DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity

Keenan, Tiarnán D L; Chen, Qingyu; Agrón, Elvira; Tham, Yih‐Chung; Goh, Jocelyn Hui Lin; Lei, Xiaofeng; Ng, Yi Pin; Liu, Yong; Xu, Xinxing; Cheng, Ching-Yu; Бикбов, М.М.; Jonas, Jost B.; Bhandari, S.; Broadhead, Geoffrey; Colyer, Marcus H.; Corsini, J.; Cousineau-Krieger, Chantal; Gensheimer, William G.; Grašić, David Josip; Lamba, T.S.; Magone, M. Teresa; Maiberger, Michele; Oshinsky, Arnold; Purt, Boonkit; Shin, Soo Young; Thavikulwat, Alisa T.; Lu, Zhiyong; Chew, Emily Y.; Ajilore, Priscilla; Akman, Alex; Azar, Nadim S.; Azar, William S.; Chan, Bryan; Cox, Victor S; Dave, Amisha D; Dhanjal, Rachna; Donovan, Mary K.; Farrell, Maureen C.; Finkel, Francisca; Goblirsch, Timothy; Ha, Wesley; Hill, Christine; Kumar, Aman; Kent, Kristen; Lee, Arielle; Patel, Pujan R.; Peprah, David; Piliponis, Emma; Selzer, Evan B; Swaby, Benjamin; Tenney, S.M.; Zeleny, Alexander

doi:10.1016/j.ophtha.2021.12.017

Cited by 40 publications

(22 citation statements)

References 52 publications

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“…More future work needs to further focus on the diagnosis and optimal treatment strategies for blinding diseases associated with systemic conditions (50). Moreover, deep learning algorithms that could rapidly and non-invasively identify pathological features of eye diseases joined ophthalmology research (23 51). Furthermore, the emergence of the COVID-19 pandemic brought about an increase in the length of patient visits due to disease control and health-related problems associated with COVID-19 infections, which had a dramatic impact on ophthalmology health care.…”

Section: Discussionmentioning

confidence: 99%

Hotspots and trends in ophthalmology in recent 5 years: Bibliometric analysis in 2017–2021

et al. 2022

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PurposeThe purpose of this study was to investigate the hotspots and research trends of ophthalmology research.MethodOphthalmology research literature published between 2017 and 2021 was obtained in the Web of Science Core Collection database. The bibliometric analysis and network visualization were performed with the VOSviewer and CiteSpace. Publication-related information, including publication volume, citation counts, countries, journals, keywords, subject categories, and publication time, was analyzed.ResultsA total of 10,469 included ophthalmology publications had been cited a total of 7,995 times during the past 5 years. The top countries and journals for the number of publications were the United States and the Ophthalmology. The top 25 global high-impact documents had been identified using the citation ranking. Keyword co-occurrence analysis showed that the hotspots in ophthalmology research were epidemiological characteristics and treatment modalities of ocular diseases, artificial intelligence and fundus imaging technology, COVID-19-related telemedicine, and screening and prevention of ocular diseases. Keyword burst analysis revealed that “neural network,” “pharmacokinetics,” “geographic atrophy,” “implementation,” “variability,” “adverse events,” “automated detection,” and “retinal images” were the research trends of research in the field of ophthalmology through 2021. The analysis of the subject categories demonstrated the close cooperation relationships that existed between different subject categories, and collaborations with non-ophthalmology-related subject categories were increasing over time in the field of ophthalmology research.ConclusionsThe hotspots in ophthalmology research were epidemiology, prevention, screening, and treatment of ocular diseases, as well as artificial intelligence and fundus imaging technology and telemedicine. Research trends in ophthalmology research were artificial intelligence, drug development, and fundus diseases. Knowledge from non-ophthalmology fields is likely to be more involved in ophthalmology research.

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Section: Discussionmentioning

confidence: 99%

Hotspots and trends in ophthalmology in recent 5 years: Bibliometric analysis in 2017–2021

et al. 2022

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“…Compared with clinical ophthalmologists, the DeepLensNet performed significantly more accurately for cortex opacity (mean squared error = 13.1) and nuclear sclerosis (mean squared error = 0.23). For the least common posterior subcapsular cataract, the grading capability was similar between the DeepLensNet (mean squared error = 16.6) and clinical ophthalmologists [ 68 ]. Objectively, silt-lamp images of good quality require a certain amount of training to reduce inter-examiner deviation.…”

Section: Lensmentioning

confidence: 99%

Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications

Shi

et al. 2023

Ophthalmol Ther

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Artificial intelligence (AI) technology is promising in the field of healthcare. With the developments of big data and image-based analysis, AI shows potential value in ophthalmology applications. Recently, machine learning and deep learning algorithms have made significant progress. Emerging evidence has demonstrated the capability of AI in the diagnosis and management of anterior segment diseases. In this review, we provide an overview of AI applications and potential future applications in anterior segment diseases, focusing on cornea, refractive surgery, cataract, anterior chamber angle detection, and refractive error prediction.

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“…There are several applications that utilise OCT before and after the cataract surgery including anterior lens capsule and lens epithelium evaluation in senile cataract and Fuchs' heterochromic cyclitis using spectral-domain anterior segment OCT (SD-OCT), investigation of clear corneal incision in manual phacoemulsification and femtosecond laser-assisted cataract surgery using SD-OCT, capsular block syndrome evaluation before and after treatment using SD-OCT, and IOL power calculation (true net power measurement) in post-myopic excimer laser eyes using SD-OCT [29]. For further knowledge of the cataract diagnosis based on digital imaging, the interested researchers are recommended to refer to [30][31][32].…”

Section: Automated Cataract Detection and Gradingmentioning

confidence: 99%

Towards a Connected Mobile Cataract Screening System: A Future Approach

et al. 2022

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Advances in computing and AI technology have promoted the development of connected health systems, indirectly influencing approaches to cataract treatment. In addition, thanks to the development of methods for cataract detection and grading using different imaging modalities, ophthalmologists can make diagnoses with significant objectivity. This paper aims to review the development and limitations of published methods for cataract detection and grading using different imaging modalities. Over the years, the proposed methods have shown significant improvement and reasonable effort towards automated cataract detection and grading systems that utilise various imaging modalities, such as optical coherence tomography (OCT), fundus, and slit-lamp images. However, more robust and fully automated cataract detection and grading systems are still needed. In addition, imaging modalities such as fundus, slit-lamps, and OCT images require medical equipment that is expensive and not portable. Therefore, the use of digital images from a smartphone as the future of cataract screening tools could be a practical and helpful solution for ophthalmologists, especially in rural areas with limited healthcare facilities.

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DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity

Cited by 40 publications

References 52 publications

Hotspots and trends in ophthalmology in recent 5 years: Bibliometric analysis in 2017–2021

Hotspots and trends in ophthalmology in recent 5 years: Bibliometric analysis in 2017–2021

Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications

Towards a Connected Mobile Cataract Screening System: A Future Approach

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