Advances in machine learning for keratoconus diagnosis
Muhsin, Zahra J. Ghafir, Ibrahim AlShawabkeh, M. Al Bdour, M. AlRyalat, S. Al-Taee, M.
Muhsin, Zahra J.
Ghafir, Ibrahim
AlShawabkeh, M.
Al Bdour, M.
AlRyalat, S.
Al-Taee, M.
Publication Date
2025-03-30
End of Embargo
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Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2025-03-06
Institution
Department
Awarded
Embargo end date
Additional title
Abstract
Purpose: To review studies reporting the role of Machine Learning (ML) techniques in the diagnosis of keratoconus (KC) over the past decade, shedding light on recent developments while also highlighting the existing gaps between academic research and practical implementation in clinical settings.
Methods: The review process begins with a systematic search of primary digital libraries using relevant keywords. A rigorous set of inclusion and exclusion criteria is then applied, resulting in the identification of 62 articles for analysis. Key research questions are formulated to address advancements in ML for KC diagnosis, corneal imaging modalities, types of datasets utilised, and the spectrum of KC conditions investigated over the past decade. A significant gap between academic research and practical implementation in clinical settings is identified, forming the basis for actionable recommendations tailored for both ML developers and ophthalmologists. Additionally, a proposed roadmap model is presented to facilitate the integration of ML models into clinical practice, enhancing diagnostic accuracy and patient care.
Results: The analysis revealed that the diagnosis of KC predominantly relies on supervised classifiers (97%), with Random Forest being the most used algorithm (27%), followed by Deep Learning including Convolution Neural Networks (16%), Feedforward and Feedback Neural Networks (12%), and Support Vector Machines (12%). Pentacam is identified as the leading corneal imaging modality (56%), and a substantial majority of studies (91%) utilize local datasets, primarily consisting of numerical corneal parameters (77%). The most studied KC conditions were non-KC (NKC) vs. clinical KC (CKC) (29%), NKC vs. Subclinical KC (SCKC) (24%), NKC vs. SCKC vs. CKC (20%), SCKC vs. CKC (7%). However, only 20% of studies focused on addressing KC severity stages, emphasizing the need for more research in this area. These findings highlight the current landscape of ML in KC diagnosis and uncover existing challenges, and suggest potential avenues for further research and development, with particular emphasis on the dominance of certain algorithms and imaging modalities.
Conclusion: Key obstacles include the lack of consensus on an objective diagnostic standard for early KC detection and severity staging, limited multidisciplinary collaboration, and restricted access to public datasets. Further research is crucial to overcome these challenges and apply findings in clinical practice.
Version
Published version
Citation
Muhsin ZJ, Qahwaji R, Ghafir I et al (2025) Advances in machine learning for keratoconus diagnosis. International Ophthalmology. 45: 128.
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Article