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Currently submitted to: Journal of Medical Internet Research

Date Submitted: Jan 25, 2021
Open Peer Review Period: Jan 24, 2021 - Mar 21, 2021
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

  • Howard Maile; 
  • Ji-Peng Olivia Li; 
  • Daniel Gore; 
  • Marcello Leucci; 
  • Padraig Mulholland; 
  • Scott Hau; 
  • Anita Szabo; 
  • Kaoru Fujinami; 
  • Alice Davidson; 
  • Petra Liskova; 
  • Alison Hardcastle; 
  • Stephen Tuft; 
  • Nikolas Pontikos

ABSTRACT

Background:

Keratoconus is a disorder characterized by progressive thinning and distortion of the cornea. If detected at an early stage corneal collagen cross linking can prevent disease progression and further visual loss. Whilst advanced forms are easily detected, reliably identifying subclinical disease can be problematic. A number of different machine learning algorithms have been used to improve the detection of subclinical keratoconus based on the analysis of single or multiple clinical measures such as corneal imaging, aberrometry, or biomechanical measurements.

Objective:

To survey and critically evaluate the literature on algorithmic detection of subclinical keratoconus and equivalent definitions.

Methods:

We performed a structured search of the following databases: Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica Database (EMBASE), Web of Science and Cochrane from Jan 1, 2010 to Oct 31, 2020. We included all full text studies that have used algorithms for the detection of subclinical keratoconus. We excluded studies that did not perform validation.

Results:

We compared the parameters measured and the design of the machine learning algorithms reported in 26 papers that met the inclusion criteria. All salient information required for detailed comparison including diagnostic criteria, demographic data, sample size, acquisition system, validation details, parameter inputs, machine learning algorithm and key results are reported in this study.

Conclusions:

Machine learning has the potential to improve the detection of subclinical keratoconus or early keratoconus in routine ophthalmic practice. Presently there is no consensus regarding the corneal parameters that should be included for assessment and the optimal design for the machine learning algorithm. We have identified avenues for further research to improve early detection and stratification of patients for early intervention to prevent disease progression. Clinical Trial: N/A


 Citation

Please cite as:

Maile H, Li JO, Gore D, Leucci M, Mulholland P, Hau S, Szabo A, Fujinami K, Davidson A, Liskova P, Hardcastle A, Tuft S, Pontikos N

Machine Learning Algorithms to Detect Subclinical Keratoconus: Systematic Review

JMIR Preprints. 25/01/2021:27363

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