates in deep learning research in ophthalmology

Cited by 30 publications

References 170 publications

A review of artificial intelligence applications in anterior segment ocular diseases

A review of artificial intelligence applications in anterior segment ocular diseases

Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank

Cultivation Path of Compound Talents in Ophthalmic Diagnosis, Treatment, and Nursing Based on Artificial Intelligence

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