Potential applications of artificial intelligence in image analysis in cornea diseases: a review

Mukhamediev RI, Popova Y, Kuchin Y, Zaiteseva E, Kalimodayev A, Symagulov A, et al. Review of artificial intelligence and machine learning technologies: classification, restrictions, opportunities and challenges. Mathematics. 2022;10(15):2552.

Article  Google Scholar 

Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64.

Article  PubMed  PubMed Central  Google Scholar 

Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160.

Article  PubMed  PubMed Central  Google Scholar 

Yang S, Zhu F, Ling X, Liu Q, Zhao P. Intelligent health care: applications of deep learning in computational medicine. Front Genet. 2021;12:607471.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Santodomingo-Rubido J, Carracedo G, Suzaki A, Villa-Collar C, Vincent SJ, Wolffsohn JS. Keratoconus: an updated review. Cont Lens Anterior Eye. 2022;45(3):101559.

Article  PubMed  Google Scholar 

Larkin DFP, Chowdhury K, Burr JM, Raynor M, Edwards M, Tuft SJ, et al. Effect of corneal cross-linking versus standard care on keratoconus progression in young patients: the KERALINK randomized controlled trial. Ophthalmology. 2021;128(11):1516–26.

Article  PubMed  Google Scholar 

Chanbour W, El Zein L, Younes MA, Issa M, Warhekar P, Chelala E, et al. Corneal cross-linking for keratoconus and post-LASIK ectasia and failure rate: a 3 years follow-up study. Cureus. 2021;13(11):e19552.

PubMed  PubMed Central  Google Scholar 

Chan C, Saad A, Randleman JB, Harissi-Dagher M, Chua D, Qazi M, et al. Analysis of cases and accuracy of 3 risk scoring systems in predicting ectasia after laser in situ keratomileusis. J Cataract Refract Surg. 2018;44(8):979–92.

Article  PubMed  Google Scholar 

Shi Y. Strategies for improving the early diagnosis of keratoconus. Clin Optom (Auckl). 2016;8:13–21.

Article  PubMed  Google Scholar 

Smolek MK, Klyce SD. Current keratoconus detection methods compared with a neural network approach. Invest Ophthalmol Vis Sci. 1997;38(11):2290–9.

CAS  PubMed  Google Scholar 

Kuo BI, Chang WY, Liao TS, Liu FY, Liu HY, Chu HS, et al. Keratoconus screening based on deep learning approach of corneal topography. Transl Vis Sci Technol. 2020;9(2):53.

Article  PubMed  PubMed Central  Google Scholar 

Mohammadpour M, Heidari Z, Hashemi H, Yaseri M, Fotouhi A. Comparison of artificial intelligence-based machine learning classifiers for early detection of keratoconus. Eur J Ophthalmol. 2022;32(3):1352–60.

Article  PubMed  Google Scholar 

Cao K, Verspoor K, Chan E, Daniell M, Sahebjada S, Baird PN. Machine learning with a reduced dimensionality representation of comprehensive Pentacam tomography parameters to identify subclinical keratoconus. Comput Biol Med. 2021;138:104884.

Article  PubMed  Google Scholar 

Feng R, Xu Z, Zheng X, Hu H, Jin X, Chen DZ, et al. KerNet: a novel deep learning approach for keratoconus and sub-clinical keratoconus detection based on raw data of the Pentacam HR system. IEEE J Biomed Health Inform. 2021;25(10):3898–910.

Article  PubMed  Google Scholar 

Lu NJ, Koppen C, Hafezi F, Ní Dhubhghaill S, Aslanides IM, Wang QM, et al. Combinations of Scheimpflug tomography, ocular coherence tomography and air-puff tonometry improve the detection of keratoconus. Cont Lens Anterior Eye. 2023;46(3):101840.

Article  PubMed  Google Scholar 

Lu NJ, Elsheikh A, Rozema JJ, Hafezi N, Aslanides IM, Hillen M, et al. Combining spectral-domain OCT and air-puff tonometry analysis to diagnose keratoconus. J Refract Surg. 2022;38(6):374–80.

Article  CAS  PubMed  Google Scholar 

Al-Timemy AH, Ghaeb NH, Mosa ZM, Escudero J. Deep transfer learning for improved detection of keratoconus using corneal topographic maps. Cogn Comput. 2022;14(5):1627–42.

Article  Google Scholar 

Yousefi S, Yousefi E, Takahashi H, Hayashi T, Tampo H, Inoda S, et al. Keratoconus severity identification using unsupervised machine learning. PLoS One. 2018;13(11):e0205998.

Article  PubMed  PubMed Central  Google Scholar 

Hashemi H, Doroodgar F, Niazi S, Khabazkhoob M, Heidari Z. Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis. Graefes Arch Clin Exp Ophthalmol. 2023. https://doi.org/10.1007/s00417-023-06154-6.

Article  PubMed  Google Scholar 

Shetty R, Kundu G, Narasimhan R, Khamar P, Gupta K, Singh N, et al. Artificial intelligence efficiently identifies regional differences in the progression of tomographic parameters of keratoconic corneas. J Refract Surg. 2021;37(4):240–8.

Article  PubMed  Google Scholar 

Kundu G, Shetty N, Shetty R, Khamar P, D’Souza S, Meda TR, et al. Artificial intelligence-based stratification of demographic, ocular surface high-risk factors in progression of keratoconus. Indian J Ophthalmol. 2023;71(5):1882–8.

Article  PubMed  PubMed Central  Google Scholar 

Zéboulon P, Debellemanière G, Bouvet M, Gatinel D. Corneal topography raw data classification using a convolutional neural network. Am J Ophthalmol. 2020;219:33–9.

Article  PubMed  Google Scholar 

Askarian B, Tabei F, Tipton GA, Chong JW. Novel keratoconus detection method using smartphone. In: Askarian B, editor. 2019 IEEE healthcare Innovations and point of care technologies, (HI-POCT). Bethesda: IEEE; 2019. p. 60–2. https://doi.org/10.1109/HI-POCT45284.2019.8962648.

Chapter  Google Scholar 

Nokas G, Kotsilieris T. Preventing keratoconus through eye rubbing activity detection: a machine learning approach. Electronics. 2023;12(4):1028.

Article  Google Scholar 

Cabrera-Aguas M, Khoo P, Watson SL. Infectious keratitis: a review. Clin Exp Ophthalmol. 2022;50(5):543–62.

Article  PubMed  PubMed Central  Google Scholar 

Ting DSJ, Ho CS, Deshmukh R, Said DG, Dua HS. Infectious keratitis: an update on epidemiology, causative microorganisms, risk factors, and antimicrobial resistance. Eye (Lond). 2021;35(4):1084–101.

Article  PubMed  Google Scholar 

Stapleton F. The epidemiology of infectious keratitis. Ocul Surf. 2023;28:351–63.

Article  PubMed  Google Scholar 

Wang L, Chen K, Wen H, Zheng Q, Chen Y, Pu J, et al. Feasibility assessment of infectious keratitis depicted on slit-lamp and smartphone photographs using deep learning. Int J Med Inform. 2021;155:104583.

Article  PubMed  Google Scholar 

Ung L, Bispo PJM, Shanbhag SS, Gilmore MS, Chodosh J. The persistent dilemma of microbial keratitis: global burden, diagnosis, and antimicrobial resistance. Surv Ophthalmol. 2019;64(3):255–71.

Article  PubMed  Google Scholar 

Khor WB, Prajna VN, Garg P, Mehta JS, Xie L, Liu Z, et al. The Asia Cornea Society Infectious Keratitis Study: a prospective multicenter study of infectious keratitis in Asia. Am J Ophthalmol. 2018;195:161–70.

Article  PubMed  Google Scholar 

Truong DT, Bui MT, Cavanagh HD. Epidemiology and outcome of microbial keratitis: private university versus urban public hospital care. Eye Contact Lens. 2018;44(Suppl 1):S82–6.

Article  PubMed  PubMed Central  Google Scholar 

Walkden A, Fullwood C, Tan SZ, Au L, Armstrong M, Brahma AK, et al. Association between season, temperature and causative organism in microbial keratitis in the UK. Cornea. 2018;37(12):1555–60.

Article  PubMed  PubMed Central  Google Scholar 

Tena D, Rodríguez N, Toribio L, González-Praetorius A. Infectious keratitis: microbiological review of 297 cases. Jpn J Infect Dis. 2019;72(2):121–3.

Article  CAS  PubMed  Google Scholar 

Henry CR, Flynn HW Jr, Miller D, Forster RK, Alfonso EC. Infectious keratitis progressing to endophthalmitis: a 15-year study of microbiology, associated factors, and clinical outcomes. Ophthalmology. 2012;119(12):2443–9.

Article  PubMed  Google Scholar 

Ghosh AK, Thammasudjarit R, Jongkhajornpong P, Attia J, Thakkinstian A. deep learning for discrimination between fungal keratitis and bacterial keratitis: DeepKeratitis. Cornea. 2022;41(5):616–22.

Article  PubMed  Google Scholar 

Liang S, Zhong J, Zeng H, Zhong P, Li S, Liu H, et al. A structure-aware convolutional neural network for automatic diagnosis of fungal keratitis with in vivo confocal microscopy images. J Digit Imaging. 2023;36(4):1624–32.

Article  PubMed  PubMed Central  Google Scholar 

Essalat M, Abolhosseini M, Le TH, Moshtaghion SM, Kanavi MR. Interpretable deep learning for diagnosis of fungal and acanthamoeba keratitis using in vivo confocal microscopy images. Sci Rep. 2023;13(1):8953.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hau SC, Dart JK, Vesaluoma M, Parmar DN, Claerhout I, Bibi K, et al. Diagnostic accuracy of microbial keratitis with in vivo scanning laser confocal microscopy. Br J Ophthalmol. 2010;94(8):982–7.

Article  PubMed  Google Scholar 

Natarajan R, Matai HD, Raman S, Kumar S, Ravichandran S, Swaminathan S, et al. Advances in the diagnosis of herpes simplex stromal necrotising keratitis: a feasibility study on deep learning approach. Indian J Ophthalmol. 2022;70(9):3279–83.

Article  PubMed  PubMed Central 

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