Exploration of Digital Health Technologies

Table 2. The summary and shortcomings of existing techniques.

Number	Existing techniques	Shortcomings
1	Manual methods	It is difficult for a specialist to make a final diagnosis of the specifications of the lens based on continuous assessments. Manual methods cause some problems with the cornea.
2	Devise based methods	A large difference between the proposed base curve and the fitting base curve (absolute difference) indicates a lack of clinical validation in these guidelines.
3	Machine learning and image processing methods	According to Table 1, the best MSE and R² are 0.014 and 0.53, respectively.

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MSE: mean squared error.

Declarations

Author contributions

LE: Methodology, Software, Validation, Formal analysis, Data curation, Writing—original draft, Visualization. HV: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing—review & editing, Visualization, Supervision, Project administration. EJ: Investigation, Resources, Writing—review & editing. SH: Software, Investigation, Resources, Data curation, Writing—review & editing, Visualization. All authors reviewed the results and approved the final version of the manuscript.

Conflicts of interest

The authors declare that there is no conflict of interest regarding the publication of this manuscript.

Ethical approval

The study was approved by the Ethical Committee of University of Tehran of Medical Sciences. No specific approval number was assigned to this study. All procedures were conducted in accordance with the ethical standards of the institutional and national research committee and with the principles of the Declaration of Helsinki.

Consent to participate

Informed consent to participate in the study was obtained from all participants.

Consent to publication

The authors affirm that human research participants provided informed consent to publication for using their data in this research project.

Availability of data and materials

The datasets generated and analyzed during the current study are not publicly available due to confidentiality and restrictions on data sharing. Requests for access to these datasets can be directed to [Ebrahim Jafarzadepour, jafarzadehpour.e@iums.ac.ir].

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Copyright

Publisher’s note

Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

References

Rigid Gas Permeable Lenses [Internet]. Bausch & Lomb Incorporated; c2010 [cited 2025 Oct 20]. Available from: https://bauschgp.com/wp-content/uploads/2014/10/RGP-MATERIAL-GUIDE.pdf

Ortiz-Toquero S, Rodriguez G, de Juan V, Martin R. Gas permeable contact lens fitting in keratoconus: Comparison of different guidelines to back optic zone radius calculations. Indian J Ophthalmol. 2019;67:1410–6. [DOI] [PubMed] [PMC]

Asiri N, Hussain M, Al Adel F, Alzaidi N. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artif Intell Med. 2019;99:101701. [DOI] [PubMed]

Hashemi S, Veisi H, Jafarzadehpur E, Rahmani R, Heshmati Z. An image processing approach for rigid gas-permeable lens base-curve identification. SIViP. 2020;14:971–9. [DOI]

Hashemi S, Veisi H, Jafarzadehpur E, Rahmani R, Heshmati Z. A CCA Approach for Multiview Analysis to Detect Rigid Gas Permeable Lens Base Curve. Proceedings of 2019 IEEE Western New York Image and Signal Processing Workshop (WNYISPW); 2019 Oct 4; Rochester, USA. IEEE; 2019. pp. 1–5. [DOI]

Hashemi S, Veisi H, Jafarzadehpur E, Rahmani R, Heshmati Z. Multi-view deep learning for rigid gas permeable lens base curve fitting based on Pentacam images. Med Biol Eng Comput. 2020;58:1467–82. [DOI] [PubMed]

Ebrahimi L, Veisi H, Hashemi S, Jafarzadepour E. The fusion of multi-view features in Pentacam four refractive maps by using neural network to determine Rigid Gas Permeable (RGP) lens properties. Proceedings of 26th Computer Society of Iran Computer Conference (CSICC); 2021 Mar 3–4; Tehran, Iran.

Zhang B, Zhou J. Multi-feature representation for burn depth classification via burn images. Artif Intell Med. 2021;118:102128. [DOI] [PubMed]

Belin MW, Khachikian SS. Keratoconus/ectasia detection with the oculus pentacam: Belin/Ambrósio enhanced ectasia display. Highlights Ophthalmol. 2007;35:5–12.

10.

Liu Y, Chen X, Wang Z, Wang ZJ, Ward RK, Wang X. Deep learning for pixel-level image fusion: Recent advances and future prospects. Inf Fusion. 2018;42:158–73. [DOI]

11.

Ker J, Wang L, Rao J, Lim T. Deep learning applications in medical image analysis. IEEE Access. 2018;6:9375–89. [DOI]

12.

Abdelmotaal H, Mostafa MM, Mostafa ANR, Mohamed AA, Abdelazeem K. Classification of Color-Coded Scheimpflug Camera Corneal Tomography Images Using Deep Learning. Transl Vis Sci Technol. 2020;9:30. [DOI] [PubMed] [PMC]

13.

Li X, Zhang G, Huang HH, Wang Z, Zheng W. Performance analysis of GPU-based convolutional neural networks. Proceedings of 2016 45th International Conference on Parallel Processing (ICPP); 2016 Aug 16–19; Philadelphia, USA. IEEE; 2016. pp. 67–76. [DOI]

14.

Al-Waisy AS, Qahwaji R, Ipson S, Al-Fahdawi S, Nagem TAM. A multi-biometric iris recognition system based on a deep learning approach. Pattern Anal Applic. 2018;21:783–802. [DOI]

15.

Rayhan F, Galata A, Cootes TF. ChoiceNet: CNN learning through choice of multiple feature map representations. Pattern Anal. Applic. 2021;24:1757–67. [DOI]

16.

Pulgar FJ, Charte F, Rivera AJ, del Jesus MJ. Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines. Inf Fusion. 2020;54:44–60. [DOI]

17.

Huang H, Ma Z, Zhang G, Wu H. Dimensionality reduction based on multi-local linear regression and global subspace projection distance minimum. Pattern Anal Applic. 2021;24:1713–30. [DOI]

18.

Castanedo F. A review of data fusion techniques. ScientificWorldJournal. 2013;2013:704504. [DOI] [PubMed] [PMC]

19.

Haghighat M, Abdel-Mottaleb M, Alhalabi W. Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition. IEEE Trans Inf Forensics Secur. 2016;11:1984–96. [DOI]

20.

Sun QS, Zeng SG, Liu Y, Heng PA, Xia DS. A new method of feature fusion and its application in image recognition. Pattern Recognit. 2005;38:2437–48. [DOI]

21.

Ceccarelli F, Mahmoud M. Multimodal temporal machine learning for Bipolar Disorder and Depression Recognition. Pattern Anal. Applic. 2022;25:493–504. [DOI]

22.

Li Y, Yang M, Zhang Z. A Survey of Multi-View Representation Learning. IEEE Trans Knowl Data Eng. 2019;31:1863–83. [DOI]

23.

Ortiz-Toquero S, Rodriguez G, de Juan V, Martin R. Rigid Gas Permeable Contact Lens Fitting Using New Software in Keratoconic Eyes. Optom Vis Sci. 2016;93:286–92. [DOI] [PubMed]

24.

Lentes de contacto gas permeable [Internet]. Córneas irregulars; [cited Year Month Day]. Available from: https://www.conoptica.es/images/documentos/productos/KAKC-N_F_I_PRO.pdf

25.

Sorbara L. Correction of Keratoconus with GP Contact Lenses. In: Fonn D, Woods C, Sivak A, editors. Waterloo: Centre for Contact Lens Research; 2010.

26.

Rajabi MT, Mohajernezhad-Fard Z, Naseri SK, Jafari F, Doostdar A, Zarrinbakhsh P, et al. Rigid contact lens fitting based on keratometry readings in keratoconus patients: predicting formula. Int J Ophthalmol. 2011;4:525–8. [DOI] [PubMed] [PMC]

27.

Romero-Jiménez M, Santodomingo-Rubido J, González-Méijome JM. An assessment of the optimal lens fit rate in keratoconus subjects using three-point-touch and apical touch fitting approaches with the rose K2 lens. Eye Contact Lens. 2013;39:269–72. [DOI] [PubMed]

28.

GAS Permeable (GP) Lens Fitting Guide [Internet]. Bausch + Lomb; c2025 [cited 2025 Oct 20]. Available from: http://bauschgp.com/wp-content/uploads/2014/10/GP-FITTING-GUIDE-2013.pdf

29.

Mannis MJ, Zadnik K, Coral-Ghanem C, Kara-José N. Contact Lenses in Ophthalmic Practice. New Yoerk: Springer; 2004. [DOI]

30.

Your Trusted Source for Quality Custom Contact Lenses. [Internet]. Valleycontax; [cited 2025 Oct 29]. Available from: https://www.valleycontax.com/

31.

Ortiz-Toquero S, Rodriguez G, de Juan V, Martin R. New web-based algorithm to improve rigid gas permeable contact lens fitting in keratoconus. Cont Lens Anterior Eye. 2017;40:143–50. [DOI] [PubMed]

32.

Ghaderi M, Sharifi A, Jafarzadeh E. Detection of irregular astigmatism through Pentacam images using mixture of multilayer perceptron experts. Proceedings of 1st International Conference on New Perspective in Electrical and Computer Engineering; 2016 Sep 9; Tehran, Iran. 2016. pp. 1–9.

33.

Risser G, Mechleb N, Muselier A, Gatinel D, Zéboulon P. Novel deep learning approach to estimate rigid gas permeable contact lens base curve for keratoconus fitting. Cont Lens Anterior Eye. 2023;46:102063. [DOI] [PubMed]

34.

Hashemi H, Mehravaran S. Day to Day Clinically Relevant Corneal Elevation, Thickness, and Curvature Parameters Using the Orbscan II Scanning Slit Topographer and the Pentacam Scheimpflug Imaging Device. Middle East Afr J Ophthalmol. 2010;17:44–55. [DOI] [PubMed] [PMC]

35.

Belin MW, Khachikian SS. New Advances and Technology with Pentacam. In: Keratoconus/Ectasia Detection with the Oculus Pentacam: Belin/Ambrósio Enhanced Ectasia Display. Wetzlar: OCULUS Optikgeräte GmbH; 2008. pp. 3–7.

36.

Yang C, Guan N. Correlation maximization machine for multi-modalities multiclass classification. Pattern Ana. Applic. 2020;23:349–58. [DOI]