Development of an application for providing corneal topography reports based on artificial intelligence

Lucena, Abrahão Rocha; Araújo, Mariana Oliveira de; Carneiro, Romulo Ferrer; Cavalcante, Tarique da Silveira; Ribeiro, Alyson B. N.; Anselmo, Francisco José Marques

doi:10.5935/0004-2749.20220051

Cited by 4 publications

(4 citation statements)

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“…The majority of the included studies regarding keratoconus or corneal ectasia diagnosis with AI used data from corneal topography ( 47 , 51 – 53 , 56 , 57 , 67 , 73 , 74 , 77 , 78 , 83 , 91 , 92 , 94 , 100 ), Scheimpflug-based tomography ( 43 , 45 , 49 , 54 , 55 , 59 , 60 , 62 , 64 , 66 , 70 , 76 , 79 , 80 , 84 , 86 – 88 , 90 , 95 , 99 ), or optical coherence tomography (OCT) ( 31 , 46 , 50 , 53 , 68 , 69 , 72 , 80 , 84 ) as the input. For example, de Almeida et al.…”

Section: Resultsmentioning

confidence: 99%

“…For example, only one specialist determined the presence of keratoconus for the reference standard of some diagnostic accuracy studies, including Almeida Jr. et al., Lucena et al., Lopes et al., Chandapura et al., Cohen et al., Zéboulon et al., Mosa et al., and Zaki et al. ( 45 , 47 , 49 , 53 , 54 , 75 , 87 , 93 ) This could have resulted in biased classification of eyes. Additionally, some studies included a relatively low sample size of study groups, which reduced the power of the study.…”

Section: Discussionmentioning

confidence: 99%

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Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Goodman,

Zhu

2024

Front. Ophthalmol.

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IntroductionThe application of artificial intelligence (AI) systems in ophthalmology is rapidly expanding. Early detection and management of keratoconus is important for preventing disease progression and the need for corneal transplant. We review studies regarding the utility of AI in the diagnosis and management of keratoconus and other corneal ectasias.MethodsWe conducted a systematic search for relevant original, English-language research studies in the PubMed, Web of Science, Embase, and Cochrane databases from inception to October 31, 2023, using a combination of the following keywords: artificial intelligence, deep learning, machine learning, keratoconus, and corneal ectasia. Case reports, literature reviews, conference proceedings, and editorials were excluded. We extracted the following data from each eligible study: type of AI, input used for training, output, ground truth or reference, dataset size, availability of algorithm/model, availability of dataset, and major study findings.ResultsNinety-three original research studies were included in this review, with the date of publication ranging from 1994 to 2023. The majority of studies were regarding the use of AI in detecting keratoconus or subclinical keratoconus (n=61). Among studies regarding keratoconus diagnosis, the most common inputs were corneal topography, Scheimpflug-based corneal tomography, and anterior segment-optical coherence tomography. This review also summarized 16 original research studies regarding AI-based assessment of severity and clinical features, 7 studies regarding the prediction of disease progression, and 6 studies regarding the characterization of treatment response. There were only three studies regarding the use of AI in identifying susceptibility genes involved in the etiology and pathogenesis of keratoconus.DiscussionAlgorithms trained on Scheimpflug-based tomography seem promising tools for the early diagnosis of keratoconus that can be particularly applied in low-resource communities. Future studies could investigate the application of AI models trained on multimodal patient information for staging keratoconus severity and tracking disease progression.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Goodman,

Zhu

2024

Front. Ophthalmol.

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“…We included 19 studies in the systematic review that contained 96,549 images or scans from over 10,500 patients. 5,7,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] Five studies assessed the diagnostic performance of DL algorithms using anterior segment OCT images, 5,[17][18][19]26 12 studies using corneal topography images (8 Scheimpflug and 4 Placido disk based), 7,[12][13][14][15][16][20][21][22][23][24]28 and 2 studies using smartphone images (topography 25 and lateral segment images 27 ). Keratoconus grading was reported in 11 studies, 4 of which used the Amsler-Krumeich classification.…”

Section: Study Characteristicsmentioning

confidence: 99%

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis

Bodmer,

Christensen,

Bachmann

et al. 2024

Cornea

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Purpose: The prevalence of keratoconus in the general population is reported to be up to 1 of 84. Over the past 2 decades, diagnosis and management evolved rapidly, but keratoconus screening in clinical practice is still challenging and asks for improving the accuracy of keratoconus detection. Deep learning (DL) offers considerable promise for improving the accuracy and speed of medical imaging interpretation. We establish an inventory of studies conducted with DL algorithms that have attempted to diagnose keratoconus. Methods: This systematic review was conducted according to the recommendations of the PRISMA statement. We searched (Pre-)MEDLINE, Embase, Science Citation Index, Conference Proceedings Citation Index, arXiv document server, and Google Scholar from inception to February 18, 2022. We included studies that evaluated the performance of DL algorithms in the diagnosis of keratoconus. The main outcome was diagnostic performance measured as sensitivity and specificity, and the methodological quality of the included studies was assessed using QUADAS-2. Results: Searches retrieved 4100 nonduplicate records, and we included 19 studies in the qualitative synthesis and 10 studies in the exploratory meta-analysis. The overall study quality was limited because of poor reporting of patient selection and the use of inadequate reference standards. We found a pooled sensitivity of 97.5% (95% confidence interval, 93.6%–99.0%) and a pooled specificity of 97.2% (95% confidence interval, 85.7%–99.5%) for topography images as input. Conclusions: Our systematic review found that the overall diagnostic performance of DL models to detect keratoconus was good, but the methodological quality of included studies was modest.

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“…Apesar de enfrentar desafios, como a dificuldade em distinguir certos tipos de astigmatismo devido a padrões não convencionais nas imagens, o aplicativo também serve como uma ferramenta educacional para médicos, melhorando a compreensão e interpretação das condições da córnea. Esta inovação não só melhora a acessibilidade ao diagnóstico oftalmológico como também aumenta sua precisão(Lucena et al, 2021). clínico entre aqueles com resultados anormais, demonstrando o potencial da IA em aprimorar tanto a eficácia quanto a eficiência do cuidado médico em populações vulneráveis.Brazilian Journal of Health Review, Curitiba, v. 7, n. 3, p. 01-13, may/jun., 2024 3.3 NEUROLOGIA A IA no radiodiagnóstico está transformando a forma como os tumores cerebrais são examinados e acompanhados.Brazilian Journal of Health Review, Curitiba, v. 7, n. 3, p. 01-13, may/jun., 2024…”

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O impacto da inteligência artificial na interpretação de exames de imagem em diagnóstico médico

Fonseca,

Matos,

Morais

et al. 2024

Braz. J. Hea. Rev.

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Introdução: A inteligência artificial (IA) refere-se à capacidade de sistemas computacionais processarem extensas quantidades de dados para simular comportamentos inteligentes, incluindo aprendizado autônomo. Objetivo: Investigar os impactos do uso da IA na interpretação de exames de imagem médicos. Metodologia: Revisão integrativa nas bases de dados nas bases científicas: LILACS, SCIELO e PUBMED. Resultados e Discussões: 17 artigos foram selecionados. A inteligência artificial está transformando o diagnóstico por imagem, melhorando a precisão, agilizando o processamento dos exames e fornecendo análises mais objetivas e reprodutíveis. Essas tecnologias também estão contribuindo para a medicina de precisão, permitindo diagnósticos e tratamentos personalizados. Além disso, modificam a configuração das estações de trabalho dos radiologistas, que agora incluem um terceiro monitor para análises assistidas por IA. Conclusão: A implementação da IA em sistemas de diagnóstico por imagem e em análises clínicas tem demonstrado um potencial significativo para personalizar e otimizar tratamentos, além de agilizar e precisar diagnósticos, o que é crucial para avanços em uma medicina centrada no paciente. Essa tecnologia promete transformações na maneira como médicos e pacientes abordam o tratamento e o acompanhamento de diversas doenças.

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Development of an application for providing corneal topography reports based on artificial intelligence

Cited by 4 publications

References 0 publications

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review

Deep Learning Models Used in the Diagnostic Workup of Keratoconus: A Systematic Review and Exploratory Meta-Analysis

O impacto da inteligência artificial na interpretação de exames de imagem em diagnóstico médico

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