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Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study
Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study
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Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study

Kamiya, Kazutaka ; Ayatsuka, Yuji ; Kato, Yudai ; Fujimura, Fusako ; Takahashi, Masahide ; Shoji, Nobuyuki ; Mori, Yosai ; Miyata, Kazunori

BMJ open, 2019-09, Vol.9 (9), p.e031313-e031313 [Periódico revisado por pares]

England: BMJ Publishing Group LTD

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2
Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review
Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review
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Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review

Pur, Daiana Roxana ; Krance, Saffire H ; Pucchio, Aidan ; Miranda, Rafael N ; Felfeli, Tina

Eye (London), 2023-07, Vol.37 (10), p.2007-2019 [Periódico revisado por pares]

England: Nature Publishing Group

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3
Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
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Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study

Fariselli, Chiara ; Vega-Estrada, Alfredo ; Arnalich-Montiel, Francisco ; Alio, Jorge L

Eye and vision (Novato, Calif.), 2020-04, Vol.7 (1), p.20-20, Article 20 [Periódico revisado por pares]

England: BioMed Central Ltd

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4
Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning
Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning
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Predicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning

Kato, Naoko ; Masumoto, Hiroki ; Tanabe, Mao ; Sakai, Chikako ; Negishi, Kazuno ; Torii, Hidemasa ; Tabuchi, Hitoshi ; Tsubota, Kazuo

Journal of clinical medicine, 2021-02, Vol.10 (4), p.844 [Periódico revisado por pares]

Switzerland: MDPI AG

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5
A Hybrid Artificial Intelligence Model for Detecting Keratoconus
A Hybrid Artificial Intelligence Model for Detecting Keratoconus
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A Hybrid Artificial Intelligence Model for Detecting Keratoconus

Alyasseri, Zaid Abdi Alkareem ; Al-Timemy, Ali H. ; Abasi, Ammar Kamal ; Lavric, Alexandru ; Mohammed, Husam Jasim ; Takahashi, Hidenori ; Milhomens Filho, Jose Arthur ; Campos, Mauro ; Hazarbassanov, Rossen M. ; Yousefi, Siamak

Applied sciences, 2022-12, Vol.12 (24), p.12979 [Periódico revisado por pares]

Basel: MDPI AG

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6
Current keratoconus detection methods compared with a neural network approach
Current keratoconus detection methods compared with a neural network approach
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Current keratoconus detection methods compared with a neural network approach

Smolek, MK ; Klyce, SD

Investigative ophthalmology & visual science, 1997-10, Vol.38 (11), p.2290-2299 [Periódico revisado por pares]

Rockville, MD: ARVO

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