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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Advances in machine learning for keratoconus diagnosis.

Zahra J Muhsin¹, Rami Qahwaji², Ibrahim Ghafir¹

¹Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK.

International Ophthalmology

|March 30, 2025

Summary

Machine learning (ML) shows promise for diagnosing keratoconus (KC), but a gap exists between research and clinical use. Overcoming challenges in standardization and data access is key for integrating ML into eye care.

Keywords:

Corneal imaging modalities Detection Early diagnosis Ophthalmology Severity staging

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Area of Science:

Ophthalmology
Medical Informatics
Artificial Intelligence

Background:

Keratoconus (KC) diagnosis relies on various clinical and imaging data.
Machine learning (ML) offers potential for improving KC detection and staging.
A gap persists between academic ML research and its clinical application in ophthalmology.

Purpose of the Study:

To review Machine Learning (ML) applications in keratoconus (KC) diagnosis over the last decade.
To identify advancements, common methodologies, and challenges in ML for KC.
To highlight the gap between ML research and clinical implementation.

Main Methods:

Systematic literature search of digital libraries for ML in KC diagnosis.
Inclusion/exclusion criteria applied to 62 identified articles.
Analysis focused on ML algorithms, imaging modalities, datasets, and KC conditions studied.

Main Results:

Supervised classifiers dominate KC diagnosis (97%), with Random Forest most frequent.
Pentacam is the leading imaging modality (56%); most studies use local numerical data (91%).
Research primarily compares normal vs. keratoconus eyes, with limited focus on KC severity stages (20%).

Conclusions:

Lack of consensus on early KC detection standards and severity staging hinders progress.
Limited multidisciplinary collaboration and restricted access to public datasets are key obstacles.
Further research and roadmap models are needed for clinical integration of ML in KC diagnosis.